diff --git a/README.md b/README.md
index 0c12981b..23fba842 100644
--- a/README.md
+++ b/README.md
@@ -31,6 +31,7 @@ Thanks to the effort of various people and institutions you can enjoy EO Browser
Your language is missing and you want to help to translate it? Contact us at info@sentinel-hub.com for more details.
- Danish (Main functionality): Carsten Skovgård Andersen (Stjernekammeret, Bellahøj Skole)
+- Estonian: ESERO Estonia
- German: ESERO Austria/ESERO Germany
- Greek: [GET](https://www.getmap.eu/?lang=en)
- Polish: ESERO Poland
diff --git a/package-lock.json b/package-lock.json
index db3f9538..07c064a1 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -18,7 +18,7 @@
"@mapbox/sphericalmercator": "^1.1.0",
"@mapbox/togeojson": "^0.16.0",
"@reduxjs/toolkit": "^1.0.4",
- "@sentinel-hub/sentinelhub-js": "^0.2.68-rc.3",
+ "@sentinel-hub/sentinelhub-js": "^0.2.69",
"@turf/bbox-polygon": "^6.5.0",
"@turf/boolean-point-in-polygon": "^6.0.1",
"@turf/center": "^6.0.1",
@@ -66,7 +66,7 @@
"react-dragula": "^1.1.17",
"react-dropzone": "^4.1.2",
"react-flags-select": "^2.1.2",
- "react-joyride": "2.0.0-14",
+ "react-joyride": "2.3.2",
"react-leaflet": "^2.6.0",
"react-markdown": "^4.3.1",
"react-onclickoutside": "^6.8.0",
@@ -90,6 +90,8 @@
"devDependencies": {
"@storybook/addon-actions": "^5.0.10",
"@storybook/react": "^5.0.10",
+ "@testing-library/jest-dom": "^5.16.1",
+ "@testing-library/react": "^12.1.2",
"axios-mock-adapter": "^1.18.1",
"cross-env": "^7.0.2",
"dotenv": "^10.0.0",
@@ -3032,9 +3034,9 @@
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},
"node_modules/@sentinel-hub/sentinelhub-js": {
- "version": "0.2.68-rc.3",
- "resolved": "https://registry.npmjs.org/@sentinel-hub/sentinelhub-js/-/sentinelhub-js-0.2.68-rc.3.tgz",
- "integrity": "sha512-zgaOzIVYRix0jORamWCb5YnPcK7joVZAKVB2nGzf8oeD+qpPLU5jgBAKanQQxcIXJFP6YaX6ZscHVLNJaIiRwg==",
+ "version": "0.2.69",
+ "resolved": "https://registry.npmjs.org/@sentinel-hub/sentinelhub-js/-/sentinelhub-js-0.2.69.tgz",
+ "integrity": "sha512-Cxm9nkMrHpqx0gL8rRLTcsUpIqeh9ZB77ButX/hRIBGcmLyn+c1RZgObM3wdu35NBfR3ub0auBmGMQUYB7OcRw==",
"dependencies": {
"@turf/area": "^6.0.1",
"@turf/helpers": "^6.1.4",
@@ -5643,14 +5645,6 @@
"url": "https://github.com/sponsors/gregberge"
}
},
- "node_modules/@svgr/plugin-svgo/node_modules/deepmerge": {
- "version": "4.2.2",
- "resolved": "https://registry.npmjs.org/deepmerge/-/deepmerge-4.2.2.tgz",
- "integrity": "sha512-FJ3UgI4gIl+PHZm53knsuSFpE+nESMr7M4v9QcgB7S63Kj/6WqMiFQJpBBYz1Pt+66bZpP3Q7Lye0Oo9MPKEdg==",
- "engines": {
- "node": ">=0.10.0"
- }
- },
"node_modules/@svgr/webpack": {
"version": "5.5.0",
"resolved": "https://registry.npmjs.org/@svgr/webpack/-/webpack-5.5.0.tgz",
@@ -5673,6 +5667,174 @@
"url": "https://github.com/sponsors/gregberge"
}
},
+ "node_modules/@testing-library/dom": {
+ "version": "8.11.1",
+ "resolved": "https://registry.npmjs.org/@testing-library/dom/-/dom-8.11.1.tgz",
+ "integrity": "sha512-3KQDyx9r0RKYailW2MiYrSSKEfH0GTkI51UGEvJenvcoDoeRYs0PZpi2SXqtnMClQvCqdtTTpOfFETDTVADpAg==",
+ "dev": true,
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+ "@babel/runtime": "^7.12.5",
+ "@types/aria-query": "^4.2.0",
+ "aria-query": "^5.0.0",
+ "chalk": "^4.1.0",
+ "dom-accessibility-api": "^0.5.9",
+ "lz-string": "^1.4.4",
+ "pretty-format": "^27.0.2"
+ },
+ "engines": {
+ "node": ">=12"
+ }
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+ "node_modules/@testing-library/dom/node_modules/@jest/types": {
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+ "resolved": "https://registry.npmjs.org/@jest/types/-/types-27.4.2.tgz",
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+ "dev": true,
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+ "@types/istanbul-reports": "^3.0.0",
+ "@types/node": "*",
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+ "dev": true,
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+ "supports-color": "^7.1.0"
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+ "source-map": "^0.6.1",
+ "source-map-resolve": "^0.6.0"
+ }
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+ "node_modules/@testing-library/jest-dom/node_modules/source-map-resolve": {
+ "version": "0.6.0",
+ "resolved": "https://registry.npmjs.org/source-map-resolve/-/source-map-resolve-0.6.0.tgz",
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+ "dev": true,
+ "dependencies": {
+ "atob": "^2.1.2",
+ "decode-uri-component": "^0.2.0"
+ }
+ },
+ "node_modules/@testing-library/react": {
+ "version": "12.1.2",
+ "resolved": "https://registry.npmjs.org/@testing-library/react/-/react-12.1.2.tgz",
+ "integrity": "sha512-ihQiEOklNyHIpo2Y8FREkyD1QAea054U0MVbwH1m8N9TxeFz+KoJ9LkqoKqJlzx2JDm56DVwaJ1r36JYxZM05g==",
+ "dev": true,
+ "dependencies": {
+ "@babel/runtime": "^7.12.5",
+ "@testing-library/dom": "^8.0.0"
+ },
+ "engines": {
+ "node": ">=12"
+ },
+ "peerDependencies": {
+ "react": "*",
+ "react-dom": "*"
+ }
+ },
"node_modules/@tootallnate/once": {
"version": "1.1.2",
"resolved": "https://registry.npmjs.org/@tootallnate/once/-/once-1.1.2.tgz",
@@ -5975,6 +6137,12 @@
"url": "https://opencollective.com/turf"
}
},
+ "node_modules/@types/aria-query": {
+ "version": "4.2.2",
+ "resolved": "https://registry.npmjs.org/@types/aria-query/-/aria-query-4.2.2.tgz",
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+ },
"node_modules/@types/babel__core": {
"version": "7.1.15",
"resolved": "https://registry.npmjs.org/@types/babel__core/-/babel__core-7.1.15.tgz",
@@ -6098,6 +6266,107 @@
"@types/istanbul-lib-report": "*"
}
},
+ "node_modules/@types/jest": {
+ "version": "27.0.3",
+ "resolved": "https://registry.npmjs.org/@types/jest/-/jest-27.0.3.tgz",
+ "integrity": "sha512-cmmwv9t7gBYt7hNKH5Spu7Kuu/DotGa+Ff+JGRKZ4db5eh8PnKS4LuebJ3YLUoyOyIHraTGyULn23YtEAm0VSg==",
+ "dev": true,
+ "dependencies": {
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+ "pretty-format": "^27.0.0"
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+ "node_modules/@types/jest/node_modules/@jest/types": {
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+ "@types/istanbul-reports": "^3.0.0",
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+ "node_modules/@types/jest/node_modules/@types/yargs": {
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+ "dev": true,
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+ "node_modules/@types/jest/node_modules/pretty-format": {
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+ "resolved": "https://registry.npmjs.org/pretty-format/-/pretty-format-27.4.2.tgz",
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+ "dev": true,
+ "dependencies": {
+ "@jest/types": "^27.4.2",
+ "ansi-regex": "^5.0.1",
+ "ansi-styles": "^5.0.0",
+ "react-is": "^17.0.1"
+ },
+ "engines": {
+ "node": "^10.13.0 || ^12.13.0 || ^14.15.0 || >=15.0.0"
+ }
+ },
+ "node_modules/@types/jest/node_modules/react-is": {
+ "version": "17.0.2",
+ "resolved": "https://registry.npmjs.org/react-is/-/react-is-17.0.2.tgz",
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+ },
"node_modules/@types/json-schema": {
"version": "7.0.9",
"resolved": "https://registry.npmjs.org/@types/json-schema/-/json-schema-7.0.9.tgz",
@@ -6240,6 +6509,15 @@
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},
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+ "resolved": "https://registry.npmjs.org/@types/testing-library__jest-dom/-/testing-library__jest-dom-5.14.2.tgz",
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+ "dev": true,
+ "dependencies": {
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+ }
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"node_modules/@types/tough-cookie": {
"version": "2.3.8",
"resolved": "https://registry.npmjs.org/@types/tough-cookie/-/tough-cookie-2.3.8.tgz",
@@ -6925,9 +7203,9 @@
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+ "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.1.tgz",
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"engines": {
"node": ">=8"
}
@@ -10643,6 +10921,12 @@
"url": "https://github.com/sponsors/fb55"
}
},
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+ "version": "1.5.1",
+ "resolved": "https://registry.npmjs.org/css.escape/-/css.escape-1.5.1.tgz",
+ "integrity": "sha1-QuJ9T6BK4y+TGktNQZH6nN3ul8s=",
+ "dev": true
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@@ -11185,9 +11469,9 @@
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"engines": {
"node": ">=0.10.0"
}
@@ -11611,6 +11895,12 @@
"node": ">=6.0.0"
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"@types/json-schema": {
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"resolved": "https://registry.npmjs.org/@types/json-schema/-/json-schema-7.0.9.tgz",
@@ -36360,6 +36870,15 @@
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},
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+ "resolved": "https://registry.npmjs.org/@types/testing-library__jest-dom/-/testing-library__jest-dom-5.14.2.tgz",
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+ "dev": true,
+ "requires": {
+ "@types/jest": "*"
+ }
+ },
"@types/tough-cookie": {
"version": "2.3.8",
"resolved": "https://registry.npmjs.org/@types/tough-cookie/-/tough-cookie-2.3.8.tgz",
@@ -36900,9 +37419,9 @@
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},
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- "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.0.tgz",
- "integrity": "sha512-bY6fj56OUQ0hU1KjFNDQuJFezqKdrAyFdIevADiqrWHwSlbmBNMHp5ak2f40Pm8JTFyM2mqxkG6ngkHO11f/lg=="
+ "version": "5.0.1",
+ "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.1.tgz",
+ "integrity": "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="
},
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@@ -39924,6 +40443,12 @@
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},
+ "css.escape": {
+ "version": "1.5.1",
+ "resolved": "https://registry.npmjs.org/css.escape/-/css.escape-1.5.1.tgz",
+ "integrity": "sha1-QuJ9T6BK4y+TGktNQZH6nN3ul8s=",
+ "dev": true
+ },
"csscolorparser": {
"version": "1.0.3",
"resolved": "https://registry.npmjs.org/csscolorparser/-/csscolorparser-1.0.3.tgz",
@@ -40406,9 +40931,9 @@
"dev": true
},
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- "resolved": "https://registry.npmjs.org/deepmerge/-/deepmerge-2.2.1.tgz",
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+ "version": "4.2.2",
+ "resolved": "https://registry.npmjs.org/deepmerge/-/deepmerge-4.2.2.tgz",
+ "integrity": "sha512-FJ3UgI4gIl+PHZm53knsuSFpE+nESMr7M4v9QcgB7S63Kj/6WqMiFQJpBBYz1Pt+66bZpP3Q7Lye0Oo9MPKEdg=="
},
"default-gateway": {
"version": "4.2.0",
@@ -40752,6 +41277,12 @@
"esutils": "^2.0.2"
}
},
+ "dom-accessibility-api": {
+ "version": "0.5.10",
+ "resolved": "https://registry.npmjs.org/dom-accessibility-api/-/dom-accessibility-api-0.5.10.tgz",
+ "integrity": "sha512-Xu9mD0UjrJisTmv7lmVSDMagQcU9R5hwAbxsaAE/35XPnPLJobbuREfV/rraiSaEj/UOvgrzQs66zyTWTlyd+g==",
+ "dev": true
+ },
"dom-align": {
"version": "1.12.2",
"resolved": "https://registry.npmjs.org/dom-align/-/dom-align-1.12.2.tgz",
@@ -40802,7 +41333,8 @@
"dom-walk": {
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"resolved": "https://registry.npmjs.org/dom-walk/-/dom-walk-0.1.2.tgz",
- "integrity": "sha512-6QvTW9mrGeIegrFXdtQi9pk7O/nSK6lSdXW2eqUspN5LWD7UTji2Fqw5V2YLjBpHEoU9Xl/eUWNpDeZvoyOv2w=="
+ "integrity": "sha512-6QvTW9mrGeIegrFXdtQi9pk7O/nSK6lSdXW2eqUspN5LWD7UTji2Fqw5V2YLjBpHEoU9Xl/eUWNpDeZvoyOv2w==",
+ "dev": true
},
"domain-browser": {
"version": "1.2.0",
@@ -43097,6 +43629,7 @@
"version": "4.3.2",
"resolved": "https://registry.npmjs.org/global/-/global-4.3.2.tgz",
"integrity": "sha1-52mJJopsdMOJCLEwWxD8DjlOnQ8=",
+ "dev": true,
"requires": {
"min-document": "^2.19.0",
"process": "~0.5.1"
@@ -44597,9 +45130,9 @@
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},
"is-lite": {
- "version": "0.2.2",
- "resolved": "https://registry.npmjs.org/is-lite/-/is-lite-0.2.2.tgz",
- "integrity": "sha512-CnO9kJOQvaXa9lJK4OSgnpao2XG9EGQHCaaPDCK9gTEqz+duD2hPXtl69Rgq879TUs+76Fuz87XBJ5H9FgYRNA=="
+ "version": "0.8.1",
+ "resolved": "https://registry.npmjs.org/is-lite/-/is-lite-0.8.1.tgz",
+ "integrity": "sha512-ekSwuewzOmwFnzzAOWuA5fRFPqOeTrLIL3GWT7hdVVi+oLuD+Rau8gCmkb94vH5hjXc1Q/CfIW/y/td1RrNQIg=="
},
"is-map": {
"version": "2.0.2",
@@ -45003,11 +45536,6 @@
"pretty-format": "^26.6.2"
},
"dependencies": {
- "deepmerge": {
- "version": "4.2.2",
- "resolved": "https://registry.npmjs.org/deepmerge/-/deepmerge-4.2.2.tgz",
- "integrity": "sha512-FJ3UgI4gIl+PHZm53knsuSFpE+nESMr7M4v9QcgB7S63Kj/6WqMiFQJpBBYz1Pt+66bZpP3Q7Lye0Oo9MPKEdg=="
- },
"jest-resolve": {
"version": "26.6.2",
"resolved": "https://registry.npmjs.org/jest-resolve/-/jest-resolve-26.6.2.tgz",
@@ -46274,6 +46802,12 @@
"yallist": "^4.0.0"
}
},
+ "lz-string": {
+ "version": "1.4.4",
+ "resolved": "https://registry.npmjs.org/lz-string/-/lz-string-1.4.4.tgz",
+ "integrity": "sha1-wNjq82BZ9wV5bh40SBHPTEmNOiY=",
+ "dev": true
+ },
"magic-string": {
"version": "0.25.7",
"resolved": "https://registry.npmjs.org/magic-string/-/magic-string-0.25.7.tgz",
@@ -46791,6 +47325,7 @@
"version": "2.19.0",
"resolved": "https://registry.npmjs.org/min-document/-/min-document-2.19.0.tgz",
"integrity": "sha1-e9KC4/WELtKVu3SM3Z8f+iyCRoU=",
+ "dev": true,
"requires": {
"dom-walk": "^0.1.0"
}
@@ -47153,9 +47688,9 @@
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},
"nested-property": {
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- "resolved": "https://registry.npmjs.org/nested-property/-/nested-property-0.0.7.tgz",
- "integrity": "sha1-/yIvIzyoeTxoKLQRcJG+pZcTD08="
+ "version": "4.0.0",
+ "resolved": "https://registry.npmjs.org/nested-property/-/nested-property-4.0.0.tgz",
+ "integrity": "sha512-yFehXNWRs4cM0+dz7QxCd06hTbWbSkV0ISsqBfkntU6TOY4Qm3Q88fRRLOddkGh2Qq6dZvnKVAahfhjcUvLnyA=="
},
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@@ -49521,7 +50056,8 @@
"process": {
"version": "0.5.2",
"resolved": "https://registry.npmjs.org/process/-/process-0.5.2.tgz",
- "integrity": "sha1-FjjYqONML0QKkduVq5rrZ3/Bhc8="
+ "integrity": "sha1-FjjYqONML0QKkduVq5rrZ3/Bhc8=",
+ "dev": true
},
"process-nextick-args": {
"version": "1.0.7",
@@ -49757,14 +50293,6 @@
"performance-now": "^2.1.0"
}
},
- "rafl": {
- "version": "1.2.2",
- "resolved": "https://registry.npmjs.org/rafl/-/rafl-1.2.2.tgz",
- "integrity": "sha1-/pMPdYIRAg1H44gV9Rlqi+QVB0A=",
- "requires": {
- "global": "~4.3.0"
- }
- },
"ramda": {
"version": "0.27.1",
"resolved": "https://registry.npmjs.org/ramda/-/ramda-0.27.1.tgz",
@@ -50403,18 +50931,6 @@
"classnames": "^2.2.6"
}
},
- "react-floater": {
- "version": "0.5.5",
- "resolved": "https://registry.npmjs.org/react-floater/-/react-floater-0.5.5.tgz",
- "integrity": "sha512-Y3MPvKJ5Qx5/ylg39ESd/4whwJLN+i6iNF4j0h6KcSi53yCqdCJnuVXibP+hIPvl6Uh0QhEIu32L5Hmb8YYsEg==",
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- "exenv": "^1.2.2",
- "is-lite": "^0.2.0",
- "popper.js": "^1.14.3",
- "react-proptype-conditional-require": "^1.0.4"
- }
- },
"react-focus-lock": {
"version": "2.5.2",
"resolved": "https://registry.npmjs.org/react-focus-lock/-/react-focus-lock-2.5.2.tgz",
@@ -50510,21 +51026,51 @@
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},
"react-joyride": {
- "version": "2.0.0-14",
- "resolved": "https://registry.npmjs.org/react-joyride/-/react-joyride-2.0.0-14.tgz",
- "integrity": "sha512-flZSGmhKKMwi9KP3nmZvjG1byIiNmM5T5QrdvklHCY+L5nmVq8o30uN+s1+sKyEfj1WYnzodHrCbFdSoU8oXhw==",
+ "version": "2.3.2",
+ "resolved": "https://registry.npmjs.org/react-joyride/-/react-joyride-2.3.2.tgz",
+ "integrity": "sha512-MPYvHdxF/ylBZXJkKzyR3nTd+jydPeTKVwQDg8y7Ctl5H0UPgvF3uCzfieyJqE/JnK2wJU4QcjkontqBU1g7Xg==",
"requires": {
- "deep-diff": "^1.0.1",
- "deepmerge": "^2.1.1",
+ "deep-diff": "^1.0.2",
+ "deepmerge": "^4.2.2",
"exenv": "^1.2.2",
- "is-lite": "^0.2.0",
- "nested-property": "^0.0.7",
- "react-floater": "^0.5.5",
- "react-proptype-conditional-require": "^1.0.4",
- "scroll": "^2.0.3",
- "scroll-doc": "^0.2.1",
+ "is-lite": "^0.8.1",
+ "nested-property": "^4.0.0",
+ "react-floater": "^0.7.3",
+ "react-is": "^16.13.1",
+ "scroll": "^3.0.1",
"scrollparent": "^2.0.1",
- "tree-changes": "^0.3.2"
+ "tree-changes": "^0.7.1"
+ },
+ "dependencies": {
+ "react-floater": {
+ "version": "0.7.3",
+ "resolved": "https://registry.npmjs.org/react-floater/-/react-floater-0.7.3.tgz",
+ "integrity": "sha512-d1wAEph+xRxQ0RJ3woMmYLlZHTaCIsja7Bv6JNo2ezsVUgdMan4CxOR4Do4/xgpmRFfsQMdlygexLAZZypWirw==",
+ "requires": {
+ "deepmerge": "^4.2.2",
+ "exenv": "^1.2.2",
+ "is-lite": "^0.8.1",
+ "popper.js": "^1.16.0",
+ "react-proptype-conditional-require": "^1.0.4",
+ "tree-changes": "^0.5.1"
+ },
+ "dependencies": {
+ "nested-property": {
+ "version": "1.0.1",
+ "resolved": "https://registry.npmjs.org/nested-property/-/nested-property-1.0.1.tgz",
+ "integrity": "sha512-BnBBoo/8bBNRdAnJc7+m79oWk7dXwW1+vCesaEQhfDGVwXGLMvmI4NwYgLTW94R/x+R2s/yr2g/hB/4w/YSAvA=="
+ },
+ "tree-changes": {
+ "version": "0.5.1",
+ "resolved": "https://registry.npmjs.org/tree-changes/-/tree-changes-0.5.1.tgz",
+ "integrity": "sha512-O873xzV2xRZ6N059Mn06QzmGKEE21LlvIPbsk2G+GS9ZX5OCur6PIwuuh0rWpAPvLWQZPj0XObyG27zZyLHUzw==",
+ "requires": {
+ "deep-diff": "^1.0.2",
+ "nested-property": "1.0.1"
+ }
+ }
+ }
+ }
}
},
"react-leaflet": {
@@ -52057,17 +52603,9 @@
}
},
"scroll": {
- "version": "2.0.3",
- "resolved": "https://registry.npmjs.org/scroll/-/scroll-2.0.3.tgz",
- "integrity": "sha512-3ncZzf8gUW739h3LeS68nSssO60O+GGjT3SxzgofQmT8PIoyHzebql9HHPJopZX8iT6TKOdwaWFMqL6LzUN3DQ==",
- "requires": {
- "rafl": "~1.2.1"
- }
- },
- "scroll-doc": {
- "version": "0.2.1",
- "resolved": "https://registry.npmjs.org/scroll-doc/-/scroll-doc-0.2.1.tgz",
- "integrity": "sha512-ZLueZaKMkdL1/SL07Fpw/P/MFYit76GaaKQYnjLSx2K6dOUmkYP1TEOyyfKZohvDyoKMwue0l9MbgPesREjqCA=="
+ "version": "3.0.1",
+ "resolved": "https://registry.npmjs.org/scroll/-/scroll-3.0.1.tgz",
+ "integrity": "sha512-pz7y517OVls1maEzlirKO5nPYle9AXsFzTMNJrRGmT951mzpIBy7sNHOg5o/0MQd/NqliCiWnAi0kZneMPFLcg=="
},
"scrollparent": {
"version": "2.0.1",
@@ -54107,12 +54645,12 @@
}
},
"tree-changes": {
- "version": "0.3.4",
- "resolved": "https://registry.npmjs.org/tree-changes/-/tree-changes-0.3.4.tgz",
- "integrity": "sha512-pNVOKZHLUTiwIv98cGoXDsRV27xYEyV9AgbPm7OvVzsdL0jnHCKq5SfiYpWIvACF0ObSAx8ZJkQgISlsEAGbsw==",
+ "version": "0.7.1",
+ "resolved": "https://registry.npmjs.org/tree-changes/-/tree-changes-0.7.1.tgz",
+ "integrity": "sha512-sPIt8PKDi0OQTglr7lsetcB9DU19Ls/ZgFSjFvK6DWJGisAn4sOxtjpmQfuqjexQE4UU9U53LNmataL1kRJ3Uw==",
"requires": {
- "deep-diff": "^1.0.2",
- "nested-property": "0.0.7"
+ "fast-deep-equal": "^3.1.3",
+ "is-lite": "^0.8.1"
}
},
"trim": {
diff --git a/package.json b/package.json
index 8f4fcced..961c1f66 100644
--- a/package.json
+++ b/package.json
@@ -15,7 +15,7 @@
"@mapbox/sphericalmercator": "^1.1.0",
"@mapbox/togeojson": "^0.16.0",
"@reduxjs/toolkit": "^1.0.4",
- "@sentinel-hub/sentinelhub-js": "^0.2.68-rc.3",
+ "@sentinel-hub/sentinelhub-js": "^0.2.69",
"@turf/bbox-polygon": "^6.5.0",
"@turf/boolean-point-in-polygon": "^6.0.1",
"@turf/center": "^6.0.1",
@@ -63,7 +63,7 @@
"react-dragula": "^1.1.17",
"react-dropzone": "^4.1.2",
"react-flags-select": "^2.1.2",
- "react-joyride": "2.0.0-14",
+ "react-joyride": "2.3.2",
"react-leaflet": "^2.6.0",
"react-markdown": "^4.3.1",
"react-onclickoutside": "^6.8.0",
@@ -111,6 +111,8 @@
"devDependencies": {
"@storybook/addon-actions": "^5.0.10",
"@storybook/react": "^5.0.10",
+ "@testing-library/jest-dom": "^5.16.1",
+ "@testing-library/react": "^12.1.2",
"axios-mock-adapter": "^1.18.1",
"cross-env": "^7.0.2",
"dotenv": "^10.0.0",
diff --git a/public/eob3d/0FDEAB1F2722E35B2E0E1024421EB5CF.cache.js b/public/eob3d/0FDEAB1F2722E35B2E0E1024421EB5CF.cache.js
index 568a4db8..46eff512 100644
--- a/public/eob3d/0FDEAB1F2722E35B2E0E1024421EB5CF.cache.js
+++ b/public/eob3d/0FDEAB1F2722E35B2E0E1024421EB5CF.cache.js
@@ -23831,7 +23831,7 @@ var Lcom_sinergise_common_util_web_MimeType$MimeParamValue_2_classLit = createFo
function $clinit_EOBrowser3D(){
$clinit_EOBrowser3D = emptyMethod;
WEBMERC_TO_WGS84 = find_0(($clinit_WorldTransforms() , POPULAR_WEB_WGS84), ($clinit_CRS() , WGS84));
- NASA_ASTER_GDEM_PROVIDER = create_4('https://gpcl01.geopedia.world/v1/AUTH_f92818-YOUR-INSTANCEID-HERE/nasa.dem.aster-gdem-v2.2011.epsg:3857');
+ NASA_ASTER_GDEM_PROVIDER = create_4('https://gpcl01.geopedia.world/v1/AUTH_f928186f-c813-4503-9c9d-223bea02b5f0/nasa.dem.aster-gdem-v2.2011.epsg:3857');
MAPZEN_PROVIDER = new MapzenTileProvider(($clinit_MapzenTileProvider$MapzenFormat() , TERRARIUM));
viewers = new HashMap;
theme3DProvider = new EOBrowser3DThemeProvider;
diff --git a/public/eob3d/BB4A5BC91C2602496BB77FF0913A032B.cache.js b/public/eob3d/BB4A5BC91C2602496BB77FF0913A032B.cache.js
index 507c7991..fc1f577c 100644
--- a/public/eob3d/BB4A5BC91C2602496BB77FF0913A032B.cache.js
+++ b/public/eob3d/BB4A5BC91C2602496BB77FF0913A032B.cache.js
@@ -24928,7 +24928,7 @@ var Lcom_sinergise_common_util_web_MimeType$MimeParamValue_2_classLit = createFo
function $clinit_EOBrowser3D(){
$clinit_EOBrowser3D = emptyMethod;
WEBMERC_TO_WGS84 = find_0(($clinit_WorldTransforms() , POPULAR_WEB_WGS84), ($clinit_CRS() , WGS84));
- NASA_ASTER_GDEM_PROVIDER = create_11('https://gpcl01.geopedia.world/v1/AUTH_f92818-YOUR-INSTANCEID-HERE/nasa.dem.aster-gdem-v2.2011.epsg:3857');
+ NASA_ASTER_GDEM_PROVIDER = create_11('https://gpcl01.geopedia.world/v1/AUTH_f928186f-c813-4503-9c9d-223bea02b5f0/nasa.dem.aster-gdem-v2.2011.epsg:3857');
MAPZEN_PROVIDER = new MapzenTileProvider(($clinit_MapzenTileProvider$MapzenFormat() , TERRARIUM));
viewers = new HashMap;
theme3DProvider = new EOBrowser3DThemeProvider;
diff --git a/public/ffmpeg.js/ffmpeg-worker-mp4.js b/public/ffmpeg.js/ffmpeg-worker-mp4.js
index 21e67352..1036cb76 100644
--- a/public/ffmpeg.js/ffmpeg-worker-mp4.js
+++ b/public/ffmpeg.js/ffmpeg-worker-mp4.js
@@ -135,7 +135,7 @@ o[1]=Na;o[2]=Pa;o[3]=_b;o[4]=mc;o[5]=$b;o[6]=Ic;o[7]=ed;o[8]=kd;o[9]=ld;o[10]=nd
)(Xa,la,ub)}},instantiate:function(a){return{then:function(b){b({instance:new U.Instance(new U.Module(a))})}}},RuntimeError:Error};
-za=[];"object"!==typeof U&&G("no native wasm support detected");var la,ub=new U.Table({initial:2921,maximum:2921,element:"anyfunc"}),Ra=!1,db="undefined"!==typeof TextDecoder?new TextDecoder("utf8"):void 0;"undefined"!==typeof TextDecoder&&new TextDecoder("utf-16le");var L,Y,Ya,m,Za=h.INITIAL_MEMORY||1073741824;h.wasmMemory?la=h.wasmMemory:la=new U.Memory({initial:Za/65536,maximum:Za/65536});if(la)var $a=la.buffer;Za=$a.byteLength;(function(a){$a=a;h.HEAP8=L=new Int8Array(a);h.HEAP16=Ya=new Int16Array(a);
+za=[];"object"!==typeof U&&G("no native wasm support detected");var la,ub=new U.Table({initial:2921,maximum:2921,element:"anyfunc"}),Ra=!1,db="undefined"!==typeof TextDecoder?new TextDecoder("utf8"):void 0;"undefined"!==typeof TextDecoder&&new TextDecoder("utf-16le");var L,Y,Ya,m,Za=h.INITIAL_MEMORY||1610612736;h.wasmMemory?la=h.wasmMemory:la=new U.Memory({initial:Za/65536,maximum:Za/65536});if(la)var $a=la.buffer;Za=$a.byteLength;(function(a){$a=a;h.HEAP8=L=new Int8Array(a);h.HEAP16=Ya=new Int16Array(a);
h.HEAP32=m=new Int32Array(a);h.HEAPU8=Y=new Uint8Array(a);h.HEAPU16=new Uint16Array(a);h.HEAPU32=new Uint32Array(a);h.HEAPF32=new Float32Array(a);h.HEAPF64=new Float64Array(a)})($a);m[584088]=7579392;var qb=[],mb=[],Hb=[],Ib=[],pb=[],wa=Math.abs,ca=Math.ceil,da=Math.floor,xa=Math.min,aa=0,Qa=null,pa=null;h.preloadedImages={};h.preloadedAudios={};var sa="data:application/octet-stream;base64,",ka="ffmpeg-worker-mp4.wasm";(String.prototype.startsWith?ka.startsWith(sa):0===ka.indexOf(sa))||(ka=yb(ka));
var A,M;mb.push({Sb:function(){Lb()}});var x={qb:function(a){return/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/.exec(a).slice(1)},Na:function(a,b){for(var c=0,e=a.length-1;0<=e;e--){var f=a[e];"."===f?a.splice(e,1):".."===f?(a.splice(e,1),c++):c&&(a.splice(e,1),c--)}if(b)for(;c;c--)a.unshift("..");return a},normalize:function(a){var b="/"===a.charAt(0),c="/"===a.substr(-1);(a=x.Na(a.split("/").filter(function(e){return!!e}),!b).join("/"))||b||(a=".");a&&c&&(a+="/");return(b?"/":
"")+a},dirname:function(a){var b=x.qb(a);a=b[0];b=b[1];if(!a&&!b)return".";b&&(b=b.substr(0,b.length-1));return a+b},basename:function(a){if("/"===a)return"/";var b=a.lastIndexOf("/");return-1===b?a:a.substr(b+1)},extname:function(a){return x.qb(a)[3]},join:function(){var a=Array.prototype.slice.call(arguments,0);return x.normalize(a.join("/"))},v:function(a,b){return x.normalize(a+"/"+b)}},S={resolve:function(){for(var a="",b=!1,c=arguments.length-1;-1<=c&&!b;c--){b=0<=c?arguments[c]:d.cwd();if("string"!==
diff --git a/public/previews/DEFAULT-THEME-dae04f-GHS-BUILT-S2.png b/public/previews/DEFAULT-THEME-dae04f-GHS-BUILT-S2.png
new file mode 100644
index 00000000..581b0681
Binary files /dev/null and b/public/previews/DEFAULT-THEME-dae04f-GHS-BUILT-S2.png differ
diff --git a/src/Controls/Timelapse/Timelapse.js b/src/Controls/Timelapse/Timelapse.js
index 63af413b..a971a770 100644
--- a/src/Controls/Timelapse/Timelapse.js
+++ b/src/Controls/Timelapse/Timelapse.js
@@ -3,8 +3,6 @@ import { connect } from 'react-redux';
import Rodal from 'rodal';
import { t } from 'ttag';
import moment from 'moment';
-import gifshot from 'gifshot';
-import FileSaver from 'file-saver';
import { isMobile } from 'react-device-detect';
import { CancelToken, MimeTypes, ProcessingDataFusionLayer } from '@sentinel-hub/sentinelhub-js';
@@ -15,30 +13,27 @@ import { TimelapseControls } from './TimelapseControls';
import { TimelapseSidebarPins } from './TimelapseSidebarPins';
import { TimelapseImages } from './TimelapseImages';
import { TimelapsePreview } from './TimelapsePreview';
-import { getLayerFromParams } from '../ImgDownload/ImageDownload.utils';
+import { constructBBoxFromBounds, getLayerFromParams } from '../ImgDownload/ImageDownload.utils';
import { constructGetMapParamsEffects } from '../../utils/effectsUtils';
import {
getFlyoversToFetch,
- fetchImage,
getMinMaxDates,
- getTimelapseBBox,
findDefaultActiveImageIndex,
isImageApplicable,
findNextActiveImageIndex,
+ generateTimelapseWithGifshot,
+ fetchTimelapseImage,
+ getTimelapseBounds,
+ determineDefaultImageSize,
+ generateTimelapseWithFFMPEG,
+ DEFAULT_IMAGE_WIDTH,
} from './Timelapse.utils';
import './Timelapse.scss';
import { applyFilterMonthsToDateRange } from '../../junk/EOBCommon/utils/filterDates';
import { getDataSourceHandler } from '../../Tools/SearchPanel/dataSourceHandlers/dataSourceHandlers';
-
-const IMAGE_WIDTH = 512;
-const IMAGE_HEIGHT = 512;
-
-export const TRANSITION = {
- none: 'none',
- fade: 'fade',
-};
+import { EXPORT_FORMAT, TRANSITION } from '../../const';
class Timelapse extends Component {
state = {
@@ -75,6 +70,13 @@ class Timelapse extends Component {
}
this.setState({ loadingLayer: false });
+
+ // update size on first load or if the ratio changes
+ const size = determineDefaultImageSize(this.props.pixelBounds, this.props.zoom, this.props.aoi);
+ if (!this.props.size || this.props.size.ratio.toFixed(3) !== size.ratio.toFixed(3)) {
+ store.dispatch(timelapseSlice.actions.setSize(size));
+ }
+
this.searchDatesAndFetchImages();
}
@@ -119,8 +121,8 @@ class Timelapse extends Component {
if (!this.layer) {
return [];
}
- const { pixelBounds, zoom } = this.props;
- const { bbox } = getTimelapseBBox(pixelBounds, zoom);
+ const { pixelBounds, zoom, aoi } = this.props;
+ const bbox = constructBBoxFromBounds(getTimelapseBounds(pixelBounds, zoom, aoi));
const dates = await this.layer.findDatesUTC(bbox, fromMoment.toDate(), toMoment.toDate());
return dates;
};
@@ -215,7 +217,7 @@ class Timelapse extends Component {
canWeFilterByCoverage,
});
- this.fetchImages(flyoversToFetch.sort((a, b) => a.toTime - b.toTime));
+ await this.fetchImages(flyoversToFetch.sort((a, b) => a.toTime - b.toTime));
};
searchDates = async (layer) => {
@@ -225,9 +227,9 @@ class Timelapse extends Component {
images: null,
activeImageIndex: null,
});
- const { pixelBounds, zoom, fromTime, toTime, filterMonths, selectedPeriod } = this.props;
+ const { pixelBounds, zoom, fromTime, toTime, filterMonths, selectedPeriod, aoi } = this.props;
- const { bbox } = getTimelapseBBox(pixelBounds, zoom);
+ const bbox = constructBBoxFromBounds(getTimelapseBounds(pixelBounds, zoom, aoi));
const intervals = applyFilterMonthsToDateRange(fromTime, toTime, filterMonths);
const reqConfig = {
@@ -282,39 +284,51 @@ class Timelapse extends Component {
};
fetchImages = async (flyoversToFetch) => {
- const { auth, maxCCPercentAllowed, minCoverageAllowed, showBorders, isSelectAllChecked } = this.props;
+ const {
+ auth,
+ maxCCPercentAllowed,
+ minCoverageAllowed,
+ showBorders,
+ isSelectAllChecked,
+ pixelBounds,
+ zoom,
+ aoi,
+ } = this.props;
const { canWeFilterByClouds, canWeFilterByCoverage } = this.state;
+ const size = determineDefaultImageSize(pixelBounds, zoom, aoi);
let images = [];
await Promise.all(
flyoversToFetch.map((flyover) =>
- fetchImage({
- ...this.props,
+ fetchTimelapseImage({
layer: flyover.visualization.layer,
datasetId: flyover.visualization.datasetId,
- effects: flyover.visualization.effects,
+ bounds: getTimelapseBounds(pixelBounds, zoom, aoi),
fromTime: flyover.fromTime,
toTime: flyover.toTime,
- width: IMAGE_WIDTH,
- height: IMAGE_HEIGHT,
+ width: size.width,
+ height: size.height,
imageFormat: MimeTypes.JPEG,
- showBorders: showBorders,
- getMapAuthToken: getGetMapAuthToken(auth),
cancelToken: this.cancelToken,
+ geometry: aoi.geometry,
+ effects: flyover.visualization.effects,
+ getMapAuthToken: getGetMapAuthToken(auth),
+ showBorders: showBorders && !(aoi && aoi.bounds),
}).then((image) => {
if (image === undefined) {
return;
}
const augmentedImage = {
- ...image,
+ url: image.url,
layer: flyover.visualization.layer,
datasetId: flyover.visualization.datasetId,
- customSelected: flyover.visualization.customSelected,
- pin: flyover.visualization.pin,
fromTime: flyover.fromTime,
toTime: flyover.toTime,
+ effects: flyover.visualization.effects,
+ customSelected: flyover.visualization.customSelected,
+ pin: flyover.visualization.pin,
isSelected: isSelectAllChecked,
averageCloudCoverPercent: flyover.meta && flyover.meta.averageCloudCoverPercent,
coveragePercent: flyover.coveragePercent,
@@ -476,7 +490,7 @@ class Timelapse extends Component {
this.setState((prevState) => {
const { images, canWeFilterByClouds, canWeFilterByCoverage, activeImageIndex } = prevState;
- const newActiveImage = findNextActiveImageIndex(
+ const nextActiveImageIndex = findNextActiveImageIndex(
images,
canWeFilterByClouds,
canWeFilterByCoverage,
@@ -485,7 +499,7 @@ class Timelapse extends Component {
activeImageIndex,
);
return {
- activeImageIndex: newActiveImage,
+ activeImageIndex: nextActiveImageIndex,
};
});
};
@@ -498,12 +512,33 @@ class Timelapse extends Component {
store.dispatch(timelapseSlice.actions.setTransition(transition));
};
- generateTimelapse = () => {
- const { maxCCPercentAllowed, minCoverageAllowed, transition } = this.props;
+ generateTimelapse = async () => {
+ const {
+ maxCCPercentAllowed,
+ minCoverageAllowed,
+ transition,
+ datasetId,
+ timelapseFPS,
+ size,
+ format,
+ pixelBounds,
+ zoom,
+ aoi,
+ auth,
+ showBorders,
+ } = this.props;
const { images, canWeFilterByClouds, canWeFilterByCoverage } = this.state;
- return new Promise((resolve, reject) => {
- const applicableImageUrls = images
+ // Cancel any previous requests
+ this.cancelToken.cancel();
+ this.cancelToken = new CancelToken();
+
+ this.setState({
+ generatingTimelapse: true,
+ });
+
+ const applicableImageUrls = await Promise.all(
+ images
.filter((image) =>
isImageApplicable(
image,
@@ -513,188 +548,52 @@ class Timelapse extends Component {
minCoverageAllowed,
),
)
- .map((image) => image.url);
-
- if (applicableImageUrls.length === 0) {
- reject();
- }
-
- if (transition === TRANSITION.none) {
- this.generateTimelapseWithoutTransition(applicableImageUrls, resolve, reject);
- } else {
- this.generateTimelapseWithTransition(applicableImageUrls, resolve, reject);
- }
- });
- };
-
- generateTimelapseWithoutTransition = (applicableImageUrls, resolve, reject) => {
- const { timelapseFPS } = this.props;
-
- this.setState({
- generatingTimelapse: true,
- generatingTimelapseProgress: null,
- });
-
- gifshot.createGIF(
- {
- images: applicableImageUrls,
- gifWidth: 512,
- interval: 1 / timelapseFPS,
- gifHeight: 512,
- numWorkers: 4,
- progressCallback: (progress) => {
- this.setState({ generatingTimelapseProgress: progress });
- },
- },
- (obj) => {
- if (obj.error) {
- this.setState({ generatingTimelapse: false });
- reject();
- } else {
- this.setState({
- generatingTimelapse: false,
- generatingTimelapseProgress: null,
- });
+ .map(async (image) => {
+ if (size.width === DEFAULT_IMAGE_WIDTH) {
+ return image.url;
+ }
- const file = new File([obj.image], this.generateTimelapseFilename() + '.gif', {
- type: obj.image.type,
+ // refetch images with custom size
+ let response = await fetchTimelapseImage({
+ layer: image.layer,
+ datasetId: image.datasetId,
+ bounds: getTimelapseBounds(pixelBounds, zoom, aoi),
+ fromTime: image.fromTime,
+ toTime: image.toTime,
+ width: size.width,
+ height: size.height,
+ imageFormat: MimeTypes.JPEG,
+ cancelToken: this.cancelToken,
+ geometry: aoi.geometry,
+ effects: image.effects,
+ getMapAuthToken: getGetMapAuthToken(auth),
+ showBorders: showBorders && !(aoi && aoi.bounds),
});
- resolve(file);
- }
- },
+ return response && response.url;
+ }),
);
- };
- generateTimelapseWithTransition = (applicableImageUrls, resolve, reject) => {
- this.setState({
- generatingTimelapse: true,
- generatingTimelapseProgress: null,
- });
-
- const worker = new Worker('ffmpeg.js/ffmpeg-worker-mp4.js');
- worker.onmessage = (e) => {
- const msg = e.data;
- switch (msg.type) {
- default:
- break;
- case 'ready':
- runFFmpegProcess();
- break;
- case 'stdout':
- console.log(msg.data);
- break;
- case 'stderr':
- console.log(msg.data);
- updateProgress(msg.data, applicableImageUrls.length);
- break;
- case 'done':
- console.log(msg.data);
- doneFFmpegProcess(msg);
- break;
- }
- };
-
- const runFFmpegProcess = () => {
- const { timelapseFPS } = this.props;
-
- worker.postMessage({
- type: 'run',
- MEMFS: applicableImageUrls.map((url, index) => ({
- name: `img${`00${index}`.slice(-3)}.png`,
- data: convertDataURIToBinary(url),
- })),
- // https://superuser.com/questions/833232/create-video-with-5-images-with-fadein-out-effect-in-ffmpeg/
- arguments: [
- ...applicableImageUrls.flatMap((url, index) => [
- '-loop',
- '1',
- '-t',
- (1 / timelapseFPS).toString(),
- '-i',
- `img${`00${index}`.slice(-3)}.png`,
- ]),
- ...(applicableImageUrls.length > 1
- ? [
- '-filter_complex',
- [
- ...applicableImageUrls
- .slice(0, -1)
- .map(
- (url, index) =>
- `[${index + 1}]fade=d=${0.5 / timelapseFPS}:t=in:alpha=1,setpts=PTS-STARTPTS+${
- (index + 1) / timelapseFPS
- }/TB[fade${index + 1}]`,
- ),
- ...applicableImageUrls
- .slice(0, -1)
- .map(
- (url, index) =>
- `[${index > 0 ? 'slice' : ''}${index}][fade${index + 1}]overlay[slice${index + 1}]`,
- ),
- ].join(';'),
- '-map',
- `[slice${applicableImageUrls.length - 1}]`,
- ]
- : []),
- '-pix_fmt',
- 'yuv420p',
- 'out.mp4',
- ],
- });
- };
-
- const updateProgress = (data, totalImages) => {
- const { timelapseFPS } = this.props;
-
- const progress = data.match(/frame=\s*(\d+)\sfps/);
- if (progress) {
- const outputFPS = 25;
- const totalFrames = (totalImages * outputFPS) / timelapseFPS;
- this.setState({ generatingTimelapseProgress: Math.min(progress[1] / totalFrames, 1) });
- }
- };
-
- const doneFFmpegProcess = (msg) => {
- this.setState({
- generatingTimelapse: false,
- generatingTimelapseProgress: null,
+ if (format === EXPORT_FORMAT.gif && transition === TRANSITION.none) {
+ return generateTimelapseWithGifshot({
+ applicableImageUrls,
+ datasetId,
+ timelapseFPS,
+ size,
+ progress: (options) => {
+ this.setState(options);
+ },
});
-
- if (msg.data?.MEMFS[0]?.data?.length > 0) {
- const file = new File([msg.data.MEMFS[0].data], this.generateTimelapseFilename() + '.mp4', {
- type: 'video/mp4',
- });
- resolve(file);
- } else {
- reject();
- }
- };
-
- const convertDataURIToBinary = (dataURI) => {
- const base64 = dataURI.split(',')[1];
- const raw = window.atob(base64);
-
- const array = new Uint8Array(new ArrayBuffer(raw.length));
- for (let i = 0; i < raw.length; i++) {
- array[i] = raw.charCodeAt(i);
- }
- return array;
- };
- };
-
- generateTimelapseFilename = () => {
- const random = Math.round(Date.now() * Math.random() * 1000);
- return `${this.props.datasetId.replace(' ', '_')}-${random}-timelapse`;
- };
-
- downloadTimelapse = async () => {
- if (this.props.previewFileUrl) {
- const link = document.createElement('a');
- link.href = this.props.previewFileUrl;
- link.click();
} else {
- const file = await this.generateTimelapse();
- FileSaver.saveAs(file, file.name);
+ return generateTimelapseWithFFMPEG({
+ applicableImageUrls,
+ datasetId,
+ timelapseFPS,
+ transition,
+ size,
+ progress: (options) => {
+ this.setState(options);
+ },
+ });
}
};
@@ -739,6 +638,20 @@ class Timelapse extends Component {
});
};
+ updateSize = (size) => {
+ store.dispatch(
+ timelapseSlice.actions.setSize({
+ width: size.width,
+ height: size.height,
+ ratio: this.props.size.ratio, // always keep initial ratio
+ }),
+ );
+ };
+
+ updateFormat = (format) => {
+ store.dispatch(timelapseSlice.actions.setFormat(format));
+ };
+
render() {
const {
images,
@@ -772,6 +685,9 @@ class Timelapse extends Component {
transition,
pins,
customSelected,
+ aoi,
+ size,
+ format,
} = this.props;
let { minDate, maxDate } = getMinMaxDates(datasetId);
@@ -845,7 +761,8 @@ class Timelapse extends Component {
minCoverageAllowed={minCoverageAllowed}
isSelectAllChecked={isSelectAllChecked}
activeImageIndex={activeImageIndex}
- showBorders={showBorders}
+ enableBorders={!(aoi && aoi.bounds)}
+ showBorders={showBorders && !(aoi && aoi.bounds)}
setMaxCCPercentAllowed={this.setMaxCCPercentAllowed}
setMinCoverageAllowed={this.setMinCoverageAllowed}
toggleImageSelected={this.toggleImageSelected}
@@ -866,9 +783,10 @@ class Timelapse extends Component {
Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":[""]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":[""]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":[""]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":[""]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":[""]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":[""]},"User Account":{"msgid":"User Account","msgstr":[""]},"Discover Tab":{"msgid":"Discover Tab","msgstr":[""]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":[""]},"Compare Tab":{"msgid":"Compare Tab","msgstr":[""]},"Pins Tab":{"msgid":"Pins Tab","msgstr":[""]},"Search Places":{"msgid":"Search Places","msgstr":[""]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":[""]},"Education Mode":{"msgid":"Education Mode","msgstr":[""]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":[""]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":[""]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":[""]},"Measure Distances":{"msgid":"Measure Distances","msgstr":[""]},"Download Image":{"msgid":"Download Image","msgstr":[""]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":[""]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":[""]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":["Kommercielle data"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":[""]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":[""]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":[""]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":[""]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":[""]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":[""]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":[""]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":[""]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":[""]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":[""]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":[""]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":[""]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":[""]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":[""]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":[""]},"Level 1":{"msgid":"Level 1","msgstr":[""]},"Level 2":{"msgid":"Level 2","msgstr":[""]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":[""]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":[""]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":[""]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":[""]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":[""]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":["Landsat 7 ETM+ L1"]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":["Landsat 7 ETM+ L2"]},"Red band":{"msgid":"Red band","msgstr":[""]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":[""]},"Blue band":{"msgid":"Blue band","msgstr":[""]},"Green band":{"msgid":"Green band","msgstr":[""]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":[""]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":[""]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":[""]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":[""]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":[""]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":[""]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":[""]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":[""]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":[""]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":[""]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":[""]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":[""]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":[""]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":[""]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":[""]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":[""]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (Atmosfærisk korrigeret)"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":[""]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":[""]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":[""]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":[""]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":[""]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":[""]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":[""]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":[""]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":[""]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":[""]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":[""]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":[""]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":[""]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":[""]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":[""]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":[""]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":[""]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":[""]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":[""]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":[""]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":[""]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":[""]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":[""]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":[""]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":[""]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":[""]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":[""]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":[""]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":[""]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":[""]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":[""]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":[""]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":["**Mapzen DEM** er baseret på SRTM30 (Shuttle Radar Topography Mission) og [andre kilder]. Bathymetry data tages fra [ETOPO1]. Det er en statisk samling (uafhængig af data) med global dækning.\n\n**Rumlig opløsning:** Oftest 90 m, for nogle arealer op til 10 m.\n\nKredit: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)"]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":["**Landsat 4-5 TM** samlingen indeholder billeder produceret med Thematic Mapper (TM) sensoren, som blev bragt om bord på Landsat 4 og 5 satellitterne.Der er 6 optiske og et termisk infrarødt bånd tilgængeligt, alt i 30 m opløsning. Data blev arkiveret med global dækning over land, tilgængelig fra 1982 til 2012. Der gives produkter med den øvre atmosfære level-1 og overflade-reflektering level-2. \n\n**Rumlig opløsning** 30 m\n\n**Tid til genoverflyvning** 16 dage\n\n** Data tilgængelighed** Globalt Level-1 fra august 1982 til maj 2012, Level-2 fra juli 1984 til maj 2012. \n\n** Almindelig brug**: Overvågning af vegetation, is-og vandressourcer, registrering af ændringer og oprettelse af landkort over arealanvendelse."]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":["**Landsat 1-5 MSS** -samlingen inkluderer billeder produceret med Multispectral Scanner System (MSS), som blev transporteret ombord på Landsat 1 gennem Landsat 5-satellitter. Der er 4 optiske bånd tilgængelige i en opløsning på 60 m. Data arkiveres og inkluderer globalt billedsprog siden 1972.\n\n**Rumlig opløsning**: 68 m x 83 m (almindeligvis samplet til 57 m eller 60 m)\n\n**Tid til genoverflyvning**: 18 dage for Landsats 1-3 og 16 dage for Landsats 4-5\n\n**Datatilgængelighed**: Globalt siden:\n- Landsat 1 fra juli 1972 til januar 1978\n- Landsat 2 fra januar 1975 til februar 1982\n- Landsat 3 fra marts 1978 til marts 1983\n- Landsat 4 fra juli 1982 til december 1993\n- Landsat 5 fra 1984 til oktober 1992 og fra juni 2012 til januar 2013\n\n**Almindelig brug**: Overvågning af vegetation, is- og vandressourcer, opdagelse af ændringer og oprettelse af kort over arealanvendelse."]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":["**Landsat 7 ETM+** inkluderer billeder produceret med en Enhanced Thematic Mapper (ETM+) sensor, som blev bragt om bord på Landsat 7 satellitten. Der er 8 optiske og 1 termisk infrarødt bånd til rådighed. Globale data er til rådighed fra 1999 med en genoverflyvnings-periode på 16 dage. Der gives produkter med den øvre atmosfære level-1 og overflade-reflektion level-2. Bemærk manglende data fra alle billeder siden den 30-5-2003 på grund af en sensor-fejl.\n\n**Rumlig opløsning**: 30 meter, 15 meter for et panchromatisk bånd\n\n**Periode for Genoverflyvning**: 16 dage\n\n**Datatilgængelighed **: globalt, siden april 1999\n\n**Almindelig brug**: Overvågning af vegetation, is- og vandressourcer, overvågning af forandringer og kort over areal-anvendelse."]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["**Landsat 4-5 TM Level-1** produkt giver billeder af reflektion i den øvre atmosfære (TOA). Level-1 data produceres ved at processere Landsat TM data med standard proces-parametre som kubisk convolution og terræn-korrektion. Lær mere [her](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) og [her](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects)."]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["**Landsat 4-5 TM Level-2** produkt fremstilles ved at behandle niveau-1-data til overfladerefleksion-et estimat af overfladespektral-reflektansen på jordoverfladen i fravær af atmosfærisk spredning og absorption. Lær mere [her] (https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) og [her] (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects = 0#qt-science_center_objects)."]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":["**Landsat 7 ETM+ Level-1** \n\nLær mere [her](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)"]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":["**Landsat 7 ETM+ Level-2** \n\nLær mere [her](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/)."]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":[""]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":[""]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":[""]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":[""]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":[""]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":[""]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":["Bestille efter:"]},"Location":{"msgid":"Location","msgstr":["Sted"]},"DatasetId":{"msgid":"DatasetId","msgstr":["DatasetId"]},"Title":{"msgid":"Title","msgstr":["Title"]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"Please select a layer":{"msgid":"Please select a layer","msgstr":[""]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":["Histogram kan kun vises under visualisering"]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":["Histogram ikke tilgængelig for "]},"Recalculate":{"msgid":"Recalculate","msgstr":["Beregn igen"]},"Histogram":{"msgid":"Histogram","msgstr":["Histogram"]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":[""]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":[""]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":[""]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":[""]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":[""]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":[""]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":[""]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":[""]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":[""]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":[""]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":[""]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":[""]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":[""]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":[""]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":[""]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":[""]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":[""]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":[""]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":[""]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":[""]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":[""]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":[""]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":[""]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":[""]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":[""]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":[""]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":[""]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":[""]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":[""]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":[""]},"Cloud base height":{"msgid":"Cloud base height","msgstr":[""]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":[""]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":[""]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":[""]},"Cloud top height":{"msgid":"Cloud top height","msgstr":[""]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":[""]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":[""]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":[""]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":[""]},"Ozone total column":{"msgid":"Ozone total column","msgstr":[""]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":[""]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":[""]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":[""]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":[""]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":[""]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":[""]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":[""]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":[""]},"B09 / B08":{"msgid":"B09 / B08","msgstr":[""]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":[""]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":[""]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":[""]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":[""]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":[""]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":[""]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":[""]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":[""]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":[""]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":[""]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":[""]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":[""]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":[""]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":[""]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":[""]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":[""]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":[""]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":[""]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":[""]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":[""]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":[""]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":[""]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":[""]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":[""]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":[""]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":[""]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":[""]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":[""]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":[""]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":[""]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":[""]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":[""]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":[""]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":[""]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":[""]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":[""]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":[""]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":[""]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":[""]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":[""]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":[""]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":[""]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":[""]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":[""]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":[""]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":[""]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":[""]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":[""]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":[""]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":[""]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":[""]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":[""]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":[""]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":[""]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":[""]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":[""]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":[""]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":[""]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":[""]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":[""]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":[""]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":[""]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":[""]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":[""]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":[""]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":[""]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":[""]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":[""]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":[""]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":[""]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":[""]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":[""]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":[""]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":[""]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":[""]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":[""]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":[""]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":[""]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":[""]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":[""]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":[""]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":[""]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":[""]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":[""]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":[""]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":[""]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":[""]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":[""]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":[""]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":[""]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":[""]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":[""]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":[""]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":[""]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":[""]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":[""]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":[""]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":[""]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":[""]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":[""]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":[""]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":[""]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":[""]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":[""]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":[""]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":[""]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":[""]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":[""]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":[""]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":[""]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":[""]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":[""]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":[""]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":[""]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":[""]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":[""]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":[""]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":[""]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":[""]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":[""]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":[""]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":[""]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":[""]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":[""]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":[""]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":[""]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":[""]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":[""]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":[""]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":[""]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":[""]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":[""]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":[""]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":[""]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":[""]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":[""]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":[""]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":["# Global forekomst af overfladevand\n\n\n\nLaget viser (intra-og interårlige) variationer i forekomsten af overfladevand i tiden fra marts 1984 til december 2020. Permanente vandområder med 100 % vandforekomst i disse 36 år vises med blå farve, mens lysere nuancer af pink og lilla angiver lavere forekomst af vand. Lær mere her."]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":["# Global Overfladevand - Forekomst og Foranderlighed\n\n\n\nLaget viser forandringer af vandforekomster gennem to forskellige epoker, den første epoke fra marts 1984 til december 1999, og den anden epoke fra januar 2000 til december 2019. Arealer med stigende vandforekomster vises med forskellige nuancer af grøn, arealer uden forandring farves sort og arealer med faldende vandforekomster vises med røde nuancer. Lær mere [her](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/)."]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":[""]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":[""]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":[""]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":[""]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Maximalt skydække:"]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Upload data"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":["Gennemse, visualiser og analyser Very High Resolution (VHR) data direkte i EO Browser, og klik på globale arkiver for Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades]((https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/)) og [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) såvel som [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/).\n\nObservér planeten ved opløsninger, der starter ved 3 meter og helt op til 0,5 meter for en pris ned til 0,9 EUR pr. km².\n\n![High resolution imagery eksempel.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nHvad du har brug for:\nEt aktivt Sentinel Hub - abonnement til søgning af metadata. Hvis du endnu ikke har en konto: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n-Forhåndskøbt kvote for nogen af konstellationerne. Gå til [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) for at oprette et abonnement og købe kommercielle dataplaner."]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]}}}}
\ No newline at end of file
+{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","plural-forms":"nplurals=2; plural=(n != 1);","language":"da_DK","mime-version":"1.0","content-transfer-encoding":"8bit","project-id-version":"","pot-creation-date":"","po-revision-date":"","last-translator":"","language-team":"","x-generator":"Poedit 3.0"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nPlural-Forms: nplurals=2; plural=(n != 1);\nLanguage: da_DK\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\nProject-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLast-Translator: \nLanguage-Team: \nx-generator: Poedit 3.0\n"]},"Education":{"msgid":"Education","msgstr":["Uddannelse"]},"Normal":{"msgid":"Normal","msgstr":["Normal"]},"Close":{"msgid":"Close","msgstr":["Luk"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Luk og vis ikke igen"]},"Previous":{"msgid":"Previous","msgstr":["Tidligere"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Afslut selvstudiet"]},"Next":{"msgid":"Next","msgstr":["Næste"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Fortsæt med selvstudiet"]},"Don't show again":{"msgid":"Don't show again","msgstr":["Vis ikke igen"]},"Show info":{"msgid":"Show info","msgstr":["Vis information"]},"Discover":{"msgid":"Discover","msgstr":["Undersøg"]},"Visualize":{"msgid":"Visualize","msgstr":["Gør synlig"]},"Compare":{"msgid":"Compare","msgstr":["Sammenlign"]},"Pins":{"msgid":"Pins","msgstr":["Pins"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Der er opstået en fejl under hentning af billeder:"]},"No tile found":{"msgid":"No tile found","msgstr":["Intet felt fundet"]},"Dataset":{"msgid":"Dataset","msgstr":["Datasæt"]},"Show":{"msgid":"Show","msgstr":["Vis"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Vis effekter og avancerede indstillinger"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Vis visualisering"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Tilføj til Pins"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Tilføj til sammenlign"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Zoom til feltet"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Skjul lag"]},"Show layer":{"msgid":"Show layer","msgstr":["Vis lag"]},"Share":{"msgid":"Share","msgstr":["Del"]},"Custom":{"msgid":"Custom","msgstr":["Tilpas selv"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Skab egen tilpasning"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Zoom ind for at se data"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Fri sign up"]},"for all features":{"msgid":"for all features","msgstr":["fr alle funktioner"]},"Powered by":{"msgid":"Powered by","msgstr":["Drevet af"]},"with contributions by":{"msgid":"with contributions by","msgstr":["med bidrag fra"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["Vælg datakilder!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["Ugyldigt tidsrum!"]},"No results found":{"msgid":"No results found","msgstr":["Ingen resultater fundet"]},"Theme":{"msgid":"Theme","msgstr":["Tema"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Administrer konfiguration af tilfælde"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Login for selv at tilpasse."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["Fejl under hentning af yderlige data!"]},"Search":{"msgid":"Search","msgstr":["Undersøg"]},"Highlights":{"msgid":"Highlights","msgstr":["Gode Eksempler"]},"Data sources":{"msgid":"Data sources","msgstr":["Datakilder"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Vælg et tema"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Tidsramme [UTC]"]},"Date":{"msgid":"Date","msgstr":["Dato"]},"Hide description":{"msgid":"Hide description","msgstr":["Skjul beskrivelse"]},"Show description":{"msgid":"Show description","msgstr":["Vis beskrivelse"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Dette tema har ingen eksempler"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Baseret på: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["1 dag (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["5 dag (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["10 dag (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["O3 (Ozon)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (Nitrogendioxid)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (Svovldioxid)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (Carbonmonoxid)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (Formaldehyd)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (Methan)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER Al (Aerosol-index)"]},"Cloud":{"msgid":"Cloud","msgstr":["Sky"]},"Other":{"msgid":"Other","msgstr":["Andre"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Maximalt skydække"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Avanceret søgning"]},"Data location":{"msgid":"Data location","msgstr":["Data-placering"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Vælg mindst et sted!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Anskaffelses-tilstand"]},"Polarization":{"msgid":"Polarization","msgstr":["Polarisering"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Vælg mindst en anskaffelses-tilstand!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Vælg mindst en polarisering!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Kredsløbs-retning"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Vælg mindst en kredsløbs-retning!"]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["**MERIS** (Medium-resolution spectrometer) var en sensor om bord på[ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellitten med det primære formål at overvåge landjorden, havets farve og atmosfæren. Den virker ikke mere og er blevetafløst af Sentinel-3.\n\n**Rumlig opløsning:** Fuld opløsning for land og kyst: 260 m x 290 m (det betyder at kun detaljer større end 260 m x 290 m kan ses).\n\n**Genbesøgelse tidsrum:** maximum 3 dage før overflyvning af samme område.\n\n**Data tilgængelighed:** Fra juni 2002 to april 2012.\n\n**Almindelig brug:** Hav måling (planteplankton, fine partikler), atmosfære (vanddamp, CO2, skyer, aerosoler), og land (vegetation index, global dækning, fugtighed)."]},"Credits:":{"msgid":"Credits:","msgstr":["Kredit:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["**GIBS** (Global Imagery Browse Services) giver hurtig adgang til mere end 600 satellitbilleder\nprodukter der dækker alle dele af Jorden. De fleste billeder er tilgængelige inden for få timer after\nsatellit-overflyvning, Nogle produkter fordeler sig over næsten 30 år."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["Serien af **Landsat** satellitter fra NASA/ U.S. Geological Survey ligner Sentinel-2 (De opfanger synlige og infrarøde bølgelængder)\nog kan også opfange termisk i infrarød (Landsat 8). Landsat serien har en lang historie, der spænder over næsten 5 årtier.\n Denne platform giver dig adgang til billeder optaget af Landsat 5, 7 and 8.\n\n**Rumlig opløsning:** 15 m, 30 m, og 100 m genprøvet til 30 m, afhængig af bølgelængden (det betydet at kun detaljer større end 10 m og 30 m, kan ses). Mere info [her](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Genoverflyvnings tidsrum:** Maximum 8 dage før samme område gen-overflyves ved brug af de tooperationelle satellitter Landsat 7 og Landsat 8.\n\n**Data tilgængelighed:** Europa og Nordafrika fra 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 indtil nu (Landsat 8) fra ESA arkiv. The global U.S. Geological Survey (USGS) arkiv siden april 2013 indtil i dag (kun Landsat 8 ) .\n\n**Almindelig brug:** Overvågning af vegetation, land-udnyttelse, landsdækkende kort, overvågning af forandringer , etc."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) henter data med det formål\nat forbedre vores forståelse af de globale processer der forekommer på landjorden. EO browser forsyner med data til\nobservation af landjorden (bånd 1-7).\n\n**Rumlig opløsning:** 250 m (bånd 1-2), 500 m (bånd 3-7), 1000 m (bånd 8-36).\n\n**Genoverflyvnings tidsrum:** Global dækning på 1 – 2 dage med både hav- og land satellitter.\n\n**Data tilgængelighed:** siden januar 2013.\n\n**Almindelig brug:** Overvågning af landjorden, skyer, havets farve i globalskala."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["**Proba-V** satellitten er en lille satellit designet til at kortlægge landjorden og vegetationens vækst\nover hele Jorden hver gang der er forløbet to dage. EO Browser forsyner afledteprodukter som minimerer skydækket\nved at kombinere med skyfrie målinger fra i løbet af 1 dag (S1), 5 dage (S5) og 10 dage (S10) i perioden.\n\n**Rumlig opløsning:** 100 m for S1 og S5, 333 m for S1 og S10, 1000 m for S1 og S10.\n\n**Genoverflyvnings tidsrum:** 1 dag for breddegrader mellem 35-75°N og 35-56°S, 2 days for breddegrader mellem 35°N\nog 35°S.\n\n**Data tilgængelighed:** Siden October 2013.\n\n**Almindelig brug:** M Observation af landjorden, vegetationens vækst, vurdering af klimaforandringer,\nvandressource-forvaltning, landbrugs-overvågning og fødevare-sikkerhedestimater, vand i landområdet\nressource-overvågning og sporing af den konstante spredning af ørkener ogskovrydning."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":["**Sentinel-1** giver dag- og nat radarbilleder af al slags vejr for landjordenog for havet. EO\nBrowseren giver data indhentet i Interferometric Wide Swath (IW) og Extra Wide Swath (EW) tilstande\nbehandlet til niveau-1 Ground Range Detected (GRD).\n\n**Pixel-afstand:** 10 m (IW), 40 m (EW).\n\n**Genoverflyvnings tidsrum:** <= 5 dage ved at bruge begge satellitter.\n\n**Genoverflyvnings tidsrum** (for asc/desc og overlap ved brug af begge satellitter): <= 3 dage, se [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data tilgængelighed:** Siden oktober 2014.\n\n**Almindelig brug:** Overvågning af hav og land, nødberedskab, klima- forandringer."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["**Sentinel-2** giver os høj-resolution billeder i de synlige og i de infrarødebølgelængder, for at overvåge vegetation, jord og vanddækning, indre vandvejeog kystområder. .\n\n**Rumlig opløsning:** 10 m, 20 m, og 60 m, afhængig af bølgelængden (Det betyder at kun detaljer større end 10 m, 20 m og 60 m kan ses). Mere info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Genoverflyvnings tidsrum:** maximum 5 dage til genbesøg af samme område ved brug af begge satellitter.\n\n**Data tilgængelighed:** Siden juni 2015. Fuld global dækning siden march 2017.\n\n**Almindelig brug:** Land-cover kort, kort over målinger af forandringer på landjorden, vegetation overvågning, overvågning af brændte områder."]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Niveau 2A data af høj kvalitet, hvor det er udelukket at forstyrrende effekter af atmosfæren kan påvirkesensorens måling af reflectioner fra Jordens overflade."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Niveau 1C data are data med tilstrækkelig god kvalitet til de fleste undersøgelser, hvor alle billedkorrektioner bliver foretaget bortset fra atmosfærisk korrektion. Dataer tilgængelige for tider fra juni 2015 og senere."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["**Sentinel-3** missionens hovedformål er at måle topografi af havoverfladen, hav- og land-overfladens temperatur, hav- og land-overfladens farve. Sentinel-3 har fire forskellige instrumenter om bord. Data erhvervet af Ocean and Land Colour Instrument (OLCI) og Sea and Land Surface Temperature Instrument (SLSTR) er tilgængelige på denne platform\n\n**Data tilgængelighed:** Siden maj 2016 ."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["**Sea and Land Surface Temperature (SLSTR)** instrumentet om bord påSentinel-3 måler den globale og regionale hav- og land-overflades\ntemperatur. SLSTR dækker synlige, kortbølge infrarøde, og thermal infrarøde bølgelængder i det elektromagnetiske spektrum. \n\n**Rumlig opløsning:** 500 m for synlige, nær- og kortbølge infrarøde bølgelængder og 1km for thermal infrared (det betyder at, kun detaljer \nstørren end 500 m og 1 km kan ses, henholdsvist.\n\n**Genoverflyvnings tidsrum:** Maximum 1 dag før genbesøg af samme område, ved brug af beggesatellitter.\n\n**Data tilgængelighed:** Siden may 2016.\n\n**Almindelig brug** Overvågning af klimaforandringer, overvågning af vegetation, aktivbrandovervågning, overvågning af temperatur på land og hav."]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["**Hav og land farve instrumentet: Ocean and Land Colour Instrument (OLCI)** om bord på Sentinel-3 er et spektrometer der \nsolindstrålingens reflection af Jorden, og den overvåger havet, miljøet, \nog klimaet. Den giver hyppigere synlige billeder end Sentinel-2 men i en mindre opløsning\nog dækkende flere bølgelængder. Sentinel-3 OLCI instrumentet viderefører de målinger,der tidligere udførtes af MERIS instrumentet om bord påEnvisat, hvis mission er afsluttet.\n\n**Rumlig opløsning:** 300 m (Det betyder, at kun detaljer større end 300 kanses).\n\n**Genoverflyvnings tidsrum:** Maximum 2 dage før genbesøg af det sammen område, ved brug af begge satellitter.\n\n**Data tilgængelighed:** Siden may 2016.\n\n**Almindelig brug:** Overflade topography, farve af hav- og land-overfladerobservationer og overvågning."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["**Sentinel-5P** er en satellit, der giver atmosfæriske målinger til brug for overvågning af luftkvalitet, ozon, UV stråling,\nog overvågning af klimaet samt opstilling af prognoser.\n\n**Rumlig opløsning:** 7 x 3.5 km (Det betyder, at kun detaljer større end 7 x 3.5 km kan sees).\n\n**Genoverflyvnings tidsrum:** Maximum 1 dag før genbesøg af det samme område.\n\n**Data tilgængelighed:** Siden april 2018.\n\n**Almindelig brug:** Overvågning af koncentrationen af carbon monoxide (CO), nitrogen dioxide (NO2) og ozone (O3) i luften. Overvågning af UV aerosol index (AER_AI) og forskellige geofysiske parametre af skyer (Cloud)."]},"Copied":{"msgid":"Copied","msgstr":["Kopieret"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["Kopier til udklipsholder"]},"Data source name":{"msgid":"Data source name","msgstr":["Datakilde navn"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Tid for måling"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Skydække"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Solhøjde"]},"MGRS location":{"msgid":"MGRS location","msgstr":["MGRS sted"]},"AWS path":{"msgid":"AWS path","msgstr":["AWS sti"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["EO sky sti"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["CreoDIAS sti"]},"SciHub link":{"msgid":"SciHub link","msgstr":["SciHub link"]},"Back to search":{"msgid":"Back to search","msgstr":["Tilbage til undersøgelse"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Visning af ${ this.state.results.length } resultat","Visende ${ this.state.results.length } resultater"]},"Load more":{"msgid":"Load more","msgstr":["Indlæs mere"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Indlæser flere resultater..."]},"Results":{"msgid":"Results","msgstr":["Resultater"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Visende ${ this.state.selectedTiles.length } resultat.","Visende ${ this.state.selectedTiles.length } resultater."]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Rediger pin beskrivelse"]},"Reject changes":{"msgid":"Reject changes","msgstr":["Afvis ændringer"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Bekræft ændringer"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Omdøb pin"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Fjern pin"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Zoom til pin location"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Bredde/Længde"]},"Zoom":{"msgid":"Zoom","msgstr":["Zoom"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["Du er ved at tilføje ${ N_PINS } pin(s) til din pin-samling. Ønsker duat fortsætte?"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["ADVARSEL: Du er ved at slette en pin. Ønsker du at fortsætte?"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":[""]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["Ingen pins. Gå til ikonet: Gør synlig for at gemme en pin eller upload en JSON file medgemte pins."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Bemærk at kun bliver gemt hvis du logger in. Ellers vil pinsblive tabt når programmet lukkes."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Fravælg alt"]},"Select all":{"msgid":"Select all","msgstr":["Vælg alt"]},"No pins.":{"msgid":"No pins.","msgstr":["Ingen pins."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Skab et link (${ selectedPins.length } pin valgt)","Skab et link (${ selectedPins.length } pins valgt)"]},"File type not supported":{"msgid":"File type not supported","msgstr":["Fil-typen understøttes ikke"]},"not supported":{"msgid":"not supported","msgstr":["understøttes ikke"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["Ingen pins blev fundet."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Fejl ved parsing af filen:"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":[""]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["Upload en JSON fil med gemte pins"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Behold existerende pins"]},"Share pins":{"msgid":"Share pins","msgstr":["Del pins"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Skab en historie fra pins"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Exporter pins til computeren"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Importer pins fra en gemt fil"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Slet alle pins"]},"Story":{"msgid":"Story","msgstr":["Historie"]},"Export":{"msgid":"Export","msgstr":["Exporter"]},"Import":{"msgid":"Import","msgstr":["Importer"]},"Clear":{"msgid":"Clear","msgstr":["Rens"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Del pins link"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Opdaterer pin samling."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Der var et problem med permanent at opdatere pin samlingen: ${ updatingPinsError }"]},"Opacity":{"msgid":"Opacity","msgstr":["Gennemsigtighed"]},"Split position":{"msgid":"Split position","msgstr":["Delt position"]},"split":{"msgid":"split","msgstr":["del"]},"opacity":{"msgid":"opacity","msgstr":["gnnemsigtighed"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["Ingen lag at sammenligne."]},"Remove all":{"msgid":"Remove all","msgstr":["Fjern alt"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Tilføj alle pins"]},"Split":{"msgid":"Split","msgstr":["Del"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Der var et problem med at downloade dine forekomster"]},"Download":{"msgid":"Download","msgstr":["Download"]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Synliggør terræn i 3D"]},"Go to Place":{"msgid":"Go to Place","msgstr":["Gå til stedet"]},"Labels":{"msgid":"Labels","msgstr":["Labels"]},"Borders":{"msgid":"Borders","msgstr":["Grænser"]},"Roads":{"msgid":"Roads","msgstr":["Veje"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Zoom ind"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Zoom ud"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["Om EO Browser"]},"Contact us":{"msgid":"Contact us","msgstr":["Kontakt os"]},"Get data":{"msgid":"Get data","msgstr":["Få data"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Du skal logge ind for at kunne bruge denne function."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Vælg lag."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["Det er ikke muligt at downloade billedet i compare mode."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["Denne datakilde er ikke understøttet."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":[""]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Statustiske oplysninger / Funktionsinformation service diagram - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["vælg et lag"]},"not available for ":{"msgid":"not available for ","msgstr":["ikke tilgængelig for "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["ikke tilgængelig for \"${ props.presetLayerName }\" (lag med værdi er ikkesat op)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Søg efter data først."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Skab en timelapse animation"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Markér et punkt af interesse"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Midter-kort på funktion"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Fjern geometri"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Areal af interesse"]},"Select mode":{"msgid":"Select mode","msgstr":["Vælg tilstand"]},"Mode:":{"msgid":"Mode:","msgstr":["Mode:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Fjern måling"]},"km":{"msgid":"km","msgstr":["km"]},"m":{"msgid":"m","msgstr":["m"]},"Gain":{"msgid":"Gain","msgstr":["Gvinst"]},"Gamma":{"msgid":"Gamma","msgstr":["Gamma"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Min. datakvalitet"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Opsamling"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Downsampling"]},"Reset all":{"msgid":"Reset all","msgstr":["Reset all"]},"filter by months":{"msgid":"filter by months","msgstr":["filtrer i månederf"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Kopier geometri til udklipsholder"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Afbestil redigering."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Tegn et areal af interesse"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Mindst skydække"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Brug yderligere dataset (avanceret)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Mosaik orden"]},"Most recent":{"msgid":"Most recent","msgstr":["Lige for nyligt"]},"Least recent":{"msgid":"Least recent","msgstr":["Lidt tidligere"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Tilpas tidsrummet"]},"Back":{"msgid":"Back","msgstr":["Tilbage"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Der skete en fejl ved indlæsning af dit script. Check din URL."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Fjern markeringen i indlæs script for at redigere koden"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Indlæs script fra URL"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["Indtast URL til dit script"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Script indlæst."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Kun HTTPS domæner er tilladte."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Indlæs script i kode-editoren"]},"Refresh":{"msgid":"Refresh","msgstr":["Prøv igen"]},"orbit":{"msgid":"orbit","msgstr":["kredsløb"]},"day":{"msgid":"day","msgstr":["dag"]},"week":{"msgid":"week","msgstr":["uge"]},"month":{"msgid":"month","msgstr":["måned"]},"year":{"msgid":"year","msgstr":["år"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Vælg et billede per:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Timelaps"]},"Select All":{"msgid":"Select All","msgstr":["Vælg alt"]},"Speed:":{"msgid":"Speed:","msgstr":["Frt:"]},"frames / s":{"msgid":"frames / s","msgstr":["rammer/s"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Forbereder..."]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Kunne ikke downloade filer:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Kan ikke downloade via lærred"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["Kunne ikke ZIP filerne:"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Der var et problem med at downloade billedet"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Der opstod en fejl under hentning af billeder: URL er tom!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Der opstod en fejl ved hentning af billedet:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["Billedet kunne ikke indlæses fra blob"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Træk bånd ind RGB felter."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Træk bånd ind i indeks-ligningen"]},"Index ":{"msgid":"Index ","msgstr":["Index "]},"Threshold":{"msgid":"Threshold","msgstr":["Grænseværdi"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Fjerne farvevælgeren"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Tilføj farvevælgeren"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Klik for at placere markøren"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Klik for at placere det første toppunkt"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Klik for at fortsætte med at tegne"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Klik på den første markør for at afslutte"]},"Show captions":{"msgid":"Show captions","msgstr":["Vis billedtekster"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Vis dias titel"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Tilføj kort-overlejringer"]},"Show legend":{"msgid":"Show legend","msgstr":["Vis legende"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["Ingen pins blev fundet i dette synsfelt."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Nogle pins (${ N_PINS_OUTSIDE_BOUNDS }) ignoreres fordi de ikke erinden for det valgte område."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["For at skabe en pin-historie, naviger til den ønskede position på kortet.\n\nalle pins i dette synsfelt vil blive brugt til at skabe historien, de resterend vil blive ignoreret."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["Filen får et logo vedhæftet."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["Et dataMask-bånd vil bliver tilføjet til de downloadede rå bånd som andet band."]},"Show logo":{"msgid":"Show logo","msgstr":["Vis logo"]},"Image format":{"msgid":"Image format","msgstr":["Billedformat"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Billedopløsning"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Koordinatsystem"]},"Layers":{"msgid":"Layers","msgstr":["Lag"]},"Visualized":{"msgid":"Visualized","msgstr":["Synliggjort"]},"Raw":{"msgid":"Raw","msgstr":["Rå"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["Kortets overlejrings-lag (placer labels, gader og politiske grænser) vil blive tilføjet til billedet."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Eksporterede billeder vil indeholde datakilde og dato, zoom skala og branding"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Tilføj en kort beskrivelse til det eksporterede billede"]},"Description":{"msgid":"Description","msgstr":["Beskrivelse"]},"Image format:":{"msgid":"Image format:","msgstr":["Billedformat:"]},"Basic":{"msgid":"Basic","msgstr":["Grundlæggende"]},"Analytical":{"msgid":"Analytical","msgstr":["Analytisk"]},"High-res print":{"msgid":"High-res print","msgstr":["Print med høj opløsning"]},"Download image":{"msgid":"Download image","msgstr":["Download billede"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["En fejl er opstået under hentningen af billederne:"]},"min/px":{"msgid":"min/px","msgstr":["min/px"]},"sec/px":{"msgid":"sec/px","msgstr":["sek/gx"]},"Resolution":{"msgid":"Resolution","msgstr":["Opløsning"]},"lat.":{"msgid":"lat.","msgstr":["bredde"]},"deg/px":{"msgid":"deg/px","msgstr":["grad/px"]},"long.":{"msgid":"long.","msgstr":["længde"]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Projekteret opløsning: ${ formattedResolution } m/px"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Error: Data fusion understøtter ikke KMZ/JPG and KMZ/PNG formater."]},"Image download":{"msgid":"Image download","msgstr":["Billed download"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Billedbredde [inches]:"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Image højde [inches]:"]},"DPI:":{"msgid":"DPI:","msgstr":["DPI:"]},"5 years":{"msgid":"5 years","msgstr":["5 år"]},"2 years":{"msgid":"2 years","msgstr":["2 år"]},"1 year":{"msgid":"1 year","msgstr":["1 år"]},"6 months":{"msgid":"6 months","msgstr":["6 måneder"]},"3 months":{"msgid":"3 months","msgstr":["3 måneder"]},"1 month":{"msgid":"1 month","msgstr":["1 måned"]},"Retry":{"msgid":"Retry","msgstr":["Prøv igen"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Indlæser, vent venligst"]},"mean":{"msgid":"mean","msgstr":["betyde"]},"median":{"msgid":"median","msgstr":["median"]},"st. dev.":{"msgid":"st. dev.","msgstr":[""]},"min / max":{"msgid":"min / max","msgstr":["min/max"]},"Export CSV":{"msgid":"Export CSV","msgstr":["Eksport CSV"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Tidsrum:"]},"Date:":{"msgid":"Date:","msgstr":["Dato:"]},"Single date":{"msgid":"Single date","msgstr":["Enkel dato"]},"Timespan":{"msgid":"Timespan","msgstr":["Tidsrum"]},"hh":{"msgid":"hh","msgstr":["hh"]},"mm":{"msgid":"mm","msgstr":["mm"]},"From:":{"msgid":"From:","msgstr":["Fra:"]},"Until:":{"msgid":"Until:","msgstr":["Indtil:"]},"Apply":{"msgid":"Apply","msgstr":["Asøge"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Dele på Facebook"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Dele på Twitter"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Se lige det her "]},"Logout":{"msgid":"Logout","msgstr":["Log ud"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["Log ind for at åbne for avancerede funktioner som timelapse, analytisk download, egne konfigurationer og mere."]},"Login":{"msgid":"Login","msgstr":["Log ind"]},"Default":{"msgid":"Default","msgstr":["Standard"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Overvågning af Jorden fra Rummet"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Landbrug"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Atmosfære og Luftforurening"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Skift Registrering via Tid"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Oversvømmelse og Tørke"]},"Geology":{"msgid":"Geology","msgstr":["Geologi"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Hav og Vandområder"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Sne og gletschere"]},"Urban":{"msgid":"Urban","msgstr":["Byer"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Vegetation og Skovbrug"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Vulkaner"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Skovbrande"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Bånd 1 - Blå - 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Bånd 2 - Grøn - 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Bånd 3 - Rød - 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Bånd 4 - NIR - 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Bånd 5 - SWIR-1 - 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Bånd 7 - SWIR-2 - 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Bånd 8 - Panchromatic - 520-900 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Bånd 2 - Blå - 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Bånd 3 - Grøn - 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Bånd 4 - Rød - 630-680 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Bånd 6 - SWIR-1 - 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Bånd 7 - SWIR-2 - 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Bånd 8 - Panchromatic - 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Bånd 9 - Cirrus - 1360-1390 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Refleksion"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Lysstyrke temperatur"]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":["Skab en timelapse af dette område"]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":["Advarsel: Følgende lag bruger dataprodukter, så den ønskede datatype kan måskeikke indstilles:"]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":["Advarsel: Evalscript er ikke i et typist V3 format og den ønskede datatypekunne ikke indstilles:"]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":["Det betyder at \"sampleType\" parameter sandsynligvis er indstillet til standard(AUTO).Dukan fixe det ved at redigere dit evalscript. Lær mere om \"sampleType\" i dokumentationen"]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":["Fejl: Du kan kun downloade visualisation med effekter i JPEG or PNG formater."]},"Measure":{"msgid":"Measure","msgstr":["Mål"]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":["Sentinel-1 tjenester er tilgængelige både på EOCloud and AWS. Mulighederneaf hver\ntjeneste varierer. Mere info ved"]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":["Det taggede billedformat (TIFF) kan indeholde et stort antal bånd.Dog kan mange almindelige billedfremvisere (fx Windows Photo Viewer) ikke viseTIFF billeder med mere end 3 bånd.\nHvis denne mulighed er aktiveret, vil kun de 3 første bånd blive inkluderet ibilledet.\nHvis denne indstilling er deaktiveret, vil alle bånd blive inkluderet i billedet, men man bliver nødt til at bruge et program der understøtter mere end 3 bånd (fx QGIS) for at vise TIFF billedet."]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":["Tilføj dataMask bånd til de rå lag"]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":["Klip ekstra bånd"]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":["EPSG:3857 er ikke tilgængelig når et AOI er angivet."]},"Creating link...":{"msgid":"Creating link...","msgstr":["Opretter link..."]},"OK":{"msgid":"OK","msgstr":["OK"]},"Hello,":{"msgid":"Hello,","msgstr":["Hallo,"]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":["Denne pin har netop nu ikke nogen beskrivelse."]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":["Din web browser understøtter ikke 3D de muligheder, der kræves for at afspille dette indhold."]},"More information":{"msgid":"More information","msgstr":["Mer information"]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":["Kan ikke forbinde til 3D tjenester! Prøv igen?"]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":["Billedet er for stort for denne enhed!\nBilledstørrelse: {0}x{1}, max: {2}"]},"Home":{"msgid":"Home","msgstr":["Hjem"]},"Shading":{"msgid":"Shading","msgstr":["Skyggende"]},"Sphere mode":{"msgid":"Sphere mode","msgstr":["Sfære tilstand"]},"Eye height":{"msgid":"Eye height","msgstr":["Øjenhøjde"]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":["Billedet kan ikke indlæses"]},"Geometries":{"msgid":"Geometries","msgstr":["Geometri"]},"Now":{"msgid":"Now","msgstr":["Nu"]},"Terrain":{"msgid":"Terrain","msgstr":[""]},"Time":{"msgid":"Time","msgstr":["Tid"]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":["Avancerede RGB effekter"]},"Left button":{"msgid":"Left button","msgstr":["Venstre knap"]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":["Klik og træk med venstre museknap for at flytte på tværs af kortet i en fast højde. Brug SHIFT + venstre knap for at rotere."]},"Right button":{"msgid":"Right button","msgstr":["Højre knap"]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":["Højreklik og træk op/ned for at ændre Kameraets højde. Højre click and\nTræk venstre/højre for at rotere kameraets synsfelt."]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":["Midterste knap/hjul"]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":["Bug scroll hjulet for at ændre højden af kameraet (samme som højre click + træk\nup/down). Klik og træk hjul knappen for at ændre kameraets vinkel."]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":["Tastaturnavigation"]},"Arrow keys":{"msgid":"Arrow keys","msgstr":["Pile taster"]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":["Brug piletasterne til at bevæge dig på tværs af kortet i en bestemt højde."]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":["SHIFT + piletaster"]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":["Hold SHIFT tasten nede mens du presser piletasten for at ændre kameraetssynsvinkel."]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":["Side op/side ned"]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":["Brug PG UP eller PG DN tasterne for at ændre højden af kameraet."]},"Map navigation":{"msgid":"Map navigation","msgstr":["Kort navigation"]},"Pan console":{"msgid":"Pan console","msgstr":["Pan konsol"]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":["Pan Konsolen tillader dig at bevæge dig i en bestemt højde. Klik og træk for at flytte dig\n. Jo fjernere fra centeret du trækker, des hurtigere vil du."]},"Camera console":{"msgid":"Camera console","msgstr":["Kamera konsol"]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":["Kamera Konsollen bevæger kun kameraets synsfelt\" Klik og træk for at ændrekameraets synsfelt."]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":["Zoom knapper"]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":["At klikke på dem vil ændre kameraets højde. Plusknappen vil bevæge kameraet\ntættere på Jorden, vil minus knappen bevæge kameraet længere bort."]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["**Copernicus DEM** repræsenterer Jordens overflade inklusiv bygninger infrastruktur og vegetation. I lighed med Mapzen DEM, er denbaseret på en kombination af forskellige DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Det er en statisk samling(uafhængig af dato) med global dækning.\n\n**Rumlig opløsning:** 90 m\n\nKredit: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":["A **DEM** (Digital Elevation Model) er en digital repræsentation af et terræn (normalt jordens overflade). Det opnås ved at dele heleglobus i gitterceller, der hver har en tilsvarende højde-værdi i meter. Afhængig af gitter-cellens størrelse, kan en DEM være mere detaljeret (høj opløsning) eller mindre detaljeret (lav opløsning). Sentinel Hub DEM data collections (Mapzen og Copernicus) er statiske (uafhængige af data) og tilgængelige globalt.\n\n**Almindelig brug:** Modellering af vandstrømme, orthorectification af Sentinel-1 billeddannelse og konstruktion."]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["**Copernicus DEM** repræsenterer Jordens overflade inklusiv bygninger infrastruktur og vegetation. I lighed med Mapzen DEM, er denbaseret på en kombination af forskellige DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Det er en statisk samling(uafhængig af dato) med global dækning.\n\n**Rumlig opløsning:** 90 m\n\nKredit: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":["Primære dataset:"]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":["Datakilde alias:"]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":["Yderlige dataset:"]},"Cancel":{"msgid":"Cancel","msgstr":["Aflys"]},"Error":{"msgid":"Error","msgstr":["Fejl"]},"Help":{"msgid":"Help","msgstr":["Hjælp"]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":["Position 3D kamera baseret på 2D kort"]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":["Muse-navigation"]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":["Dine bruger-forekomster kunne ikke indlæses da din Sentinel Hub konto erikke konfigureret/udløbet. Du kan stadig bruge EO Browser men du vil ikke være i standtil at bruge personlige bruger-forekomster. For at kunne konfigurere en personligbruger kan du ansøge om en gratis prøveperiode eller overveje at abonnere på en af planerne: "]},"User Instances":{"msgid":"User Instances","msgstr":["Bruger forekomster"]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":["Disse er tema-dele som indeholder u-tilgængelige datakilder:"]},"Disabled":{"msgid":"Disabled","msgstr":["Afbrudt"]},"Yes":{"msgid":"Yes","msgstr":["Ja"]},"Orthorectification":{"msgid":"Orthorectification","msgstr":["Orthorectification"]},"Zoom to location":{"msgid":"Zoom to location","msgstr":["Zoom til lokation"]},"Remove layer":{"msgid":"Remove layer","msgstr":["Fjern lag"]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":["Bånd 10 - Thermal Infrarød (TIRS) - 10895 nm"]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":["Bånd 11 - Thermal Infrarød (TIRS) - 12005 nm"]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":["Hoved-klassificering af diskret jorddækning i henhold til FAO LCCS ordning"]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":["Klassificerings-sandsynlighed, en kvalitetsindikator for den diskreteklassifikation"]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":["Skovtype for alle pixels hvor træ-dækningen er større end 1 %"]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":["Procent-dækning (%) af den nøgne og sparsomme vegetation"]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":["Procent-dækning (%) af de dyrkede marker"]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":["Procent-dækning (%) af de urteagtige planter"]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":["Procent-dækning (%) af mosser og laver"]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":["Procent-dækning (%) af buske"]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":["Procent-dækning (%) af sne og is"]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":["Procent-dækning (%) af skove"]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":["Procent-dækning (%) af bygninger"]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":[""]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":["Procent-dækning (%) af sæsonbestemte vådområder i indlandet"]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":["Data- tætheds-indikator der viser kvaliteten af EO input data (0 = dårlig, 100 = perfekte data)"]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":["Kvalitets-lag vedrørende måling af forandringer i det løbende kortlagte år i forhold til det tidligere kortlagte år. Det er et 3 niveauer for alle CONSO og NRT kort med værdi definitioner som:\n0 = ingen forandring.\n1 - Potentiel tillid.\n2 - Middel tillid.\n3 = Høj tillid.\nNOTE: Værdierne af Forandring_ Tillid_Lag af båndet i 2015 data vises ikke korrekt, derfor bør dette bånd i 2015 ikke bruges."]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":["Træk klasser til RGB felter."]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":["**CORINE Land Cover (CLC)** inventory er et vektorbaseret data-set der består af 44 land-dæknings og land-anvendelsesklasser, afledt fra en serie afsatellit-missioner. I fleste europæiske lande dannes CLC ved hjælp af visuel fortolkning af satellitbilleder i høj opløsning. I enkelte lande er der indført semi-automatiske løsninger ved brug af national in-situ data, satellit-billed-processering, GIS-integration og generalisation. Mere information [her](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Dækning**: I det meste af Europa.\n\n**Data tilgængelighed**:\nCLC data opdateres hvert 6. år. I EO Browser, data er tilgængelig på de følgende datoer:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Almindelig brug**:\novervågning af arealanvendelse, analyse og forudsigelse af ændring forforskellige applikationer, som miljø, landbrug, transport ogrumlig planlægning."]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":["**Global Land Cover** produkterne giver en diskret klassifikation af landdække- kort i henhold til UN-FAO Land Cover Classification System. Yderligere kontinuerlige fraktionslag for alle grundlæggende landdækningsklasser er inkluderetsom bånd, for at give mere detaljerede oplysninger om hver landdækningsklasse.Mere information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Dækning**: Global.\n\n**Data tilgængelighed**:\nOpdateres på årlig basis. I EO Browser, data er tilgængelige på defølgende datoer:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Almindelig brug**: \novervågning af arealanvendelse og jorddækning der bruges til at støtte politiske beslutninger om forskelligespørgsmål, herunder landbrug og fødevaresikkerhed, biodiversitet, klima-forandringer, skov- og vandressourcer, jordforringelse og ørkendannelse ogudvikling i landdistrikterne."]},"File upload":{"msgid":"File upload","msgstr":["Fil upload"]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Upload a KML/KMZ, GPX or GEOJSON/JSON fil for at skabe et areal af interesse. Arealet vil blive brugt til klipning når man eksporterer et billed."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":[""]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":["De største vand-forekomsters detekterings-lag der viser vand pixels og ikke-vand pixels\n0 = Hav\n70 = Wandr\n251 = Ingen data\n255 = Intet vand"]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":["Kvalitets-lag der giver information om vandforekomsterter \n0 = Hav\n71 = Meget lav forekomst\n72 = Lav forekomst\n73 = Middel forekomst\n74 = Høj forekomst\n75 = Meget høj forekomst\n76 = Permanent forekomst\n251 = Ingen data\n252 = Skyer\n255 = Ikke vand"]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":["Produktet **Water Bodies** viser vandoverfladers udbredelse til landsvand i en permanent, sæsonbestemt eller lejlighedsvist på en global skala. Denindeholder et hovedlag for detektering af vandforekomster (WB) og et kvalitets-lag(QUAL), der giver information om sæsonudsving i de opdagedevandforekomster. Mere information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Dækning**:\nGlobal dækning fra længdegrad -180°Ø to +180°V og breddegrad +80°N til -60°S. Afhængig af måneden, dækkes nogle høj-breddegrads-områder ikke af Sentinel-2 satellites.\n\n**Data Tilgængelighed**:\nSiden oktober 2020, Opdateres hver måned. \n\n**Almindelig brug**\nOvervåger vandforekomster, tørke, oversvømmelser og klimaændringer."]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":["Ultra Blå (443 nm)"]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":["Blå (482 nm)"]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":["Grøn (561.5 nm)"]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":["Red (654.5 nm)"]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":["Nær Infrarød (NIR) (865 nm)"]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":["Shortwave Infrarød (SWIR) 1 (1608.5 nm"]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":["Shortwave Infrarød (SWIR) 2 (2200.5 nm)"]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":["Thermal Infrarød (TIRS) 1(10895 nm)"]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":["**Level-1** data (fra **Landsat Collection 2**) giver målinger af reflection og temperatur i den øverste atmosfære. \n\nData gennemgår adskillige processerings-trin inklusiv geometriske og radiometriske forbedringer. \n\nMere info om Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) og [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)"]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":["**Level-2** data (from **Landsat Collection 2**) giver videnskabelige produkter af globale overflade-målinger af reflection og overfladetemperatur (CEOS Analysis Ready Data). \n\nData-produkterne er genereret fra Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLær mere om Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/)."]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["**Landsat 8** er den senest lancerede Landsat satellit (producere af NASA/USGS) og bærer instrumentet Operational Land Imager (OLI) og Thermal Infrared Sensor (TIRS) instrumenterne, med 9 optiske og 2 termiske bånd. Disse to instrumenter giver sæson-dækning af de globale landmasser.\n\n**Rumlig opløsning:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Gen-besøgelses tidsrum:** 16 days\n\n**Data tilgængelighed:** Siden februar 2013\n\n**Almindelig brug:** Overvågning af vegetation, arealanvendelse, land-dækningskort, overvågning af forandringer , etc."]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":["Forandringer i vandforekomster mellem to epoker, den første rækkende fra 1984 til 1999 og den anden dækkende 2000 to 2019."]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":["Maximum udbredelse af overflader af vandforekomster i 36-års perioden."]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":["Intra- and mellem-årlig frekvens af forekomster af vandoverflader i tidsrummetmellem 1984 og 2019."]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":["Mellem-årlig variation i forekomsten af overfladevand i en defineret periode inden for hele tidsrammen fra 1984 til 2019."]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":["Intra-årlig fordeling af vandoverflader i 2019."]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":["Synliggør forandringer i de tre overfladevand-klasser (1) Ikke vand, (2) sæson vand, og (3) permanent vand mellem det første og det sidste år i 36-års perioden."]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":[""]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":[""]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":[""]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":[""]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":[""]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":[""]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":[""]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":[""]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":[""]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":[""]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":[""]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":[""]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":[""]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":[""]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":[""]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":[""]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":[""]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":[""]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":[""]},"User Account":{"msgid":"User Account","msgstr":[""]},"Discover Tab":{"msgid":"Discover Tab","msgstr":[""]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":[""]},"Compare Tab":{"msgid":"Compare Tab","msgstr":[""]},"Pins Tab":{"msgid":"Pins Tab","msgstr":[""]},"Search Places":{"msgid":"Search Places","msgstr":[""]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":[""]},"Education Mode":{"msgid":"Education Mode","msgstr":[""]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":[""]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":[""]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":[""]},"Measure Distances":{"msgid":"Measure Distances","msgstr":[""]},"Download Image":{"msgid":"Download Image","msgstr":[""]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":[""]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":[""]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":["Kommercielle data"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":[""]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":[""]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":[""]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":[""]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":[""]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":[""]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":[""]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":[""]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":[""]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":[""]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":[""]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":[""]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":[""]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":[""]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":[""]},"Level 1":{"msgid":"Level 1","msgstr":[""]},"Level 2":{"msgid":"Level 2","msgstr":[""]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":[""]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":[""]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":[""]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":[""]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":[""]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":["Landsat 7 ETM+ L1"]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":["Landsat 7 ETM+ L2"]},"Red band":{"msgid":"Red band","msgstr":[""]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":[""]},"Blue band":{"msgid":"Blue band","msgstr":[""]},"Green band":{"msgid":"Green band","msgstr":[""]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":[""]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":[""]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":[""]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":[""]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":[""]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":[""]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":[""]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":[""]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":[""]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":[""]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":[""]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":[""]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":[""]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":[""]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":[""]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":[""]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (Atmosfærisk korrigeret)"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":[""]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":[""]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":[""]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":[""]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":[""]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":[""]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":[""]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":[""]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":[""]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":[""]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":[""]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":[""]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":[""]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":[""]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":[""]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":[""]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":[""]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":[""]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":[""]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":[""]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":[""]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":[""]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":[""]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":[""]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":[""]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":[""]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":[""]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":[""]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":[""]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":[""]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":[""]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":[""]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":["**Mapzen DEM** er baseret på SRTM30 (Shuttle Radar Topography Mission) og [andre kilder]. Bathymetry data tages fra [ETOPO1]. Det er en statisk samling (uafhængig af data) med global dækning.\n\n**Rumlig opløsning:** Oftest 90 m, for nogle arealer op til 10 m.\n\nKredit: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)"]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":["**Landsat 4-5 TM** samlingen indeholder billeder produceret med Thematic Mapper (TM) sensoren, som blev bragt om bord på Landsat 4 og 5 satellitterne.Der er 6 optiske og et termisk infrarødt bånd tilgængeligt, alt i 30 m opløsning. Data blev arkiveret med global dækning over land, tilgængelig fra 1982 til 2012. Der gives produkter med den øvre atmosfære level-1 og overflade-reflektering level-2. \n\n**Rumlig opløsning** 30 m\n\n**Tid til genoverflyvning** 16 dage\n\n** Data tilgængelighed** Globalt Level-1 fra august 1982 til maj 2012, Level-2 fra juli 1984 til maj 2012. \n\n** Almindelig brug**: Overvågning af vegetation, is-og vandressourcer, registrering af ændringer og oprettelse af landkort over arealanvendelse."]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":["**Landsat 1-5 MSS** -samlingen inkluderer billeder produceret med Multispectral Scanner System (MSS), som blev transporteret ombord på Landsat 1 gennem Landsat 5-satellitter. Der er 4 optiske bånd tilgængelige i en opløsning på 60 m. Data arkiveres og inkluderer globalt billedsprog siden 1972.\n\n**Rumlig opløsning**: 68 m x 83 m (almindeligvis samplet til 57 m eller 60 m)\n\n**Tid til genoverflyvning**: 18 dage for Landsats 1-3 og 16 dage for Landsats 4-5\n\n**Datatilgængelighed**: Globalt siden:\n- Landsat 1 fra juli 1972 til januar 1978\n- Landsat 2 fra januar 1975 til februar 1982\n- Landsat 3 fra marts 1978 til marts 1983\n- Landsat 4 fra juli 1982 til december 1993\n- Landsat 5 fra 1984 til oktober 1992 og fra juni 2012 til januar 2013\n\n**Almindelig brug**: Overvågning af vegetation, is- og vandressourcer, opdagelse af ændringer og oprettelse af kort over arealanvendelse."]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":["**Landsat 7 ETM+** inkluderer billeder produceret med en Enhanced Thematic Mapper (ETM+) sensor, som blev bragt om bord på Landsat 7 satellitten. Der er 8 optiske og 1 termisk infrarødt bånd til rådighed. Globale data er til rådighed fra 1999 med en genoverflyvnings-periode på 16 dage. Der gives produkter med den øvre atmosfære level-1 og overflade-reflektion level-2. Bemærk manglende data fra alle billeder siden den 30-5-2003 på grund af en sensor-fejl.\n\n**Rumlig opløsning**: 30 meter, 15 meter for et panchromatisk bånd\n\n**Periode for Genoverflyvning**: 16 dage\n\n**Datatilgængelighed **: globalt, siden april 1999\n\n**Almindelig brug**: Overvågning af vegetation, is- og vandressourcer, overvågning af forandringer og kort over areal-anvendelse."]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["**Landsat 4-5 TM Level-1** produkt giver billeder af reflektion i den øvre atmosfære (TOA). Level-1 data produceres ved at processere Landsat TM data med standard proces-parametre som kubisk convolution og terræn-korrektion. Lær mere [her](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) og [her](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects)."]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["**Landsat 4-5 TM Level-2** produkt fremstilles ved at behandle niveau-1-data til overfladerefleksion-et estimat af overfladespektral-reflektansen på jordoverfladen i fravær af atmosfærisk spredning og absorption. Lær mere [her] (https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) og [her] (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects = 0#qt-science_center_objects)."]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":["**Landsat 7 ETM+ Level-1** \n\nLær mere [her](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)"]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":["**Landsat 7 ETM+ Level-2** \n\nLær mere [her](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/)."]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":[""]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":[""]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":[""]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":[""]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":[""]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":[""]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":["Bestille efter:"]},"Location":{"msgid":"Location","msgstr":["Sted"]},"DatasetId":{"msgid":"DatasetId","msgstr":["DatasetId"]},"Title":{"msgid":"Title","msgstr":["Title"]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"Please select a layer":{"msgid":"Please select a layer","msgstr":[""]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":["Histogram kan kun vises under visualisering"]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":["Histogram ikke tilgængelig for "]},"Recalculate":{"msgid":"Recalculate","msgstr":["Beregn igen"]},"Histogram":{"msgid":"Histogram","msgstr":["Histogram"]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":[""]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":[""]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":[""]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":[""]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":[""]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":[""]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":[""]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":[""]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":[""]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":[""]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":[""]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":[""]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":[""]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":[""]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":[""]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":[""]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":[""]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":[""]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":[""]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":[""]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":[""]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":[""]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":[""]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":[""]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":[""]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":[""]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":[""]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":[""]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":[""]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":[""]},"Cloud base height":{"msgid":"Cloud base height","msgstr":[""]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":[""]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":[""]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":[""]},"Cloud top height":{"msgid":"Cloud top height","msgstr":[""]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":[""]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":[""]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":[""]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":[""]},"Ozone total column":{"msgid":"Ozone total column","msgstr":[""]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":[""]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":[""]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":[""]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":[""]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":[""]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":[""]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":[""]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":[""]},"B09 / B08":{"msgid":"B09 / B08","msgstr":[""]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":[""]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":[""]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":[""]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":[""]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":[""]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":[""]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":[""]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":[""]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":[""]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":[""]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":[""]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":[""]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":[""]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":[""]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":[""]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":[""]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":[""]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":[""]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":[""]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":[""]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":[""]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":[""]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":[""]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":[""]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":[""]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":[""]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":[""]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":[""]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":[""]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":[""]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":[""]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":[""]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":[""]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":[""]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":[""]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":[""]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":[""]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":[""]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":[""]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":[""]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":[""]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":[""]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":[""]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":[""]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":[""]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":[""]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":[""]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":[""]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":[""]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":[""]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":[""]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":[""]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":[""]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":[""]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":[""]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":[""]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":[""]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":[""]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":[""]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":[""]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":[""]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":[""]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":[""]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":[""]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":[""]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":[""]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":[""]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":[""]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":[""]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":[""]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":[""]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":[""]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":[""]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":[""]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":[""]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":[""]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":[""]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":[""]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":[""]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":[""]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":[""]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":[""]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":[""]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":[""]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":[""]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":[""]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":[""]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":[""]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":[""]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":[""]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":[""]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":[""]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":[""]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":[""]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":[""]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":[""]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":[""]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":[""]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":[""]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":[""]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":[""]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":[""]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":[""]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":[""]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":[""]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":[""]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":[""]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":[""]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":[""]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":[""]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":[""]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":[""]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":[""]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":[""]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":[""]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":[""]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":[""]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":[""]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":[""]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":[""]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":[""]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":[""]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":[""]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":[""]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":[""]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":[""]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":[""]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":[""]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":[""]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":[""]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":[""]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":[""]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":[""]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":[""]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":[""]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":[""]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":[""]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":["# Global forekomst af overfladevand\n\n\n\nLaget viser (intra-og interårlige) variationer i forekomsten af overfladevand i tiden fra marts 1984 til december 2020. Permanente vandområder med 100 % vandforekomst i disse 36 år vises med blå farve, mens lysere nuancer af pink og lilla angiver lavere forekomst af vand. Lær mere her."]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":["# Global Overfladevand - Forekomst og Foranderlighed\n\n\n\nLaget viser forandringer af vandforekomster gennem to forskellige epoker, den første epoke fra marts 1984 til december 1999, og den anden epoke fra januar 2000 til december 2019. Arealer med stigende vandforekomster vises med forskellige nuancer af grøn, arealer uden forandring farves sort og arealer med faldende vandforekomster vises med røde nuancer. Lær mere [her](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/)."]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":[""]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":[""]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":[""]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":[""]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Maximalt skydække:"]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Upload data"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":["Gennemse, visualiser og analyser Very High Resolution (VHR) data direkte i EO Browser, og klik på globale arkiver for Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades]((https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/)) og [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) såvel som [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/).\n\nObservér planeten ved opløsninger, der starter ved 3 meter og helt op til 0,5 meter for en pris ned til 0,9 EUR pr. km².\n\n![High resolution imagery eksempel.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nHvad du har brug for:\nEt aktivt Sentinel Hub - abonnement til søgning af metadata. Hvis du endnu ikke har en konto: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n-Forhåndskøbt kvote for nogen af konstellationerne. Gå til [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) for at oprette et abonnement og købe kommercielle dataplaner."]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]},"Built-up probability [0 - 100 %] at 10 m spatial resolution":{"msgid":"Built-up probability [0 - 100 %] at 10 m spatial resolution","msgstr":[""]},"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).":{"msgid":"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).","msgstr":[""]},"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.":{"msgid":"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.","msgstr":[""]},"Resampling kernel":{"msgid":"Resampling kernel","msgstr":[""]},"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)":{"msgid":"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)","msgstr":[""]},"4x4 cubic convolution":{"msgid":"4x4 cubic convolution","msgstr":[""]},"nearest neighbour":{"msgid":"nearest neighbour","msgstr":[""]},"proprietary MTF kernel":{"msgid":"proprietary MTF kernel","msgstr":[""]},"Width":{"msgid":"Width","msgstr":[""]},"Height":{"msgid":"Height","msgstr":[""]},"Format":{"msgid":"Format","msgstr":[""]},"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.":{"msgid":"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.","msgstr":[""]},"Could not generate timelapse animation file. Try using lower resolution or fewer frames.":{"msgid":"Could not generate timelapse animation file. Try using lower resolution or fewer frames.","msgstr":[""]}}}}
\ No newline at end of file
diff --git a/src/translations/de.po b/src/translations/de.po
index a9413133..2b327ebb 100644
--- a/src/translations/de.po
+++ b/src/translations/de.po
@@ -7221,4 +7221,82 @@ msgid ""
msgstr ""
msgid "Currently only collections on services.sentinel-hub are supported."
+msgstr ""
+
+msgid "Built-up probability [0 - 100 %] at 10 m spatial resolution"
+msgstr ""
+
+msgid ""
+"# The Global Human Settlement Layer GHS-BUILT-S2 \n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"The Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up "
+"areas (expressed as probabilities from 0 to 100 %) at 10 m spatial "
+"resolution. It was derived from a Sentinel-2 global image composite for the "
+"reference year 2018 using Convolutional Neural Networks.\n"
+"\n"
+"This script visualises the built-up probabilities stretched to 0-255.\n"
+"\n"
+"For more information about the layer, visit [this "
+"website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-"
+"human-settlement-layer-ghs-built-s2/)."
+msgstr ""
+
+msgid ""
+"The **Global Human Settlement** (GHS) framework produces global maps of "
+"built-up areas, population density and settlements to monitor human "
+"presence on Earth over time.\n"
+"\n"
+"**Coverage**: Global coverage with longitude from 180°W to 180°E and "
+"latitude from 72°N to 56°S\n"
+"\n"
+"**Data Availability**: Reference year 2018\n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Knowledge of population distribution and density has a "
+"number of applications, including disaster risk management or the study and "
+"management of urbanisation processes, not only but also in relation to the "
+"challenges of climate change and environmental degradation."
+msgstr ""
+
+msgid "Resampling kernel"
+msgstr ""
+
+msgid ""
+"Select the resampling kernel to use: \n"
+"- 4x4 cubic convolution (CC), \n"
+"- nearest neighbour (NN), \n"
+"- or the proprietary MTF kernel (MTF)"
+msgstr ""
+
+msgid "4x4 cubic convolution"
+msgstr ""
+
+msgid "nearest neighbour"
+msgstr ""
+
+msgid "proprietary MTF kernel"
+msgstr ""
+
+msgid "Width"
+msgstr ""
+
+msgid "Height"
+msgstr ""
+
+msgid "Format"
+msgstr ""
+
+msgid ""
+"For optimisation reasons MPEG4 output format will always be used for "
+"generating a timelapse with transition \"fade\", even when GIF is selected."
+msgstr ""
+
+msgid ""
+"Could not generate timelapse animation file. Try using lower resolution or "
+"fewer frames."
msgstr ""
\ No newline at end of file
diff --git a/src/translations/de.po.json b/src/translations/de.po.json
index 0cc8e0b6..d6073b93 100644
--- a/src/translations/de.po.json
+++ b/src/translations/de.po.json
@@ -1 +1 @@
-{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","language":"de","mime-version":"1.0","content-transfer-encoding":"8bit","project-id-version":"","pot-creation-date":"","po-revision-date":"","last-translator":"","language-team":"","x-generator":"Poedit 2.4.3","plural-forms":"nplurals=2; plural=(n != 1);"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nLanguage: de\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\nProject-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLast-Translator: \nLanguage-Team: \nx-generator: Poedit 2.4.3\nPlural-Forms: nplurals=2; plural=(n != 1);\n"]},"Education":{"msgid":"Education","msgstr":["Bildung"]},"Normal":{"msgid":"Normal","msgstr":["Normal"]},"Close":{"msgid":"Close","msgstr":["Schließen"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Schließen und nicht mehr anzeigen"]},"Previous":{"msgid":"Previous","msgstr":["Zurück"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Tutorial beenden"]},"Next":{"msgid":"Next","msgstr":["Weiter"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Tutorial fortsetzen"]},"Don't show again":{"msgid":"Don't show again","msgstr":["Nicht mehr anzeigen"]},"Show info":{"msgid":"Show info","msgstr":["Information anzeigen"]},"Discover":{"msgid":"Discover","msgstr":["Entdecken"]},"Visualize":{"msgid":"Visualize","msgstr":["Anzeigen"]},"Compare":{"msgid":"Compare","msgstr":["Vergleichen"]},"Pins":{"msgid":"Pins","msgstr":["Pins"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Es ist ein Fehler beim Laden der Bilder aufgetreten:"]},"No tile found":{"msgid":"No tile found","msgstr":["Kachel nicht gefunden"]},"Dataset":{"msgid":"Dataset","msgstr":["Datensatz"]},"Show":{"msgid":"Show","msgstr":["Zeige"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Effekte und erweiterte Optionen anzeigen"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Visualisierung anzeigen"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Zu Pins hinzufügen"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Zum Vergleich hinzufügen"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Zoom auf Kachel"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Ebene ausblenden"]},"Show layer":{"msgid":"Show layer","msgstr":["Ebene anzeigen"]},"Share":{"msgid":"Share","msgstr":["Teilen"]},"Custom":{"msgid":"Custom","msgstr":["Benutzerdefiniert"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Benutzerdefinierte Visualisierung erstellen"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Vergrößern, um Daten anzuzeigen"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Kostenlose Anmeldung"]},"for all features":{"msgid":"for all features","msgstr":["für alle Funktionen"]},"Powered by":{"msgid":"Powered by","msgstr":["Präsentiert von"]},"with contributions by":{"msgid":"with contributions by","msgstr":["mit Unterstützung der"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["Bitte Datenquelle(n) auswählen!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["Ungültiger Zeitraum!"]},"No results found":{"msgid":"No results found","msgstr":["Keine Ergebnisse gefunden"]},"Theme":{"msgid":"Theme","msgstr":["Thema"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Verwalten von Konfigurationeinstellungen"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Anmelden, um benutzerdefinierte Konfigurationseinstellungen zu verwenden."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["Fehler beim Abrufen von zusätzlichen Daten!"]},"Search":{"msgid":"Search","msgstr":["Suche"]},"Highlights":{"msgid":"Highlights","msgstr":["Highlights"]},"Data sources":{"msgid":"Data sources","msgstr":["Datensätze"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Bitte wählen Sie ein Thema"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Zeitraum [UTC]"]},"Date":{"msgid":"Date","msgstr":["Datum"]},"Hide description":{"msgid":"Hide description","msgstr":["Beschreibung ausblenden"]},"Show description":{"msgid":"Show description","msgstr":["Beschreibung anzeigen"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Dieses Thema hat keine Highlights"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Basierend auf: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["1 Tag (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["5 Tage (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["10 Tage (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["O3 (Ozon)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (Stickstoffdioxid)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (Schwefeldioxid)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (Kohlenmonoxid)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (Formaldehyd)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (Methan)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER AI (Aerosol-Index)"]},"Cloud":{"msgid":"Cloud","msgstr":["Wolken"]},"Other":{"msgid":"Other","msgstr":["Sonstiges"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Max. Wolkenbedeckung"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Erweiterte Suche"]},"Data location":{"msgid":"Data location","msgstr":["Speicherort der Daten"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Bitte wählen Sie mindestens einen Standort aus!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Datenerfassungsmodus"]},"Polarization":{"msgid":"Polarization","msgstr":["Polarisation"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Bitte wählen Sie mindestens einen Datenerfassungsmodus aus!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Bitte wählen Sie mindestens eine Polarisation aus!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Richtung des Orbits"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Bitte wählen Sie mindestens eine Orbitrichtung aus!"]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["**MERIS** (Medium-resolution spectrometer) war ein Sensor an Bord des Satelliten [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) mit der Hauptaufgabe, die Farbe von Land und Ozean sowie die Atmosphäre zu beobachten. Er ist nicht mehr aktiv und wurde durch Sentinel-3 ersetzt.\n\n**Räumliche Auflösung:** Volle Auflösung Land & Küste: 260m x 290m (d.h. nur Details, die größer als 260m x 290m sind, können gesehen werden).\n\n**Wiederholrate:** Maximal 3 Tage, um das gleiche Gebiet erneut zu besuchen.\n\n**Datenverfügbarkeit:** Von Juni 2002 bis April 2012.\n\n**Häufige Verwendung:** Meeresüberwachung (Phytoplankton, Schwebstoffe), Atmosphäre (Wasserdampf, CO2, Wolken, Aerosole) und Land (Vegetationsindex, globale Abdeckung, Feuchtigkeit)."]},"Credits:":{"msgid":"Credits:","msgstr":["Referenz:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["**GIBS** (Global Imagery Browse Services) bietet schnellen Zugriff auf über 600 Satelliten-\nbildprodukte, die jeden Teil der Welt abdecken. Die meisten Bilder sind innerhalb weniger Stunden nach Satellitenüberflug verfügbar. Einige Produkte umfassen fast 30 Jahre."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["Die Serie der **Landsat**-Satelliten der NASA/U.S. Geological Survey erfassen, ähnlich wie Sentinel-2, sichtbare und infrarote Wellenlängen. Sie können zusätzlich thermales Infrarot erfassen (Landsat 8). Die Landsat-Serie umfasst fast fünf Jahrzehnte von Aufnahmen.\nMit dieser Plattform haben Sie Zugriff auf Bilder, die von Landsat 5, 7 und 8 aufgenommen wurden.\n\n**Räumliche Auflösung:** 15 m, 30 m und 100 m umgerechnet auf 30 m, abhängig von der Wellenlänge (d. h. nur Details größer als 10 m und 30 m können gesehen werden). Mehr Infos [hier](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Wiederholrate:** Maximal 8 Tage, um dasselbe Gebiet mit den beiden operationellen Satelliten Landsat 7 und Landsat 8 erneut zu besuchen.\n\n**Datenverfügbarkeit:** Europa und Nordafrika von 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 bis heute (Landsat 8) aus dem ESA-Archiv. Das globale Archiv des U.S. Geological Survey (USGS) von April 2013 bis heute (nur Landsat 8) .\n\n**Häufige Verwendung:** Vegetationsüberwachung, Landnutzung, Landbedeckungskarten, Überwachung von Veränderungen, etc."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["Das **MODIS** (Moderate Resolution Imaging Spectroradiometer) der NASA sammelt Daten mit dem Ziel, unser Verständnis von globalen Prozessen, die auf dem Land stattfinden, zu verbessern. Der EO-Browser liefert Daten zur Beobachtung von Land (Bänder 1-7).\n\n**Räumliche Auflösung:** 250 m (Bänder 1-2), 500 m (Bänder 3-7), 1000 m (Bänder 8-36).\n\n**Wiederholrate:** Globale Abdeckung in 1 - 2 Tagen sowohl mit Aqua- als auch mit Terra-Satelliten.\n\n**Datenverfügbarkeit:** Seit Januar 2013.\n\n**Häufige Verwendung:** Überwachung von Land, Wolken, Ozeanfarbe auf globaler Ebene."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["Der **Proba-V**-Satellit ist ein Kleinsatellit, der die Landbedeckung und das Wachstum der Vegetation alle zwei Tage über dem gesamten Globus aufnimmt. Der EO-Browser liefert abgeleitete Produkte, welche die Wolkenbedeckung durch die Kombination von wolkenfreien Messungen innerhalb eines Zeitraums von 1 Tag (S1), 5 Tagen (S5) und 10 Tagen (S10) berücksichtigt.\n\n**Räumliche Auflösung:** 100 m für S1 und S5, 333 m für S1 und S10, 1000 m für S1 und S10.\n\n**Wiederholrate:** 1 Tag für Breitengrade 35-75°N und 35-56°S, 2 Tage für Breitengrade zwischen 35°N und 35°S.\n\n**Datenverfügbarkeit:** Seit Oktober 2013.\n\n**Häufige Verwendung:** Die Beobachtung der Landbedeckung, Vegetationswachstum, Klimafolgenabschätzung, Wasserressourcenmanagement, landwirtschaftliche Überwachung und Abschätzung der Ernährungssicherheit, Überwachung von Binnengewässern, Überwachung von Binnenwasserressourcen und Verfolgung der stetigen Ausbreitung von Wüsten und Entwaldung."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":["**Sentinel-1** liefert Allwetter-, Tag- und Nacht-Radarbilder für Land- und Ozeandienste. Der EO-Browser liefert Daten, die im Interferometric Wide Swath (IW) und Extra Wide Swath (EW) Modus aufgenommen und zu Level-1 Ground Range Detected (GRD) verarbeitet werden.\n\n**Pixelabstände:** 10 m (IW), 40 m (EW).\n\n**Wiederholrate:** <= 5 Tage mit beiden Satelliten.\n\n**Wiederholrate:** (für aufsteigend/absteigend und Überlappung unter Verwendung beider Satelliten): <= 3 Tage, siehe [Beobachtungsszenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Datenverfügbarkeit:** Seit Oktober 2014.\n\n**Häufige Verwendung:** Überwachung von Meer und Land, Notfallmaßnahmen, Klimawandel."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["**Sentinel-2** liefert hochauflösende Bilder im sichtbaren und infraroten Wellenlängenbereich, um Vegetation, Boden- und Wasserbedeckung, Binnengewässer und Küstengebiete zu überwachen. \n\n**Räumliche Auflösung:** 10 m, 20 m und 60 m, abhängig von der Wellenlänge (d. h. nur Details größer als 10 m, 20 m und 60 m können gesehen werden). Mehr Infos [hier](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Wiederholrate:** maximal 5 Tage, um dasselbe Gebiet mit beiden Satelliten erneut zu besuchen.\n\n**Datenverfügbarkeit:** Seit Juni 2015. Volle globale Abdeckung seit März 2017.\n\n**Häufige Verwendung:** Landbedeckungskarten, Karten zur Erkennung von Landveränderungen, Vegetationsüberwachung, Überwachung von verbrannten Gebieten."]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Level-2A-Daten sind Daten von hoher Qualität, bei denen die Auswirkungen der Atmosphäre auf das Licht, das von der Erdoberfläche reflektiert wird und den Sensor erreicht, entfernt wurden. Die Daten sind seit März 2017 weltweit verfügbar.\n\nWeitere Informationen zur atmosphärischen Korrektur [hier](https://fis.rub.de/recherchetools/infobox/profis/bildvorverarbeitung/atmosph%C3%A4renkorrektur)."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Level-1C-Daten sind Daten von ausreichender Qualität für die meisten Untersuchungen, da bei ihnen alle Bildkorrekturen bis auf die atmosphärische Korrektur durchgeführt wurden. Die Daten sind seit Juni 2015 global verfügbar."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["Das Hauptziel der **Sentinel-3**-Mission ist die Messung der Topografie der Meeresoberfläche, der Temperatur der Meeres- und Landoberfläche sowie der Farbe der Meeres- und Landoberfläche. Sentinel-3 hat vier verschiedene Instrumente an Bord. Die vom Ocean and Land Colour Instrument (OLCI) und dem Sea and Land Surface Temperature Instrument (SLSTR) erfassten Daten sind auf dieser Plattform verfügbar.\n\n**Datenverfügbarkeit:** Seit Mai 2016."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["Das **Sea and Land Surface Temperature (SLSTR)**-Instrument an Bord von Sentinel-3 misst die globale und regionale Meeres- und Landoberflächentemperatur. Das SLSTR deckt die sichtbaren, kurzwelligen Infrarot- und thermalen Infrarot-Wellenlängen des elektromagnetischen Spektrums ab. \n\n**Räumliche Auflösung:** 500 m für sichtbare, nah- und kurzwellige Infrarot-Wellenlängen und 1 km für thermales Infrarot (d. h. nur Details, die größer als 500 m bzw. 1 km sind, können gesehen werden).\n\n**Wiederholrate:** Maximal 1 Tag, um dasselbe Gebiet unter Verwendung beider Satelliten erneut zu besuchen.\n\n**Datenverfügbarkeit:** Seit Mai 2016.\n\n**Häufige Verwendung: ** Überwachung des Klimawandels, Vegetationsüberwachung, aktive Feuererkennung, Überwachung der Land- und Meeresoberflächentemperatur."]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["Das **Ocean and Land Colour Instrument (OLCI)** an Bord von Sentinel-3 ist ein Spektrometer, das die von der Erde reflektierte Sonnenstrahlung misst und den Ozean, die Umwelt und das Klima überwacht. Es liefert häufiger sichtbare Bilder als Sentinel-2, jedoch mit einer geringeren Auflösung und mit mehr abgedeckten Wellenlängen. Das Sentinel-3 OLCI setzt die Messungen fort, die zuvor vom MERIS-Instrument an Bord von Envisat durchgeführt wurden, dessen Mission beendet war.\n\n**Räumliche Auflösung:** 300 m (d. h. nur Details größer als 300 m können gesehen werden).\n\n**Wiederholrate:** Maximal 2 Tage, um dasselbe Gebiet unter Verwendung beider Satelliten erneut zu besuchen.\n\n**Datenverfügbarkeit:** Seit Mai 2016.\n\n**Häufige Verwendung: ** Oberflächentopografie, Farbbeobachtung und Überwachung der Meeres- und Landoberfläche."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["**Sentinel-5P** ist ein Satellit, der atmosphärische Messungen liefert, die für die Überwachung von Luftqualität, Ozon, UV-Strahlung, und Klimaüberwachung und -vorhersage verwendet werden.\n\n**Räumliche Auflösung:** 7 x 3,5 km (d. h. nur Details, die größer als 7 x 3,5 km sind, können gesehen werden).\n\n**Wiederholrate:** Maximal 1 Tag, um das gleiche Gebiet erneut zu besuchen.\n\n**Datenverfügbarkeit:** Seit April 2018.\n\n**Häufige Verwendung:** Überwachung der Konzentration von Kohlenmonoxid (CO), Stickstoffdioxid (NO2) und Ozon (O3) in der Luft. Überwachung des UV-Aerosol-Index (AER_AI) und verschiedener geophysikalischer Parameter von Wolken (Cloud)."]},"Copied":{"msgid":"Copied","msgstr":["Kopiert"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["In die Zwischenablage kopieren"]},"Data source name":{"msgid":"Data source name","msgstr":["Name der Datenquelle"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Aufnahmezeitpunkt"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Wolkenbedeckung"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Sonnenhöhe"]},"MGRS location":{"msgid":"MGRS location","msgstr":["MGRS-Standort"]},"AWS path":{"msgid":"AWS path","msgstr":["AWS-Pfad"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["EO-Cloud-Pfad"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["CreoDIAS-Pfad"]},"SciHub link":{"msgid":"SciHub link","msgstr":["SciHub-Link"]},"Back to search":{"msgid":"Back to search","msgstr":["Zurück zur Suche"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Zeige ${ this.state.results.length } Ergebnis an","Zeige ${ this.state.results.length } Ergebnisse an"]},"Load more":{"msgid":"Load more","msgstr":["Mehr laden"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Weitere Ergebnisse werden geladen..."]},"Results":{"msgid":"Results","msgstr":["Ergebnisse"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Zeige ${ this.state.selectedTiles.length } Ergebnis.","Zeige ${ this.state.selectedTiles.length } Ergebnisse."]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Pin-Beschreibung bearbeiten"]},"Reject changes":{"msgid":"Reject changes","msgstr":["Änderungen verwerfen"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Änderungen bestätigen"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Pin umbenennen"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Pin entfernen"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Zoom auf angeheftete Position"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Breite/Länge"]},"Zoom":{"msgid":"Zoom","msgstr":["Zoom"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["Sie sind dabei, ${ N_PINS } Pin(s) zu Ihrer Pin-Sammlung hinzuzufügen. Möchten Sie fortfahren?"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["WARNUNG: Sie sind im Begriff, eine Pin zu löschen. Möchten Sie fortfahren?"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":["WARNUNG: Sie sind im Begriff, alle Pins zu löschen. Möchten Sie fortfahren?"]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["Keine Pins. Gehen Sie zur Registerkarte \"Anzeigen\", um einen Pin zu speichern oder laden Sie eine JSON-Datei mit gespeicherten Pins hoch."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Beachten Sie, dass die Pins nur gespeichert werden, wenn Sie sich anmelden. Andernfalls gehen die Pins verloren, sobald die Anwendung geschlossen wird."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Alle abwählen"]},"Select all":{"msgid":"Select all","msgstr":["Alle auswählen"]},"No pins.":{"msgid":"No pins.","msgstr":["Keine Pins."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Link erstellen (${ selectedPins.length } Pins ausgewählt)","Link erstellen (${ selectedPins.length } Pins ausgewählt)"]},"File type not supported":{"msgid":"File type not supported","msgstr":["Dateityp nicht unterstützt"]},"not supported":{"msgid":"not supported","msgstr":["nicht unterstützt"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["Es wurden keine Pins gefunden."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Fehler beim Parsen der Datei:"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":["Laden Sie eine JSON-Datei mit gespeicherten Pins hoch."]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["JSON-Datei ablegen oder auf dem Computer suchen"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Vorhandene Pins beibehalten"]},"Share pins":{"msgid":"Share pins","msgstr":["Pins teilen"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Erstellen einer Story aus Pins"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Pins auf den Computer exportieren"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Importieren von Pins aus einer gespeicherten Datei"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Alle Pins löschen"]},"Story":{"msgid":"Story","msgstr":["Story"]},"Export":{"msgid":"Export","msgstr":["Exportieren"]},"Import":{"msgid":"Import","msgstr":["Importieren"]},"Clear":{"msgid":"Clear","msgstr":["Löschen"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Link für Pins freigeben"]},"Creating link...":{"msgid":"Creating link...","msgstr":["Link erstellen..."]},"OK":{"msgid":"OK","msgstr":["OK"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Aktualisieren der Pin-Sammlung."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Es gab ein Problem beim permanenten Aktualisieren der Pin-Sammlung: ${ updatingPinsError }."]},"Hello,":{"msgid":"Hello,","msgstr":["Hallo,"]},"Opacity":{"msgid":"Opacity","msgstr":["Deckkraft"]},"Split position":{"msgid":"Split position","msgstr":["Position teilen"]},"split":{"msgid":"split","msgstr":["aufteilen"]},"opacity":{"msgid":"opacity","msgstr":["Deckkraft"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["Keine Ebenen zum Vergleichen."]},"Remove all":{"msgid":"Remove all","msgstr":["Alle entfernen"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Alle Pins hinzufügen"]},"Split":{"msgid":"Split","msgstr":["Aufteilen"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Beim Herunterladen Ihrer Einstellungen ist ein Problem aufgetreten"]},"Download":{"msgid":"Download","msgstr":["Download"]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Gelände in 3D visualisieren"]},"Go to Place":{"msgid":"Go to Place","msgstr":["Gehe zu Ort"]},"Labels":{"msgid":"Labels","msgstr":["Beschriftungen"]},"Borders":{"msgid":"Borders","msgstr":["Grenzen"]},"Roads":{"msgid":"Roads","msgstr":["Straßen"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Vergrößern"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Verkleinern"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["Über den EO-Browser"]},"Contact us":{"msgid":"Contact us","msgstr":["Kontaktieren Sie uns"]},"Get data":{"msgid":"Get data","msgstr":["Daten beschaffen"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Sie müssen sich anmelden, um diese Funktion nutzen zu können."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Bitte wählen Sie eine Ebene aus."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["Das Herunterladen von Bildern im Vergleichsmodus ist nicht möglich."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["Diese Datenquelle wird nicht unterstützt."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":["Statistische Information / spektrale Informationen im Zeitraum"]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Statistische Information / spektrale Informationen im Zeitraum - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["Bitte wählen Sie eine Ebene"]},"not available for ":{"msgid":"not available for ","msgstr":["nicht verfügbar für "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["nicht verfügbar für \"${ props.presetLayerName }\" (Ebene mit Wert ist nicht eingerichtet)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Suchen Sie zuerst nach Daten."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Zeitrafferanimation erstellen"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Interessenfokus markieren"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Karte auf Merkmal zentrieren"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Geometrie entfernen"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Untersuchungsgebiet"]},"Select mode":{"msgid":"Select mode","msgstr":["Modus auswählen"]},"Mode:":{"msgid":"Mode:","msgstr":["Modus:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Messung entfernen"]},"km":{"msgid":"km","msgstr":["km"]},"m":{"msgid":"m","msgstr":["m"]},"Gain":{"msgid":"Gain","msgstr":["Kontrast"]},"Gamma":{"msgid":"Gamma","msgstr":["Helligkeit"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Minimale Datenqualität"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Upsampling"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Downsampling"]},"Reset all":{"msgid":"Reset all","msgstr":["Alle zurücksetzen"]},"filter by months":{"msgid":"filter by months","msgstr":["nach Monaten filtern"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Geometrie in die Zwischenablage kopieren"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Bearbeitung abbrechen."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Untersuchungsgebiet einzeichnen"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Geringste Wolkenbedeckung"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Zusätzliche Datensätze verwenden (erweitert)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Mosaikanordnung"]},"Most recent":{"msgid":"Most recent","msgstr":["Aktuellste"]},"Least recent":{"msgid":"Least recent","msgstr":["Älteste"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Zeitraum anpassen"]},"Back":{"msgid":"Back","msgstr":["Zurück"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Fehler beim Laden des Skripts. Prüfen Sie Ihre URL."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Deaktivieren Sie \"Skript aus URL laden\", um den Code zu bearbeiten"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Skript aus URL laden"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["URL zu Ihrem Skript eintragen"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Skript geladen."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Es sind nur HTTPS-Domänen erlaubt."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Skript in Code-Editor laden"]},"Refresh":{"msgid":"Refresh","msgstr":["Aktualisieren"]},"orbit":{"msgid":"orbit","msgstr":["Orbit"]},"day":{"msgid":"day","msgstr":["Tag"]},"week":{"msgid":"week","msgstr":["Woche"]},"month":{"msgid":"month","msgstr":["Monat"]},"year":{"msgid":"year","msgstr":["Jahr"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Wählen Sie 1 Bild pro:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Zeitraffer"]},"Select All":{"msgid":"Select All","msgstr":["Alle auswählen"]},"Speed:":{"msgid":"Speed:","msgstr":["Geschwindigkeit:"]},"frames / s":{"msgid":"frames / s","msgstr":["Bilder / s"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Bereite vor..."]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Dateien konnten nicht heruntergeladen werden:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Kann nicht über Canvas heruntergeladen werden"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["Dateien konnten nicht gezippt werden:"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Es gab ein Problem beim Herunterladen des Bildes"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Fehler beim Laden des Bildes: URL ist leer!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Fehler beim Laden des Bildes:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["Konnte Bild nicht aus dem Datenobjekt (blob) laden"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Ziehen Sie die Kanäle auf RGB-Felder."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Bänder in die Indexgleichung ziehen"]},"Index ":{"msgid":"Index ","msgstr":["Index "]},"Threshold":{"msgid":"Threshold","msgstr":["Grenzwert"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Farbwähler entfernen"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Farbwähler hinzufügen"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Klicken Sie, um die Markierung zu setzen"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Klicken Sie, um den ersten Scheitelpunkt zu platzieren"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Zum Weiterzeichnen anklicken"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Klicken Sie zum Beenden auf die erste Markierung"]},"Show captions":{"msgid":"Show captions","msgstr":["Beschriftungen anzeigen"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Folientitel anzeigen"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Karten-Beschriftung hinzufügen"]},"Show legend":{"msgid":"Show legend","msgstr":["Legende anzeigen"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["Es wurden keine Pins innerhalb des aktuellen Sichtfelds gefunden."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Einige Pins (${ N_PINS_OUTSIDE_BOUNDS }) werden ignoriert, da sie sich nicht innerhalb des ausgewählten Bereichs befinden."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["Um eine Pin-Story zu erstellen, navigieren Sie zur gewünschten Position auf der Karte.\n\nAlle Pins innerhalb des aktuellen Sichtfelds werden zum Erstellen der Story verwendet, der Rest wird ignoriert."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["Die Datei wird mit einem Logo versehen."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["Ein dataMask-Band wird in den heruntergeladenen Rohbändern als zweites Band enthalten sein."]},"Show logo":{"msgid":"Show logo","msgstr":["Logo anzeigen"]},"Image format":{"msgid":"Image format","msgstr":["Bildformat"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Bildauflösung"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Koordinatensystem"]},"Layers":{"msgid":"Layers","msgstr":["Ebenen"]},"Visualized":{"msgid":"Visualized","msgstr":["Visualisiert"]},"Raw":{"msgid":"Raw","msgstr":["Rohdaten"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["Die Beschriftungs-Ebenen der Karte (Ortsbezeichnungen, Straßen und politische Grenzen) werden dem Bild hinzugefügt."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Exportierte Bilder enthalten Datenquelle und Datum, Zoom-Skala und Branding"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Fügen Sie eine kurze Beschreibung zum exportierten Bild hinzu"]},"Description":{"msgid":"Description","msgstr":["Beschreibung"]},"Image format:":{"msgid":"Image format:","msgstr":["Bildformat:"]},"Basic":{"msgid":"Basic","msgstr":["Einfach"]},"Analytical":{"msgid":"Analytical","msgstr":["Analytisch"]},"High-res print":{"msgid":"High-res print","msgstr":["Hochauflösender Druck"]},"Download image":{"msgid":"Download image","msgstr":["Bild herunterladen"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["Beim Abrufen einiger Bilder ist ein Fehler aufgetreten:"]},"min/px":{"msgid":"min/px","msgstr":["min/px"]},"sec/px":{"msgid":"sec/px","msgstr":["sek/px"]},"Resolution":{"msgid":"Resolution","msgstr":["Auflösung"]},"lat.":{"msgid":"lat.","msgstr":["Breite"]},"deg/px":{"msgid":"deg/px","msgstr":["Grad/px"]},"long.":{"msgid":"long.","msgstr":["Länge"]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Projizierte Auflösung: ${ formattedResolution } m/px"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Fehler: Die Formate KMZ/JPG und KMZ/PNG werden bei der Datenfusion nicht unterstützt."]},"Image download":{"msgid":"Image download","msgstr":["Bild-Download"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Bildbreite [Zoll]:"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Bildhöhe [Zoll]:"]},"DPI:":{"msgid":"DPI:","msgstr":["DPI:"]},"5 years":{"msgid":"5 years","msgstr":["5 Jahre"]},"2 years":{"msgid":"2 years","msgstr":["2 Jahre"]},"1 year":{"msgid":"1 year","msgstr":["1 Jahr"]},"6 months":{"msgid":"6 months","msgstr":["6 Monate"]},"3 months":{"msgid":"3 months","msgstr":["3 Monate"]},"1 month":{"msgid":"1 month","msgstr":["1 Monat"]},"Retry":{"msgid":"Retry","msgstr":["Nochmal versuchen"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Wird geladen, bitte warten"]},"mean":{"msgid":"mean","msgstr":["Mittelwert"]},"median":{"msgid":"median","msgstr":["Median"]},"st. dev.":{"msgid":"st. dev.","msgstr":["St. Abw."]},"min / max":{"msgid":"min / max","msgstr":["min / max"]},"Export CSV":{"msgid":"Export CSV","msgstr":["CSV exportieren"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Zeitspanne:"]},"Date:":{"msgid":"Date:","msgstr":["Datum:"]},"Single date":{"msgid":"Single date","msgstr":["Einzeldatum"]},"Timespan":{"msgid":"Timespan","msgstr":["Zeitraum"]},"hh":{"msgid":"hh","msgstr":["hh"]},"mm":{"msgid":"mm","msgstr":["mm"]},"From:":{"msgid":"From:","msgstr":["Von:"]},"Until:":{"msgid":"Until:","msgstr":["Bis:"]},"Apply":{"msgid":"Apply","msgstr":["Anwenden"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Auf Facebook teilen"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Auf Twitter teilen"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Schauen Sie sich das an "]},"Logout":{"msgid":"Logout","msgstr":["Logout"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["Melden Sie sich an, um erweiterte Funktionen wie Zeitraffer, analytischen Download, eigene Konfigurationen und mehr freizuschalten."]},"Login":{"msgid":"Login","msgstr":["Login"]},"Default":{"msgid":"Default","msgstr":["Standard"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Überwachung der Erde aus dem Weltraum"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Landwirtschaft"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Atmosphäre und Luftverschmutzung"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Veränderungen im Laufe der Zeit"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Überschwemmungen und Dürren"]},"Geology":{"msgid":"Geology","msgstr":["Geologie"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Ozean und Gewässer"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Schnee und Gletscher"]},"Urban":{"msgid":"Urban","msgstr":["Stadt"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Vegetation und Forstwirtschaft"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Vulkane"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Waldbrände"]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":["# Willkommen beim EO-Browser!\n\nEin komplettes Archiv von Sentinel-1-, Sentinel-2-, Sentinel-3-, Sentinel-5P-Produkten, \nESAs Archiv von Landsat 5, 7 und 8, globale Abdeckung von Landsat 8, Envisat Meris-, \nMODIS-, Proba-V- und GIBS-Produkten an einem Ort.\n\n[EO-Browser-Präsentationsseite] (https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser-Benutzerhandbuch] (https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":["#### Kurze Übersicht über die EO-Browser-Funktionen\n\nDer EO-Browser kombiniert ein komplettes Archiv von Sentinel-1-, Sentinel-2-, Sentinel-3-, Sentinel-5P-Produkten, das ESA-Archiv von Landsat 5, 7 und 8, die globale Abdeckung von Landsat 8, Envisat Meris-, MODIS-, Proba-V- und GIBS-Produkten an einem Ort und ermöglicht es, Bilder in voller Auflösung aus diesen Quellen zu durchsuchen und zu vergleichen. Sie gehen einfach zu dem für Sie interessanten Gebiet, wählen Datenquellen, Zeitspanne und Wolken-Abdeckung aus und überprüfen die erhaltenen Daten.\n\nSie können das Tutorial fortsetzen, indem Sie auf die Schaltfläche \"Weiter\" klicken oder sie schließen es. Wenn Sie auf das Info-Symbol in der oberen rechten Ecke klicken, können Sie das Tutorial immer fortsetzen, falls Sie es versehentlich geschlossen haben oder weil Sie Dinge ausprobieren möchten."]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":["Auf der Registerkarte **Entdecken** können Sie:\n\n- **Themen** wählen. \n- nach Daten **Suche**n.\n- **Highlights** Themen anzeigen.\n\nDie Auswahlliste **Themen** bietet Ihnen verschiedene vorkonfigurierte Designs sowie eigene benutzerdefinierte konfigurierte Einstellungen, wenn Sie angemeldet sind. Um eine Einstellung zu erstellen, klicken Sie auf\ndas Einstellungssymbol und melden Sie sich mit den gleichen Anmeldeinformationen an, die Sie für den EO-Browser verwendet haben.\n\nUnter **Suche** können Sie Suchkriterien festlegen:\n - Wählen Sie aus, von welchem Satelliten Sie Daten erhalten möchten, indem Sie Checkboxen auswählen.\n - Wählen Sie ggf. zusätzliche Optionen aus, z. B. Wolkenbedeckung mit dem Schieberegler.\n - Wählen Sie einen Zeitraum aus, indem Sie entweder das Datum eingeben oder das Datum aus dem Kalender auswählen.\n\nSie können Informationen zu den Satelliten lesen, indem Sie auf das Fragezeichen\nneben dem Datenquellennamen klicken.\n\nSobald Sie auf Suchen klicken, erhalten Sie eine Liste von Ergebnissen. Jedes Ergebnis wird \nmit einem Vorschaubild und mit für die Datenquelle relevanten Daten angezeigt. Für einige Datenquellen ist das Linksymbol auch für jedes Ergebnis sichtbar.\nEin Klick darauf zeigt direkte Links zum Rohbild des Ergebnisses auf EO-Cloud oder SciHub. Wenn Sie auf die Schaltfläche Anzeigen klicken, wird die Registerkarte **Anzeigen** für das ausgewählte Ergebnis geöffnet.\n\nUnter **Highlight** finden Sie eine Auswahl von für das Thema interessanten Orten und Aufnahmen."]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":["Auf der Registerkarte Anzeigen können Sie verschiedene vorinstallierte oder benutzerdefinierte Spektralbandkombinationen auswählen, um Daten für das ausgewählte Ergebnis zu visualisieren.\n\nEinige der gängigen Optionen:\n- **Echtfarbe** - Visuelle Interpretation der Landbedeckung.\n- **Falschfarbe** - Visuelle Interpretation der Vegetation.\n- **NDVI** - Normalisierter differenzierter Vegetations-Index.\n- **Feuchtigkeitsindex** - Feuchte-Index\n- **SWIR** - Kurzwellen-Infrarot-Index.\n- **NDWI** - Normalisierter differenzierter Wasser-Index.\n- **NDSI** - Normalisierter differenzierter Schnee-Index.\n\nDie meisten Visualisierungen sind mit einer Beschreibung und einer Legende versehen, die Sie durch Klicken auf das Expandieren-Symbol auswählen können.\n \nFür die meisten Datenquellen ist die Option **Benutzerdefiniertes Skript** verfügbar. Klicken Sie darauf, um benutzerdefinierte Bandkombinationen, Indexkombinationen oder ein eigenes Klassifizierungsskript für die Visualisierung von Daten zu schreiben. Sie können auch benutzerdefinierte Skripte verwenden, die an anderer Stelle gespeichert sind, entweder auf Google Drive, GitHub oder in unserem [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nFügen Sie die URL des Skripts in ein Textfeld im erweiterten Skriptbearbeitungsfenster ein und klicken Sie auf Aktualisieren.\n \nSie können das Datum direkt in der Registerkarte Anzeigen ändern, ohne zurück zur Registerkarte **Entdecken** zu gehen. Geben Sie es ein oder wählen Sie es aus dem Kalender .\n\nOberhalb der Visualisierungen haben Sie eine Reihe von zusätzlichen Werkzeugen. Beachten Sie, dass deren Verfügbarkeit von der Datenquelle abhängt.\n- **Pin-Ebene**, um sie in der Anwendung für die spätere Verwendung zu speichern - durch Klicken auf das Pin-Symbol .\n- Wählen Sie **erweiterte Optionen** wie Resampling-Methoden oder wenden Sie verschiedene **Effekte** wie Kontrast und Helligkeit an - durch Klicken auf das Effekt-Schieberegler-Symbol .\n- Fügen Sie auf der Registerkarte **Vergleichen** durch Klicken auf das Vergleichssymbol eine Ebene zum späteren Vergleich hinzu.\n- **Zoomen** Sie in die Mitte der Kachel - durch Klicken auf das Fadenkreuz .\n- **Sichtbarkeit der Ebene** umschalten - durch Klicken auf das Sichtbarkeitssymbol .\n- **Teilen** Sie Ihre Visualisierung in sozialen Medien - durch Klicken auf das Teilen-Symbol ."]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":["In der Registerkarte **Vergleichen** finden Sie alle Visualisierungen, die Sie über zu **Vergleichen** hinzugefügt haben. \n\nEs gibt zwei Modi:\n - **Deckkraft** (Ziehen Sie den Deckkraftregler nach links oder rechts, um zwischen den verglichenen Bildern zu blenden)\n - **Aufteilen** (Ziehen Sie den Aufteilen-Schieberegler nach links oder rechts, um die Grenze zwischen den verglichenen Bildern festzulegen)\n\nSie können alle Pins mit **Alle Pins hinzufügen** zum Vergleichsfenster hinzufügen oder alle Visualisierungen\n mit der Schaltfläche **Alle entfernen** aus der Registerkarte **Vergleichen** entfernen."]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":["Die Registerkarte **Pins** enthält Ihre angepinnten (favorisierten/gespeicherten) Objekte. Angepinnte Objekte enthalten Informationen über den Standort, die Datenquelle und deren spezifische Ebene, die Zoomstufe und die Zeit.\n\nFür jeden Pin haben Sie mehrere Möglichkeiten, wie Sie mit einem einzelnen Pin interagieren können:\n\n- Ändern der **Reihenfolge** - durch Klicken auf das Verschiebesymbol\n\n \n \n \nin der linken oberen Ecke des Pins und Ziehen des Pins in der Liste nach oben oder unten.\n- **Umbenennen** - durch Klicken auf das Bleistiftsymbol neben dem Namen des Pins.\n- Hinzufügen zur Registerkarte **Vergleichen** - durch Klicken auf das Vergleichssymbol \n- Eine **Beschreibung** eingeben - durch Klicken auf das Erweiterungssymbol .\n- **Entfernen** - durch Klicken auf das Entfernen-Symbol .\n- **Zoomen** auf den Standort des Pins- durch Anklicken des Breitengrad/Längengrad.\n\nIn der Zeile über allen Pins haben Sie verschiedene Optionen, die für alle Pins gelten:\n- Erstellen Sie aus den Pins eine eigene Story - durch Klick auf **Story**.\n- Ihre Pins über einen Link mit anderen teilen - durch Klick auf **Teilen**.\n- Pins als JSON-Datei exportieren - durch Klicken auf **Exportieren**.\n- Pins aus einer JSON-Datei importieren - durch Klicken auf **Importieren**.\n- Alle Pins löschen - durch Klicken auf **Löschen**."]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":["Suchen Sie einen Ort, indem Sie entweder mit der Maus durch die Karte scrollen oder den Ort in das Suchfeld eingeben."]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":["Hier können Sie auswählen, welche Basisebene und Zusatzinformationen (Straßen, Grenzen, Beschriftungen) auf der Karte angezeigt werden."]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":["Hier können Sie zwischen dem **Normal**en und dem **Bildung**smodus wechseln. Der **Bildung**smodus bietet Ihnen eine leicht vereinfachte Version der App.\nSie kann auch direkt über die [dedizierte URL](https://apps.sentinel-hub.com/eo-browser-education/) aufgerufen werden."]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":["Sie können sich das Tutorial jederzeit ansehen, indem Sie auf dieses Info-Symbol klicken\n\n\n."]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":["Mit diesem Werkzeug können Sie ein Polygon auf der Karte zeichnen und die Größe des Polygons anzeigen.\n\nAlle Ebenen, die einen einzelnen Wert zurückgeben (z. B. NDVI, Feuchtigkeitsindex, NDWI,...) unterstützen die Anzeige des Index für den ausgewählten Bereich im Zeitverlauf. Durch Klicken auf das Diagrammsymbol werden die Diagramme angezeigt. Sie können das Polygon entfernen, indem Sie auf das Symbol zum Entfernen klicken.\n\nSie können auch eine KML/KMZ-, GPX- oder GEOJSON/JSON-Datei mit einer Polygongeometrie hochladen.\n\nMit dem Zwei-Blatt-Symbol können Sie die Polygonkoordinaten als GEOJSON kopieren, das Fadenkreuz \nzentriert die Karte auf das gezeichnete Polygon.\n\nExportierte Bilder werden bei analytischen Downloads auf das Untersuchungsgebiet zugeschnitten."]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":["Mit diesem Werkzeug können Sie einen Punkt auf der Karte markieren.\n\nSie können auch statistische Daten für einige Ebenen anzeigen, indem Sie auf das Diagrammsymbol klicken\n. \nSie können die Markierung entfernen, indem Sie auf das Symbol zum Entfernen klicken.\n"]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":["Mit diesem Werkzeug können Sie Strecken und Flächen auf der Karte messen.\n\nJeder Mausklick erzeugt einen neuen Punkt auf dem Pfad. Um das Hinzufügen von Punkten zu stoppen, drücken Sie die Esc
-Taste oder klicken Sie doppelt auf die Karte. \nSie können die Messung entfernen, indem Sie auf das Entfernen-Symbol klicken."]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":["Mit diesem Werkzeug können Sie ein Bild der visualisierten Daten für den angezeigten Standort herunterladen. Sie können wählen, ob Sie Beschriftungen anzeigen, und Sie können Ihre eigene Beschreibung hinzufügen.\nWenn Sie den Analysemodus aktivieren, können Sie zwischen verschiedenen Bildformaten, Bildauflösungen und Koordinatensystemen wählen. Sie können auch mehrere Ebenen auswählen und sie als .zip
-Datei herunterladen.\n\nKlicken Sie auf die Schaltfläche Download\nDownload\nund Ihr(e) Bild(er) beginnt/beginnen mit dem Download. Der Vorgang kann einige Sekunden dauern, abhängig von der gewählten Auflösung und der Anzahl der ausgewählten Ebenen.\n\nVor dem Herunterladen können Sie ein Untersuchungsgebiet definieren, indem Sie auf das Symbol des Bereichsauswahlwerkzeugs klicken. Ihre Daten werden so zugeschnitten, dass sie diesem Bereich entsprechen."]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":["Sie haben das Ende des Tutorials erreicht. Wenn Sie weitere Fragen haben, können Sie uns diese gerne im [Forum] (https://forum.sentinel-hub.com/) stellen oder uns [per E-Mail] (mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback) kontaktieren.\n\n\nWenn Sie sich das Tutorial in Zukunft ansehen möchten, können Sie es jederzeit mit einem Klick auf das Info-Symbol\n\n\n\nin der rechten oberen Ecke öffnen."]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":["#### Kurzer Überblick über die Funktionen des EO-Browsers\n\nWenn Sie einen kleinen Bildschirm haben, klicken Sie bitte [hier](https://www.sentinel-hub.com/explore/eobrowser/user-guide/), um unsere Bedienungsanleitung einzusehen.\n\nSie können diese Info jederzeit wieder aufrufen, indem Sie auf das Info-Symbol\n\n\n\nin der rechten oberen Ecke klicken.\n\n#### Andere Quellen\n- [EO Browser Präsentationsseite](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Sommer 2018 Updates - Video](https://www.youtube.com/embed/m3pron0C0kE)"]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":["Was ist der EO-Browser?"]},"User Account":{"msgid":"User Account","msgstr":["Benutzerkonto"]},"Discover Tab":{"msgid":"Discover Tab","msgstr":["Registerkarte \"Entdecken\""]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":["Registerkarte \"Anzeigen\""]},"Compare Tab":{"msgid":"Compare Tab","msgstr":["Registerkarte \"Vergleichen\""]},"Pins Tab":{"msgid":"Pins Tab","msgstr":["Registerkarte \"Pins\""]},"Search Places":{"msgid":"Search Places","msgstr":["Orte suchen"]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":["Ebenen und Beschriftungen"]},"Education Mode":{"msgid":"Education Mode","msgstr":["Bildungsmodus"]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":["Informationen und Tutorial"]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":["Untersuchungsgebiet zeichnen"]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":["Interessenfokus setzen"]},"Measure Distances":{"msgid":"Measure Distances","msgstr":["Entfernungen messen"]},"Download Image":{"msgid":"Download Image","msgstr":["Bild herunterladen"]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":["Zeitraffer-Animation erstellen"]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":["Viel Spaß beim Stöbern!"]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":["Willkommen beim EO-Browser!"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":["Band 1 - Gelbe Substanz und Detritalpigmente - 412,5 nm"]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":["Band 3 - Chlorophyll und andere Pigmente - 490 nm"]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":["Band 4 - Schwebstoffe, Rotalgenblüte - 510 nm"]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":["Band 5 - Chlorophyll-Absorptionsminimum - 560 nm"]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":["Band 6 - Schwebstoffe - 620 nm"]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":["Band 7 - Chlorophyll-Absorption & fluo. Referenz - 665 nm"]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":["Band 8 - Chlorophyll-Fluoreszenz-Peak - 681 nm"]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":["Band 9 - Fluo. Referenz, Atmosphärenkorrekturen - 709 nm"]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":["Band 10 - Vegetation, Wolken - 753 nm"]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":["Band 12 - Atmosphärenkorrekturen - 779 nm"]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":["Band 13 - Vegetation, Wasserdampf-Referenz - 865 nm"]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":["Band 14 - Atmosphärenkorrekturen - 885 nm"]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":["Band 15 - Wasserdampf, Land - 900 nm"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Band 1 - Blau - 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Band 2 - Grün - 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Band 3 - Rot - 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Band 4 - Nahinfrarot (NIR) - 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Band 5 - kurzwelliges Infrarot (SWIR-1) - 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Band 7 - kurzwelliges Infrarot (SWIR-2) - 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Band 8 - Panchromatisch - 520-900 nm"]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":["Band 1 - Küstengebiet/Aerosole - 433-453 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Band 2 - Blau - 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Band 3 - Grün - 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Band 4 - Rot - 630-680 nm"]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":["Band 5 - Nahinfrarot (NIR) - 845-885 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Band 6 - kurzwelliges Infrarot (SWIR-1) - 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Band 7 - kurzwelliges Infrarot (SWIR-2) - 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Band 8 - Panchromatisch - 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Band 9 - Zirrus - 1360-1390 nm"]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":["Landsat 5 (ESA-Archiv)"]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":["Landsat 7 (ESA-Archiv)"]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":["Landsat 8 (ESA-Archiv)"]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":["Landsat 8 (USGS-Archiv)"]},"Red band":{"msgid":"Red band","msgstr":["Rotes Band"]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":["841 - 876 nm (Nahinfrarot NIR)"]},"Blue band":{"msgid":"Blue band","msgstr":["Blaues Band"]},"Green band":{"msgid":"Green band","msgstr":["Grünes Band"]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":["1230 - 1250 nm"]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":["1628 - 1652 nm"]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":["2105 - 2155 nm"]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":["Band 1 - Küstenaerosole - 443 nm"]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":["Band 2 - Blau - 490 nm"]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":["Band 3 - Grün - 560 nm"]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":["Band 4 - Rot - 665 nm"]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":["Band 5 - Vegetation Red Edge - 705 nm"]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":["Band 6 - Vegetation Red Edge - 740 nm"]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":["Band 7 - Vegetation Red Edge - 783 nm"]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":["Band 8 - Nahinfrarot (NIR) - 842 nm"]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":["Band 9 - Wasserdampf - 945 nm"]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":["Band 10 - kurzwelliges Infrarot (SWIR) - Cirrus - 1375 nm"]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":["Band 11 - kurzwelliges Infrarot (SWIR) - 1610 nm"]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":["Band 12 - kurzwelliges Infrarot (SWIR) - 2190 nm"]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":["Band 8A - Vegetation Red Edge - 865 nm"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":["Band 1 - Aerosolkorrektur, verbesserte Abfrage der Wasserinhaltsstoffe - 400 nm"]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":["Band 2 - Gelbe Substanz und Detritalpigmente (Trübung)-412 nm"]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":["Band 3 - Chl-Absorptionsmaximum, Biogeochemie, Vegetation - 442,5 nm"]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":["Band 4 - Hohes Chl, andere Pigmente - 490 nm"]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":["Band 5 - Chl, Sediment, Trübung, Rotalgenblüte - 510 nm"]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":["Band 6 - Chlorophyll-Referenz (Chl-Minimum) - 560 nm"]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":["Band 7 - Sedimentbelastung - 620 nm"]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":["Band 8 - Chl (2. Chl abs. max.), Sediment, gelbe Substanz/Vegetation - 665 nm"]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":["Band 9 - Zur verbesserten Fluoreszenzabfrage und zur besseren Berücksichtigung des Smile-Effekts zwischen den Bändern 8 (665 nm) und 10 (681.25 nm) nm - 673,75 nm"]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":["Band 10 - Chl-Fluoreszenzpeak, Red Edge - 681,25 nm"]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":["Band 11 - Chl-Fluoreszenz-Basislinie, Red Edge-Übergang - 708,75 nm"]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":["Band 12 - O2-Absorption/Wolken, Vegetation - 753,75 nm"]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":["Band 13 - O2-Absorptionsband/Aerosolkorrektur - 761,25 nm"]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":["Band 14 - Atmosphärische Korrektur - 764,375 nm"]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":["Band 15 - O2A verwendet für Wolkenoberdruck, Fluoreszenz über Land - 767,5 nm"]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":["Band 16 - Atmosphären-/Aerosol-Korrektur - 778,75 nm"]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":["Band 17 - Atmosphären-/Aerosol-Korrektur, Wolken, Pixel-Koregistrierung - 865 nm"]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":["Band 18 - Referenzband für Wasserdampfabsorption. Gemeinsames Referenzband mit dem SLSTR-Instrument. Überwachung der Vegetation - 885 nm"]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":["Band 19 - Wasserdampfabsorption/Vegetationsüberwachung (max. Reflexionsgrad) - 900 nm"]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":["Band 20 - Wasserdampfabsorption, Atmosphären-/Aerosol-Korrektur - 940 nm"]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":["Band 21 - Atmosphären-/Aerosol-Korrektur - 1020 nm"]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":["Band F1 - Thermale IR-Feueremission - Aktives Feuer - 3742,00 nm"]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":["Band F1 - Thermale IR-Feueremission - Aktives Feuer - 3742,00 nm"]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":["Band S1 - VNIR - Wolkenmaskierung, Vegetationsüberwachung, Aerosole - 554,27 nm"]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":["Band S2 - VNIR - NDVI, Vegetationsüberwachung, Aerosole - 659,47 nm"]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":["Band S3 - VNIR - NDVI, Wolkenmarkierung, Pixel-Koregistrierung - 868,00 nm"]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":["Band S4 - kurzwelliges Infrarot (SWIR) - Zirrus-Erkennung über Land - 1374.80 nm"]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":["Band S5 - kurzwelliges Infrarot (SWIR) - Wolkenauflösung, Eis, Schnee, Vegetationsüberwachung - 1613,40 nm"]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":["Band S6 - kurzwelliges Infrarot (SWIR) - Vegetationszustand und Wolkenauflösung - 2255,70 nm"]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":["Band S7 - Thermale IR-Umgebung - SST, LST, aktives Feuer - 3742,00 nm"]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":["Band S8 - Thermale IR-Umgebung - SST, LST, aktives Feuer - 10854,00 nm"]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":["Band S9 - Thermale IR-Umgebung - SST, LST - 12022,50 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Reflexionsgrad"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Strahlungtemperatur"]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":["Basierend auf der Kombination der Bänder 4, 3, 2"]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":["Basierend auf der Kombination der Bänder (B04-B03)/(B04+B03)"]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":["Basierend auf der Kombination der Bänder 5, 4, 3"]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":["Basierend auf den Echtfarbbändern 4, 3, 2 und einem Panband 8"]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":["Basierend auf der Kombination der Bänder (B05-B04)/(B05+B04)"]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":["VV - lineares Gamma 0 - orthorektifiziert"]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":["VV - lineares Gamma 0 - nicht orthorektifiziert"]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":["VH - lineares Gamma 0 - orthorektifiziert"]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":["Basierend auf der Kombination der Bänder 3, 2, 1"]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":["VH - lineares Gamma 0 - nicht orthorektifiziert"]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":["Farbbild durch Mapping der Eingangsbänder. Wert [RGB] = [VV, 2 VH, VV / VH / 100.0] - lineares Gamma 0 - orthorektifiziert"]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":["VV - Dezibel Gamma 0 [-20,0] - orthorektifiziert"]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":["VH - Dezibel Gamma 0 [-20,0] - orthorektifiziert"]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":["Gibt ein Komposit aus (VH, VV, VH-VV) zurück"]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":["HH - lineares Gamma 0 - orthorektifiziert"]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":["HV - lineares Gamma 0 - orthorektifiziert"]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":["Farbbild durch Mapping der Eingangsbänder. Wert [RGB] = [HH, 2 HV, HH / HV / 100.0] - lineares Gamma 0 - orthorektifiziert"]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":["HH - Dezibel Gamma 0 [-20,0] - orthorektifiziert"]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":["HV - Dezibel Gamma 0 [-20,0] - orthorektifiziert"]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":["HH - lineares Gamma 0 - nicht orthorektifiziert"]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":["Basierend auf den Bändern 4,3,2"]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":["Basierend auf den Bändern 8,4,3"]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":["Basierend auf den Bändern 12,11,4"]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Basierend auf der Kombination der Bänder (B8 - B4)/(B8 + B4)"]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":["Basierend auf der Kombination der Bänder (B8A - B11)/(B8A + B11)"]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":["Basierend auf den Bändern 12, 8A, 4"]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":["Basierend auf der Kombination der Bänder (B3 - B8)/(B3 + B8)"]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":["Basierend auf der Kombination der Bänder (B3 - B11)/(B3 + B11); Werte über 0,42 werden als schneereich angesehen"]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":["Klassifizierung der Sentinel-2-Daten auf Basis des ESA-Szenenklassifizierungsalgorithmus."]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":["UV-Aerosol-Index von 380 und 340 nm"]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":["Basierend auf der Kombination der Bänder (B3 - B11)/(B3 + B11)"]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":["OLCI Terrestrischer Chlorophyll-Index, basierend auf der Kombination der Bänder (B12 - B11)/(B11 - B10)"]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":["UV-Aerosol-Index von 388 und 354 nm"]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":["Säulengemitteltes Trockenluft-Mischungsverhältnis von Methan"]},"Cloud base height":{"msgid":"Cloud base height","msgstr":["Höhe der Wolkenuntergrenze"]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":["Wolkenbasisdruck"]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":["Effektiver radiometrischer Wolkenanteil"]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":["Optische Wolkendicke"]},"Cloud top height":{"msgid":"Cloud top height","msgstr":["Höhe der Wolkenobergrenze"]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":["Druck der Wolkenobergrenze"]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":["Kohlenmonoxid Gesamtsäule"]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":["Troposphärische Formaldehyd-Vertikalsäule"]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":["Troposphärische Stickstoffdioxid-Säule"]},"Ozone total column":{"msgid":"Ozone total column","msgstr":["Ozon-Gesamtsäule"]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":["Schwefeldioxid-Gesamtsäule"]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":["Basierend auf den Bändern 2, 1, 4"]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":["Basierend auf der Kombination der Bänder (B02 - B01)/(B02 + B01)"]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":["Basierend auf der Kombination der Bänder (B02 - B05)/(B02 + B05)"]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":["Basierend auf der Kombination der Bänder (B06 - B07)/(B06 + B07)"]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":["Basierend auf den Bändern 1, 4, 3"]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":["Basierend auf der Kombination der Bänder 7, 5, 3"]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":["(B09 - B08)/(B09 + B08)"]},"B09 / B08":{"msgid":"B09 / B08","msgstr":["B09 / B08"]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":["Basierend auf der Kombination der Bänder 13, 5, 2"]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":["Basierend auf den Bändern 13, 4, 1"]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":["Basierend auf den Bändern 13, 5, 2"]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":["Basierend auf der Kombination der Bänder (B13 - B07) / (B13 + B07)"]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":["Terrestrischer Chlorophyll-Index"]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V 10-tägige Synthese\nBaumobergrenze (Atmosphärisch korrigiert)\nZeitliche Auflösung: 10-tägig\nRäumliche Auflösung: 333 m (Pixelgröße)"]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V tägliche Synthese\nObere Grenze der Atmosphäre\nZeitliche Auflösung: täglich\nRäumliche Auflösung: 333 m (Pixelgröße)"]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-tägige Synthese\nObere Grenze der Atmosphäre\nZeitliche Auflösung: 5-tägig\nRäumliche Auflösung: 100 m (Pixelgröße)"]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V tägliche Synthese\nBaumobergrenze (Atmosphärisch korrigiert)\nZeitliche Auflösung: täglich\nRäumliche Auflösung: 333 m (Pixelgröße)"]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":["Umlaufbahn_ Aqua_Absteigend"]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-tägige Synthese\nBaumobergrenze (Atmosphärisch korrigiert)\nZeitliche Auflösung: 5-tägig\nRäumliche Auflösung: 100 m (Pixelgröße)"]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":["Umlaufbahn_ Aqua_Aufsteigend"]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":["Umlaufbahn_Aura_Aufsteigend"]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":["Umlaufbahn_Aura_Absteigend"]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":["Umlaufbahn_CloudSat_Aufsteigend"]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":["Umlaufbahn_Calipso_Aufsteigend"]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":["Umlaufbahn_Calipso_Absteigend"]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":["Umlaufbahn_CloudSat_Absteigend"]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":["Umlaufbahn_CYGNSS_Aufsteigend"]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":["Umlaufbahn_CYGNSS_Absteigend"]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":["Umlaufbahn_GCOM-C_Aufsteigend"]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":["Umlaufbahn_GCOM-C_Absteigend"]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":["Umlaufbahn_GCOM-W1_Aufsteigend"]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":["Umlaufbahn_GCOM-W1_Absteigend"]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":["Umlaufbahn_GOSAT-2_Aufsteigend"]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":["Umlaufbahn_GOSAT-2_Absteigend"]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":["Umlaufbahn_GOSAT_Aufsteigend"]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":["Umlaufbahn_GOSAT_Absteigend"]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":["Umlaufbahn_GPM_Aufsteigend"]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":["Umlaufbahn_GPM_Absteigend"]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":["Umlaufbahn_ICESAT-2_Aufsteigend"]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":["Umlaufbahn_ICESAT-2_Absteigend"]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":["Umlaufbahn_ISS_Aufsteigend"]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":["Umlaufbahn_ISS_Absteigend"]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":["Umlaufbahn_Landsat-7_Aufsteigend"]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":["Umlaufbahn_Landsat-7_Absteigend"]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":["Umlaufbahn_Landsat-8_Aufsteigend"]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":["Umlaufbahn_METOP-A_Aufsteigend"]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":["Umlaufbahn_METOP-B_Absteigend"]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":["Umlaufbahn_Landsat-8_Absteigend"]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":["Umlaufbahn_METOP-C_Aufsteigend"]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":["Umlaufbahn_METOP-C_Absteigend"]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":["Umlaufbahn_NOAA-20_Aufsteigend"]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":["Umlaufbahn_NOAA-20_Absteigend"]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":["Umlaufbahn_METOP-B_Aufsteigend"]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":["Umlaufbahn_OCO-2_Aufsteigend"]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":["Umlaufbahn_OCO-2_Absteigend"]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":["Umlaufbahn_SAOCOM1-A_Aufsteigend"]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":["Umlaufbahn_SAOCOM1-A_Absteigend"]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":["Umlaufbahn_Sentinel-1A_Aufsteigend"]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":["Umlaufbahn_Sentinel-1B_Aufsteigend"]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":["Umlaufbahn_Sentinel-1A_Absteigend"]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":["Umlaufbahn_METOP-A_Absteigend"]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":["Umlaufbahn_Sentinel-1B_Absteigend"]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":["Umlaufbahn_Sentinel-2A_Aufsteigend"]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":["Umlaufbahn_Sentinel-2A_Absteigend"]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":["Umlaufbahn_Sentinel-2B_Aufsteigend"]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":["Umlaufbahn_Sentinel-2B_Absteigend"]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":["Umlaufbahn_Sentinel-5P_Aufsteigend"]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":["Umlaufbahn_Sentinel-5P_Absteigend"]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":["Umlaufbahn_SMAP_Aufsteigend"]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":["Umlaufbahn_SMAP_Aufsteigend"]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":["Umlaufbahn_Suomi_NPP_Absteigend"]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":["Umlaufbahn_Suomi_NPP_Aufsteigend"]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":["Umlaufbahn_Terra_Absteigend"]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":["Umlaufbahn_Terra_Aufsteigend"]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":["Basierend auf den Bändern 4, 3, 2, erweitert um die Bänder 12 und 11."]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":["Basierend auf den Bändern B07, B06, B4"]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Basierend auf der Kombination der Bänder (B8 - B4)/(B8 + B4)"]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":["Basierend auf dem thermalen Band 10"]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":["Basierend auf den Bändern B12, B11, B8A"]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":["Basierend auf der Kombination der Bänder (B8 - B12)/(B8 + B12)"]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":["Verbesserte natürliche Farbdarstellung"]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":["Basierend auf der Kombination der Bänder 8, 6, 4"]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":["Verbesserter Vegetationsindex"]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":["Basierend auf der Kombination: BSI, B08, B11"]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":["Klassifiziertes NDMI für Bewässerung"]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":["Basierend auf den Bändern B11, B08, B02"]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":["Falschfarben 13, 5, 2"]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":["Basierend auf der Kombination der Bänder (B13 - B07) / (B13 + B07)"]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":["Basierend auf den Bändern 12, 8, 2"]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":["Basierend auf den Bändern 4, 3, 2"]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":["Basierend auf den Bändern 8, 4, 3"]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":["Basierend auf den Bändern 12, 8, 2"]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":["Basierend auf den Bändern 8, 11, 12"]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":["Wassersedimentation und Chlorophyllgehalt"]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":["Basierend auf den Bändern 12, 8A, 4"]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":["Basierend auf NDSI"]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":["Basierend auf der Kombination der Bänder 11, 8, 2"]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":["Basierend auf den Bändern B12, B11, B04"]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":["Basierend auf den Bändern 4, 3 ,2"]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":["Atmosphärisch resistenter Vegetationsindex"]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":["Bodenangepasster Vegetationsindex"]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":["# Thermale IR-Feueremissionsbänder\n\nDas Sentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) verfügt über zwei dedizierte Kanäle (F1 und F2) zur Erfassung der Landoberflächentemperatur (LST). Der F2-Kanal mit einer zentralen Wellenlänge von 10854 nm misst im thermalen Infrarot (TIR). Er ist sehr nützlich für die Überwachung von Bränden und Hochtemperaturereignissen mit einer Auflösung von 1 km.\n\n\n\nMehr Informationen [hier.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)"]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":["# Methan (CH4)\n\n\n\nMethan ist nach Kohlendioxid der wichtigste Beitrag zum anthropogenen (durch menschliche Aktivitäten verursachten) verstärkten Treibhauseffekt. Die Messungen werden in Teilen pro Milliarde (ppb) mit einer räumlichen Auflösung von 7 km x 3,5 km angegeben.\n\n\n\nMehr Informationen [hier.](http://www.tropomi.eu/data-products/methane)"]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":["# Formaldehyd (HCHO)\n\n\n\nLangzeit-Satellitenbeobachtungen von troposphärischem Formaldehyd (HCHO) sind essentiell für die Unterstützung von Luftqualität und Chemie-Klima-bezogenen Studien von der regionalen bis zur globalen Skala. Die saisonalen und halbjährlichen Variationen der Formaldehyd-Verteilung hängen hauptsächlich mit Temperaturänderungen und Feuerereignissen zusammen, aber auch mit Änderungen der anthropogenen (vom Menschen verursachten) Aktivitäten. Da die Lebensdauer von HCHO in der Größenordnung von einigen Stunden liegt, können die HCHO-Konzentrationen in der Grenzschicht direkt mit der Freisetzung von kurzlebigen Kohlenwasserstoffen in Verbindung gebracht werden, die meist nicht direkt aus dem Weltraum beobachtet werden können. Die Messungen erfolgen in Mol pro Quadratmeter (mol/m^2).\n\n\n\nMehr Infos [hier.](http://www.tropomi.eu/data-products/formaldehyde)"]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":["# Schwefeldioxid (SO2)\n\n\n\nSchwefeldioxid gelangt sowohl durch natürliche als auch anthropogene (vom Menschen verursachte) Prozesse in die Erdatmosphäre. Es spielt in der Chemie auf lokaler und globaler Ebene eine Rolle und seine Auswirkungen reichen von kurzfristiger Verschmutzung bis hin zu Auswirkungen auf das Klima. Nur etwa 30 % des emittierten SO2 stammt aus natürlichen Quellen; der Großteil ist anthropogenen Ursprungs. Das Instrument Sentinel-5P/TROPOMI tastet die Erdoberfläche mit einer Wiederholungszeit von einem Tag und einer räumlichen Auflösung von 3,5 x 7 km ab, was die Auflösung feiner Details einschließlich der Erkennung kleinerer SO2-Fahnen ermöglicht. Die Messungen erfolgen in Mol pro Quadratmeter (mol/m^2).\n\n\n\nMehr Informationen [hier.](http://www.tropomi.eu/data-products/sulphur-dioxide)"]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":["# Ozon (O3)\n\n\n\nOzon ist von entscheidender Bedeutung für das Gleichgewicht der Erdatmosphäre. In der Stratosphäre schirmt die Ozonschicht die Biosphäre vor gefährlicher solarer Ultraviolettstrahlung ab. In der Troposphäre wirkt es als effizientes Reinigungsmittel, wird aber bei hoher Konzentration auch gesundheitsschädlich für Mensch, Tier und Vegetation. Ozon ist auch ein wichtiges Treibhausgas, das zum laufenden Klimawandel beiträgt. Seit der Entdeckung des antarktischen Ozonlochs in den 1980er Jahren und dem darauf folgenden Montreal-Protokoll, das die Produktion von chlorhaltigen, ozonabbauenden Substanzen regelt, wird Ozon routinemäßig vom Boden und aus dem Weltraum überwacht. Die Messungen erfolgen in Mol pro Quadratmeter (mol/m^2)\n\n\n\nMehr Informationen [hier.](http://www.tropomi.eu/data-products/total-ozone-column)"]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":["# Stickstoffdioxid (NO2)\n\n\n\nStickstoffdioxid (NO2) und Stickstoffoxid (NO) werden zusammen meist als Stickoxide bezeichnet. Sie sind wichtige Spurengase in der Erdatmosphäre, die sowohl in der Troposphäre als auch in der Stratosphäre vorkommen. Sie gelangen durch anthropogene Aktivitäten (insbesondere Verbrennung fossiler Brennstoffe und Biomasse) und natürliche Prozesse (wie mikrobiologische Prozesse in Böden, Waldbrände und Blitze) in die Atmosphäre. Die Messungen erfolgen in Mol pro Quadratmeter (mol/m^2).\n\n\n\nMehr Infos [hier.](http://www.tropomi.eu/data-products/nitrogen-dioxide)"]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":["# Kohlenmonoxid (CO)\n\n\n\nKohlenmonoxid (CO) ist ein wichtiges atmosphärisches Spurengas. In bestimmten städtischen Gebieten ist es ein wichtiger Luftschadstoff. Hauptquellen für CO sind die Verbrennung fossiler Brennstoffe, die Verbrennung von Biomasse und die atmosphärische Oxidation von Methan und anderen Kohlenwasserstoffen. Die Gesamtsäule des Kohlenmonoxids wird in Mol pro Quadratmeter (mol/ m^2) gemessen.\n\n\n\nMehr Informationen [hier.](http://www.tropomi.eu/data-products/carbon-monoxide)"]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":["# Aerosol-Index\n\nDer Aerosol-Index (AI) ist ein qualitativer Index, der das Vorhandensein erhöhter Schichten von Aerosolen in der Atmosphäre anzeigt. Er kann verwendet werden, um das Auftreten von UV-absorbierenden Aerosolen wie Wüstenstaub und Vulkanaschewolken zu erkennen. Positive Werte (von hellblau bis rot) zeigen das Auftreten von UV-absorbierendem Aerosol an. Dieser Index wird für zwei Paare von Wellenlängen berechnet: 340/380 nm und 354/388 nm.\n\nMehr Informationen [hier.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)"]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":["# Höhe der Wolkenuntergrenze\n\nHöhe der Wolkenuntergrenze, gemessen in Metern (m)."]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":["# Wolkenbasisdruck\n\nAn der Wolkenuntergrenze gemessener Druck in Pascal (Pa)."]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":["# Optische Wolkendicke\n\nDie Wolkendicke ist ein Schlüsselparameter zur Charakterisierung der optischen Eigenschaften von Wolken. Sie ist ein Maß dafür, wie viel Sonnenlicht durch die Wolke dringt, um die Erdoberfläche zu erreichen. Je höher die optische Dicke einer Wolke ist, desto mehr Sonnenlicht wird von der Wolke gestreut und reflektiert. Dunkelblau zeigt an, wo es niedrige Werte für die optische Wolkendicke gibt, und rot zeigt eine größere optische Wolkendicke an."]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":["# Höhe der Wolkenobergrenze\n\nHöhe der Wolkenobergrenze, gemessen in Metern (m)."]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":["# Wolkenoberdruck\n\nAn der Wolkenobergrenze gemessener Druck in Pascal (Pa)."]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":["# Normalisierter differenzierter Vegetations-Index (NDVI)\n\nDer normalisierte differenzierte Vegetations-Index ist ein einfacher, aber effektiver Index zur Quantifizierung der grünen Vegetation. Er ist ein Maß für den Gesundheitszustand der Vegetation, das darauf basiert, wie Pflanzen Licht bestimmter Wellenlängen reflektieren. Der Wertebereich des NDVI liegt zwischen -1 und 1. Negative Werte des NDVI (Werte, die sich -1 annähern) entsprechen dem Wasser. Werte nahe Null (-0,1 bis 0,1) entsprechen im Allgemeinen kargen Flächen aus Fels, Sand oder Schnee. Niedrige, positive Werte stehen für Strauch- und Grasland (ca. 0,2 bis 0,4), während hohe Werte auf gemäßigte und tropische Regenwälder hinweisen (Werte nahe 1).\n\nWeitere Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/faszination-fernerkundung/infrarote-pflanzen), [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) und [hier.](https://eos.com/ndvi/)"]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":["# Erweiterter Vegetationsindex (EVI)\n\nDer erweiterte Vegetationsindex (EVI) ist ein \"optimierter\" Vegetationsindex, da er Bodenhintergrundsignale und atmosphärische Einflüsse korrigiert. Er ist sehr nützlich in Gebieten mit dichtem Blätterdach. Der Wertebereich für den EVI liegt zwischen -1 und 1, wobei eine gesunde Vegetation im Allgemeinen zwischen 0,20 und 0,80 liegt.\n\n\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) und [hier.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)"]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Atmosphärisch resistenter Vegetationsindex (ARVI)\n\nDer Atmosphärisch resistenter Vegetationsindex (ARVI) ist ein Vegetationsindex, der die Auswirkungen der atmosphärischen Streuung minimiert. Er ist besonders nützlich für Regionen mit hohem Gehalt an atmosphärischem Aerosolen (Nebel, Staub, Rauch, Luftverschmutzung). Der Bereich für einen ARVI liegt bei -1 bis 1, wobei grüne Vegetation im Allgemeinen zwischen Werten von 0,20 bis 0,80 liegt.\n\n\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) und [hier.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Bodenangepasster Vegetationsindex (SAVI)\n\nDer Bodenangepasste Vegetationsindex (Soil Adjusted Vegetation Index, SAVI) ähnelt dem normalisierten differenzierten Vegetations-Index (NDVI), wird aber in Gebieten mit geringer Vegetationsbedeckung (< 40 %) verwendet. Der Index ist ein Umwandlungsverfahren, das die Einflüsse der Bodenhelligkeit in spektralen Vegetationsindizes mit roten und Nahinfrarot (NIR)-Wellenlängen minimiert. Der Index ist hilfreich bei der Analyse von jungen Pflanzen, trockenen Regionen mit spärlicher Vegetation und exponierten Bodenflächen.\n\n\n\n\n\nMehr Infos [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) und [hier.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":["# Modifizierter Anthocyanin-Reflexionsindex (mARI/ARI2)\n\nAnthocyane sind Pigmente, die in höheren Pflanzen vorkommen und für deren rote, blaue und violette Färbung verantwortlich sind. Sie liefern wertvolle Informationen über den physiologischen Zustand von Pflanzen, da sie als Indikatoren für verschiedene Arten von Pflanzenstress gelten. Der Reflexionsgrad von Anthocyanin ist um 550 nm am höchsten. Die gleichen Wellenlängen werden jedoch auch von Chlorophyll reflektiert. Um die Anthocyane zu isolieren, wird das 700nm-Spektralband, welches nur von Chlorophyll und nicht von Anthocyanen reflektiert wird, subtrahiert.\n\nUm die Blattdichte und -dicke zu korrigieren, wird das Spektralband im Nahinfrarot (in den empfohlenen Wellenlängen von 760-800 nm), die mit der Blattstreuung zusammenhängt, zum Basis-ARI-Index hinzugefügt. Der neue Index wird modifizierter ARI oder mARI (auch ARI2) genannt.\n\nDie mARI-Werte für die untersuchten Bäume in [diesem Originalartikel](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) reichten von 0 bis 8.\n\n\n\n\n\nMehr Infos [hier.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)"]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":["# Grüne-Stadt-Skript\n\nDas Grüne-Stadt-Skript zielt darauf ab, das Bewusstsein für Grünflächen in Städten auf der ganzen Welt zu erhöhen. Das Skript berücksichtigt den normalisierten differenzierten Vegetations-Index (NDVI) und Echtfarb-Wellenlängen; es trennt bebaute von bewachsenen Flächen und ist damit für die Erkennung von Stadtgebieten nützlich. Bebaute Gebiete werden grau und die Vegetation grün dargestellt.\n\n\n\n\n\nMehr Infos [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":["# Stadtklassifizierungs-Skript\n\nDas Stadtklassifizierungs-Skript zielt darauf ab, bebaute Gebiete zu erkennen, indem es sie von unfruchtbarem Boden, Vegetation und Wasser trennt. Bereiche mit einem hohen Feuchtigkeitsgehalt werden in blau dargestellt; Bereiche, die bebaute Flächen anzeigen, werden in weiß dargestellt; bewachsene Flächen werden in grün zurückgegeben; alles andere zeigt unfruchtbaren Boden an und wird in braunen Farben angezeigt.\n\n\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)"]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":["# Urbanes Land Infrarot-Farbskript\n\nDieses Skript von Leo Tolari kombiniert die Visualisierung von Echtfarben mit den Wellenlängen des Nahinfrarot (NIR) und des kurzwelligen Infrarots (SWIR). Das Skript hebt urbane Gebiete besser als Echtfarbe hervor, während es trotzdem natürlich aussieht.\n\n\n\n\n\nMehr Infos [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)"]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":["# NDMI für Feuchtigkeitsstress\n\nDer Normalized Difference Moisture Index (NDMI) für Feuchtigkeitsstress kann verwendet werden, um Bewässerung zu erkennen. Für alle Indexwerte über 0 kann bei Kenntnis der Landnutzung und Landbedeckung festgestellt werden, ob eine Bewässerung stattgefunden hat. Wenn man die Art der angebauten Pflanzen kennt (z. B. Zitrusfrüchte), kann festgestellt werden, ob die Bewässerung während der entscheidenden Wachstumsperiode im Sommer effektiv ist oder nicht, und auch herausfinden, ob einige Teile des Betriebs unter- oder überbewässert werden.\n\n\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)"]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":["# Normalisierter differenzierter Feuchte-Index (NDMI)\n\nDer Normalized Difference Moisture Index (NDMI) wird zur Bestimmung des Wassergehalts der Vegetation und zur Überwachung von Trockenperioden verwendet. Der Wertebereich des NDMI ist -1 bis 1. Negative Werte des NDMI (Werte, die sich -1 nähern) entsprechen unfruchtbarem Boden. Werte um Null (-0,2 bis 0,4) entsprechen im Allgemeinen Wasserstress. Hohe, positive Werte stehen für ein hohes Kronendach ohne Wasserstress (etwa 0,4 bis 1).\n\n\n\nMehr Infos [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":["# Normalized Difference Water Index (NDWI)\n\nDer normalisierte differenzierte Wasser-Index ist für die Wasserflächenkartierung am besten geeignet. Werte für Wasserflächen sind größer als 0,5. Vegetation führt zu kleineren Werten. Bebaute Flächen führen zu positiven Werten zwischen 0 und 0,2.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":["# Normalized Difference Water Index (NDWI)\n\nDer normalisierte differenzierte Wasser-Index ist für die Wasserflächenkartierung am besten geeignet. Werte für Wasserflächen sind größer als 0,5. Vegetation führt zu kleineren Werten. Bebaute Flächen führen zu positiven Werten zwischen Null und 0,2."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Falschfarbenkomposit\n\nEin Falschfarbenkomposit verwendet mindestens eine nicht-sichtbare Wellenlänge zur Abbildung der Erde. Das Falschfarbenkomposit unter Verwendung von Nahinfrarot, roten und grünen Bändern ist sehr beliebt (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Das Falschfarbenkomposit wird am häufigsten zur Beurteilung der Pflanzendichte und -gesundheit verwendet, da Pflanzen nahes Infrarot- und grünes Licht reflektieren, während sie rotes absorbieren. Städte und freiliegender Boden sind grau oder hellbraun, und Wasser erscheint blau oder schwarz.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) und [hier.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Falschfarbenkomposit\n\nEin Falschfarbenkomposit verwendet mindestens eine nicht-sichtbare Wellenlänge, um die Erde abzubilden. Das Falschfarbenkomposit unter Verwendung von Nahinfrarot, roten und grünen Bändern ist sehr beliebt (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Das Falschfarbenkomposit wird am häufigsten zur Beurteilung der Pflanzendichte und -gesundheit verwendet, da Pflanzen nahes Infrarot- und grünes Licht reflektieren, während sie rotes absorbieren. Städte und freiliegender Boden sind grau oder hellbraun, und Wasser erscheint blau oder schwarz.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) und [hier.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":["# Falschfarbenkomposit\n\nEin Falschfarbenkomposit verwendet mindestens eine nicht-sichtbare Wellenlänge zur Abbildung der Erde. Das Falschfarbenkomposit unter Verwendung von Nahinfrarot, roten und grünen Bändern ist sehr beliebt (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Das Falschfarbenkomposit wird am häufigsten zur Beurteilung der Pflanzendichte und -gesundheit verwendet, da Pflanzen nahes Infrarot- und grünes Licht reflektieren, während sie rotes absorbieren. Städte und freiliegender Boden sind grau oder hellbraun, und Wasser erscheint blau oder schwarz.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) und [hier.](https://earthobservatory.nasa.gov/features/FalseColor)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Falschfarbenkomposit\n\nEin Falschfarbenkomposit verwendet mindestens eine nicht-sichtbare Wellenlänge, um die Erde abzubilden. Das Falschfarbenkomposit unter Verwendung von Nahinfrarot, roten und grünen Bändern ist sehr beliebt (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Das Falschfarbenkomposit wird am häufigsten zur Beurteilung der Pflanzendichte und -gesundheit verwendet, da Pflanzen nahes Infrarot- und grünes Licht reflektieren, während sie rotes absorbieren. Städte und freiliegender Boden sind grau oder hellbraun, und Wasser erscheint blau oder schwarz.\n\n\n\nMehr Informationen [hier.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jeder Bereich des Spektrums wird als Band bezeichnet. Sentinel-2 hat 13 Bänder. Das Echtfarbenkomposit verwendet die Bänder des sichtbaren Lichts aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen. Das Ergebnis ist ein natürliches Farbprodukt, das die Erde in etwa darstellt, wie Menschen sie natürlich sehen würden.\n\n\n\nWeitere Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/was-ist-fernerkundung/licht-und-farbe), [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) und [hier.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy)."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jeder Bereich des Spektrums wird als Band bezeichnet. Landsat 5 hat 7 Bänder. Das Echtfarbenkomposit verwendet die Bänder des sichtbaren Lichts aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen, was zu einem natürlich gefärbten Produkt führt, das die Erde in etwa darstellt, wie Menschen sie natürlich sehen würden.\n\n\n\nWeitere Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/was-ist-fernerkundung/licht-und-farbe), [hier](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) und [hier.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jeder Bereich des Spektrums wird als Band bezeichnet. Landsat 7 hat 8 Bänder. Ein Echtfarbenkomposit verwendet die Bänder des sichtbaren Lichts aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen, was zu einem natürlich gefärbten Produkt führt, das die Erde in etwa darstellt, wie Menschen sie natürlich sehen würden.\n\n\n\nWeitere Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/was-ist-fernerkundung/licht-und-farbe), [hier](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) und [hier.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jede Region im Spektrum wird als Band bezeichnet. Das Echtfarbenkomposit verwendet die Bänder des sichtbaren Lichts aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen, was zu einem Produkt mit natürlichen Farben führt, das die Erde in etwa darstellt, wie Menschen sie natürlich sehen würden.\n\n\n\nMehr Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/was-ist-fernerkundung/licht-und-farbe), [hier.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jeder Bereich des Spektrums wird als Band bezeichnet. Das Echtfarbenkomposit verwendet die die Bänder des sichtbaren Lichts aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen, was zu einem Produkt mit natürlichen Farben führt, das die Erde in etwa darstellt, wie Menschen sie natürlich sehen würden.\n\n\n\nMehr Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/was-ist-fernerkundung/licht-und-farbe), [hier.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)"]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Geschärftes Echtfarbenkomposit\n\nDas geschärftes Echtfarbenkomposit wird erstellt, indem die üblichen Echtfarbdaten (Rot, Grün und Blau (RGB)) verwendet und durch die Verwendung des panchromatischen Bandes 8, oder Pan-Bandes, verbessert werden (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern aufnehmen). Ein Bild aus dem Pan-Band ist ähnlich wie ein Schwarz-Weiß-Film: Es kombiniert Licht aus dem roten, grünen und blauen Teil des Spektrums zu einem einzigen Wert für die sichtbare Gesamtreflexion. So geschärfte Bilder haben eine vierfach höhere Auflösung als die üblichen Echtfarbbilder, was den Nutzen von Landsat-Bildern erheblich steigert.\n\n\n\nWeitere Informationen [hier](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) und [hier.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":["# Falschfarben-Stadtkomposit\n\nDieses Komposit wird verwendet, um verstädterte Gebiete deutlicher zu visualisieren. Die Vegetation ist in Grüntönen sichtbar, während verstädterte Bereiche durch Weiß, Grau oder Violett dargestellt werden. Böden, Sand und Mineralien werden in verschiedenen Farben dargestellt. Schnee und Eis erscheinen in Dunkelblau, Wasser in Schwarz oder Blau. Überschwemmte Gebiete sind sehr dunkelblau und fast schwarz. Das Komposit ist nützlich für die Erkennung von Waldbränden und Vulkancalderen, da diese in Rot- und Gelbtönen dargestellt werden.\n\n\n\nMehr Infos [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) und [hier.](https://eos.com/false-color/)"]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Falschfarben-Stadtkomposit\n\nDieses Komposit verwendet eine Kombination von Bändern im sichtbaren und im kurzwelligen Infrarotbereich (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Es zeigt die Vegetation in Grüntönen an. Während dunklere Grüntöne eine dichtere Vegetation anzeigen, hat spärliche Vegetation hellere Farbtöne. Städtische Gebiete sind blau und Böden haben verschiedene Brauntöne.\n\n\n\nMehr Informationen [hier.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":["# Landwirtschaftskomposit \n\nDieses Komposit verwendet kurzwellige Infrarot-, Nahinfrarot- und blaue Bänder, um die Gesundheit der Pflanzen zu überwachen (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Sowohl das kurzwellige als auch das nahe Infrarotband sind besonders gut geeignet, um dichte Vegetation hervorzuheben, die im Kompositbild dunkelgrün erscheint. Feldfrüchte erscheinen in einem leuchtenden Grün und freiliegende Erde erscheint in Magenta.\n\n\n\nWeitere Informationen [hier](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) und [hier.](https://gisgeography.com/sentinel-2-bands-combinations/)"]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":["# Schnee-Klassifikator\n\nDer Algorithmus des Schnee-Klassifikators zielt darauf ab, Schnee zu erkennen, indem er Pixel auf der Grundlage unterschiedlicher Helligkeits- und der Normalized Difference Snow Index (NDSI)-Schwellenwerte klassifiziert. Werte, die als Schnee klassifiziert werden, werden in hellem, leuchtendem Blau zurückgegeben. Das Skript kann Schneeflächen gegenüber Wolken überschätzen.\n\n\n\nMehr Infos [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":["# Ulyssys Wasserqualitätsanzeige (UWQV)\n\nDas Skript zielt darauf ab, den Chlorophyll- und Sedimentzustand von Wasserkörpern dynamisch zu visualisieren, die primäre Indikatoren für die Wasserqualität sind. Der Chlorophyllgehalt reicht farblich von dunkelblau (niedriger Chlorophyllgehalt) über grün bis rot (hoher Chlorophyllgehalt). Sedimentkonzentrationen sind braun gefärbt; undurchsichtiges Braun zeigt einen hohen Sedimentgehalt an.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)"]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":["# Kurzwellen-Infrarot-Komposit (SWIR)\n\nMessungen im kurzwelligen Infrarot (Short-Wave InfraRed, SWIR) können Wissenschaftler:innen helfen, abzuschätzen, wie viel Wasser in Pflanzen und Böden vorhanden ist, da Wasser SWIR-Wellenlängen absorbiert. Kurzwellige Infrarotbänder (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden) sind auch nützlich, um zwischen Wolkentypen (Wasserwolken gegenüber Eiswolken), Schnee und Eis zu unterscheiden, die alle im sichtbaren Licht weiß erscheinen. In diesem Komposit erscheint die Vegetation in Grüntönen, Böden und städtische Gebiete in verschiedenen Brauntöne und Wasser schwarz. Frisch verbranntes Land reflektiert stark in den SWIR-Bändern, was sie für die Kartierung von Brandschäden wertvoll macht. Jede Gesteinsart reflektiert kurzwelliges Infrarotlicht anders, sodass es möglich ist, die Geologie durch Vergleich des reflektierten SWIR-Lichts zu kartieren.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)"]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":["# Highlight-optimierte natürliche Farbe\n\nDieses Skript zielt darauf ab, die Erde in schönen, natürlichen Farbbildern darzustellen. Es verwendet Highlight-Optimierung, um ausgebrannte Pixel zu vermeiden und die Belichtung auszugleichen.\n\n\n\nMehr Infos [hier.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)"]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":["# Geologie 12, 8, 2 Komposit\n\nDieses Komposit nutzt das kurzwellige Infrarot (SWIR) Band 12, um zwischen verschiedenen Gesteinsarten zu unterscheiden (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Jeder Gesteins- und Mineraltyp reflektiert kurzwelliges Infrarotlicht anders, sodass es möglich ist, die Geologie durch den Vergleich des reflektierten SWIR-Lichts abzubilden. Das Nahinfrarot (NIR)-Band 8 hebt Vegetation hervor und Band 2 erkennt Feuchtigkeit. Beides trägt zur Unterscheidung von Bodenmaterialien bei. Das Komposit ist nützlich für das Auffinden von geologischen Formationen und Merkmalen (z.B. Verwerfungen, Brüche), Lithologie (z.B. Granit, Basalt, etc.) und Bergbauanwendungen.\n\n\n\nMehr Infos [hier.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)"]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":["# Geologie 8, 11, 12 Komposit\n\nDieses Komposit nutzt die beiden kurzwelligen Infrarotbänder (SWIR) 11 und 12, um zwischen verschiedenen Gesteinsarten zu unterscheiden (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Jeder Gesteins- und Mineraltyp reflektiert kurzwelliges Infrarotlicht anders, sodass es möglich ist, die Geologie durch den Vergleich des reflektierten SWIR-Lichts abzubilden. Das Nahinfrarot (NIR) Band 8 hebt die Vegetation hervor und trägt so zur Unterscheidung von Bodenmaterialien bei. Die Vegetation im Komposit erscheint rot. Das Komposit ist nützlich für die Unterscheidung von Vegetation und Land, insbesondere von geologischen Merkmalen, die für den Bergbau und die Mineralienexploration nützlich sein können.\n\n\n\nWeitere Informationen [hier](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) und [hier.](http://murphygeological.com/new---sentinel-2.html#)"]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":["# Waldbrände\n\nDieses Skript, erstellt von Pierre Markuse, visualisiert Waldbrände anhand von Sentinel-2-Daten. Es kombiniert den natürlichen Farbhintergrund mit einigen NIR/SWIR-Daten für die Rauchdurchdringung und mehr Details, während es Highlights von B11 und B12 hinzufügt, um Brände in roten und orangenen Farben zu zeigen.\n\n\n\nMehr Infos [hier.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)"]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":["# Verbesserte Echtfarben\n\nDieses Skript, das von Pierre Markuse erstellt wurde, verwendet mehrere Bänder (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden) sowie Sättigungs- und Helligkeitssteuerung, um die Echtfarbendarstellung zu verbessern.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)"]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":["# Brandflächen-Index\n\nDer Brandflächen-Index nutzt das breitere Spektrum der sichtbaren, Red-Edge-, NIR- und SWIR-Bänder.\n\nWertebeschreibung:()=> Der Wertebereich für den Index ist `-1` bis `1` für Brandnarben und `1` - `6` für aktive Brände. Unterschiedliche Feuerintensitäten können zu unterschiedlichen Schwellenwerten führen; die aktuellen Werte wurden, laut Originalautor, an überwiegend mediterranen Regionen kalibriert.\n\n\n\nMehr Infos [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)"]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":["# Normalisiertes Verbrennungsverhältnis (NBR)\n\nDas Normalisierte Verbrennungsverhältnis wird häufig zur Abschätzung der Verbrennungsschwere verwendet. Es verwendet Wellenlängen im Nahinfrarot (NIR) und im kurzwelligen Infrarot (SWIR). Gesunde Vegetation hat einen hohen Reflexionsgrad im Nahinfrarotbereich des Spektrums und einen niedrigen Reflexionsgrad im kurzwelligen Infrarot. Andererseits haben verbrannte Gebiete eine hohe Reflexion im kurzwelligen Infrarot, aber eine niedrige Reflexion im Nahinfrarot. Dunklere Pixel zeigen verbrannte Gebiete an.\n\n\n\nWeitere Informationen [hier](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) und [hier.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":["# Atmosphärische Durchdringung\n\nDieses Komposit verwendet verschiedene Bänder (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden) im nicht-sichtbaren Teil des elektromagnetischen Spektrums, um den Einfluss der Atmosphäre im Bild zu reduzieren. Die Wellenlängen, die von den kurzwelligen Infrarotbändern 11 und 12 aufgenommen werden, werden von den erhitzten Bereichen stark reflektiert und sind daher für die Kartierung von Bränden und verbrannten Gebieten nützlich. Die Wellenlängen, die vom kurzwelligen Infrarotband 8 aufgenommen werden, werden dagegen stark von der Vegetation reflektiert, was bedeutet, dass kein Feuer vorhanden ist. Die Vegetation erscheint blau und zeigt Details in Bezug auf die Vegetationsstärke an. Gesunde Vegetation wird in Hellblau dargestellt, während gestresste, spärliche oder/und trockene Vegetation in mattem Blau erscheint. Städtische Merkmale sind weiß, grau, cyan oder violett.\n\n\n\nMehr Informationen [hier.](https://eos.com/atmospheric-penetration/)"]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":["# Visualisierung von unfruchtbarem Boden\n\nDie Visualisierung von unfruchtbarem Boden kann für die Bodenkartierung nützlich sein, um die Lage von Erdrutschen oder das Ausmaß der Erosion in nicht-begrünten Gebieten zu untersuchen. Diese Visualisierung zeigt alle Vegetation in grün und den unfruchtbaren Boden in rot. Wasser erscheint schwarz.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) und [hier.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)"]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":["# Echtfarbenkomposit mit IR-Highlights\n\nDieses Komposit verbessert die Echtfarbendarstellung durch Hinzufügen der kurzwelligen Infrarot-Wellenlängen, um Details zu verstärken. Es zeigt erwärmte Bereiche in Rot/Orange an.\n\n\n\nMehr Informationen [hier.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)"]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":["# Erkennung von verbrannten Flächen\n\nDieses Skript wird verwendet, um großflächige, kürzlich verbrannte Bereiche zu erkennen. Rot gefärbte Pixel markieren verbrannte Gebiete, alle anderen Pixel werden in Echtfarbe zurückgegeben. Das Skript überschätzt manchmal verbrannte Flächen über Wasser und Wolken.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":["# Terrestrischer Chlorophyll-Index (OTCI)\n\n\n\nDer terrestrische Chlorophyll-Index (OTCI) wird auf der Grundlage des Chlorophyll-Gehalts in der terrestrischen Vegetation geschätzt und kann zur Überwachung des Zustands und der Gesundheit der Vegetation verwendet werden. Niedrige OTCI-Werte weisen normalerweise auf Wasser, Sand oder Schnee hin. Extrem hohe Werte, die mit Weiß angezeigt werden, deuten normalerweise auch auf das Fehlen von Chlorophyll hin. Sie repräsentieren im Allgemeinen entweder kahlen Boden, Fels oder Wolken. Die Chlorophyll-Werte dazwischen, die von rot (niedrige Chlorophyll-Werte) bis dunkelgrün (hohe Chlorophyll-Werte) reichen, können zur Bestimmung der Gesundheit der Vegetation verwendet werden.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":["# Normalisierter differenzierter Salzgehalt-Index\n\nDer Index veranschaulicht die Menge an Salz, die in Böden vorhanden ist. Die Versalzung des Bodens ist einer der häufigsten Landabbauprozesse, insbesondere in trockenen und halbtrocken Regionen, in denen die Niederschläge die Verdunstung übersteigen. \n\nHöhere Werte deuten auf einen höheren Salzgehalt hin, niedrige Werte auf einen niedrigeren Salzgehalt.\n\nLesen Sie mehr [hier,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [hier](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) und [hier.](https://www.indexdatabase.de/db/i-single.php?id=57)"]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":["**Eingeloggte Nutzer** können Ihre benutzerdefinierten Themen nutzen, Pins speichern und laden, eine Pin-Story erstellen, Entfernungen messen, einen\nZeitraffer erstellen und die erweiterten Bild-Downloadfunktionen nutzen.\n\nUm einen kostenlosen Account zu erstellen, einfach [hier] klicken\noder innerhalb der App unter **Login** und dann **Anmelden (Sign up)**."]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":["Mit diesem Tool können Sie einen Zeitraffer der dargestellten Layer und der ausgewählten Position erstellen.\n\nZunächst wählen Sie einen Zeitraum aus. Sie können Ihre Suchergebnisse weiter verfeinern, indem Sie nach dem Monat filtern\n(Filter nach \"Monat\"-Checkbox) und/oder, indem ein Bild pro Zeitspanne (Orbit, Tag, Woche, Monat, Jahr)\nausgewählt wird.\n\nDann pressen Sie Suchen und wählen Ihre Bilder aus.\nSie können alle über die Checkbox auswählen oder die Bilder mithilfe des Schiebereglers nach Bewölkungsgrad filtern. Alternativ können alle Bilder auch einzeln ausgewählt werden,\nindem Sie in der Liste ausgewählt werden. Mithilfe der Checkbox **Grenzen** können die Grenzen auf dem Bild ein-/ausgeschalten werden.\n\nÜber den Knopf \"Play\" kann eine Vorschau des Zeitraffers abgespielt werden. Die Geschwindigkeit kann auch eingestellt werden.\n(Bilder pro Sekunde).\n\nWenn Sie mit dem Ergebnis zufrieden sind, klicken Sie auf den Knopf \"Herunterladen\" und der\nZeitraffer wird als .gif
heruntergeladen."]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":["Ihr Nutzeraccount konnte nicht geladen werden, weil der zugehörige Sentinel Hub Account nicht erstellt wurde/abgelaufen ist. Sie können den EO-Browser weiterhin nutzen, aber nicht die persönliche Nutzerkonfiguration. Um die persönliche Nutzerkonfiguration nutzen zu können, können Sie sich für einen 30-Tage Probeaccount anmelden oder sich für einen unserer Abonnements entscheiden: "]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":["Band 2 - Chlorophyll-Absorptionsmaximum - 442 nm"]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":["Band 11 - O2 R-Zweig Absorptionsband - 761 nm"]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (Atmosphärisch korrigiert)"]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":["Die Sentinel-1 Services sind sowohl auf EO-Cloud als auch auf AWS verfügbar. \nDie Möglichkeiten der Services unterscheiden sich. Mehr Informationen unter"]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":["Ein **DEM** (Digital Elevation Model) ist eine digitale Repräsentation des Geländes (normalerweise des der Erde). Es wird erstellt, indem die ganze Erde in ein Gitternetz unterteilt wird, worin jede Zelle einen Höhenwert beinhaltet. Abhängig von der Auflösung des Gitternetzes kann ein DEM genauer (höhere Auflösung) oder ungenauer (niedrige Auflösung) sein. Die Sentinel Hub DEM-Datensets (Mapzen und Copernicus) sind statisch (Datumsunabhängig) und weltweit verfügbar.\n\n**Häufiger Einsatzbereich:** Wasserflussmodellierung, Orthorektifizierung von Satellitenbildern und Ingenieurswesen."]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["Das **Copernicus DEM** stellt die Oberfläche der Erde mit ihren Gebäuden, der Infrastruktur und Vegetation dar. Ähnlich dem Mapzen DEM basiert es auf einer Zusammenstellung verschiedener DEMs (Basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Es handelt sich um eine statische Aufnahme (Datumsunabhängig) mit globaler Abdeckung.\n\n**Räumliche Auflösung:** 30 m, aufgefüllt mit 90 m an Orten, an denen das 30 m DEM noch nicht freigegeben wurde.\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["Das **Copernicus DEM** stellt die Oberfläche der Erde mit ihren Gebäuden, der Infrastruktur und Vegetation dar. Ähnlich dem Mapzen DEM basiert es auf einer Zusammenstellung verschiedener DEMs (Basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Es handelt sich um eine statische Aufnahme (Datumsunabhängig) mit globaler Abdeckung.\n\n**Räumliche Auflösung:** 90 m.\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":["Dieser Pin hat zur Zeit keine Beschreibung."]},"User Instances":{"msgid":"User Instances","msgstr":["Nutzerkonfiguration"]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":["Mausnavigation"]},"Left button":{"msgid":"Left button","msgstr":["Linke Maustaste"]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":["Klicken und ziehen Sie mit gedrückter linker Maustaste, um sich bei fixierter Höhe auf der Karte zu bewegen. Benutzten Sie SHIFT + linke Maustaste zum Drehen."]},"Right button":{"msgid":"Right button","msgstr":["Rechte Maustaste"]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":["Rechtsklick und hoch/runter ziehen, um die Höhe der Kamera zu ändern. Rechtsklick und\nnach links/rechts ziehen, um die Kameraperspektive zu drehen."]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":["Mittlere Maustaste/Mausrad"]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":["Nutzen Sie das Mausrad, um die Höhe der Kamera zu ändern (Rechtsklick + runter/hoch \nziehen). Klicken und ziehen Sie mit dem Mausrad, um den Winkel der Kamera zu ändern."]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":["Tastaturnavigation"]},"Arrow keys":{"msgid":"Arrow keys","msgstr":["Pfeiltasten"]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":["Nutzen Sie die Pfeiltasten, um sich bei fixierter Höhe auf der Karte zu bewegen."]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":["SHIFT + Pfeiltasten"]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":["Halten Sie SHIFT gedrückt und drücken Sie die Pfeiltasten, um die Kameraperspektive zu ändern."]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":["Bild auf/Bild ab"]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":["Nutzen Sie die Tasten \"Bild auf\" oder \"Bild ab\", um die Höhe der Kamera zu ändern."]},"Map navigation":{"msgid":"Map navigation","msgstr":["Kartennavigation"]},"Pan console":{"msgid":"Pan console","msgstr":["Schwenkkonsole"]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":["Die Schwenkkonsole erlaubt Ihnen, sich bei fixierter Höhe über die Karte zu bewegen. Klicken und ziehen Sie, um\nsich gleichmäßig zu bewegen. Je weiter entfern Sie vom Mittelpunkt sind, desto schneller bewegen Sie sich."]},"Camera console":{"msgid":"Camera console","msgstr":["Kamerakonsole"]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":["Die Kamerakonsole bewegt nur die Kameraperspektive. Klicken und ziehen Sie, um die Kameraperspektive zu ändern.\nJe weiter Sie vom Mittelpunkt weg ziehen, desto schneller wird sich die Ansicht ändern."]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":["Zoom"]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":["Durch Klicken wird die Höhe der Kamera geändert. Der Plusknopf bewegt die Kamera\nnäher zur Erde, der Minusknopf bewegt die Kamera weiter weg von der Erde."]},"Measure":{"msgid":"Measure","msgstr":["Messen"]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":["Erweiterte RGB-Effekte"]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":["Hauptdatenset:"]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":["Datenset-Alias:"]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":["Weitere Datensets:"]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":["Erstellen Sie einen Zeitraffer der markierten Fläche"]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":["Das TIFF-Format kann eine große Anzahl an Bändern beinhalten. Viele gebräuchliche Bildbearbeitungsprogramme (z. B. der Windows Photo Viewer) können TIFFs mit mehr als 3 Bändern nicht richtig anzeigen.\nWenn diese Option ausgewählt ist, werden nur die ersten 3 Bänder im Bild hinzugefügt.\nWenn diese Option nicht ausgewählt ist, werden alle Bänder hinzugefügt. Zum Betrachten des TIFF-Bildes muss ein Programm genutzt werden, dass mehr als 3 Bänder darstellen kann (z. B. QGIS)."]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":["EPSG:3857 ist nicht verfügbar wenn ein Untersuchungsgebiet erstellt wurde."]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":["Fügen Sie das dataMask-Band zu den unbearbeiteten Layern hinzu."]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":["Schneide zusätzliche Bänder zu"]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":["Fehler: Sie können Bilder mit Effekten nur als JPEG oder PNG runterladen."]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":["Achtung: Die folgenden Layer nutzen dataProducts. Daher ist der gewünschte Datentyp eventuell nicht gesetzt:"]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":["Achtung: Das Evalscript ist nicht im gebräuchlichen V3-Format und der gewünschte Datentyp konnte nicht gesetzt werden für:"]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":["Das heißt, der \"sampleType\" Parameter ist vermutlich automatisch auf den Default-Wert gesetzt. Du kannst dein Evalscript verändern, um den Fehler zu korrigieren. Erfahren Sie mehr über den \"sampleType\" in unserer Dokumentation"]},"Cancel":{"msgid":"Cancel","msgstr":["Abbrechen"]},"Error":{"msgid":"Error","msgstr":["Fehler"]},"Help":{"msgid":"Help","msgstr":["Hilfe"]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":["Ihr Browser unterstützt keine 3D-Funktionen, die notwendig sind, um diesen Inhalt anzuzeigen."]},"More information":{"msgid":"More information","msgstr":["Mehr Informationen"]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":["Keine Verbindung zum 3D-Service möglich! Nochmal versuchen?"]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":["Das Bild ist zu groß für dieses Gerät! Bildgröße: {0}x{1}, max.: {2}"]},"Home":{"msgid":"Home","msgstr":["Startseite"]},"Shading":{"msgid":"Shading","msgstr":["Schattierung"]},"Sphere mode":{"msgid":"Sphere mode","msgstr":["Sphären-Modus"]},"Eye height":{"msgid":"Eye height","msgstr":["Augenhöhe"]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":["Laden des Bildes nicht möglich"]},"Geometries":{"msgid":"Geometries","msgstr":["Geometrien"]},"Now":{"msgid":"Now","msgstr":["Jetzt"]},"Terrain":{"msgid":"Terrain","msgstr":["Gelände"]},"Time":{"msgid":"Time","msgstr":["Zeit"]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":["Positioniere die 3D Kamera basierend auf der 2D Karte"]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":["Modifizierter Anthocyanin-Reflexionsindex"]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":["# Effektiver radiometrischer Wolkenanteil\n\nDer Wolkenanteil ist der Teil der Erdoberfläche, der von Wolken bedeckt ist, im Verhältnis zur gesamten Erdoberfläche. Wolken wirken sich durch Abschirmung, Albedo und wolkeninterne Absorption auf die Bestimmung von Spurengasen aus. Der effektive radiometrische Wolkenanteil ist ein wichtiger Parameter zur Korrektur dieser Effekte."]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Thermales Band 10\n\nDiese thermale Visualisierung basiert auf Band 10 (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Bei der zentralen Wellenlänge von 10.895 nm misst es im thermalen Infrarot, oder TIR. Anstatt die Temperatur der Luft zu messen, wie es Wetterstationen tun, zeigt Band 10 die Temperatur über den Boden selbst, der oft viel heißer ist. Das Thermalband 10 ist nützlich, um Oberflächentemperaturen zu liefern und wird mit einer Auflösung von 100 m aufgenommen.\n\n\n\nWeitere Informationen [hier](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jeder einzeln aufgenommene Bereich des Spektrums wird als Band bezeichnet. Landsat 8 hat 11 Bänder. Das Echtfarbenkomposit verwendet die Bänder des sichtbaren Lichtes aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen, was zu einem Produkt mit natürlichen Farben führt. Die ist eine gute Darstellung der Erde, wie Menschen sie natürlich sehen würden.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":["# Verbessertes Echtfarbenbild\n\nDieses Skript nutzte die Verbesserung von Highlights, um ausgebrannte Pixel zu vermeiden und Überbelichtung auszugleichen. Es führt dazu, dass Wolken natürlicher aussehen und so viele sichtbaren Informationen wie möglich erhalten werden. Sentinel-3-OLCI-Kacheln decken ein großes Gebiet ab, sodass es möglich ist, große Wolkenformationen wie Wirbelstürme zu beobachten.\n\n\n\nWeitere Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)"]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":["Dies sind die Themen, die nicht verfügbare Daten enthalten:"]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":["# Normalisierter differenzierter Schnee-Index (NDSI)\n\nDer normalisierte differenzierte Schnee-Index von Sentinel-2 kann zur Unterscheidung zwischen Wolken- und Schneedecke verwendet werden, da Schnee im kurzwelligen Infrarotlicht absorbiert, aber im sichtbaren Licht reflektiert, während Wolken im Allgemeinen in beiden Wellenlängen reflektieren. Die Schneedecke wird in einem hellen, leuchtenden Blau dargestellt.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/)."]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":["# Szenenklassifizierung\n\n\n\nDie Szenenklassifizierung wurde entwickelt, um zwischen bewölkten Pixeln, klaren Pixeln und Wasserpixeln der Sentinel-2-Daten zu unterscheiden, und funktioniert auf Basis des Szenenklassifizierungsalgorithmus der ESA. Es werden zwölf verschiedene Klassen bereitgestellt, darunter Klassen für Wolken, Vegetation, Böden/Wüste, Wasser und Schnee. Es handelt sich nicht um eine Landbedeckungsklassifizierungskarte im engeren Sinne.\n\n\n\nMehr Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/)."]},"Disabled":{"msgid":"Disabled","msgstr":["Deaktivieren"]},"Yes":{"msgid":"Yes","msgstr":["Ja"]},"Orthorectification":{"msgid":"Orthorectification","msgstr":["Orthorektifizierung"]},"Histogram":{"msgid":"Histogram","msgstr":["Histogramm"]},"Recalculate":{"msgid":"Recalculate","msgstr":["Neuberechnen"]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":["Band 10 - Thermale Infrarot (TIRS) - 10895 nm"]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":["Band 11 - Thermale Infrarot (TIRS) - 12005 nm"]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":[""]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":[""]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":[""]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":[""]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":[""]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":[""]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":[""]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":[""]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":[""]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":[""]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":[""]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":[""]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":[""]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":[""]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":[""]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":["Ziehe die Klassen auf die RGB-Felder."]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":[""]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":[""]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":[""]},"File upload":{"msgid":"File upload","msgstr":["Datei hochladen"]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Laden Sie eine KML/KMZ-, GPX- oder GEOJSON/JSON-Datei hoch, um einen Untersuchungsgebiet zu erstellen. Das Gebiet wird beim Exportieren eines Bildes zum Ausschneiden verwendet."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":["Eine KML/KMZ-, GPX-, GEOJSON/JSON-Datei ablegen oder den Computer durchsuchen"]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":[""]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":[""]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":[""]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":[""]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":[""]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":["Landsat 8 L1"]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":["Landsat 8 L2"]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":["Ultrablau (443 nm)"]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":["Blau (482 nm)"]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":["Grün (561.5 nm)"]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":["Rot (654.5 nm)"]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":["Nahinfrarot (NIR) (865 nm)"]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":["Kurzwelliges Infrarot (SWIR) 1 (1608.5 nm)"]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":["Kurzwelliges Infrarot (SWIR) 2 (2200.5 nm)"]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":[""]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":[""]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":[""]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":[""]},"Zoom to location":{"msgid":"Zoom to location","msgstr":["Zoom zur Position"]},"Remove layer":{"msgid":"Remove layer","msgstr":["Ebene entfernen"]},"Please select a layer":{"msgid":"Please select a layer","msgstr":["Bitte wählen Sie eine Ebene aus"]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":[""]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":[""]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":[""]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":[""]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":[""]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":[""]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":[""]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":[""]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":[""]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":[""]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":[""]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":[""]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":[""]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":[""]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":["Blau (450-520 nm)"]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":["Grün (520-600 nm)"]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":["Rot (630-690 nm)"]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":["Nahinfrarot (NIR) (760-900 nm)"]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":["Kurzwelliges Infrarot (SWIR) 1 (1550-1750 nm)"]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":["Thermale Infrarot (10400-12500 nm)"]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":["Kurzwelliges Infrarot (SWIR) 2 (2080-2350 nm)"]},"Level 1":{"msgid":"Level 1","msgstr":["Level 1"]},"Level 2":{"msgid":"Level 2","msgstr":["Level 2"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":[""]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":["Das **Mapzen DEM** basiert auf der SRTM30 (Shuttle Radar Topography Mission) und [anderen Quellen]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). Die bathymetrischen Daten stammen von [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). Es handelt sich um eine statische Aufnahme (Datumsunabhängig) mit globaler Abdeckung.\n\n**Räumliche Auflösung:** Meist 90 m, in einigen Bereichen bis zu 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)"]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":["Flughäfen"]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Max. Wolkenbedeckung:"]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Daten hochladen"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":[""]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]}}}}
\ No newline at end of file
+{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","language":"de","mime-version":"1.0","content-transfer-encoding":"8bit","project-id-version":"","pot-creation-date":"","po-revision-date":"","last-translator":"","language-team":"","x-generator":"Poedit 2.4.3","plural-forms":"nplurals=2; plural=(n != 1);"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nLanguage: de\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\nProject-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLast-Translator: \nLanguage-Team: \nx-generator: Poedit 2.4.3\nPlural-Forms: nplurals=2; plural=(n != 1);\n"]},"Education":{"msgid":"Education","msgstr":["Bildung"]},"Normal":{"msgid":"Normal","msgstr":["Normal"]},"Close":{"msgid":"Close","msgstr":["Schließen"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Schließen und nicht mehr anzeigen"]},"Previous":{"msgid":"Previous","msgstr":["Zurück"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Tutorial beenden"]},"Next":{"msgid":"Next","msgstr":["Weiter"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Tutorial fortsetzen"]},"Don't show again":{"msgid":"Don't show again","msgstr":["Nicht mehr anzeigen"]},"Show info":{"msgid":"Show info","msgstr":["Information anzeigen"]},"Discover":{"msgid":"Discover","msgstr":["Entdecken"]},"Visualize":{"msgid":"Visualize","msgstr":["Anzeigen"]},"Compare":{"msgid":"Compare","msgstr":["Vergleichen"]},"Pins":{"msgid":"Pins","msgstr":["Pins"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Es ist ein Fehler beim Laden der Bilder aufgetreten:"]},"No tile found":{"msgid":"No tile found","msgstr":["Kachel nicht gefunden"]},"Dataset":{"msgid":"Dataset","msgstr":["Datensatz"]},"Show":{"msgid":"Show","msgstr":["Zeige"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Effekte und erweiterte Optionen anzeigen"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Visualisierung anzeigen"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Zu Pins hinzufügen"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Zum Vergleich hinzufügen"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Zoom auf Kachel"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Ebene ausblenden"]},"Show layer":{"msgid":"Show layer","msgstr":["Ebene anzeigen"]},"Share":{"msgid":"Share","msgstr":["Teilen"]},"Custom":{"msgid":"Custom","msgstr":["Benutzerdefiniert"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Benutzerdefinierte Visualisierung erstellen"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Vergrößern, um Daten anzuzeigen"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Kostenlose Anmeldung"]},"for all features":{"msgid":"for all features","msgstr":["für alle Funktionen"]},"Powered by":{"msgid":"Powered by","msgstr":["Präsentiert von"]},"with contributions by":{"msgid":"with contributions by","msgstr":["mit Unterstützung der"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["Bitte Datenquelle(n) auswählen!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["Ungültiger Zeitraum!"]},"No results found":{"msgid":"No results found","msgstr":["Keine Ergebnisse gefunden"]},"Theme":{"msgid":"Theme","msgstr":["Thema"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Verwalten von Konfigurationeinstellungen"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Anmelden, um benutzerdefinierte Konfigurationseinstellungen zu verwenden."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["Fehler beim Abrufen von zusätzlichen Daten!"]},"Search":{"msgid":"Search","msgstr":["Suche"]},"Highlights":{"msgid":"Highlights","msgstr":["Highlights"]},"Data sources":{"msgid":"Data sources","msgstr":["Datensätze"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Bitte wählen Sie ein Thema"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Zeitraum [UTC]"]},"Date":{"msgid":"Date","msgstr":["Datum"]},"Hide description":{"msgid":"Hide description","msgstr":["Beschreibung ausblenden"]},"Show description":{"msgid":"Show description","msgstr":["Beschreibung anzeigen"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Dieses Thema hat keine Highlights"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Basierend auf: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["1 Tag (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["5 Tage (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["10 Tage (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["O3 (Ozon)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (Stickstoffdioxid)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (Schwefeldioxid)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (Kohlenmonoxid)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (Formaldehyd)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (Methan)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER AI (Aerosol-Index)"]},"Cloud":{"msgid":"Cloud","msgstr":["Wolken"]},"Other":{"msgid":"Other","msgstr":["Sonstiges"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Max. Wolkenbedeckung"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Erweiterte Suche"]},"Data location":{"msgid":"Data location","msgstr":["Speicherort der Daten"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Bitte wählen Sie mindestens einen Standort aus!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Datenerfassungsmodus"]},"Polarization":{"msgid":"Polarization","msgstr":["Polarisation"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Bitte wählen Sie mindestens einen Datenerfassungsmodus aus!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Bitte wählen Sie mindestens eine Polarisation aus!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Richtung des Orbits"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Bitte wählen Sie mindestens eine Orbitrichtung aus!"]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["**MERIS** (Medium-resolution spectrometer) war ein Sensor an Bord des Satelliten [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) mit der Hauptaufgabe, die Farbe von Land und Ozean sowie die Atmosphäre zu beobachten. Er ist nicht mehr aktiv und wurde durch Sentinel-3 ersetzt.\n\n**Räumliche Auflösung:** Volle Auflösung Land & Küste: 260m x 290m (d.h. nur Details, die größer als 260m x 290m sind, können gesehen werden).\n\n**Wiederholrate:** Maximal 3 Tage, um das gleiche Gebiet erneut zu besuchen.\n\n**Datenverfügbarkeit:** Von Juni 2002 bis April 2012.\n\n**Häufige Verwendung:** Meeresüberwachung (Phytoplankton, Schwebstoffe), Atmosphäre (Wasserdampf, CO2, Wolken, Aerosole) und Land (Vegetationsindex, globale Abdeckung, Feuchtigkeit)."]},"Credits:":{"msgid":"Credits:","msgstr":["Referenz:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["**GIBS** (Global Imagery Browse Services) bietet schnellen Zugriff auf über 600 Satelliten-\nbildprodukte, die jeden Teil der Welt abdecken. Die meisten Bilder sind innerhalb weniger Stunden nach Satellitenüberflug verfügbar. Einige Produkte umfassen fast 30 Jahre."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["Die Serie der **Landsat**-Satelliten der NASA/U.S. Geological Survey erfassen, ähnlich wie Sentinel-2, sichtbare und infrarote Wellenlängen. Sie können zusätzlich thermales Infrarot erfassen (Landsat 8). Die Landsat-Serie umfasst fast fünf Jahrzehnte von Aufnahmen.\nMit dieser Plattform haben Sie Zugriff auf Bilder, die von Landsat 5, 7 und 8 aufgenommen wurden.\n\n**Räumliche Auflösung:** 15 m, 30 m und 100 m umgerechnet auf 30 m, abhängig von der Wellenlänge (d. h. nur Details größer als 10 m und 30 m können gesehen werden). Mehr Infos [hier](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Wiederholrate:** Maximal 8 Tage, um dasselbe Gebiet mit den beiden operationellen Satelliten Landsat 7 und Landsat 8 erneut zu besuchen.\n\n**Datenverfügbarkeit:** Europa und Nordafrika von 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 bis heute (Landsat 8) aus dem ESA-Archiv. Das globale Archiv des U.S. Geological Survey (USGS) von April 2013 bis heute (nur Landsat 8) .\n\n**Häufige Verwendung:** Vegetationsüberwachung, Landnutzung, Landbedeckungskarten, Überwachung von Veränderungen, etc."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["Das **MODIS** (Moderate Resolution Imaging Spectroradiometer) der NASA sammelt Daten mit dem Ziel, unser Verständnis von globalen Prozessen, die auf dem Land stattfinden, zu verbessern. Der EO-Browser liefert Daten zur Beobachtung von Land (Bänder 1-7).\n\n**Räumliche Auflösung:** 250 m (Bänder 1-2), 500 m (Bänder 3-7), 1000 m (Bänder 8-36).\n\n**Wiederholrate:** Globale Abdeckung in 1 - 2 Tagen sowohl mit Aqua- als auch mit Terra-Satelliten.\n\n**Datenverfügbarkeit:** Seit Januar 2013.\n\n**Häufige Verwendung:** Überwachung von Land, Wolken, Ozeanfarbe auf globaler Ebene."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["Der **Proba-V**-Satellit ist ein Kleinsatellit, der die Landbedeckung und das Wachstum der Vegetation alle zwei Tage über dem gesamten Globus aufnimmt. Der EO-Browser liefert abgeleitete Produkte, welche die Wolkenbedeckung durch die Kombination von wolkenfreien Messungen innerhalb eines Zeitraums von 1 Tag (S1), 5 Tagen (S5) und 10 Tagen (S10) berücksichtigt.\n\n**Räumliche Auflösung:** 100 m für S1 und S5, 333 m für S1 und S10, 1000 m für S1 und S10.\n\n**Wiederholrate:** 1 Tag für Breitengrade 35-75°N und 35-56°S, 2 Tage für Breitengrade zwischen 35°N und 35°S.\n\n**Datenverfügbarkeit:** Seit Oktober 2013.\n\n**Häufige Verwendung:** Die Beobachtung der Landbedeckung, Vegetationswachstum, Klimafolgenabschätzung, Wasserressourcenmanagement, landwirtschaftliche Überwachung und Abschätzung der Ernährungssicherheit, Überwachung von Binnengewässern, Überwachung von Binnenwasserressourcen und Verfolgung der stetigen Ausbreitung von Wüsten und Entwaldung."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":["**Sentinel-1** liefert Allwetter-, Tag- und Nacht-Radarbilder für Land- und Ozeandienste. Der EO-Browser liefert Daten, die im Interferometric Wide Swath (IW) und Extra Wide Swath (EW) Modus aufgenommen und zu Level-1 Ground Range Detected (GRD) verarbeitet werden.\n\n**Pixelabstände:** 10 m (IW), 40 m (EW).\n\n**Wiederholrate:** <= 5 Tage mit beiden Satelliten.\n\n**Wiederholrate:** (für aufsteigend/absteigend und Überlappung unter Verwendung beider Satelliten): <= 3 Tage, siehe [Beobachtungsszenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Datenverfügbarkeit:** Seit Oktober 2014.\n\n**Häufige Verwendung:** Überwachung von Meer und Land, Notfallmaßnahmen, Klimawandel."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["**Sentinel-2** liefert hochauflösende Bilder im sichtbaren und infraroten Wellenlängenbereich, um Vegetation, Boden- und Wasserbedeckung, Binnengewässer und Küstengebiete zu überwachen. \n\n**Räumliche Auflösung:** 10 m, 20 m und 60 m, abhängig von der Wellenlänge (d. h. nur Details größer als 10 m, 20 m und 60 m können gesehen werden). Mehr Infos [hier](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Wiederholrate:** maximal 5 Tage, um dasselbe Gebiet mit beiden Satelliten erneut zu besuchen.\n\n**Datenverfügbarkeit:** Seit Juni 2015. Volle globale Abdeckung seit März 2017.\n\n**Häufige Verwendung:** Landbedeckungskarten, Karten zur Erkennung von Landveränderungen, Vegetationsüberwachung, Überwachung von verbrannten Gebieten."]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Level-2A-Daten sind Daten von hoher Qualität, bei denen die Auswirkungen der Atmosphäre auf das Licht, das von der Erdoberfläche reflektiert wird und den Sensor erreicht, entfernt wurden. Die Daten sind seit März 2017 weltweit verfügbar.\n\nWeitere Informationen zur atmosphärischen Korrektur [hier](https://fis.rub.de/recherchetools/infobox/profis/bildvorverarbeitung/atmosph%C3%A4renkorrektur)."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Level-1C-Daten sind Daten von ausreichender Qualität für die meisten Untersuchungen, da bei ihnen alle Bildkorrekturen bis auf die atmosphärische Korrektur durchgeführt wurden. Die Daten sind seit Juni 2015 global verfügbar."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["Das Hauptziel der **Sentinel-3**-Mission ist die Messung der Topografie der Meeresoberfläche, der Temperatur der Meeres- und Landoberfläche sowie der Farbe der Meeres- und Landoberfläche. Sentinel-3 hat vier verschiedene Instrumente an Bord. Die vom Ocean and Land Colour Instrument (OLCI) und dem Sea and Land Surface Temperature Instrument (SLSTR) erfassten Daten sind auf dieser Plattform verfügbar.\n\n**Datenverfügbarkeit:** Seit Mai 2016."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["Das **Sea and Land Surface Temperature (SLSTR)**-Instrument an Bord von Sentinel-3 misst die globale und regionale Meeres- und Landoberflächentemperatur. Das SLSTR deckt die sichtbaren, kurzwelligen Infrarot- und thermalen Infrarot-Wellenlängen des elektromagnetischen Spektrums ab. \n\n**Räumliche Auflösung:** 500 m für sichtbare, nah- und kurzwellige Infrarot-Wellenlängen und 1 km für thermales Infrarot (d. h. nur Details, die größer als 500 m bzw. 1 km sind, können gesehen werden).\n\n**Wiederholrate:** Maximal 1 Tag, um dasselbe Gebiet unter Verwendung beider Satelliten erneut zu besuchen.\n\n**Datenverfügbarkeit:** Seit Mai 2016.\n\n**Häufige Verwendung: ** Überwachung des Klimawandels, Vegetationsüberwachung, aktive Feuererkennung, Überwachung der Land- und Meeresoberflächentemperatur."]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["Das **Ocean and Land Colour Instrument (OLCI)** an Bord von Sentinel-3 ist ein Spektrometer, das die von der Erde reflektierte Sonnenstrahlung misst und den Ozean, die Umwelt und das Klima überwacht. Es liefert häufiger sichtbare Bilder als Sentinel-2, jedoch mit einer geringeren Auflösung und mit mehr abgedeckten Wellenlängen. Das Sentinel-3 OLCI setzt die Messungen fort, die zuvor vom MERIS-Instrument an Bord von Envisat durchgeführt wurden, dessen Mission beendet war.\n\n**Räumliche Auflösung:** 300 m (d. h. nur Details größer als 300 m können gesehen werden).\n\n**Wiederholrate:** Maximal 2 Tage, um dasselbe Gebiet unter Verwendung beider Satelliten erneut zu besuchen.\n\n**Datenverfügbarkeit:** Seit Mai 2016.\n\n**Häufige Verwendung: ** Oberflächentopografie, Farbbeobachtung und Überwachung der Meeres- und Landoberfläche."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["**Sentinel-5P** ist ein Satellit, der atmosphärische Messungen liefert, die für die Überwachung von Luftqualität, Ozon, UV-Strahlung, und Klimaüberwachung und -vorhersage verwendet werden.\n\n**Räumliche Auflösung:** 7 x 3,5 km (d. h. nur Details, die größer als 7 x 3,5 km sind, können gesehen werden).\n\n**Wiederholrate:** Maximal 1 Tag, um das gleiche Gebiet erneut zu besuchen.\n\n**Datenverfügbarkeit:** Seit April 2018.\n\n**Häufige Verwendung:** Überwachung der Konzentration von Kohlenmonoxid (CO), Stickstoffdioxid (NO2) und Ozon (O3) in der Luft. Überwachung des UV-Aerosol-Index (AER_AI) und verschiedener geophysikalischer Parameter von Wolken (Cloud)."]},"Copied":{"msgid":"Copied","msgstr":["Kopiert"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["In die Zwischenablage kopieren"]},"Data source name":{"msgid":"Data source name","msgstr":["Name der Datenquelle"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Aufnahmezeitpunkt"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Wolkenbedeckung"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Sonnenhöhe"]},"MGRS location":{"msgid":"MGRS location","msgstr":["MGRS-Standort"]},"AWS path":{"msgid":"AWS path","msgstr":["AWS-Pfad"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["EO-Cloud-Pfad"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["CreoDIAS-Pfad"]},"SciHub link":{"msgid":"SciHub link","msgstr":["SciHub-Link"]},"Back to search":{"msgid":"Back to search","msgstr":["Zurück zur Suche"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Zeige ${ this.state.results.length } Ergebnis an","Zeige ${ this.state.results.length } Ergebnisse an"]},"Load more":{"msgid":"Load more","msgstr":["Mehr laden"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Weitere Ergebnisse werden geladen..."]},"Results":{"msgid":"Results","msgstr":["Ergebnisse"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Zeige ${ this.state.selectedTiles.length } Ergebnis.","Zeige ${ this.state.selectedTiles.length } Ergebnisse."]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Pin-Beschreibung bearbeiten"]},"Reject changes":{"msgid":"Reject changes","msgstr":["Änderungen verwerfen"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Änderungen bestätigen"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Pin umbenennen"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Pin entfernen"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Zoom auf angeheftete Position"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Breite/Länge"]},"Zoom":{"msgid":"Zoom","msgstr":["Zoom"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["Sie sind dabei, ${ N_PINS } Pin(s) zu Ihrer Pin-Sammlung hinzuzufügen. Möchten Sie fortfahren?"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["WARNUNG: Sie sind im Begriff, eine Pin zu löschen. Möchten Sie fortfahren?"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":["WARNUNG: Sie sind im Begriff, alle Pins zu löschen. Möchten Sie fortfahren?"]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["Keine Pins. Gehen Sie zur Registerkarte \"Anzeigen\", um einen Pin zu speichern oder laden Sie eine JSON-Datei mit gespeicherten Pins hoch."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Beachten Sie, dass die Pins nur gespeichert werden, wenn Sie sich anmelden. Andernfalls gehen die Pins verloren, sobald die Anwendung geschlossen wird."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Alle abwählen"]},"Select all":{"msgid":"Select all","msgstr":["Alle auswählen"]},"No pins.":{"msgid":"No pins.","msgstr":["Keine Pins."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Link erstellen (${ selectedPins.length } Pins ausgewählt)","Link erstellen (${ selectedPins.length } Pins ausgewählt)"]},"File type not supported":{"msgid":"File type not supported","msgstr":["Dateityp nicht unterstützt"]},"not supported":{"msgid":"not supported","msgstr":["nicht unterstützt"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["Es wurden keine Pins gefunden."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Fehler beim Parsen der Datei:"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":["Laden Sie eine JSON-Datei mit gespeicherten Pins hoch."]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["JSON-Datei ablegen oder auf dem Computer suchen"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Vorhandene Pins beibehalten"]},"Share pins":{"msgid":"Share pins","msgstr":["Pins teilen"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Erstellen einer Story aus Pins"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Pins auf den Computer exportieren"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Importieren von Pins aus einer gespeicherten Datei"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Alle Pins löschen"]},"Story":{"msgid":"Story","msgstr":["Story"]},"Export":{"msgid":"Export","msgstr":["Exportieren"]},"Import":{"msgid":"Import","msgstr":["Importieren"]},"Clear":{"msgid":"Clear","msgstr":["Löschen"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Link für Pins freigeben"]},"Creating link...":{"msgid":"Creating link...","msgstr":["Link erstellen..."]},"OK":{"msgid":"OK","msgstr":["OK"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Aktualisieren der Pin-Sammlung."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Es gab ein Problem beim permanenten Aktualisieren der Pin-Sammlung: ${ updatingPinsError }."]},"Hello,":{"msgid":"Hello,","msgstr":["Hallo,"]},"Opacity":{"msgid":"Opacity","msgstr":["Deckkraft"]},"Split position":{"msgid":"Split position","msgstr":["Position teilen"]},"split":{"msgid":"split","msgstr":["aufteilen"]},"opacity":{"msgid":"opacity","msgstr":["Deckkraft"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["Keine Ebenen zum Vergleichen."]},"Remove all":{"msgid":"Remove all","msgstr":["Alle entfernen"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Alle Pins hinzufügen"]},"Split":{"msgid":"Split","msgstr":["Aufteilen"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Beim Herunterladen Ihrer Einstellungen ist ein Problem aufgetreten"]},"Download":{"msgid":"Download","msgstr":["Download"]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Gelände in 3D visualisieren"]},"Go to Place":{"msgid":"Go to Place","msgstr":["Gehe zu Ort"]},"Labels":{"msgid":"Labels","msgstr":["Beschriftungen"]},"Borders":{"msgid":"Borders","msgstr":["Grenzen"]},"Roads":{"msgid":"Roads","msgstr":["Straßen"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Vergrößern"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Verkleinern"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["Über den EO-Browser"]},"Contact us":{"msgid":"Contact us","msgstr":["Kontaktieren Sie uns"]},"Get data":{"msgid":"Get data","msgstr":["Daten beschaffen"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Sie müssen sich anmelden, um diese Funktion nutzen zu können."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Bitte wählen Sie eine Ebene aus."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["Das Herunterladen von Bildern im Vergleichsmodus ist nicht möglich."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["Diese Datenquelle wird nicht unterstützt."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":["Statistische Information / spektrale Informationen im Zeitraum"]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Statistische Information / spektrale Informationen im Zeitraum - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["Bitte wählen Sie eine Ebene"]},"not available for ":{"msgid":"not available for ","msgstr":["nicht verfügbar für "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["nicht verfügbar für \"${ props.presetLayerName }\" (Ebene mit Wert ist nicht eingerichtet)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Suchen Sie zuerst nach Daten."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Zeitrafferanimation erstellen"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Interessenfokus markieren"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Karte auf Merkmal zentrieren"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Geometrie entfernen"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Untersuchungsgebiet"]},"Select mode":{"msgid":"Select mode","msgstr":["Modus auswählen"]},"Mode:":{"msgid":"Mode:","msgstr":["Modus:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Messung entfernen"]},"km":{"msgid":"km","msgstr":["km"]},"m":{"msgid":"m","msgstr":["m"]},"Gain":{"msgid":"Gain","msgstr":["Kontrast"]},"Gamma":{"msgid":"Gamma","msgstr":["Helligkeit"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Minimale Datenqualität"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Upsampling"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Downsampling"]},"Reset all":{"msgid":"Reset all","msgstr":["Alle zurücksetzen"]},"filter by months":{"msgid":"filter by months","msgstr":["nach Monaten filtern"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Geometrie in die Zwischenablage kopieren"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Bearbeitung abbrechen."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Untersuchungsgebiet einzeichnen"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Geringste Wolkenbedeckung"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Zusätzliche Datensätze verwenden (erweitert)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Mosaikanordnung"]},"Most recent":{"msgid":"Most recent","msgstr":["Aktuellste"]},"Least recent":{"msgid":"Least recent","msgstr":["Älteste"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Zeitraum anpassen"]},"Back":{"msgid":"Back","msgstr":["Zurück"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Fehler beim Laden des Skripts. Prüfen Sie Ihre URL."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Deaktivieren Sie \"Skript aus URL laden\", um den Code zu bearbeiten"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Skript aus URL laden"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["URL zu Ihrem Skript eintragen"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Skript geladen."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Es sind nur HTTPS-Domänen erlaubt."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Skript in Code-Editor laden"]},"Refresh":{"msgid":"Refresh","msgstr":["Aktualisieren"]},"orbit":{"msgid":"orbit","msgstr":["Orbit"]},"day":{"msgid":"day","msgstr":["Tag"]},"week":{"msgid":"week","msgstr":["Woche"]},"month":{"msgid":"month","msgstr":["Monat"]},"year":{"msgid":"year","msgstr":["Jahr"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Wählen Sie 1 Bild pro:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Zeitraffer"]},"Select All":{"msgid":"Select All","msgstr":["Alle auswählen"]},"Speed:":{"msgid":"Speed:","msgstr":["Geschwindigkeit:"]},"frames / s":{"msgid":"frames / s","msgstr":["Bilder / s"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Bereite vor..."]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Dateien konnten nicht heruntergeladen werden:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Kann nicht über Canvas heruntergeladen werden"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["Dateien konnten nicht gezippt werden:"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Es gab ein Problem beim Herunterladen des Bildes"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Fehler beim Laden des Bildes: URL ist leer!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Fehler beim Laden des Bildes:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["Konnte Bild nicht aus dem Datenobjekt (blob) laden"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Ziehen Sie die Kanäle auf RGB-Felder."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Bänder in die Indexgleichung ziehen"]},"Index ":{"msgid":"Index ","msgstr":["Index "]},"Threshold":{"msgid":"Threshold","msgstr":["Grenzwert"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Farbwähler entfernen"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Farbwähler hinzufügen"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Klicken Sie, um die Markierung zu setzen"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Klicken Sie, um den ersten Scheitelpunkt zu platzieren"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Zum Weiterzeichnen anklicken"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Klicken Sie zum Beenden auf die erste Markierung"]},"Show captions":{"msgid":"Show captions","msgstr":["Beschriftungen anzeigen"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Folientitel anzeigen"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Karten-Beschriftung hinzufügen"]},"Show legend":{"msgid":"Show legend","msgstr":["Legende anzeigen"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["Es wurden keine Pins innerhalb des aktuellen Sichtfelds gefunden."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Einige Pins (${ N_PINS_OUTSIDE_BOUNDS }) werden ignoriert, da sie sich nicht innerhalb des ausgewählten Bereichs befinden."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["Um eine Pin-Story zu erstellen, navigieren Sie zur gewünschten Position auf der Karte.\n\nAlle Pins innerhalb des aktuellen Sichtfelds werden zum Erstellen der Story verwendet, der Rest wird ignoriert."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["Die Datei wird mit einem Logo versehen."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["Ein dataMask-Band wird in den heruntergeladenen Rohbändern als zweites Band enthalten sein."]},"Show logo":{"msgid":"Show logo","msgstr":["Logo anzeigen"]},"Image format":{"msgid":"Image format","msgstr":["Bildformat"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Bildauflösung"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Koordinatensystem"]},"Layers":{"msgid":"Layers","msgstr":["Ebenen"]},"Visualized":{"msgid":"Visualized","msgstr":["Visualisiert"]},"Raw":{"msgid":"Raw","msgstr":["Rohdaten"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["Die Beschriftungs-Ebenen der Karte (Ortsbezeichnungen, Straßen und politische Grenzen) werden dem Bild hinzugefügt."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Exportierte Bilder enthalten Datenquelle und Datum, Zoom-Skala und Branding"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Fügen Sie eine kurze Beschreibung zum exportierten Bild hinzu"]},"Description":{"msgid":"Description","msgstr":["Beschreibung"]},"Image format:":{"msgid":"Image format:","msgstr":["Bildformat:"]},"Basic":{"msgid":"Basic","msgstr":["Einfach"]},"Analytical":{"msgid":"Analytical","msgstr":["Analytisch"]},"High-res print":{"msgid":"High-res print","msgstr":["Hochauflösender Druck"]},"Download image":{"msgid":"Download image","msgstr":["Bild herunterladen"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["Beim Abrufen einiger Bilder ist ein Fehler aufgetreten:"]},"min/px":{"msgid":"min/px","msgstr":["min/px"]},"sec/px":{"msgid":"sec/px","msgstr":["sek/px"]},"Resolution":{"msgid":"Resolution","msgstr":["Auflösung"]},"lat.":{"msgid":"lat.","msgstr":["Breite"]},"deg/px":{"msgid":"deg/px","msgstr":["Grad/px"]},"long.":{"msgid":"long.","msgstr":["Länge"]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Projizierte Auflösung: ${ formattedResolution } m/px"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Fehler: Die Formate KMZ/JPG und KMZ/PNG werden bei der Datenfusion nicht unterstützt."]},"Image download":{"msgid":"Image download","msgstr":["Bild-Download"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Bildbreite [Zoll]:"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Bildhöhe [Zoll]:"]},"DPI:":{"msgid":"DPI:","msgstr":["DPI:"]},"5 years":{"msgid":"5 years","msgstr":["5 Jahre"]},"2 years":{"msgid":"2 years","msgstr":["2 Jahre"]},"1 year":{"msgid":"1 year","msgstr":["1 Jahr"]},"6 months":{"msgid":"6 months","msgstr":["6 Monate"]},"3 months":{"msgid":"3 months","msgstr":["3 Monate"]},"1 month":{"msgid":"1 month","msgstr":["1 Monat"]},"Retry":{"msgid":"Retry","msgstr":["Nochmal versuchen"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Wird geladen, bitte warten"]},"mean":{"msgid":"mean","msgstr":["Mittelwert"]},"median":{"msgid":"median","msgstr":["Median"]},"st. dev.":{"msgid":"st. dev.","msgstr":["St. Abw."]},"min / max":{"msgid":"min / max","msgstr":["min / max"]},"Export CSV":{"msgid":"Export CSV","msgstr":["CSV exportieren"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Zeitspanne:"]},"Date:":{"msgid":"Date:","msgstr":["Datum:"]},"Single date":{"msgid":"Single date","msgstr":["Einzeldatum"]},"Timespan":{"msgid":"Timespan","msgstr":["Zeitraum"]},"hh":{"msgid":"hh","msgstr":["hh"]},"mm":{"msgid":"mm","msgstr":["mm"]},"From:":{"msgid":"From:","msgstr":["Von:"]},"Until:":{"msgid":"Until:","msgstr":["Bis:"]},"Apply":{"msgid":"Apply","msgstr":["Anwenden"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Auf Facebook teilen"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Auf Twitter teilen"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Schauen Sie sich das an "]},"Logout":{"msgid":"Logout","msgstr":["Logout"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["Melden Sie sich an, um erweiterte Funktionen wie Zeitraffer, analytischen Download, eigene Konfigurationen und mehr freizuschalten."]},"Login":{"msgid":"Login","msgstr":["Login"]},"Default":{"msgid":"Default","msgstr":["Standard"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Überwachung der Erde aus dem Weltraum"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Landwirtschaft"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Atmosphäre und Luftverschmutzung"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Veränderungen im Laufe der Zeit"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Überschwemmungen und Dürren"]},"Geology":{"msgid":"Geology","msgstr":["Geologie"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Ozean und Gewässer"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Schnee und Gletscher"]},"Urban":{"msgid":"Urban","msgstr":["Stadt"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Vegetation und Forstwirtschaft"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Vulkane"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Waldbrände"]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":["# Willkommen beim EO-Browser!\n\nEin komplettes Archiv von Sentinel-1-, Sentinel-2-, Sentinel-3-, Sentinel-5P-Produkten, \nESAs Archiv von Landsat 5, 7 und 8, globale Abdeckung von Landsat 8, Envisat Meris-, \nMODIS-, Proba-V- und GIBS-Produkten an einem Ort.\n\n[EO-Browser-Präsentationsseite] (https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser-Benutzerhandbuch] (https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":["#### Kurze Übersicht über die EO-Browser-Funktionen\n\nDer EO-Browser kombiniert ein komplettes Archiv von Sentinel-1-, Sentinel-2-, Sentinel-3-, Sentinel-5P-Produkten, das ESA-Archiv von Landsat 5, 7 und 8, die globale Abdeckung von Landsat 8, Envisat Meris-, MODIS-, Proba-V- und GIBS-Produkten an einem Ort und ermöglicht es, Bilder in voller Auflösung aus diesen Quellen zu durchsuchen und zu vergleichen. Sie gehen einfach zu dem für Sie interessanten Gebiet, wählen Datenquellen, Zeitspanne und Wolken-Abdeckung aus und überprüfen die erhaltenen Daten.\n\nSie können das Tutorial fortsetzen, indem Sie auf die Schaltfläche \"Weiter\" klicken oder sie schließen es. Wenn Sie auf das Info-Symbol in der oberen rechten Ecke klicken, können Sie das Tutorial immer fortsetzen, falls Sie es versehentlich geschlossen haben oder weil Sie Dinge ausprobieren möchten."]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":["Auf der Registerkarte **Entdecken** können Sie:\n\n- **Themen** wählen. \n- nach Daten **Suche**n.\n- **Highlights** Themen anzeigen.\n\nDie Auswahlliste **Themen** bietet Ihnen verschiedene vorkonfigurierte Designs sowie eigene benutzerdefinierte konfigurierte Einstellungen, wenn Sie angemeldet sind. Um eine Einstellung zu erstellen, klicken Sie auf\ndas Einstellungssymbol und melden Sie sich mit den gleichen Anmeldeinformationen an, die Sie für den EO-Browser verwendet haben.\n\nUnter **Suche** können Sie Suchkriterien festlegen:\n - Wählen Sie aus, von welchem Satelliten Sie Daten erhalten möchten, indem Sie Checkboxen auswählen.\n - Wählen Sie ggf. zusätzliche Optionen aus, z. B. Wolkenbedeckung mit dem Schieberegler.\n - Wählen Sie einen Zeitraum aus, indem Sie entweder das Datum eingeben oder das Datum aus dem Kalender auswählen.\n\nSie können Informationen zu den Satelliten lesen, indem Sie auf das Fragezeichen\nneben dem Datenquellennamen klicken.\n\nSobald Sie auf Suchen klicken, erhalten Sie eine Liste von Ergebnissen. Jedes Ergebnis wird \nmit einem Vorschaubild und mit für die Datenquelle relevanten Daten angezeigt. Für einige Datenquellen ist das Linksymbol auch für jedes Ergebnis sichtbar.\nEin Klick darauf zeigt direkte Links zum Rohbild des Ergebnisses auf EO-Cloud oder SciHub. Wenn Sie auf die Schaltfläche Anzeigen klicken, wird die Registerkarte **Anzeigen** für das ausgewählte Ergebnis geöffnet.\n\nUnter **Highlight** finden Sie eine Auswahl von für das Thema interessanten Orten und Aufnahmen."]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":["Auf der Registerkarte Anzeigen können Sie verschiedene vorinstallierte oder benutzerdefinierte Spektralbandkombinationen auswählen, um Daten für das ausgewählte Ergebnis zu visualisieren.\n\nEinige der gängigen Optionen:\n- **Echtfarbe** - Visuelle Interpretation der Landbedeckung.\n- **Falschfarbe** - Visuelle Interpretation der Vegetation.\n- **NDVI** - Normalisierter differenzierter Vegetations-Index.\n- **Feuchtigkeitsindex** - Feuchte-Index\n- **SWIR** - Kurzwellen-Infrarot-Index.\n- **NDWI** - Normalisierter differenzierter Wasser-Index.\n- **NDSI** - Normalisierter differenzierter Schnee-Index.\n\nDie meisten Visualisierungen sind mit einer Beschreibung und einer Legende versehen, die Sie durch Klicken auf das Expandieren-Symbol auswählen können.\n \nFür die meisten Datenquellen ist die Option **Benutzerdefiniertes Skript** verfügbar. Klicken Sie darauf, um benutzerdefinierte Bandkombinationen, Indexkombinationen oder ein eigenes Klassifizierungsskript für die Visualisierung von Daten zu schreiben. Sie können auch benutzerdefinierte Skripte verwenden, die an anderer Stelle gespeichert sind, entweder auf Google Drive, GitHub oder in unserem [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nFügen Sie die URL des Skripts in ein Textfeld im erweiterten Skriptbearbeitungsfenster ein und klicken Sie auf Aktualisieren.\n \nSie können das Datum direkt in der Registerkarte Anzeigen ändern, ohne zurück zur Registerkarte **Entdecken** zu gehen. Geben Sie es ein oder wählen Sie es aus dem Kalender .\n\nOberhalb der Visualisierungen haben Sie eine Reihe von zusätzlichen Werkzeugen. Beachten Sie, dass deren Verfügbarkeit von der Datenquelle abhängt.\n- **Pin-Ebene**, um sie in der Anwendung für die spätere Verwendung zu speichern - durch Klicken auf das Pin-Symbol .\n- Wählen Sie **erweiterte Optionen** wie Resampling-Methoden oder wenden Sie verschiedene **Effekte** wie Kontrast und Helligkeit an - durch Klicken auf das Effekt-Schieberegler-Symbol .\n- Fügen Sie auf der Registerkarte **Vergleichen** durch Klicken auf das Vergleichssymbol eine Ebene zum späteren Vergleich hinzu.\n- **Zoomen** Sie in die Mitte der Kachel - durch Klicken auf das Fadenkreuz .\n- **Sichtbarkeit der Ebene** umschalten - durch Klicken auf das Sichtbarkeitssymbol .\n- **Teilen** Sie Ihre Visualisierung in sozialen Medien - durch Klicken auf das Teilen-Symbol ."]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":["In der Registerkarte **Vergleichen** finden Sie alle Visualisierungen, die Sie über zu **Vergleichen** hinzugefügt haben. \n\nEs gibt zwei Modi:\n - **Deckkraft** (Ziehen Sie den Deckkraftregler nach links oder rechts, um zwischen den verglichenen Bildern zu blenden)\n - **Aufteilen** (Ziehen Sie den Aufteilen-Schieberegler nach links oder rechts, um die Grenze zwischen den verglichenen Bildern festzulegen)\n\nSie können alle Pins mit **Alle Pins hinzufügen** zum Vergleichsfenster hinzufügen oder alle Visualisierungen\n mit der Schaltfläche **Alle entfernen** aus der Registerkarte **Vergleichen** entfernen."]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":["Die Registerkarte **Pins** enthält Ihre angepinnten (favorisierten/gespeicherten) Objekte. Angepinnte Objekte enthalten Informationen über den Standort, die Datenquelle und deren spezifische Ebene, die Zoomstufe und die Zeit.\n\nFür jeden Pin haben Sie mehrere Möglichkeiten, wie Sie mit einem einzelnen Pin interagieren können:\n\n- Ändern der **Reihenfolge** - durch Klicken auf das Verschiebesymbol\n\n \n \n \nin der linken oberen Ecke des Pins und Ziehen des Pins in der Liste nach oben oder unten.\n- **Umbenennen** - durch Klicken auf das Bleistiftsymbol neben dem Namen des Pins.\n- Hinzufügen zur Registerkarte **Vergleichen** - durch Klicken auf das Vergleichssymbol \n- Eine **Beschreibung** eingeben - durch Klicken auf das Erweiterungssymbol .\n- **Entfernen** - durch Klicken auf das Entfernen-Symbol .\n- **Zoomen** auf den Standort des Pins- durch Anklicken des Breitengrad/Längengrad.\n\nIn der Zeile über allen Pins haben Sie verschiedene Optionen, die für alle Pins gelten:\n- Erstellen Sie aus den Pins eine eigene Story - durch Klick auf **Story**.\n- Ihre Pins über einen Link mit anderen teilen - durch Klick auf **Teilen**.\n- Pins als JSON-Datei exportieren - durch Klicken auf **Exportieren**.\n- Pins aus einer JSON-Datei importieren - durch Klicken auf **Importieren**.\n- Alle Pins löschen - durch Klicken auf **Löschen**."]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":["Suchen Sie einen Ort, indem Sie entweder mit der Maus durch die Karte scrollen oder den Ort in das Suchfeld eingeben."]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":["Hier können Sie auswählen, welche Basisebene und Zusatzinformationen (Straßen, Grenzen, Beschriftungen) auf der Karte angezeigt werden."]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":["Hier können Sie zwischen dem **Normal**en und dem **Bildung**smodus wechseln. Der **Bildung**smodus bietet Ihnen eine leicht vereinfachte Version der App.\nSie kann auch direkt über die [dedizierte URL](https://apps.sentinel-hub.com/eo-browser-education/) aufgerufen werden."]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":["Sie können sich das Tutorial jederzeit ansehen, indem Sie auf dieses Info-Symbol klicken\n\n\n."]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":["Mit diesem Werkzeug können Sie ein Polygon auf der Karte zeichnen und die Größe des Polygons anzeigen.\n\nAlle Ebenen, die einen einzelnen Wert zurückgeben (z. B. NDVI, Feuchtigkeitsindex, NDWI,...) unterstützen die Anzeige des Index für den ausgewählten Bereich im Zeitverlauf. Durch Klicken auf das Diagrammsymbol werden die Diagramme angezeigt. Sie können das Polygon entfernen, indem Sie auf das Symbol zum Entfernen klicken.\n\nSie können auch eine KML/KMZ-, GPX- oder GEOJSON/JSON-Datei mit einer Polygongeometrie hochladen.\n\nMit dem Zwei-Blatt-Symbol können Sie die Polygonkoordinaten als GEOJSON kopieren, das Fadenkreuz \nzentriert die Karte auf das gezeichnete Polygon.\n\nExportierte Bilder werden bei analytischen Downloads auf das Untersuchungsgebiet zugeschnitten."]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":["Mit diesem Werkzeug können Sie einen Punkt auf der Karte markieren.\n\nSie können auch statistische Daten für einige Ebenen anzeigen, indem Sie auf das Diagrammsymbol klicken\n. \nSie können die Markierung entfernen, indem Sie auf das Symbol zum Entfernen klicken.\n"]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":["Mit diesem Werkzeug können Sie Strecken und Flächen auf der Karte messen.\n\nJeder Mausklick erzeugt einen neuen Punkt auf dem Pfad. Um das Hinzufügen von Punkten zu stoppen, drücken Sie die Esc
-Taste oder klicken Sie doppelt auf die Karte. \nSie können die Messung entfernen, indem Sie auf das Entfernen-Symbol klicken."]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":["Mit diesem Werkzeug können Sie ein Bild der visualisierten Daten für den angezeigten Standort herunterladen. Sie können wählen, ob Sie Beschriftungen anzeigen, und Sie können Ihre eigene Beschreibung hinzufügen.\nWenn Sie den Analysemodus aktivieren, können Sie zwischen verschiedenen Bildformaten, Bildauflösungen und Koordinatensystemen wählen. Sie können auch mehrere Ebenen auswählen und sie als .zip
-Datei herunterladen.\n\nKlicken Sie auf die Schaltfläche Download\nDownload\nund Ihr(e) Bild(er) beginnt/beginnen mit dem Download. Der Vorgang kann einige Sekunden dauern, abhängig von der gewählten Auflösung und der Anzahl der ausgewählten Ebenen.\n\nVor dem Herunterladen können Sie ein Untersuchungsgebiet definieren, indem Sie auf das Symbol des Bereichsauswahlwerkzeugs klicken. Ihre Daten werden so zugeschnitten, dass sie diesem Bereich entsprechen."]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":["Sie haben das Ende des Tutorials erreicht. Wenn Sie weitere Fragen haben, können Sie uns diese gerne im [Forum] (https://forum.sentinel-hub.com/) stellen oder uns [per E-Mail] (mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback) kontaktieren.\n\n\nWenn Sie sich das Tutorial in Zukunft ansehen möchten, können Sie es jederzeit mit einem Klick auf das Info-Symbol\n\n\n\nin der rechten oberen Ecke öffnen."]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":["#### Kurzer Überblick über die Funktionen des EO-Browsers\n\nWenn Sie einen kleinen Bildschirm haben, klicken Sie bitte [hier](https://www.sentinel-hub.com/explore/eobrowser/user-guide/), um unsere Bedienungsanleitung einzusehen.\n\nSie können diese Info jederzeit wieder aufrufen, indem Sie auf das Info-Symbol\n\n\n\nin der rechten oberen Ecke klicken.\n\n#### Andere Quellen\n- [EO Browser Präsentationsseite](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Sommer 2018 Updates - Video](https://www.youtube.com/embed/m3pron0C0kE)"]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":["Was ist der EO-Browser?"]},"User Account":{"msgid":"User Account","msgstr":["Benutzerkonto"]},"Discover Tab":{"msgid":"Discover Tab","msgstr":["Registerkarte \"Entdecken\""]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":["Registerkarte \"Anzeigen\""]},"Compare Tab":{"msgid":"Compare Tab","msgstr":["Registerkarte \"Vergleichen\""]},"Pins Tab":{"msgid":"Pins Tab","msgstr":["Registerkarte \"Pins\""]},"Search Places":{"msgid":"Search Places","msgstr":["Orte suchen"]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":["Ebenen und Beschriftungen"]},"Education Mode":{"msgid":"Education Mode","msgstr":["Bildungsmodus"]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":["Informationen und Tutorial"]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":["Untersuchungsgebiet zeichnen"]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":["Interessenfokus setzen"]},"Measure Distances":{"msgid":"Measure Distances","msgstr":["Entfernungen messen"]},"Download Image":{"msgid":"Download Image","msgstr":["Bild herunterladen"]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":["Zeitraffer-Animation erstellen"]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":["Viel Spaß beim Stöbern!"]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":["Willkommen beim EO-Browser!"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":["Band 1 - Gelbe Substanz und Detritalpigmente - 412,5 nm"]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":["Band 3 - Chlorophyll und andere Pigmente - 490 nm"]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":["Band 4 - Schwebstoffe, Rotalgenblüte - 510 nm"]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":["Band 5 - Chlorophyll-Absorptionsminimum - 560 nm"]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":["Band 6 - Schwebstoffe - 620 nm"]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":["Band 7 - Chlorophyll-Absorption & fluo. Referenz - 665 nm"]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":["Band 8 - Chlorophyll-Fluoreszenz-Peak - 681 nm"]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":["Band 9 - Fluo. Referenz, Atmosphärenkorrekturen - 709 nm"]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":["Band 10 - Vegetation, Wolken - 753 nm"]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":["Band 12 - Atmosphärenkorrekturen - 779 nm"]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":["Band 13 - Vegetation, Wasserdampf-Referenz - 865 nm"]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":["Band 14 - Atmosphärenkorrekturen - 885 nm"]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":["Band 15 - Wasserdampf, Land - 900 nm"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Band 1 - Blau - 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Band 2 - Grün - 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Band 3 - Rot - 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Band 4 - Nahinfrarot (NIR) - 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Band 5 - kurzwelliges Infrarot (SWIR-1) - 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Band 7 - kurzwelliges Infrarot (SWIR-2) - 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Band 8 - Panchromatisch - 520-900 nm"]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":["Band 1 - Küstengebiet/Aerosole - 433-453 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Band 2 - Blau - 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Band 3 - Grün - 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Band 4 - Rot - 630-680 nm"]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":["Band 5 - Nahinfrarot (NIR) - 845-885 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Band 6 - kurzwelliges Infrarot (SWIR-1) - 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Band 7 - kurzwelliges Infrarot (SWIR-2) - 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Band 8 - Panchromatisch - 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Band 9 - Zirrus - 1360-1390 nm"]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":["Landsat 5 (ESA-Archiv)"]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":["Landsat 7 (ESA-Archiv)"]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":["Landsat 8 (ESA-Archiv)"]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":["Landsat 8 (USGS-Archiv)"]},"Red band":{"msgid":"Red band","msgstr":["Rotes Band"]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":["841 - 876 nm (Nahinfrarot NIR)"]},"Blue band":{"msgid":"Blue band","msgstr":["Blaues Band"]},"Green band":{"msgid":"Green band","msgstr":["Grünes Band"]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":["1230 - 1250 nm"]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":["1628 - 1652 nm"]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":["2105 - 2155 nm"]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":["Band 1 - Küstenaerosole - 443 nm"]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":["Band 2 - Blau - 490 nm"]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":["Band 3 - Grün - 560 nm"]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":["Band 4 - Rot - 665 nm"]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":["Band 5 - Vegetation Red Edge - 705 nm"]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":["Band 6 - Vegetation Red Edge - 740 nm"]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":["Band 7 - Vegetation Red Edge - 783 nm"]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":["Band 8 - Nahinfrarot (NIR) - 842 nm"]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":["Band 9 - Wasserdampf - 945 nm"]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":["Band 10 - kurzwelliges Infrarot (SWIR) - Cirrus - 1375 nm"]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":["Band 11 - kurzwelliges Infrarot (SWIR) - 1610 nm"]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":["Band 12 - kurzwelliges Infrarot (SWIR) - 2190 nm"]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":["Band 8A - Vegetation Red Edge - 865 nm"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":["Band 1 - Aerosolkorrektur, verbesserte Abfrage der Wasserinhaltsstoffe - 400 nm"]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":["Band 2 - Gelbe Substanz und Detritalpigmente (Trübung)-412 nm"]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":["Band 3 - Chl-Absorptionsmaximum, Biogeochemie, Vegetation - 442,5 nm"]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":["Band 4 - Hohes Chl, andere Pigmente - 490 nm"]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":["Band 5 - Chl, Sediment, Trübung, Rotalgenblüte - 510 nm"]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":["Band 6 - Chlorophyll-Referenz (Chl-Minimum) - 560 nm"]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":["Band 7 - Sedimentbelastung - 620 nm"]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":["Band 8 - Chl (2. Chl abs. max.), Sediment, gelbe Substanz/Vegetation - 665 nm"]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":["Band 9 - Zur verbesserten Fluoreszenzabfrage und zur besseren Berücksichtigung des Smile-Effekts zwischen den Bändern 8 (665 nm) und 10 (681.25 nm) nm - 673,75 nm"]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":["Band 10 - Chl-Fluoreszenzpeak, Red Edge - 681,25 nm"]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":["Band 11 - Chl-Fluoreszenz-Basislinie, Red Edge-Übergang - 708,75 nm"]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":["Band 12 - O2-Absorption/Wolken, Vegetation - 753,75 nm"]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":["Band 13 - O2-Absorptionsband/Aerosolkorrektur - 761,25 nm"]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":["Band 14 - Atmosphärische Korrektur - 764,375 nm"]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":["Band 15 - O2A verwendet für Wolkenoberdruck, Fluoreszenz über Land - 767,5 nm"]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":["Band 16 - Atmosphären-/Aerosol-Korrektur - 778,75 nm"]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":["Band 17 - Atmosphären-/Aerosol-Korrektur, Wolken, Pixel-Koregistrierung - 865 nm"]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":["Band 18 - Referenzband für Wasserdampfabsorption. Gemeinsames Referenzband mit dem SLSTR-Instrument. Überwachung der Vegetation - 885 nm"]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":["Band 19 - Wasserdampfabsorption/Vegetationsüberwachung (max. Reflexionsgrad) - 900 nm"]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":["Band 20 - Wasserdampfabsorption, Atmosphären-/Aerosol-Korrektur - 940 nm"]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":["Band 21 - Atmosphären-/Aerosol-Korrektur - 1020 nm"]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":["Band F1 - Thermale IR-Feueremission - Aktives Feuer - 3742,00 nm"]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":["Band F1 - Thermale IR-Feueremission - Aktives Feuer - 3742,00 nm"]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":["Band S1 - VNIR - Wolkenmaskierung, Vegetationsüberwachung, Aerosole - 554,27 nm"]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":["Band S2 - VNIR - NDVI, Vegetationsüberwachung, Aerosole - 659,47 nm"]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":["Band S3 - VNIR - NDVI, Wolkenmarkierung, Pixel-Koregistrierung - 868,00 nm"]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":["Band S4 - kurzwelliges Infrarot (SWIR) - Zirrus-Erkennung über Land - 1374.80 nm"]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":["Band S5 - kurzwelliges Infrarot (SWIR) - Wolkenauflösung, Eis, Schnee, Vegetationsüberwachung - 1613,40 nm"]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":["Band S6 - kurzwelliges Infrarot (SWIR) - Vegetationszustand und Wolkenauflösung - 2255,70 nm"]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":["Band S7 - Thermale IR-Umgebung - SST, LST, aktives Feuer - 3742,00 nm"]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":["Band S8 - Thermale IR-Umgebung - SST, LST, aktives Feuer - 10854,00 nm"]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":["Band S9 - Thermale IR-Umgebung - SST, LST - 12022,50 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Reflexionsgrad"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Strahlungtemperatur"]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":["Basierend auf der Kombination der Bänder 4, 3, 2"]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":["Basierend auf der Kombination der Bänder (B04-B03)/(B04+B03)"]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":["Basierend auf der Kombination der Bänder 5, 4, 3"]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":["Basierend auf den Echtfarbbändern 4, 3, 2 und einem Panband 8"]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":["Basierend auf der Kombination der Bänder (B05-B04)/(B05+B04)"]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":["VV - lineares Gamma 0 - orthorektifiziert"]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":["VV - lineares Gamma 0 - nicht orthorektifiziert"]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":["VH - lineares Gamma 0 - orthorektifiziert"]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":["Basierend auf der Kombination der Bänder 3, 2, 1"]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":["VH - lineares Gamma 0 - nicht orthorektifiziert"]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":["Farbbild durch Mapping der Eingangsbänder. Wert [RGB] = [VV, 2 VH, VV / VH / 100.0] - lineares Gamma 0 - orthorektifiziert"]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":["VV - Dezibel Gamma 0 [-20,0] - orthorektifiziert"]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":["VH - Dezibel Gamma 0 [-20,0] - orthorektifiziert"]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":["Gibt ein Komposit aus (VH, VV, VH-VV) zurück"]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":["HH - lineares Gamma 0 - orthorektifiziert"]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":["HV - lineares Gamma 0 - orthorektifiziert"]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":["Farbbild durch Mapping der Eingangsbänder. Wert [RGB] = [HH, 2 HV, HH / HV / 100.0] - lineares Gamma 0 - orthorektifiziert"]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":["HH - Dezibel Gamma 0 [-20,0] - orthorektifiziert"]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":["HV - Dezibel Gamma 0 [-20,0] - orthorektifiziert"]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":["HH - lineares Gamma 0 - nicht orthorektifiziert"]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":["Basierend auf den Bändern 4,3,2"]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":["Basierend auf den Bändern 8,4,3"]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":["Basierend auf den Bändern 12,11,4"]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Basierend auf der Kombination der Bänder (B8 - B4)/(B8 + B4)"]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":["Basierend auf der Kombination der Bänder (B8A - B11)/(B8A + B11)"]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":["Basierend auf den Bändern 12, 8A, 4"]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":["Basierend auf der Kombination der Bänder (B3 - B8)/(B3 + B8)"]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":["Basierend auf der Kombination der Bänder (B3 - B11)/(B3 + B11); Werte über 0,42 werden als schneereich angesehen"]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":["Klassifizierung der Sentinel-2-Daten auf Basis des ESA-Szenenklassifizierungsalgorithmus."]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":["UV-Aerosol-Index von 380 und 340 nm"]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":["Basierend auf der Kombination der Bänder (B3 - B11)/(B3 + B11)"]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":["OLCI Terrestrischer Chlorophyll-Index, basierend auf der Kombination der Bänder (B12 - B11)/(B11 - B10)"]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":["UV-Aerosol-Index von 388 und 354 nm"]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":["Säulengemitteltes Trockenluft-Mischungsverhältnis von Methan"]},"Cloud base height":{"msgid":"Cloud base height","msgstr":["Höhe der Wolkenuntergrenze"]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":["Wolkenbasisdruck"]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":["Effektiver radiometrischer Wolkenanteil"]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":["Optische Wolkendicke"]},"Cloud top height":{"msgid":"Cloud top height","msgstr":["Höhe der Wolkenobergrenze"]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":["Druck der Wolkenobergrenze"]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":["Kohlenmonoxid Gesamtsäule"]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":["Troposphärische Formaldehyd-Vertikalsäule"]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":["Troposphärische Stickstoffdioxid-Säule"]},"Ozone total column":{"msgid":"Ozone total column","msgstr":["Ozon-Gesamtsäule"]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":["Schwefeldioxid-Gesamtsäule"]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":["Basierend auf den Bändern 2, 1, 4"]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":["Basierend auf der Kombination der Bänder (B02 - B01)/(B02 + B01)"]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":["Basierend auf der Kombination der Bänder (B02 - B05)/(B02 + B05)"]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":["Basierend auf der Kombination der Bänder (B06 - B07)/(B06 + B07)"]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":["Basierend auf den Bändern 1, 4, 3"]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":["Basierend auf der Kombination der Bänder 7, 5, 3"]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":["(B09 - B08)/(B09 + B08)"]},"B09 / B08":{"msgid":"B09 / B08","msgstr":["B09 / B08"]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":["Basierend auf der Kombination der Bänder 13, 5, 2"]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":["Basierend auf den Bändern 13, 4, 1"]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":["Basierend auf den Bändern 13, 5, 2"]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":["Basierend auf der Kombination der Bänder (B13 - B07) / (B13 + B07)"]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":["Terrestrischer Chlorophyll-Index"]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V 10-tägige Synthese\nBaumobergrenze (Atmosphärisch korrigiert)\nZeitliche Auflösung: 10-tägig\nRäumliche Auflösung: 333 m (Pixelgröße)"]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V tägliche Synthese\nObere Grenze der Atmosphäre\nZeitliche Auflösung: täglich\nRäumliche Auflösung: 333 m (Pixelgröße)"]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-tägige Synthese\nObere Grenze der Atmosphäre\nZeitliche Auflösung: 5-tägig\nRäumliche Auflösung: 100 m (Pixelgröße)"]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V tägliche Synthese\nBaumobergrenze (Atmosphärisch korrigiert)\nZeitliche Auflösung: täglich\nRäumliche Auflösung: 333 m (Pixelgröße)"]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":["Umlaufbahn_ Aqua_Absteigend"]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-tägige Synthese\nBaumobergrenze (Atmosphärisch korrigiert)\nZeitliche Auflösung: 5-tägig\nRäumliche Auflösung: 100 m (Pixelgröße)"]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":["Umlaufbahn_ Aqua_Aufsteigend"]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":["Umlaufbahn_Aura_Aufsteigend"]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":["Umlaufbahn_Aura_Absteigend"]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":["Umlaufbahn_CloudSat_Aufsteigend"]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":["Umlaufbahn_Calipso_Aufsteigend"]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":["Umlaufbahn_Calipso_Absteigend"]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":["Umlaufbahn_CloudSat_Absteigend"]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":["Umlaufbahn_CYGNSS_Aufsteigend"]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":["Umlaufbahn_CYGNSS_Absteigend"]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":["Umlaufbahn_GCOM-C_Aufsteigend"]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":["Umlaufbahn_GCOM-C_Absteigend"]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":["Umlaufbahn_GCOM-W1_Aufsteigend"]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":["Umlaufbahn_GCOM-W1_Absteigend"]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":["Umlaufbahn_GOSAT-2_Aufsteigend"]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":["Umlaufbahn_GOSAT-2_Absteigend"]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":["Umlaufbahn_GOSAT_Aufsteigend"]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":["Umlaufbahn_GOSAT_Absteigend"]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":["Umlaufbahn_GPM_Aufsteigend"]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":["Umlaufbahn_GPM_Absteigend"]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":["Umlaufbahn_ICESAT-2_Aufsteigend"]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":["Umlaufbahn_ICESAT-2_Absteigend"]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":["Umlaufbahn_ISS_Aufsteigend"]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":["Umlaufbahn_ISS_Absteigend"]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":["Umlaufbahn_Landsat-7_Aufsteigend"]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":["Umlaufbahn_Landsat-7_Absteigend"]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":["Umlaufbahn_Landsat-8_Aufsteigend"]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":["Umlaufbahn_METOP-A_Aufsteigend"]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":["Umlaufbahn_METOP-B_Absteigend"]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":["Umlaufbahn_Landsat-8_Absteigend"]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":["Umlaufbahn_METOP-C_Aufsteigend"]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":["Umlaufbahn_METOP-C_Absteigend"]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":["Umlaufbahn_NOAA-20_Aufsteigend"]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":["Umlaufbahn_NOAA-20_Absteigend"]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":["Umlaufbahn_METOP-B_Aufsteigend"]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":["Umlaufbahn_OCO-2_Aufsteigend"]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":["Umlaufbahn_OCO-2_Absteigend"]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":["Umlaufbahn_SAOCOM1-A_Aufsteigend"]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":["Umlaufbahn_SAOCOM1-A_Absteigend"]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":["Umlaufbahn_Sentinel-1A_Aufsteigend"]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":["Umlaufbahn_Sentinel-1B_Aufsteigend"]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":["Umlaufbahn_Sentinel-1A_Absteigend"]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":["Umlaufbahn_METOP-A_Absteigend"]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":["Umlaufbahn_Sentinel-1B_Absteigend"]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":["Umlaufbahn_Sentinel-2A_Aufsteigend"]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":["Umlaufbahn_Sentinel-2A_Absteigend"]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":["Umlaufbahn_Sentinel-2B_Aufsteigend"]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":["Umlaufbahn_Sentinel-2B_Absteigend"]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":["Umlaufbahn_Sentinel-5P_Aufsteigend"]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":["Umlaufbahn_Sentinel-5P_Absteigend"]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":["Umlaufbahn_SMAP_Aufsteigend"]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":["Umlaufbahn_SMAP_Aufsteigend"]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":["Umlaufbahn_Suomi_NPP_Absteigend"]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":["Umlaufbahn_Suomi_NPP_Aufsteigend"]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":["Umlaufbahn_Terra_Absteigend"]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":["Umlaufbahn_Terra_Aufsteigend"]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":["Basierend auf den Bändern 4, 3, 2, erweitert um die Bänder 12 und 11."]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":["Basierend auf den Bändern B07, B06, B4"]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Basierend auf der Kombination der Bänder (B8 - B4)/(B8 + B4)"]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":["Basierend auf dem thermalen Band 10"]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":["Basierend auf den Bändern B12, B11, B8A"]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":["Basierend auf der Kombination der Bänder (B8 - B12)/(B8 + B12)"]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":["Verbesserte natürliche Farbdarstellung"]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":["Basierend auf der Kombination der Bänder 8, 6, 4"]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":["Verbesserter Vegetationsindex"]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":["Basierend auf der Kombination: BSI, B08, B11"]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":["Klassifiziertes NDMI für Bewässerung"]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":["Basierend auf den Bändern B11, B08, B02"]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":["Falschfarben 13, 5, 2"]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":["Basierend auf der Kombination der Bänder (B13 - B07) / (B13 + B07)"]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":["Basierend auf den Bändern 12, 8, 2"]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":["Basierend auf den Bändern 4, 3, 2"]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":["Basierend auf den Bändern 8, 4, 3"]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":["Basierend auf den Bändern 12, 8, 2"]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":["Basierend auf den Bändern 8, 11, 12"]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":["Wassersedimentation und Chlorophyllgehalt"]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":["Basierend auf den Bändern 12, 8A, 4"]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":["Basierend auf NDSI"]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":["Basierend auf der Kombination der Bänder 11, 8, 2"]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":["Basierend auf den Bändern B12, B11, B04"]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":["Basierend auf den Bändern 4, 3 ,2"]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":["Atmosphärisch resistenter Vegetationsindex"]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":["Bodenangepasster Vegetationsindex"]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":["# Thermale IR-Feueremissionsbänder\n\nDas Sentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) verfügt über zwei dedizierte Kanäle (F1 und F2) zur Erfassung der Landoberflächentemperatur (LST). Der F2-Kanal mit einer zentralen Wellenlänge von 10854 nm misst im thermalen Infrarot (TIR). Er ist sehr nützlich für die Überwachung von Bränden und Hochtemperaturereignissen mit einer Auflösung von 1 km.\n\n\n\nMehr Informationen [hier.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)"]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":["# Methan (CH4)\n\n\n\nMethan ist nach Kohlendioxid der wichtigste Beitrag zum anthropogenen (durch menschliche Aktivitäten verursachten) verstärkten Treibhauseffekt. Die Messungen werden in Teilen pro Milliarde (ppb) mit einer räumlichen Auflösung von 7 km x 3,5 km angegeben.\n\n\n\nMehr Informationen [hier.](http://www.tropomi.eu/data-products/methane)"]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":["# Formaldehyd (HCHO)\n\n\n\nLangzeit-Satellitenbeobachtungen von troposphärischem Formaldehyd (HCHO) sind essentiell für die Unterstützung von Luftqualität und Chemie-Klima-bezogenen Studien von der regionalen bis zur globalen Skala. Die saisonalen und halbjährlichen Variationen der Formaldehyd-Verteilung hängen hauptsächlich mit Temperaturänderungen und Feuerereignissen zusammen, aber auch mit Änderungen der anthropogenen (vom Menschen verursachten) Aktivitäten. Da die Lebensdauer von HCHO in der Größenordnung von einigen Stunden liegt, können die HCHO-Konzentrationen in der Grenzschicht direkt mit der Freisetzung von kurzlebigen Kohlenwasserstoffen in Verbindung gebracht werden, die meist nicht direkt aus dem Weltraum beobachtet werden können. Die Messungen erfolgen in Mol pro Quadratmeter (mol/m^2).\n\n\n\nMehr Infos [hier.](http://www.tropomi.eu/data-products/formaldehyde)"]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":["# Schwefeldioxid (SO2)\n\n\n\nSchwefeldioxid gelangt sowohl durch natürliche als auch anthropogene (vom Menschen verursachte) Prozesse in die Erdatmosphäre. Es spielt in der Chemie auf lokaler und globaler Ebene eine Rolle und seine Auswirkungen reichen von kurzfristiger Verschmutzung bis hin zu Auswirkungen auf das Klima. Nur etwa 30 % des emittierten SO2 stammt aus natürlichen Quellen; der Großteil ist anthropogenen Ursprungs. Das Instrument Sentinel-5P/TROPOMI tastet die Erdoberfläche mit einer Wiederholungszeit von einem Tag und einer räumlichen Auflösung von 3,5 x 7 km ab, was die Auflösung feiner Details einschließlich der Erkennung kleinerer SO2-Fahnen ermöglicht. Die Messungen erfolgen in Mol pro Quadratmeter (mol/m^2).\n\n\n\nMehr Informationen [hier.](http://www.tropomi.eu/data-products/sulphur-dioxide)"]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":["# Ozon (O3)\n\n\n\nOzon ist von entscheidender Bedeutung für das Gleichgewicht der Erdatmosphäre. In der Stratosphäre schirmt die Ozonschicht die Biosphäre vor gefährlicher solarer Ultraviolettstrahlung ab. In der Troposphäre wirkt es als effizientes Reinigungsmittel, wird aber bei hoher Konzentration auch gesundheitsschädlich für Mensch, Tier und Vegetation. Ozon ist auch ein wichtiges Treibhausgas, das zum laufenden Klimawandel beiträgt. Seit der Entdeckung des antarktischen Ozonlochs in den 1980er Jahren und dem darauf folgenden Montreal-Protokoll, das die Produktion von chlorhaltigen, ozonabbauenden Substanzen regelt, wird Ozon routinemäßig vom Boden und aus dem Weltraum überwacht. Die Messungen erfolgen in Mol pro Quadratmeter (mol/m^2)\n\n\n\nMehr Informationen [hier.](http://www.tropomi.eu/data-products/total-ozone-column)"]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":["# Stickstoffdioxid (NO2)\n\n\n\nStickstoffdioxid (NO2) und Stickstoffoxid (NO) werden zusammen meist als Stickoxide bezeichnet. Sie sind wichtige Spurengase in der Erdatmosphäre, die sowohl in der Troposphäre als auch in der Stratosphäre vorkommen. Sie gelangen durch anthropogene Aktivitäten (insbesondere Verbrennung fossiler Brennstoffe und Biomasse) und natürliche Prozesse (wie mikrobiologische Prozesse in Böden, Waldbrände und Blitze) in die Atmosphäre. Die Messungen erfolgen in Mol pro Quadratmeter (mol/m^2).\n\n\n\nMehr Infos [hier.](http://www.tropomi.eu/data-products/nitrogen-dioxide)"]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":["# Kohlenmonoxid (CO)\n\n\n\nKohlenmonoxid (CO) ist ein wichtiges atmosphärisches Spurengas. In bestimmten städtischen Gebieten ist es ein wichtiger Luftschadstoff. Hauptquellen für CO sind die Verbrennung fossiler Brennstoffe, die Verbrennung von Biomasse und die atmosphärische Oxidation von Methan und anderen Kohlenwasserstoffen. Die Gesamtsäule des Kohlenmonoxids wird in Mol pro Quadratmeter (mol/ m^2) gemessen.\n\n\n\nMehr Informationen [hier.](http://www.tropomi.eu/data-products/carbon-monoxide)"]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":["# Aerosol-Index\n\nDer Aerosol-Index (AI) ist ein qualitativer Index, der das Vorhandensein erhöhter Schichten von Aerosolen in der Atmosphäre anzeigt. Er kann verwendet werden, um das Auftreten von UV-absorbierenden Aerosolen wie Wüstenstaub und Vulkanaschewolken zu erkennen. Positive Werte (von hellblau bis rot) zeigen das Auftreten von UV-absorbierendem Aerosol an. Dieser Index wird für zwei Paare von Wellenlängen berechnet: 340/380 nm und 354/388 nm.\n\nMehr Informationen [hier.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)"]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":["# Höhe der Wolkenuntergrenze\n\nHöhe der Wolkenuntergrenze, gemessen in Metern (m)."]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":["# Wolkenbasisdruck\n\nAn der Wolkenuntergrenze gemessener Druck in Pascal (Pa)."]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":["# Optische Wolkendicke\n\nDie Wolkendicke ist ein Schlüsselparameter zur Charakterisierung der optischen Eigenschaften von Wolken. Sie ist ein Maß dafür, wie viel Sonnenlicht durch die Wolke dringt, um die Erdoberfläche zu erreichen. Je höher die optische Dicke einer Wolke ist, desto mehr Sonnenlicht wird von der Wolke gestreut und reflektiert. Dunkelblau zeigt an, wo es niedrige Werte für die optische Wolkendicke gibt, und rot zeigt eine größere optische Wolkendicke an."]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":["# Höhe der Wolkenobergrenze\n\nHöhe der Wolkenobergrenze, gemessen in Metern (m)."]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":["# Wolkenoberdruck\n\nAn der Wolkenobergrenze gemessener Druck in Pascal (Pa)."]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":["# Normalisierter differenzierter Vegetations-Index (NDVI)\n\nDer normalisierte differenzierte Vegetations-Index ist ein einfacher, aber effektiver Index zur Quantifizierung der grünen Vegetation. Er ist ein Maß für den Gesundheitszustand der Vegetation, das darauf basiert, wie Pflanzen Licht bestimmter Wellenlängen reflektieren. Der Wertebereich des NDVI liegt zwischen -1 und 1. Negative Werte des NDVI (Werte, die sich -1 annähern) entsprechen dem Wasser. Werte nahe Null (-0,1 bis 0,1) entsprechen im Allgemeinen kargen Flächen aus Fels, Sand oder Schnee. Niedrige, positive Werte stehen für Strauch- und Grasland (ca. 0,2 bis 0,4), während hohe Werte auf gemäßigte und tropische Regenwälder hinweisen (Werte nahe 1).\n\nWeitere Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/faszination-fernerkundung/infrarote-pflanzen), [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) und [hier.](https://eos.com/ndvi/)"]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":["# Erweiterter Vegetationsindex (EVI)\n\nDer erweiterte Vegetationsindex (EVI) ist ein \"optimierter\" Vegetationsindex, da er Bodenhintergrundsignale und atmosphärische Einflüsse korrigiert. Er ist sehr nützlich in Gebieten mit dichtem Blätterdach. Der Wertebereich für den EVI liegt zwischen -1 und 1, wobei eine gesunde Vegetation im Allgemeinen zwischen 0,20 und 0,80 liegt.\n\n\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) und [hier.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)"]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Atmosphärisch resistenter Vegetationsindex (ARVI)\n\nDer Atmosphärisch resistenter Vegetationsindex (ARVI) ist ein Vegetationsindex, der die Auswirkungen der atmosphärischen Streuung minimiert. Er ist besonders nützlich für Regionen mit hohem Gehalt an atmosphärischem Aerosolen (Nebel, Staub, Rauch, Luftverschmutzung). Der Bereich für einen ARVI liegt bei -1 bis 1, wobei grüne Vegetation im Allgemeinen zwischen Werten von 0,20 bis 0,80 liegt.\n\n\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) und [hier.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Bodenangepasster Vegetationsindex (SAVI)\n\nDer Bodenangepasste Vegetationsindex (Soil Adjusted Vegetation Index, SAVI) ähnelt dem normalisierten differenzierten Vegetations-Index (NDVI), wird aber in Gebieten mit geringer Vegetationsbedeckung (< 40 %) verwendet. Der Index ist ein Umwandlungsverfahren, das die Einflüsse der Bodenhelligkeit in spektralen Vegetationsindizes mit roten und Nahinfrarot (NIR)-Wellenlängen minimiert. Der Index ist hilfreich bei der Analyse von jungen Pflanzen, trockenen Regionen mit spärlicher Vegetation und exponierten Bodenflächen.\n\n\n\n\n\nMehr Infos [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) und [hier.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":["# Modifizierter Anthocyanin-Reflexionsindex (mARI/ARI2)\n\nAnthocyane sind Pigmente, die in höheren Pflanzen vorkommen und für deren rote, blaue und violette Färbung verantwortlich sind. Sie liefern wertvolle Informationen über den physiologischen Zustand von Pflanzen, da sie als Indikatoren für verschiedene Arten von Pflanzenstress gelten. Der Reflexionsgrad von Anthocyanin ist um 550 nm am höchsten. Die gleichen Wellenlängen werden jedoch auch von Chlorophyll reflektiert. Um die Anthocyane zu isolieren, wird das 700nm-Spektralband, welches nur von Chlorophyll und nicht von Anthocyanen reflektiert wird, subtrahiert.\n\nUm die Blattdichte und -dicke zu korrigieren, wird das Spektralband im Nahinfrarot (in den empfohlenen Wellenlängen von 760-800 nm), die mit der Blattstreuung zusammenhängt, zum Basis-ARI-Index hinzugefügt. Der neue Index wird modifizierter ARI oder mARI (auch ARI2) genannt.\n\nDie mARI-Werte für die untersuchten Bäume in [diesem Originalartikel](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) reichten von 0 bis 8.\n\n\n\n\n\nMehr Infos [hier.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)"]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":["# Grüne-Stadt-Skript\n\nDas Grüne-Stadt-Skript zielt darauf ab, das Bewusstsein für Grünflächen in Städten auf der ganzen Welt zu erhöhen. Das Skript berücksichtigt den normalisierten differenzierten Vegetations-Index (NDVI) und Echtfarb-Wellenlängen; es trennt bebaute von bewachsenen Flächen und ist damit für die Erkennung von Stadtgebieten nützlich. Bebaute Gebiete werden grau und die Vegetation grün dargestellt.\n\n\n\n\n\nMehr Infos [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":["# Stadtklassifizierungs-Skript\n\nDas Stadtklassifizierungs-Skript zielt darauf ab, bebaute Gebiete zu erkennen, indem es sie von unfruchtbarem Boden, Vegetation und Wasser trennt. Bereiche mit einem hohen Feuchtigkeitsgehalt werden in blau dargestellt; Bereiche, die bebaute Flächen anzeigen, werden in weiß dargestellt; bewachsene Flächen werden in grün zurückgegeben; alles andere zeigt unfruchtbaren Boden an und wird in braunen Farben angezeigt.\n\n\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)"]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":["# Urbanes Land Infrarot-Farbskript\n\nDieses Skript von Leo Tolari kombiniert die Visualisierung von Echtfarben mit den Wellenlängen des Nahinfrarot (NIR) und des kurzwelligen Infrarots (SWIR). Das Skript hebt urbane Gebiete besser als Echtfarbe hervor, während es trotzdem natürlich aussieht.\n\n\n\n\n\nMehr Infos [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)"]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":["# NDMI für Feuchtigkeitsstress\n\nDer Normalized Difference Moisture Index (NDMI) für Feuchtigkeitsstress kann verwendet werden, um Bewässerung zu erkennen. Für alle Indexwerte über 0 kann bei Kenntnis der Landnutzung und Landbedeckung festgestellt werden, ob eine Bewässerung stattgefunden hat. Wenn man die Art der angebauten Pflanzen kennt (z. B. Zitrusfrüchte), kann festgestellt werden, ob die Bewässerung während der entscheidenden Wachstumsperiode im Sommer effektiv ist oder nicht, und auch herausfinden, ob einige Teile des Betriebs unter- oder überbewässert werden.\n\n\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)"]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":["# Normalisierter differenzierter Feuchte-Index (NDMI)\n\nDer Normalized Difference Moisture Index (NDMI) wird zur Bestimmung des Wassergehalts der Vegetation und zur Überwachung von Trockenperioden verwendet. Der Wertebereich des NDMI ist -1 bis 1. Negative Werte des NDMI (Werte, die sich -1 nähern) entsprechen unfruchtbarem Boden. Werte um Null (-0,2 bis 0,4) entsprechen im Allgemeinen Wasserstress. Hohe, positive Werte stehen für ein hohes Kronendach ohne Wasserstress (etwa 0,4 bis 1).\n\n\n\nMehr Infos [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":["# Normalized Difference Water Index (NDWI)\n\nDer normalisierte differenzierte Wasser-Index ist für die Wasserflächenkartierung am besten geeignet. Werte für Wasserflächen sind größer als 0,5. Vegetation führt zu kleineren Werten. Bebaute Flächen führen zu positiven Werten zwischen 0 und 0,2.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":["# Normalized Difference Water Index (NDWI)\n\nDer normalisierte differenzierte Wasser-Index ist für die Wasserflächenkartierung am besten geeignet. Werte für Wasserflächen sind größer als 0,5. Vegetation führt zu kleineren Werten. Bebaute Flächen führen zu positiven Werten zwischen Null und 0,2."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Falschfarbenkomposit\n\nEin Falschfarbenkomposit verwendet mindestens eine nicht-sichtbare Wellenlänge zur Abbildung der Erde. Das Falschfarbenkomposit unter Verwendung von Nahinfrarot, roten und grünen Bändern ist sehr beliebt (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Das Falschfarbenkomposit wird am häufigsten zur Beurteilung der Pflanzendichte und -gesundheit verwendet, da Pflanzen nahes Infrarot- und grünes Licht reflektieren, während sie rotes absorbieren. Städte und freiliegender Boden sind grau oder hellbraun, und Wasser erscheint blau oder schwarz.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) und [hier.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Falschfarbenkomposit\n\nEin Falschfarbenkomposit verwendet mindestens eine nicht-sichtbare Wellenlänge, um die Erde abzubilden. Das Falschfarbenkomposit unter Verwendung von Nahinfrarot, roten und grünen Bändern ist sehr beliebt (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Das Falschfarbenkomposit wird am häufigsten zur Beurteilung der Pflanzendichte und -gesundheit verwendet, da Pflanzen nahes Infrarot- und grünes Licht reflektieren, während sie rotes absorbieren. Städte und freiliegender Boden sind grau oder hellbraun, und Wasser erscheint blau oder schwarz.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) und [hier.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":["# Falschfarbenkomposit\n\nEin Falschfarbenkomposit verwendet mindestens eine nicht-sichtbare Wellenlänge zur Abbildung der Erde. Das Falschfarbenkomposit unter Verwendung von Nahinfrarot, roten und grünen Bändern ist sehr beliebt (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Das Falschfarbenkomposit wird am häufigsten zur Beurteilung der Pflanzendichte und -gesundheit verwendet, da Pflanzen nahes Infrarot- und grünes Licht reflektieren, während sie rotes absorbieren. Städte und freiliegender Boden sind grau oder hellbraun, und Wasser erscheint blau oder schwarz.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) und [hier.](https://earthobservatory.nasa.gov/features/FalseColor)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Falschfarbenkomposit\n\nEin Falschfarbenkomposit verwendet mindestens eine nicht-sichtbare Wellenlänge, um die Erde abzubilden. Das Falschfarbenkomposit unter Verwendung von Nahinfrarot, roten und grünen Bändern ist sehr beliebt (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Das Falschfarbenkomposit wird am häufigsten zur Beurteilung der Pflanzendichte und -gesundheit verwendet, da Pflanzen nahes Infrarot- und grünes Licht reflektieren, während sie rotes absorbieren. Städte und freiliegender Boden sind grau oder hellbraun, und Wasser erscheint blau oder schwarz.\n\n\n\nMehr Informationen [hier.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jeder Bereich des Spektrums wird als Band bezeichnet. Sentinel-2 hat 13 Bänder. Das Echtfarbenkomposit verwendet die Bänder des sichtbaren Lichts aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen. Das Ergebnis ist ein natürliches Farbprodukt, das die Erde in etwa darstellt, wie Menschen sie natürlich sehen würden.\n\n\n\nWeitere Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/was-ist-fernerkundung/licht-und-farbe), [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) und [hier.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy)."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jeder Bereich des Spektrums wird als Band bezeichnet. Landsat 5 hat 7 Bänder. Das Echtfarbenkomposit verwendet die Bänder des sichtbaren Lichts aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen, was zu einem natürlich gefärbten Produkt führt, das die Erde in etwa darstellt, wie Menschen sie natürlich sehen würden.\n\n\n\nWeitere Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/was-ist-fernerkundung/licht-und-farbe), [hier](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) und [hier.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jeder Bereich des Spektrums wird als Band bezeichnet. Landsat 7 hat 8 Bänder. Ein Echtfarbenkomposit verwendet die Bänder des sichtbaren Lichts aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen, was zu einem natürlich gefärbten Produkt führt, das die Erde in etwa darstellt, wie Menschen sie natürlich sehen würden.\n\n\n\nWeitere Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/was-ist-fernerkundung/licht-und-farbe), [hier](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) und [hier.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jede Region im Spektrum wird als Band bezeichnet. Das Echtfarbenkomposit verwendet die Bänder des sichtbaren Lichts aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen, was zu einem Produkt mit natürlichen Farben führt, das die Erde in etwa darstellt, wie Menschen sie natürlich sehen würden.\n\n\n\nMehr Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/was-ist-fernerkundung/licht-und-farbe), [hier.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jeder Bereich des Spektrums wird als Band bezeichnet. Das Echtfarbenkomposit verwendet die die Bänder des sichtbaren Lichts aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen, was zu einem Produkt mit natürlichen Farben führt, das die Erde in etwa darstellt, wie Menschen sie natürlich sehen würden.\n\n\n\nMehr Informationen [hier](https://fis.rub.de/recherchetools/infobox/profis/was-ist-fernerkundung/licht-und-farbe), [hier.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)"]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Geschärftes Echtfarbenkomposit\n\nDas geschärftes Echtfarbenkomposit wird erstellt, indem die üblichen Echtfarbdaten (Rot, Grün und Blau (RGB)) verwendet und durch die Verwendung des panchromatischen Bandes 8, oder Pan-Bandes, verbessert werden (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern aufnehmen). Ein Bild aus dem Pan-Band ist ähnlich wie ein Schwarz-Weiß-Film: Es kombiniert Licht aus dem roten, grünen und blauen Teil des Spektrums zu einem einzigen Wert für die sichtbare Gesamtreflexion. So geschärfte Bilder haben eine vierfach höhere Auflösung als die üblichen Echtfarbbilder, was den Nutzen von Landsat-Bildern erheblich steigert.\n\n\n\nWeitere Informationen [hier](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) und [hier.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":["# Falschfarben-Stadtkomposit\n\nDieses Komposit wird verwendet, um verstädterte Gebiete deutlicher zu visualisieren. Die Vegetation ist in Grüntönen sichtbar, während verstädterte Bereiche durch Weiß, Grau oder Violett dargestellt werden. Böden, Sand und Mineralien werden in verschiedenen Farben dargestellt. Schnee und Eis erscheinen in Dunkelblau, Wasser in Schwarz oder Blau. Überschwemmte Gebiete sind sehr dunkelblau und fast schwarz. Das Komposit ist nützlich für die Erkennung von Waldbränden und Vulkancalderen, da diese in Rot- und Gelbtönen dargestellt werden.\n\n\n\nMehr Infos [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) und [hier.](https://eos.com/false-color/)"]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Falschfarben-Stadtkomposit\n\nDieses Komposit verwendet eine Kombination von Bändern im sichtbaren und im kurzwelligen Infrarotbereich (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Es zeigt die Vegetation in Grüntönen an. Während dunklere Grüntöne eine dichtere Vegetation anzeigen, hat spärliche Vegetation hellere Farbtöne. Städtische Gebiete sind blau und Böden haben verschiedene Brauntöne.\n\n\n\nMehr Informationen [hier.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":["# Landwirtschaftskomposit \n\nDieses Komposit verwendet kurzwellige Infrarot-, Nahinfrarot- und blaue Bänder, um die Gesundheit der Pflanzen zu überwachen (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Sowohl das kurzwellige als auch das nahe Infrarotband sind besonders gut geeignet, um dichte Vegetation hervorzuheben, die im Kompositbild dunkelgrün erscheint. Feldfrüchte erscheinen in einem leuchtenden Grün und freiliegende Erde erscheint in Magenta.\n\n\n\nWeitere Informationen [hier](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) und [hier.](https://gisgeography.com/sentinel-2-bands-combinations/)"]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":["# Schnee-Klassifikator\n\nDer Algorithmus des Schnee-Klassifikators zielt darauf ab, Schnee zu erkennen, indem er Pixel auf der Grundlage unterschiedlicher Helligkeits- und der Normalized Difference Snow Index (NDSI)-Schwellenwerte klassifiziert. Werte, die als Schnee klassifiziert werden, werden in hellem, leuchtendem Blau zurückgegeben. Das Skript kann Schneeflächen gegenüber Wolken überschätzen.\n\n\n\nMehr Infos [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":["# Ulyssys Wasserqualitätsanzeige (UWQV)\n\nDas Skript zielt darauf ab, den Chlorophyll- und Sedimentzustand von Wasserkörpern dynamisch zu visualisieren, die primäre Indikatoren für die Wasserqualität sind. Der Chlorophyllgehalt reicht farblich von dunkelblau (niedriger Chlorophyllgehalt) über grün bis rot (hoher Chlorophyllgehalt). Sedimentkonzentrationen sind braun gefärbt; undurchsichtiges Braun zeigt einen hohen Sedimentgehalt an.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)"]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":["# Kurzwellen-Infrarot-Komposit (SWIR)\n\nMessungen im kurzwelligen Infrarot (Short-Wave InfraRed, SWIR) können Wissenschaftler:innen helfen, abzuschätzen, wie viel Wasser in Pflanzen und Böden vorhanden ist, da Wasser SWIR-Wellenlängen absorbiert. Kurzwellige Infrarotbänder (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden) sind auch nützlich, um zwischen Wolkentypen (Wasserwolken gegenüber Eiswolken), Schnee und Eis zu unterscheiden, die alle im sichtbaren Licht weiß erscheinen. In diesem Komposit erscheint die Vegetation in Grüntönen, Böden und städtische Gebiete in verschiedenen Brauntöne und Wasser schwarz. Frisch verbranntes Land reflektiert stark in den SWIR-Bändern, was sie für die Kartierung von Brandschäden wertvoll macht. Jede Gesteinsart reflektiert kurzwelliges Infrarotlicht anders, sodass es möglich ist, die Geologie durch Vergleich des reflektierten SWIR-Lichts zu kartieren.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)"]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":["# Highlight-optimierte natürliche Farbe\n\nDieses Skript zielt darauf ab, die Erde in schönen, natürlichen Farbbildern darzustellen. Es verwendet Highlight-Optimierung, um ausgebrannte Pixel zu vermeiden und die Belichtung auszugleichen.\n\n\n\nMehr Infos [hier.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)"]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":["# Geologie 12, 8, 2 Komposit\n\nDieses Komposit nutzt das kurzwellige Infrarot (SWIR) Band 12, um zwischen verschiedenen Gesteinsarten zu unterscheiden (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Jeder Gesteins- und Mineraltyp reflektiert kurzwelliges Infrarotlicht anders, sodass es möglich ist, die Geologie durch den Vergleich des reflektierten SWIR-Lichts abzubilden. Das Nahinfrarot (NIR)-Band 8 hebt Vegetation hervor und Band 2 erkennt Feuchtigkeit. Beides trägt zur Unterscheidung von Bodenmaterialien bei. Das Komposit ist nützlich für das Auffinden von geologischen Formationen und Merkmalen (z.B. Verwerfungen, Brüche), Lithologie (z.B. Granit, Basalt, etc.) und Bergbauanwendungen.\n\n\n\nMehr Infos [hier.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)"]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":["# Geologie 8, 11, 12 Komposit\n\nDieses Komposit nutzt die beiden kurzwelligen Infrarotbänder (SWIR) 11 und 12, um zwischen verschiedenen Gesteinsarten zu unterscheiden (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Jeder Gesteins- und Mineraltyp reflektiert kurzwelliges Infrarotlicht anders, sodass es möglich ist, die Geologie durch den Vergleich des reflektierten SWIR-Lichts abzubilden. Das Nahinfrarot (NIR) Band 8 hebt die Vegetation hervor und trägt so zur Unterscheidung von Bodenmaterialien bei. Die Vegetation im Komposit erscheint rot. Das Komposit ist nützlich für die Unterscheidung von Vegetation und Land, insbesondere von geologischen Merkmalen, die für den Bergbau und die Mineralienexploration nützlich sein können.\n\n\n\nWeitere Informationen [hier](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) und [hier.](http://murphygeological.com/new---sentinel-2.html#)"]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":["# Waldbrände\n\nDieses Skript, erstellt von Pierre Markuse, visualisiert Waldbrände anhand von Sentinel-2-Daten. Es kombiniert den natürlichen Farbhintergrund mit einigen NIR/SWIR-Daten für die Rauchdurchdringung und mehr Details, während es Highlights von B11 und B12 hinzufügt, um Brände in roten und orangenen Farben zu zeigen.\n\n\n\nMehr Infos [hier.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)"]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":["# Verbesserte Echtfarben\n\nDieses Skript, das von Pierre Markuse erstellt wurde, verwendet mehrere Bänder (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden) sowie Sättigungs- und Helligkeitssteuerung, um die Echtfarbendarstellung zu verbessern.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)"]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":["# Brandflächen-Index\n\nDer Brandflächen-Index nutzt das breitere Spektrum der sichtbaren, Red-Edge-, NIR- und SWIR-Bänder.\n\nWertebeschreibung:()=> Der Wertebereich für den Index ist `-1` bis `1` für Brandnarben und `1` - `6` für aktive Brände. Unterschiedliche Feuerintensitäten können zu unterschiedlichen Schwellenwerten führen; die aktuellen Werte wurden, laut Originalautor, an überwiegend mediterranen Regionen kalibriert.\n\n\n\nMehr Infos [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)"]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":["# Normalisiertes Verbrennungsverhältnis (NBR)\n\nDas Normalisierte Verbrennungsverhältnis wird häufig zur Abschätzung der Verbrennungsschwere verwendet. Es verwendet Wellenlängen im Nahinfrarot (NIR) und im kurzwelligen Infrarot (SWIR). Gesunde Vegetation hat einen hohen Reflexionsgrad im Nahinfrarotbereich des Spektrums und einen niedrigen Reflexionsgrad im kurzwelligen Infrarot. Andererseits haben verbrannte Gebiete eine hohe Reflexion im kurzwelligen Infrarot, aber eine niedrige Reflexion im Nahinfrarot. Dunklere Pixel zeigen verbrannte Gebiete an.\n\n\n\nWeitere Informationen [hier](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) und [hier.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":["# Atmosphärische Durchdringung\n\nDieses Komposit verwendet verschiedene Bänder (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden) im nicht-sichtbaren Teil des elektromagnetischen Spektrums, um den Einfluss der Atmosphäre im Bild zu reduzieren. Die Wellenlängen, die von den kurzwelligen Infrarotbändern 11 und 12 aufgenommen werden, werden von den erhitzten Bereichen stark reflektiert und sind daher für die Kartierung von Bränden und verbrannten Gebieten nützlich. Die Wellenlängen, die vom kurzwelligen Infrarotband 8 aufgenommen werden, werden dagegen stark von der Vegetation reflektiert, was bedeutet, dass kein Feuer vorhanden ist. Die Vegetation erscheint blau und zeigt Details in Bezug auf die Vegetationsstärke an. Gesunde Vegetation wird in Hellblau dargestellt, während gestresste, spärliche oder/und trockene Vegetation in mattem Blau erscheint. Städtische Merkmale sind weiß, grau, cyan oder violett.\n\n\n\nMehr Informationen [hier.](https://eos.com/atmospheric-penetration/)"]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":["# Visualisierung von unfruchtbarem Boden\n\nDie Visualisierung von unfruchtbarem Boden kann für die Bodenkartierung nützlich sein, um die Lage von Erdrutschen oder das Ausmaß der Erosion in nicht-begrünten Gebieten zu untersuchen. Diese Visualisierung zeigt alle Vegetation in grün und den unfruchtbaren Boden in rot. Wasser erscheint schwarz.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) und [hier.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)"]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":["# Echtfarbenkomposit mit IR-Highlights\n\nDieses Komposit verbessert die Echtfarbendarstellung durch Hinzufügen der kurzwelligen Infrarot-Wellenlängen, um Details zu verstärken. Es zeigt erwärmte Bereiche in Rot/Orange an.\n\n\n\nMehr Informationen [hier.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)"]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":["# Erkennung von verbrannten Flächen\n\nDieses Skript wird verwendet, um großflächige, kürzlich verbrannte Bereiche zu erkennen. Rot gefärbte Pixel markieren verbrannte Gebiete, alle anderen Pixel werden in Echtfarbe zurückgegeben. Das Skript überschätzt manchmal verbrannte Flächen über Wasser und Wolken.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":["# Terrestrischer Chlorophyll-Index (OTCI)\n\n\n\nDer terrestrische Chlorophyll-Index (OTCI) wird auf der Grundlage des Chlorophyll-Gehalts in der terrestrischen Vegetation geschätzt und kann zur Überwachung des Zustands und der Gesundheit der Vegetation verwendet werden. Niedrige OTCI-Werte weisen normalerweise auf Wasser, Sand oder Schnee hin. Extrem hohe Werte, die mit Weiß angezeigt werden, deuten normalerweise auch auf das Fehlen von Chlorophyll hin. Sie repräsentieren im Allgemeinen entweder kahlen Boden, Fels oder Wolken. Die Chlorophyll-Werte dazwischen, die von rot (niedrige Chlorophyll-Werte) bis dunkelgrün (hohe Chlorophyll-Werte) reichen, können zur Bestimmung der Gesundheit der Vegetation verwendet werden.\n\n\n\nMehr Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":["# Normalisierter differenzierter Salzgehalt-Index\n\nDer Index veranschaulicht die Menge an Salz, die in Böden vorhanden ist. Die Versalzung des Bodens ist einer der häufigsten Landabbauprozesse, insbesondere in trockenen und halbtrocken Regionen, in denen die Niederschläge die Verdunstung übersteigen. \n\nHöhere Werte deuten auf einen höheren Salzgehalt hin, niedrige Werte auf einen niedrigeren Salzgehalt.\n\nLesen Sie mehr [hier,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [hier](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) und [hier.](https://www.indexdatabase.de/db/i-single.php?id=57)"]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":["**Eingeloggte Nutzer** können Ihre benutzerdefinierten Themen nutzen, Pins speichern und laden, eine Pin-Story erstellen, Entfernungen messen, einen\nZeitraffer erstellen und die erweiterten Bild-Downloadfunktionen nutzen.\n\nUm einen kostenlosen Account zu erstellen, einfach [hier] klicken\noder innerhalb der App unter **Login** und dann **Anmelden (Sign up)**."]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":["Mit diesem Tool können Sie einen Zeitraffer der dargestellten Layer und der ausgewählten Position erstellen.\n\nZunächst wählen Sie einen Zeitraum aus. Sie können Ihre Suchergebnisse weiter verfeinern, indem Sie nach dem Monat filtern\n(Filter nach \"Monat\"-Checkbox) und/oder, indem ein Bild pro Zeitspanne (Orbit, Tag, Woche, Monat, Jahr)\nausgewählt wird.\n\nDann pressen Sie Suchen und wählen Ihre Bilder aus.\nSie können alle über die Checkbox auswählen oder die Bilder mithilfe des Schiebereglers nach Bewölkungsgrad filtern. Alternativ können alle Bilder auch einzeln ausgewählt werden,\nindem Sie in der Liste ausgewählt werden. Mithilfe der Checkbox **Grenzen** können die Grenzen auf dem Bild ein-/ausgeschalten werden.\n\nÜber den Knopf \"Play\" kann eine Vorschau des Zeitraffers abgespielt werden. Die Geschwindigkeit kann auch eingestellt werden.\n(Bilder pro Sekunde).\n\nWenn Sie mit dem Ergebnis zufrieden sind, klicken Sie auf den Knopf \"Herunterladen\" und der\nZeitraffer wird als .gif
heruntergeladen."]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":["Ihr Nutzeraccount konnte nicht geladen werden, weil der zugehörige Sentinel Hub Account nicht erstellt wurde/abgelaufen ist. Sie können den EO-Browser weiterhin nutzen, aber nicht die persönliche Nutzerkonfiguration. Um die persönliche Nutzerkonfiguration nutzen zu können, können Sie sich für einen 30-Tage Probeaccount anmelden oder sich für einen unserer Abonnements entscheiden: "]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":["Band 2 - Chlorophyll-Absorptionsmaximum - 442 nm"]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":["Band 11 - O2 R-Zweig Absorptionsband - 761 nm"]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (Atmosphärisch korrigiert)"]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":["Die Sentinel-1 Services sind sowohl auf EO-Cloud als auch auf AWS verfügbar. \nDie Möglichkeiten der Services unterscheiden sich. Mehr Informationen unter"]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":["Ein **DEM** (Digital Elevation Model) ist eine digitale Repräsentation des Geländes (normalerweise des der Erde). Es wird erstellt, indem die ganze Erde in ein Gitternetz unterteilt wird, worin jede Zelle einen Höhenwert beinhaltet. Abhängig von der Auflösung des Gitternetzes kann ein DEM genauer (höhere Auflösung) oder ungenauer (niedrige Auflösung) sein. Die Sentinel Hub DEM-Datensets (Mapzen und Copernicus) sind statisch (Datumsunabhängig) und weltweit verfügbar.\n\n**Häufiger Einsatzbereich:** Wasserflussmodellierung, Orthorektifizierung von Satellitenbildern und Ingenieurswesen."]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["Das **Copernicus DEM** stellt die Oberfläche der Erde mit ihren Gebäuden, der Infrastruktur und Vegetation dar. Ähnlich dem Mapzen DEM basiert es auf einer Zusammenstellung verschiedener DEMs (Basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Es handelt sich um eine statische Aufnahme (Datumsunabhängig) mit globaler Abdeckung.\n\n**Räumliche Auflösung:** 30 m, aufgefüllt mit 90 m an Orten, an denen das 30 m DEM noch nicht freigegeben wurde.\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["Das **Copernicus DEM** stellt die Oberfläche der Erde mit ihren Gebäuden, der Infrastruktur und Vegetation dar. Ähnlich dem Mapzen DEM basiert es auf einer Zusammenstellung verschiedener DEMs (Basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Es handelt sich um eine statische Aufnahme (Datumsunabhängig) mit globaler Abdeckung.\n\n**Räumliche Auflösung:** 90 m.\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":["Dieser Pin hat zur Zeit keine Beschreibung."]},"User Instances":{"msgid":"User Instances","msgstr":["Nutzerkonfiguration"]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":["Mausnavigation"]},"Left button":{"msgid":"Left button","msgstr":["Linke Maustaste"]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":["Klicken und ziehen Sie mit gedrückter linker Maustaste, um sich bei fixierter Höhe auf der Karte zu bewegen. Benutzten Sie SHIFT + linke Maustaste zum Drehen."]},"Right button":{"msgid":"Right button","msgstr":["Rechte Maustaste"]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":["Rechtsklick und hoch/runter ziehen, um die Höhe der Kamera zu ändern. Rechtsklick und\nnach links/rechts ziehen, um die Kameraperspektive zu drehen."]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":["Mittlere Maustaste/Mausrad"]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":["Nutzen Sie das Mausrad, um die Höhe der Kamera zu ändern (Rechtsklick + runter/hoch \nziehen). Klicken und ziehen Sie mit dem Mausrad, um den Winkel der Kamera zu ändern."]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":["Tastaturnavigation"]},"Arrow keys":{"msgid":"Arrow keys","msgstr":["Pfeiltasten"]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":["Nutzen Sie die Pfeiltasten, um sich bei fixierter Höhe auf der Karte zu bewegen."]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":["SHIFT + Pfeiltasten"]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":["Halten Sie SHIFT gedrückt und drücken Sie die Pfeiltasten, um die Kameraperspektive zu ändern."]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":["Bild auf/Bild ab"]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":["Nutzen Sie die Tasten \"Bild auf\" oder \"Bild ab\", um die Höhe der Kamera zu ändern."]},"Map navigation":{"msgid":"Map navigation","msgstr":["Kartennavigation"]},"Pan console":{"msgid":"Pan console","msgstr":["Schwenkkonsole"]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":["Die Schwenkkonsole erlaubt Ihnen, sich bei fixierter Höhe über die Karte zu bewegen. Klicken und ziehen Sie, um\nsich gleichmäßig zu bewegen. Je weiter entfern Sie vom Mittelpunkt sind, desto schneller bewegen Sie sich."]},"Camera console":{"msgid":"Camera console","msgstr":["Kamerakonsole"]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":["Die Kamerakonsole bewegt nur die Kameraperspektive. Klicken und ziehen Sie, um die Kameraperspektive zu ändern.\nJe weiter Sie vom Mittelpunkt weg ziehen, desto schneller wird sich die Ansicht ändern."]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":["Zoom"]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":["Durch Klicken wird die Höhe der Kamera geändert. Der Plusknopf bewegt die Kamera\nnäher zur Erde, der Minusknopf bewegt die Kamera weiter weg von der Erde."]},"Measure":{"msgid":"Measure","msgstr":["Messen"]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":["Erweiterte RGB-Effekte"]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":["Hauptdatenset:"]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":["Datenset-Alias:"]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":["Weitere Datensets:"]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":["Erstellen Sie einen Zeitraffer der markierten Fläche"]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":["Das TIFF-Format kann eine große Anzahl an Bändern beinhalten. Viele gebräuchliche Bildbearbeitungsprogramme (z. B. der Windows Photo Viewer) können TIFFs mit mehr als 3 Bändern nicht richtig anzeigen.\nWenn diese Option ausgewählt ist, werden nur die ersten 3 Bänder im Bild hinzugefügt.\nWenn diese Option nicht ausgewählt ist, werden alle Bänder hinzugefügt. Zum Betrachten des TIFF-Bildes muss ein Programm genutzt werden, dass mehr als 3 Bänder darstellen kann (z. B. QGIS)."]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":["EPSG:3857 ist nicht verfügbar wenn ein Untersuchungsgebiet erstellt wurde."]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":["Fügen Sie das dataMask-Band zu den unbearbeiteten Layern hinzu."]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":["Schneide zusätzliche Bänder zu"]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":["Fehler: Sie können Bilder mit Effekten nur als JPEG oder PNG runterladen."]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":["Achtung: Die folgenden Layer nutzen dataProducts. Daher ist der gewünschte Datentyp eventuell nicht gesetzt:"]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":["Achtung: Das Evalscript ist nicht im gebräuchlichen V3-Format und der gewünschte Datentyp konnte nicht gesetzt werden für:"]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":["Das heißt, der \"sampleType\" Parameter ist vermutlich automatisch auf den Default-Wert gesetzt. Du kannst dein Evalscript verändern, um den Fehler zu korrigieren. Erfahren Sie mehr über den \"sampleType\" in unserer Dokumentation"]},"Cancel":{"msgid":"Cancel","msgstr":["Abbrechen"]},"Error":{"msgid":"Error","msgstr":["Fehler"]},"Help":{"msgid":"Help","msgstr":["Hilfe"]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":["Ihr Browser unterstützt keine 3D-Funktionen, die notwendig sind, um diesen Inhalt anzuzeigen."]},"More information":{"msgid":"More information","msgstr":["Mehr Informationen"]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":["Keine Verbindung zum 3D-Service möglich! Nochmal versuchen?"]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":["Das Bild ist zu groß für dieses Gerät! Bildgröße: {0}x{1}, max.: {2}"]},"Home":{"msgid":"Home","msgstr":["Startseite"]},"Shading":{"msgid":"Shading","msgstr":["Schattierung"]},"Sphere mode":{"msgid":"Sphere mode","msgstr":["Sphären-Modus"]},"Eye height":{"msgid":"Eye height","msgstr":["Augenhöhe"]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":["Laden des Bildes nicht möglich"]},"Geometries":{"msgid":"Geometries","msgstr":["Geometrien"]},"Now":{"msgid":"Now","msgstr":["Jetzt"]},"Terrain":{"msgid":"Terrain","msgstr":["Gelände"]},"Time":{"msgid":"Time","msgstr":["Zeit"]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":["Positioniere die 3D Kamera basierend auf der 2D Karte"]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":["Modifizierter Anthocyanin-Reflexionsindex"]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":["# Effektiver radiometrischer Wolkenanteil\n\nDer Wolkenanteil ist der Teil der Erdoberfläche, der von Wolken bedeckt ist, im Verhältnis zur gesamten Erdoberfläche. Wolken wirken sich durch Abschirmung, Albedo und wolkeninterne Absorption auf die Bestimmung von Spurengasen aus. Der effektive radiometrische Wolkenanteil ist ein wichtiger Parameter zur Korrektur dieser Effekte."]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Thermales Band 10\n\nDiese thermale Visualisierung basiert auf Band 10 (ein Band ist ein Bereich des elektromagnetischen Spektrums; ein Satellitensensor kann die Erde in verschiedenen Bändern abbilden). Bei der zentralen Wellenlänge von 10.895 nm misst es im thermalen Infrarot, oder TIR. Anstatt die Temperatur der Luft zu messen, wie es Wetterstationen tun, zeigt Band 10 die Temperatur über den Boden selbst, der oft viel heißer ist. Das Thermalband 10 ist nützlich, um Oberflächentemperaturen zu liefern und wird mit einer Auflösung von 100 m aufgenommen.\n\n\n\nWeitere Informationen [hier](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Echtfarbenkomposit\n\nVon Satelliten mitgeführte Sensoren können die Erde in verschiedenen Bereichen des elektromagnetischen Spektrums abbilden. Jeder einzeln aufgenommene Bereich des Spektrums wird als Band bezeichnet. Landsat 8 hat 11 Bänder. Das Echtfarbenkomposit verwendet die Bänder des sichtbaren Lichtes aus Rot, Grün und Blau in den entsprechenden roten, grünen und blauen Farbkanälen, was zu einem Produkt mit natürlichen Farben führt. Die ist eine gute Darstellung der Erde, wie Menschen sie natürlich sehen würden.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":["# Verbessertes Echtfarbenbild\n\nDieses Skript nutzte die Verbesserung von Highlights, um ausgebrannte Pixel zu vermeiden und Überbelichtung auszugleichen. Es führt dazu, dass Wolken natürlicher aussehen und so viele sichtbaren Informationen wie möglich erhalten werden. Sentinel-3-OLCI-Kacheln decken ein großes Gebiet ab, sodass es möglich ist, große Wolkenformationen wie Wirbelstürme zu beobachten.\n\n\n\nWeitere Informationen [hier.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)"]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":["Dies sind die Themen, die nicht verfügbare Daten enthalten:"]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":["# Normalisierter differenzierter Schnee-Index (NDSI)\n\nDer normalisierte differenzierte Schnee-Index von Sentinel-2 kann zur Unterscheidung zwischen Wolken- und Schneedecke verwendet werden, da Schnee im kurzwelligen Infrarotlicht absorbiert, aber im sichtbaren Licht reflektiert, während Wolken im Allgemeinen in beiden Wellenlängen reflektieren. Die Schneedecke wird in einem hellen, leuchtenden Blau dargestellt.\n\n\n\nWeitere Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/)."]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":["# Szenenklassifizierung\n\n\n\nDie Szenenklassifizierung wurde entwickelt, um zwischen bewölkten Pixeln, klaren Pixeln und Wasserpixeln der Sentinel-2-Daten zu unterscheiden, und funktioniert auf Basis des Szenenklassifizierungsalgorithmus der ESA. Es werden zwölf verschiedene Klassen bereitgestellt, darunter Klassen für Wolken, Vegetation, Böden/Wüste, Wasser und Schnee. Es handelt sich nicht um eine Landbedeckungsklassifizierungskarte im engeren Sinne.\n\n\n\nMehr Informationen [hier](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/)."]},"Disabled":{"msgid":"Disabled","msgstr":["Deaktivieren"]},"Yes":{"msgid":"Yes","msgstr":["Ja"]},"Orthorectification":{"msgid":"Orthorectification","msgstr":["Orthorektifizierung"]},"Histogram":{"msgid":"Histogram","msgstr":["Histogramm"]},"Recalculate":{"msgid":"Recalculate","msgstr":["Neuberechnen"]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":["Band 10 - Thermale Infrarot (TIRS) - 10895 nm"]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":["Band 11 - Thermale Infrarot (TIRS) - 12005 nm"]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":[""]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":[""]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":[""]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":[""]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":[""]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":[""]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":[""]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":[""]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":[""]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":[""]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":[""]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":[""]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":[""]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":[""]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":[""]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":["Ziehe die Klassen auf die RGB-Felder."]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":[""]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":[""]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":[""]},"File upload":{"msgid":"File upload","msgstr":["Datei hochladen"]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Laden Sie eine KML/KMZ-, GPX- oder GEOJSON/JSON-Datei hoch, um einen Untersuchungsgebiet zu erstellen. Das Gebiet wird beim Exportieren eines Bildes zum Ausschneiden verwendet."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":["Eine KML/KMZ-, GPX-, GEOJSON/JSON-Datei ablegen oder den Computer durchsuchen"]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":[""]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":[""]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":[""]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":[""]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":[""]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":["Landsat 8 L1"]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":["Landsat 8 L2"]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":["Ultrablau (443 nm)"]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":["Blau (482 nm)"]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":["Grün (561.5 nm)"]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":["Rot (654.5 nm)"]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":["Nahinfrarot (NIR) (865 nm)"]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":["Kurzwelliges Infrarot (SWIR) 1 (1608.5 nm)"]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":["Kurzwelliges Infrarot (SWIR) 2 (2200.5 nm)"]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":[""]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":[""]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":[""]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":[""]},"Zoom to location":{"msgid":"Zoom to location","msgstr":["Zoom zur Position"]},"Remove layer":{"msgid":"Remove layer","msgstr":["Ebene entfernen"]},"Please select a layer":{"msgid":"Please select a layer","msgstr":["Bitte wählen Sie eine Ebene aus"]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":[""]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":[""]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":[""]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":[""]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":[""]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":[""]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":[""]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":[""]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":[""]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":[""]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":[""]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":[""]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":[""]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":[""]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":["Blau (450-520 nm)"]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":["Grün (520-600 nm)"]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":["Rot (630-690 nm)"]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":["Nahinfrarot (NIR) (760-900 nm)"]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":["Kurzwelliges Infrarot (SWIR) 1 (1550-1750 nm)"]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":["Thermale Infrarot (10400-12500 nm)"]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":["Kurzwelliges Infrarot (SWIR) 2 (2080-2350 nm)"]},"Level 1":{"msgid":"Level 1","msgstr":["Level 1"]},"Level 2":{"msgid":"Level 2","msgstr":["Level 2"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":[""]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":["Das **Mapzen DEM** basiert auf der SRTM30 (Shuttle Radar Topography Mission) und [anderen Quellen]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). Die bathymetrischen Daten stammen von [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). Es handelt sich um eine statische Aufnahme (Datumsunabhängig) mit globaler Abdeckung.\n\n**Räumliche Auflösung:** Meist 90 m, in einigen Bereichen bis zu 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)"]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":["Flughäfen"]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Max. Wolkenbedeckung:"]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Daten hochladen"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":[""]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]},"Built-up probability [0 - 100 %] at 10 m spatial resolution":{"msgid":"Built-up probability [0 - 100 %] at 10 m spatial resolution","msgstr":[""]},"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).":{"msgid":"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).","msgstr":[""]},"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.":{"msgid":"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.","msgstr":[""]},"Resampling kernel":{"msgid":"Resampling kernel","msgstr":[""]},"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)":{"msgid":"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)","msgstr":[""]},"4x4 cubic convolution":{"msgid":"4x4 cubic convolution","msgstr":[""]},"nearest neighbour":{"msgid":"nearest neighbour","msgstr":[""]},"proprietary MTF kernel":{"msgid":"proprietary MTF kernel","msgstr":[""]},"Width":{"msgid":"Width","msgstr":[""]},"Height":{"msgid":"Height","msgstr":[""]},"Format":{"msgid":"Format","msgstr":[""]},"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.":{"msgid":"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.","msgstr":[""]},"Could not generate timelapse animation file. Try using lower resolution or fewer frames.":{"msgid":"Could not generate timelapse animation file. Try using lower resolution or fewer frames.","msgstr":[""]}}}}
\ No newline at end of file
diff --git a/src/translations/el.po b/src/translations/el.po
index c4de7c36..23b35fb2 100644
--- a/src/translations/el.po
+++ b/src/translations/el.po
@@ -7670,4 +7670,82 @@ msgid ""
msgstr ""
msgid "Currently only collections on services.sentinel-hub are supported."
+msgstr ""
+
+msgid "Built-up probability [0 - 100 %] at 10 m spatial resolution"
+msgstr ""
+
+msgid ""
+"# The Global Human Settlement Layer GHS-BUILT-S2 \n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"The Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up "
+"areas (expressed as probabilities from 0 to 100 %) at 10 m spatial "
+"resolution. It was derived from a Sentinel-2 global image composite for the "
+"reference year 2018 using Convolutional Neural Networks.\n"
+"\n"
+"This script visualises the built-up probabilities stretched to 0-255.\n"
+"\n"
+"For more information about the layer, visit [this "
+"website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-"
+"human-settlement-layer-ghs-built-s2/)."
+msgstr ""
+
+msgid ""
+"The **Global Human Settlement** (GHS) framework produces global maps of "
+"built-up areas, population density and settlements to monitor human "
+"presence on Earth over time.\n"
+"\n"
+"**Coverage**: Global coverage with longitude from 180°W to 180°E and "
+"latitude from 72°N to 56°S\n"
+"\n"
+"**Data Availability**: Reference year 2018\n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Knowledge of population distribution and density has a "
+"number of applications, including disaster risk management or the study and "
+"management of urbanisation processes, not only but also in relation to the "
+"challenges of climate change and environmental degradation."
+msgstr ""
+
+msgid "Resampling kernel"
+msgstr ""
+
+msgid ""
+"Select the resampling kernel to use: \n"
+"- 4x4 cubic convolution (CC), \n"
+"- nearest neighbour (NN), \n"
+"- or the proprietary MTF kernel (MTF)"
+msgstr ""
+
+msgid "4x4 cubic convolution"
+msgstr ""
+
+msgid "nearest neighbour"
+msgstr ""
+
+msgid "proprietary MTF kernel"
+msgstr ""
+
+msgid "Width"
+msgstr ""
+
+msgid "Height"
+msgstr ""
+
+msgid "Format"
+msgstr ""
+
+msgid ""
+"For optimisation reasons MPEG4 output format will always be used for "
+"generating a timelapse with transition \"fade\", even when GIF is selected."
+msgstr ""
+
+msgid ""
+"Could not generate timelapse animation file. Try using lower resolution or "
+"fewer frames."
msgstr ""
\ No newline at end of file
diff --git a/src/translations/el.po.json b/src/translations/el.po.json
index b7dd4d5a..f3a37cb3 100644
--- a/src/translations/el.po.json
+++ b/src/translations/el.po.json
@@ -1 +1 @@
-{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","plural-forms":"nplurals=2; plural=(n != 1);","mime-version":"1.0","content-transfer-encoding":"8bit","project-id-version":"","pot-creation-date":"","po-revision-date":"","language-team":"","x-generator":"Poedit 3.0","last-translator":"","language":"el"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nPlural-Forms: nplurals=2; plural=(n != 1);\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\nProject-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLanguage-Team: \nx-generator: Poedit 3.0\nLast-Translator: \nLanguage: el\n"]},"Education":{"msgid":"Education","msgstr":["Εκπαίδευση"]},"Normal":{"msgid":"Normal","msgstr":["Κανονικό"]},"Close":{"msgid":"Close","msgstr":["Κλείσιμο"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Να κλείσει και να μην εμφανιστεί ξανά"]},"Previous":{"msgid":"Previous","msgstr":["Προηγούμενο"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Τερματισμός εκμάθησης"]},"Next":{"msgid":"Next","msgstr":["Επόμενο"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Συνεχίστε με το πρόγραμμα εκμάθησης"]},"Don't show again":{"msgid":"Don't show again","msgstr":["Να μην εμφανιστεί ξανά"]},"Show info":{"msgid":"Show info","msgstr":["Εμφάνιση πληροφοριών"]},"Discover":{"msgid":"Discover","msgstr":["Εξερευνήστε"]},"Visualize":{"msgid":"Visualize","msgstr":["Οπτικοποιήστε"]},"Compare":{"msgid":"Compare","msgstr":["Συγκρίνετε"]},"Pins":{"msgid":"Pins","msgstr":["Πινέζες"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Παρουσιάστηκε σφάλμα κατά τη λήψη εικόνων:"]},"No tile found":{"msgid":"No tile found","msgstr":["Δε βρέθηκε πινακίδα"]},"Dataset":{"msgid":"Dataset","msgstr":["Σύνολο δεδομένων"]},"Show":{"msgid":"Show","msgstr":["Εμφάνιση"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Εμφανίση εφέ και προηγμένων επιλογών"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Εμφάνιση οπτικοποίησης"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Προσθήκη στις πινέζες"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Προσθήκη στη σύγκριση"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Εστίαση στην πινακίδα"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Απόκρυψη επιπέδου"]},"Show layer":{"msgid":"Show layer","msgstr":["Εμφάνιση επιπέδου"]},"Share":{"msgid":"Share","msgstr":["Διαμοιρασμός"]},"Custom":{"msgid":"Custom","msgstr":["Προσαρμοσμένο"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Δημιουργία προσαρμοσμένης οπτικοποίησης"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Εστίαση για προβολή δεδομένων"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Δωρεάν εγγραφή"]},"for all features":{"msgid":"for all features","msgstr":["για όλα τα χαρακτηριστικά"]},"Powered by":{"msgid":"Powered by","msgstr":["Δημιουργήθηκε από"]},"with contributions by":{"msgid":"with contributions by","msgstr":["με τη συμβολή των"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["Παρακαλώ επιλέξτε πηγή(ές) δεδομένων!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["Μη έγκυρο χρονικό εύρος!"]},"No results found":{"msgid":"No results found","msgstr":["Δε βρέθηκαν αποτελέσματα"]},"Theme":{"msgid":"Theme","msgstr":["Θέμα"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Διαχείριση διαμόρφωσης στιγμιότυπων"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Συνδεθείτε για να χρησιμοποιήσετε προσαρμοσμένα στιγμιότυπα που έχετε διαμορφώσει."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["Σφάλμα κατά την ανάκτηση συμπληρωματικών δεδομένων!"]},"Search":{"msgid":"Search","msgstr":["Αναζήτηση"]},"Highlights":{"msgid":"Highlights","msgstr":["Επισημάνσεις"]},"Data sources":{"msgid":"Data sources","msgstr":["Πηγές δεδομένων"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Παρακαλώ επιλέξτε θέμα"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Χρονικό εύρος (UTC)"]},"Date":{"msgid":"Date","msgstr":["Ημερομηνία"]},"Hide description":{"msgid":"Hide description","msgstr":["Απόκρυψη περιγραφής"]},"Show description":{"msgid":"Show description","msgstr":["Εμφάνιση περιγραφής"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Το θέμα δεν περιλαμβάνει επισημάνσεις"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Βασίζεται σε: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["Μέρα 1 (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["Μέρα 5 (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["Μέρα 10 (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["Ο3 (Όζον)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (διοξείδιο του αζώτου)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (διοξείδιο του θείου)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (μονοξείδιο του άνθρακα)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (Φορμαλδεΰδη)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (μεθάνιο)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER AI (δείκτης αερολύματος)"]},"Cloud":{"msgid":"Cloud","msgstr":["Σύννεφα"]},"Other":{"msgid":"Other","msgstr":["Άλλο"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Μέγιστη νεφοκάλυψη"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Προηγμένη αναζήτηση"]},"Data location":{"msgid":"Data location","msgstr":["Τοποθεσία δεδομένων"]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":["Οι υπηρεσίες Sentinel-1 είναι διαθέσιμες τόσο στο EOCloud όσο και στο AWS. Οι δυνατότητες της κάθε\nυπηρεσίας διαφέρουν. Περισσότερες πληροφορίες στο"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Παρακαλώ επιλέξτε τουλάχιστον μια τοποθεσία!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Λειτουργία λήψης"]},"Polarization":{"msgid":"Polarization","msgstr":["Πόλωση"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Παρακαλώ επιλέξτε τουλάχιστον μία λειτουργία λήψης δεδομένων!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Επιλέξτε τουλάχιστον ένα είδος πόλωσης!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Κατεύθυνση τροχιάς"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Επιλέξτε τουλάχιστον μία κατεύθυνση τροχιάς!"]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["** Το MERIS ** (φασματόμετρο μέσης ανάλυσης) ήταν ένας αισθητήρας του δορυφόρου [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) με πρωταρχικό στόχο την παρατήρηση του εδάφους, των ωκεανών και της ατμόσφαιρας. Δεν είναι πλέον εν ενεργεία και ο Sentinel-3 έχει διαδεχτεί το MERIS.\n\n** Χωρική ανάλυση: ** Πλήρης ανάλυση εδάφους και ακτής: 260m x 290m (που μπορεί να δει μόνο λεπτομέρειες μεγαλύτερες από 260m x 290m).\n\n** Χρόνος επίσκεψης: ** το πολύ 3 ημέρες για επίσκεψη στην ίδια περιοχή.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Ιούνιο του 2002 έως τον Απρίλιο του 2012.\n\n** Κοινή χρήση: ** Παρακολούθηση ωκεανών (φυτοπλαγκτόν, αιωρούμενη ύλη), ατμόσφαιρα (υδρατμοί, CO2, σύννεφα, αερολύματα) και γη (δείκτης βλάστησης, παγκόσμια κάλυψη, υγρασία)."]},"Credits:":{"msgid":"Credits:","msgstr":["Συντελεστές:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["** Το GIBS ** (Παγκόσμιες υπηρεσίες αναζήτησης εικόνων) παρέχει γρήγορη πρόσβαση σε περισσότερες από 600 δορυφορικές εικόνες\nκαι προϊόντα, με παγκόσμια κάλυψη. Οι περισσότερες εικόνες είναι διαθέσιμες μέσα σε λίγες ώρες μετά\nτη λήψη και ορισμένα προϊόντα διαρκούν σχεδόν 30 χρόνια."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["Οι δορυφόροι ** Landsat ** της NASA / U.S. Geological Survey είναι παρόμοιοι με τους Sentinel-2 (δηλαδή καταγράφουν ορατά και υπέρυθρα μήκη κύματος)\nκαι επιπλέον μπορεί να καταγράψουν θερμικές υπέρυθρες ακτίνες (Landsat 8). Η σειρά Landsat έχει μακρά ιστορία που εκτείνεται σχεδόν σε πέντε δεκαετίες.\n Αυτή η πλατφόρμα παρέχει πρόσβαση σε εικόνες που αποκτήθηκαν από το Landsat 5, 7 και 8.\n\n** Χωρική ανάλυση: ** 15m, 30m και 100m (επανασύσταση στα 30m), ανάλογα με το μήκος κύματος (δηλαδή, μπορούν να φανούν μόνο λεπτομέρειες μεγαλύτερες από 10m και 30m). Περισσότερες πληροφορίες [εδώ](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n** Χρόνος επίσκεψης: ** Μέγιστος χρόνος 8 ημερών για κάλυψη της ίδιας περιοχή με τη χρήση των δύο λειτουργικών δορυφόρων Landsat 7 και Landsat 8.\n\n** Διαθεσιμότητα δεδομένων: ** Ευρώπη και Βόρεια Αφρική από το 1984 έως το 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 έως σήμερα (Landsat 8) από το αρχείο ESA. Το παγκόσμιο αρχείο Γεωλογικής Έρευνας των ΗΠΑ (USGS) από τον Απρίλιο του 2013 έως σήμερα (μόνο για το Landsat 8).\n\n** Κοινή χρήση: ** Παρακολούθηση βλάστησης, χρήσεις γης, χάρτες κάλυψης γης, παρακολούθηση αλλαγών κ.λπ."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["Ο δορυφόρος ** MODIS ** της Nasa - (Spectroradiometer Imaging Moderate Resolution) αποκτά δεδομένα με στόχο\nτην καταγραφή των διεργασιών που συμβαίνουν στην ξηρά. Το πρόγραμμα περιήγησης EO παρέχει δεδομένα για\nπαρατήρηση της γης (κανάλια 1-7).\n\n** Χωρική ανάλυση: ** 250m (ζώνες 1-2), 500m (ζώνες 3-7), 1000m (ζώνες 8-36).\n\n** Χρόνος επίσκεψης: ** Παγκόσμια κάλυψη σε 1-2 ημέρες με τους δορυφόρους Aqua και Terra.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Ιανουάριο του 2013.\n\n** Κοινή χρήση: ** Παρακολούθηση γης, νεφών, ωκεανών σε παγκόσμια κλίμακα."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["Ο δορυφόρος ** Proba-V ** είναι ένας μικρός δορυφόρος σχεδιασμένος για να χαρτογραφήσει την κάλυψη της γης και την ανάπτυξη της βλάστησης,\nσε ολόκληρο τον κόσμο, κάθε δύο ημέρες. Το EO Browser παρέχει παράγωγα προϊόντα που ελαχιστοποιούν τη νεφοκάλυψη\nσυνδυάζοντας λήψεις χωρίς σύννεφα εντός χρονικής περιόδου 1 ημέρας (S1), 5 ημερών (S5) και 10 ημερών (S10).\n\n** Χωρική ανάλυση: ** 100m για S1 και S5, 333m για S1 και S10, 1000m για S1 και S10.\n\n** Χρόνος επίσκεψης: ** 1 ημέρα για γεωγραφικά πλάτη 35-75 ° Β και 35-56 ° Ν, 2 ημέρες για γεωγραφικά πλάτη μεταξύ 35 ° Β\nκαι 35 ° Ν.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Οκτώβριο του 2013.\n\n** Κοινή χρήση: ** Η παρατήρηση της κάλυψης της γης, η ανάπτυξη της βλάστησης, η αξιολόγηση των κλιματικών επιπτώσεων,\nη διαχείριση των υδατικών πόρων, παρακολούθηση γεωργικών εκτάσεων και εκτίμηση της επισιτιστικής ασφάλειας, εσωτερικά ύδατα\nπαρακολούθηση των φυσικών πόρων και ανίχνευση της σταθερής εξάπλωσης των ερήμων και της αποψίλωσης των δασών."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":["Ο δορυφόρος ** Sentinel-1 ** παρέχει εικόνες ραντάρ παντός καιρού, την ημέρα και τη νύχτα, για χερσαίες και ωκεάνιες περιοχές. Ο \nEOBrowser παρέχει δεδομένα που αποκτήθηκαν σε λειτουργίες Interferometric Wide Swath (IW) και Extra Wide Swath (EW)\nκαι έχουν μετατραπεί σε προϊόντα Level-1 Ground Range Detected (GRD).\n\n** Μέγεθος εικονοψηφίδας: ** 10m (IW), 40m (EW).\n\n** Χρόνος επίσκεψης: ** <= 5 ημέρες με την αξιοποίηση και των δύο δορυφόρων.\n\n** Χρόνος επίσκεψης ** (για αύξουσα και φθίνουσα τροχιά, με την αξιοποίηση και των δύο δορυφόρων): <= 3 ημέρες, δείτε το [σενάριο παρατήρησης](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation -σενάριο)\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Οκτώβριο του 2014.\n\n** Κοινή χρήση: ** Παρακολούθηση θαλάσσιων και χερσαίων περιοχών, διαχείριση έκτακτων αναγκών, κλιματική αλλαγή."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["Ο δορυφόρος ** Sentinel-2 ** παρέχει εικόνες υψηλής ανάλυσης στα ορατά και υπέρυθρα μήκη κύματος, για την παρακολούθηση της βλάστησης, του εδάφους και του νερού, των εσωτερικών υδάτων και των παράκτιων περιοχών. .\n\n** Χωρική ανάλυση: ** 10m, 20m και 60m, ανάλογα με το μήκος κύματος (δηλαδή, εμφανίζονται μόνο λεπτομέρειες μεγαλύτερες από 10m, 20m και 60m). Περισσότερες πληροφορίες [εδώ](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial).\n\n** Χρόνος επίσκεψης: ** το πολύ 5 ημέρες για επίσκεψη της ίδιας περιοχή, με την αξιοποίηση και των δύο δορυφόρων.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Ιούνιο του 2015. Πλήρης παγκόσμια κάλυψη από το Μάρτιο του 2017.\n\n** Κοινή χρήση: ** χάρτες κάλυψης γης, ανίχνευσης αλλαγών, παρακολούθηση βλάστησης, παρακολούθηση καμένων περιοχών."]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Τα δεδομένα επιπέδου 2Α είναι δεδομένα υψηλής ποιότητας στα οποία έχει εφαρμοστεί ατμοσφαιρική διόρθωση. Τα δεδομένα είναι διαθέσιμα παγκοσμίως από τον Μάρτιο του 2017.\n\nΠερισσότερες πληροφορίες σχετικά με την ατμοσφαιρική διόρθωση [εδώ](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction)."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Τα δεδομένα επιπέδου 1C είναι δεδομένα επαρκούς ποιότητας για τις περισσότερες έρευνες. Σε αυτά έχουν εφαρμοστεί όλες οι διορθώσεις εικόνας εκτός από την ατμοσφαιρική. Τα δεδομένα είναι διαθέσιμα παγκοσμίως από τον Ιούνιο του 2015 και μετά."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["** Κύριος στόχος της αποστολής Sentinel-3 ** είναι η μέτρηση της τοπογραφίας της επιφάνειας της θάλασσας, της θερμοκρασίας της επιφάνειας της θάλασσας και της ξηράς, του χρώματος της επιφάνειας του ωκεανού και της ξηράς. Το Sentinel-3 διαθέτει τέσσερις αισθητήρες. Τα δεδομένα που αποκτήθηκαν από το Ocean and Land Color Instrument (OLCI) και το Sea and Land Surface Temperature Instrument (SLSTR) είναι διαθέσιμα σε αυτήν την πλατφόρμα.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Μάιο του 2016 και μετά."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["Ο αισθητήρας ** Sea and Land Surface Temperature (SLSTR) ** του δορυφόρου Sentinel-3 καταγράφει τη θερμοκρασία της επιφάνειας της θάλασσας και της ξηράς\nσε παγκόσμιο επίπεδο. Το SLSTR καταγράφει τα ορατά μήκη κύματος, το βραχυκυματικό και θερμικό υπέρυθρο του ηλεκτρομαγνητικού φάσματος.\n\n** Χωρική ανάλυση: ** 500 μέτρα για ορατά μήκη κύματος και το εγγύς και βραχυκυματικό υπέρυθρο και 1 χλμ για το θερμικό κανάλι\n(δηλαδή, εμφανίζονται μόνο λεπτομέρειες μεγαλύτερες από 500m και 1km αντίστοιχα).\n\n** Χρόνος επίσκεψης: ** Μέγιστη 1 ημέρα για επίσκεψη στην ίδια περιοχή, χρησιμοποιώντας και τους δύο δορυφόρους.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Μάιο του 2016 και μετά.\n\n** Κοινή χρήση: ** Παρακολούθηση της αλλαγής του κλίματος, παρακολούθηση της βλάστησης, ενεργός εντοπισμός πυρκαγιάς, παρακολούθηση της θερμοκρασίας της ξηράς και της θάλασσας."]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["Ο αισθητήρας ** Ocean and Land Color Instrument (OLCI) ** του δορυφόρου Sentinel-3 είναι ένα φασματόμετρο που\nμετρά την ηλιακή ακτινοβολία που αντανακλάται από τη Γη και παρακολουθεί τον ωκεανό, το περιβάλλον,\nκαι το κλίμα. Παρέχει συχνότερες λήψεις στο ορατό φάσμα από το Sentinel-2 αλλά σε χαμηλότερη ανάλυση\nκαι με περισσότερα μήκη κύματος. Ο Sentinel-3 OLCI αποτελεί διάδοχο του αισθητήρα MERIS στο δορυφόρο Envisat, του οποίου η αποστολή ολοκληρώθηκε.\n\n** Χωρική ανάλυση: ** 300 μέτρα (δηλαδή, μπορούν να καταγραφούν λεπτομέρειες μεγαλύτερες από 300 μέτρα).\n\n** Χρόνος επίσκεψης: ** Μέγιστο 2 ημέρες για επίσκεψη της ίδιας περιοχή, με την αξιοποίηση και των δύο δορυφόρων.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Μάιο του 2016 και μετά.\n\n** Κοινή χρήση: ** Παρακολούθηση της τοπογραφίας και του χρώματος της ξηράς και της θάλασσας."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["Ο ** Sentinel-5P ** είναι ένας δορυφόρος που παρέχει ατμοσφαιρικές μετρήσεις που χρησιμοποιούνται για την εκτίμηση της ποιότητας του αέρα, την παρακολούθηση του όζοντος, την υπεριώδη ακτινοβολία,\nκαι κλιματική παρακολούθηση και πρόβλεψη.\n\n** Χωρική ανάλυση: ** 7 x 3,5 χιλιόμετρα (δηλαδή, εμφανίζονται μόνο λεπτομέρειες μεγαλύτερες από 7 x 3,5 χιλιόμετρα).\n\n** Ώρα επίσκεψης: ** Μέγιστη 1 ημέρα για επίσκεψη στην ίδια περιοχή.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Απρίλιο του 2018 και μετά.\n\n** Κοινή χρήση: ** Παρακολούθηση της συγκέντρωσης του μονοξειδίου του άνθρακα (CO), του διοξειδίου του αζώτου (NO2) και του όζοντος (O3) στον αέρα. Παρακολούθηση του δείκτη UV αερολύματος (AER_AI) και διαφόρων γεωφυσικών παραμέτρων των νεφών (Cloud)."]},"Copied":{"msgid":"Copied","msgstr":["Αντιγράφηκε"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["Αντιγραφή στο πρόχειρο"]},"Data source name":{"msgid":"Data source name","msgstr":["Ονομασία πηγής δεδομένων"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Χρόνος λήψης"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Νεφοκάλυψη"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Υψόμετρο του ήλιου"]},"MGRS location":{"msgid":"MGRS location","msgstr":["Τοποθεσία MGRS"]},"AWS path":{"msgid":"AWS path","msgstr":["Διαδρομή AWS"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["Διαδρομή EO Cloud"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["Διαδρομή CreoDIAS"]},"SciHub link":{"msgid":"SciHub link","msgstr":["Σύνδεσμος SciHub"]},"Back to search":{"msgid":"Back to search","msgstr":["Πίσω στην αναζήτηση"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Εμφάνιση αποτελέσματος ${ this.state.results.length }","Εμφάνιση αποτελεσμάτων ${ this.state.results.length }"]},"Load more":{"msgid":"Load more","msgstr":["Φόρτωσε περισσότερα"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Φόρτωση περισσότερων αποτελεσμάτων ..."]},"Results":{"msgid":"Results","msgstr":["Αποτελέσματα"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Εμφάνιση αποτελέσματος ${ this.state.selectedTiles.length }.","Εμφάνιση αποτελεσμάτων ${ this.state.selectedTiles.length }"]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Επεξεργασία περιγραφής πινέζας"]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":["Αυτή η πινέζα προς το παρόν δεν έχει περιγραφή."]},"Reject changes":{"msgid":"Reject changes","msgstr":["Απόρριψη αλλαγών"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Αποδοχή αλλαγών"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Μετονομασία πινέζας"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Αφαίρεση πινέζας"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Εστίαση στην τοποθεσία της πινέζας"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Πλάτος/Μήκος"]},"Zoom":{"msgid":"Zoom","msgstr":["Εστίαση"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["Πρόκειται να προσθέσετε ${ N_PINS } πινέζα(ες) στη συλλογή σας. Θέλετε να συνεχίσετε;"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["ΠΡΟΕΙΔΟΠΟΙΗΣΗ: Πρόκειται να διαγράψετε μια πινέζα. Θέλετε να συνεχίσετε;"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":["ΠΡΟΕΙΔΟΠΟΙΗΣΗ: Πρόκειται να διαγράψετε όλες τις πινέζες. Θέλετε να συνεχίσετε;"]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["Χωρίς πινέζες. Μεταβείτε στην καρτέλα Οπτικοποίηση για να αποθηκεύσετε μια πινέζα ή να ανεβάσετε ένα αρχείο JSON με αποθηκευμένες πινέζες."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Να σημειωθεί ότι οι πινέζες θα αποθηκευτούν μόνο αν συνδεθείτε. Διαφορετικά, οι πινέζες θα χαθούν μόλις κλείσει η εφαρμογή."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Αποεπιλογή όλων"]},"Select all":{"msgid":"Select all","msgstr":["Επιλογή όλων"]},"No pins.":{"msgid":"No pins.","msgstr":["Καμία πινέζα."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Δημιουργία συνδέσμου (Επιλογή πινέζας ${ selectedPins.length })","Δημιουργία συνδέσμου (Επιλογή πινεζών ${ selectedPins.length })"]},"File type not supported":{"msgid":"File type not supported","msgstr":["Ο τύπος του αρχείου δεν υποστηρίζεται"]},"not supported":{"msgid":"not supported","msgstr":["δεν υποστηρίζεται"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["Καμία πινέζα δε βρέθηκε."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Σφάλμα κατά τη συντακτική ανάλυση του αρχείου:"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":["Μεταφόρτωση αρχείου JSON με αποθηκευμένες πινέζες."]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["Αποθέστε αρχείο JSON ή αναζητήστε στον υπολογιστή"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Διατηρήστε τις υπάρχουσες πινέζες"]},"Share pins":{"msgid":"Share pins","msgstr":["Κοινή χρήση πινέζων"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Δημιουργήστε μια ιστορία απο τις πινέζες"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Εξάγετε τις πινέζες στον υπολογιστή σας"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Εισάγετε πινέζες απο αποθηκευμένο αρχείο"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Διαγράψτε όλες τις πινέζες"]},"Story":{"msgid":"Story","msgstr":["Ιστορία"]},"Export":{"msgid":"Export","msgstr":["Εξαγωγή"]},"Import":{"msgid":"Import","msgstr":["Εισαγωγή"]},"Clear":{"msgid":"Clear","msgstr":["Καθαρισμός"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Κοινή χρήση του συνδέσμου με τις πινέζες"]},"Creating link...":{"msgid":"Creating link...","msgstr":["Δημιουργία συνδέσμου..."]},"OK":{"msgid":"OK","msgstr":["Εντάξει"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Ενημέρωση συλλογής με τις πινέζες."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Παρουσιάστηκε ένα πρόβλημα κατά την ενημέρωση της συλλογής με τις πινέζες: $ {updatingPinsError}."]},"Hello,":{"msgid":"Hello,","msgstr":["Γειά,"]},"Opacity":{"msgid":"Opacity","msgstr":["Αδιαφάνεια"]},"Split position":{"msgid":"Split position","msgstr":["Θέση διαχωριστικού"]},"split":{"msgid":"split","msgstr":["διαχωρισμός"]},"opacity":{"msgid":"opacity","msgstr":["αδιαφάνεια"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["Δεν υπάρχουν επίπεδα για σύγκριση."]},"Remove all":{"msgid":"Remove all","msgstr":["Αφαίρεση όλων"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Προσθήκη όλων των πινέζων"]},"Split":{"msgid":"Split","msgstr":["Διαχωρισμός"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Παρουσιάστηκε σφάλμα κατά τη λήψη των στιγμιοτύπων σας"]},"Download":{"msgid":"Download","msgstr":["Λήψη"]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Οπτικοποίηση εδάφους σε 3D"]},"Left button":{"msgid":"Left button","msgstr":["Αριστερό πλήκτρο"]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":["Κάντε κλικ και σύρετε χρησιμοποιώντας το αριστερό πλήκτρο του ποντικιού για να μετακινηθείτε στο χάρτη σε σταθερό ύψος. Χρησιμοποιήστε το συνδυασμό πλήκτρων SHIFT + αριστερό κλικ για περιστροφή."]},"Right button":{"msgid":"Right button","msgstr":["Δεξί πλήκτρο"]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":["Κάντε δεξί κλικ και σύρετε προς τα πάνω / κάτω για να αλλάξετε το ύψος της κάμερας. Κάντε δεξί κλικ και\nσύρετε αριστερά / δεξιά για περιστροφή του οπτικού πεδίου της κάμερας."]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":["Μεσαίο πλήκτρο / τροχός"]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":["Χρησιμοποιήστε τον τροχό κύλισης για να αλλάξετε το ύψος της κάμερας (όπως το δεξί κλικ + σύρετε\nπάνω κάτω). Κάντε κλικ και σύρετε το κουμπί τροχού για να αλλάξετε τη γωνία της κάμερας."]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":["Πλοήγηση με το πληκτρολόγιο"]},"Arrow keys":{"msgid":"Arrow keys","msgstr":["Πλήκτρα με βέλη"]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":["Χρησιμοποιήστε τα πλήκτρα με τα βέλη για να μετακινηθείτε στο χάρτη σε σταθερό ύψος."]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":["SHIFT + πλήκτρα με βέλη"]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":["Κρατήστε πατημένο το πλήκτρο SHIFT ενώ πατάτε τα πλήκτρα με τα βέλη για να αλλάξετε την προβολή της κάμερας."]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":["Σελίδα πάνω/Σελίδα κάτω"]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":["Χρησιμοποιήστε τα πλήκτρα PG UP ή PG DN για να αλλάξετε το υψόμετρο της κάμερας."]},"Map navigation":{"msgid":"Map navigation","msgstr":["Πλοήγηση στο χάρτη"]},"Pan console":{"msgid":"Pan console","msgstr":["Έλεγχος κίνησης στην ίδια κλίμακα (pan)"]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":["Ο έλεγχος κίνησης στην ίδια κλίμακα σας επιτρέπει να μετακινηθείτε στο χάρτη σε σταθερή κλίμακα/εστίαση. Κάντε κλικ και σύρετε για μετακίνηση\nσυνεχώς. Όσο πιο μακριά σύρετε από το κέντρο, τόσο πιο γρήγορα θα κινηθείτε."]},"Camera console":{"msgid":"Camera console","msgstr":["Κονσόλα κάμερας"]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":["Η κονσόλα κάμερας μετακινεί μόνο την προβολή της κάμερας. Κάντε κλικ και σύρετε για να αλλάξετε την προβολή της κάμερας.\nΌσο πιο μακριά σύρετε από το κέντρο, τόσο πιο γρήγορα θα αλλάξετε την προβολή."]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":["Πλήκτρα εστίασης"]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":["Κάνοντας κλικ σε αυτά θα αλλάξει το ύψος της κάμερας. Το πλήκτρο συν θα μετακινήσει την κάμερα\nπιο κοντά στη γη, το πλήκτρο μείον θα μετακινήσει την κάμερα πιο μακριά."]},"Go to Place":{"msgid":"Go to Place","msgstr":["Μετάβαση σε Τοποθεσία"]},"Labels":{"msgid":"Labels","msgstr":["Ετικέτες"]},"Borders":{"msgid":"Borders","msgstr":["Όρια"]},"Roads":{"msgid":"Roads","msgstr":["Οδικό δίκτυο"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Εστίαση"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Αποεστίαση"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["Σχετικά με το EO Browser"]},"Contact us":{"msgid":"Contact us","msgstr":["Επικοινωνήστε μαζί μας"]},"Get data":{"msgid":"Get data","msgstr":["Λήψη δεδομένων"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Πρέπει να συνδεθείτε για να χρησιμοποιήσετε αυτήν τη λειτουργία."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Παρακαλώ επιλέξτε ένα επίπεδο."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["Δεν είναι δυνατή η λήψη εικόνας στη λειτουργία σύγκρισης."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["Αυτή η πηγή δεδομένων δεν υποστηρίζεται."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":["Γράφημα υπηρεσίας Statistical Info / Feature Info"]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Γράφημα υπηρεσίας Statistical Info / Feature Info - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["παρακαλώ επιλέξτε ένα επίπεδο"]},"not available for ":{"msgid":"not available for ","msgstr":["δεν είναι διαθέσιμο για "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["δεν είναι διαθέσιμο για ${ props.presetLayerName }\" (το επίπεδο με τιμή δεν έχει καθοριστεί)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Πρώτα αναζητήστε δεδομένα."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Δημιουργήστε κινούμενη εικόνα σε timelapse"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Επισήμανση σημείου ενδιαφέροντος"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Κεντράρετε το χάρτη στο χαρακτηριστικό"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Αφαίρεση γεωμετρίας"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Περιοχή ενδιαφέροντος"]},"Select mode":{"msgid":"Select mode","msgstr":["Λειτουργία επιλογής"]},"Mode:":{"msgid":"Mode:","msgstr":["Λειτουργία:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Αφαίρεση μέτρησης"]},"Measure":{"msgid":"Measure","msgstr":["Μέτρηση"]},"km":{"msgid":"km","msgstr":["χλμ"]},"m":{"msgid":"m","msgstr":["μ"]},"Gain":{"msgid":"Gain","msgstr":["Ενίσχυση"]},"Gamma":{"msgid":"Gamma","msgstr":["Γάμμα"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Ελάχιστη ποιότητα δεδομένων"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Κλιμάκωση προς τα επάνω"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Κλιμάκωση προς τα κάτω"]},"Reset all":{"msgid":"Reset all","msgstr":["Επαναφορά όλων"]},"filter by months":{"msgid":"filter by months","msgstr":["φιλτράρισμα κατά μήνες"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Αντιγραφή γεωμετρίας στο πρόχειρο"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Απόρριψη επεξεργασίας."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Σχεδιάστε την περιοχή ενδιαφέροντος"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Λιγότερη νεφοκάλυψη"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Χρησιμοποιήστε πρόσθετα σύνολα δεδομένων (για προχωρημένους)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Σειρά προβολής τμημάτων μωσαϊκού"]},"Most recent":{"msgid":"Most recent","msgstr":["Τα πιο πρόσφατα"]},"Least recent":{"msgid":"Least recent","msgstr":["Λιγότερο πρόσφατα"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Προσαρμόστε το χρονικό διάστημα"]},"Back":{"msgid":"Back","msgstr":["Πίσω"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Σφάλμα κατά τη φόρτωση αλγορίθμου. Ελέγξτε τη διεύθυνση URL σας."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Καταργήστε την επιλογή Φόρτωση αλγορίθμου από τη διεύθυνση URL για επεξεργασία του κώδικα"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Φόρτωση αλγορίθμου από διεύθυνση URL"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["Εισάγετε σύνδεσμο URL προς τον αλγόριθμο"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Ο αλγόριθμος φορτώθηκε."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Επιτρέπονται μόνο τομείς HTTPS."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Φόρτωση σεναρίου στον επεξεργαστή κώδικα"]},"Refresh":{"msgid":"Refresh","msgstr":["Ανανέωση"]},"orbit":{"msgid":"orbit","msgstr":["τροχιά"]},"day":{"msgid":"day","msgstr":["ημέρα"]},"week":{"msgid":"week","msgstr":["εβδομάδα"]},"month":{"msgid":"month","msgstr":["μήνας"]},"year":{"msgid":"year","msgstr":["έτος"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Επιλέξτε 1 εικόνα ανά:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Τεχνική Timelapse"]},"Select All":{"msgid":"Select All","msgstr":["Επιλογή όλων"]},"Speed:":{"msgid":"Speed:","msgstr":["Ταχύτητα:"]},"frames / s":{"msgid":"frames / s","msgstr":["καρέ / δευτερόλεπτο"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Προετοιμασία..."]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Δεν είναι δυνατή η λήψη των αρχείων:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Δεν είναι δυνατή η λήψη μέσω χάρτη"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["Δεν είναι δυνατή η συμπίεση των αρχείων:"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Παρουσιάστηκε πρόβλημα κατά τη λήψη της εικόνας"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Σφάλμα κατά τη λήψη της εικόνας: η διεύθυνση url είναι άδεια!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Σφάλμα κατά την ανάκτηση εικόνας:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["Δεν ήταν δυνατή η φόρτωση της εικόνας από blob"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Σύρετε τα κανάλια στα πεδία RGB."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Σύρετε κανάλια στην εξίσωση του δείκτη"]},"Index ":{"msgid":"Index ","msgstr":["Δείκτης "]},"Threshold":{"msgid":"Threshold","msgstr":["Κατώφλι"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Αφαίρεση εργαλείου επιλογής χρωμάτων"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Προσθήκη εργαλείου επιλογής χρωμάτων"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Κάντε κλικ για να τοποθετήσετε το δείκτη"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Κάντε κλικ για να τοποθετήσετε την πρώτη κορυφή"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Κάντε κλικ για να συνεχίσετε τη σχεδίαση"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Κάντε κλικ στο πρώτο σημείο για να ολοκληρώσετε τη σχεδίαση"]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":["Δημιουργία timelapse αυτής της περιοχής"]},"Show captions":{"msgid":"Show captions","msgstr":["Εμφάνιση υπότιτλων"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Εμφάνιση τίτλου διαφάνειας"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Προσθήκη επικαλύψεων χάρτη"]},"Show legend":{"msgid":"Show legend","msgstr":["Εμφάνιση υπομνήματος"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["Δεν βρέθηκαν πινέζες στο τρέχον οπτικό πεδίο."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Ορισμένες πινέζες ($ {N_PINS_OUTSIDE_BOUNDS}) αγνοούνται επειδή δε βρίσκονται στην επιλεγμένη περιοχή."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["Για να δημιουργήσετε μια ιστορία με τις πινέζες, μεταβείτε στην επιθυμητή θέση στον χάρτη.\n\nΌλες οι πινέζες στο τρέχον οπτικό πεδίο θα χρησιμοποιηθούν για τη δημιουργία της ιστορίας, ενώ οι υπόλοιπες θα αγνοηθούν."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["Το αρχείο θα έχει συνημμένο το λογότυπο."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["Ένα κανάλι-μάσκα δεδομένων θα συμπεριληφθεί στα ληφθέντα ακατέργαστα κανάλια ως δεύτερο κανάλι."]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":["Ο μορφότυπος αρχείου Tagged Image File Format (TIFF) μπορεί να περιέχει μεγάλο αριθμό καναλιών, ωστόσο συνήθεις εφαρμογές θέασης εικόνων (π.χ. Windows Photo Viewer) δεν μπορούν να εμφανίσουν εικόνες TIFF με περισσότερες από 3 κανάλια.\nΕάν αυτή η επιλογή είναι ενεργοποιημένη, μόνο τα τρία πρώτα κανάλια θα συμπεριληφθούν στην εικόνα.\nΕάν αυτή η επιλογή είναι απενεργοποιημένη, όλα τα κανάλια θα συμπεριληφθούν, αλλά θα πρέπει να χρησιμοποιήσετε μια εφαρμογή που υποστηρίζει εικόνες με περισσότερα από 3 κανάλια (π.χ. QGIS) για την εμφάνιση της εικόνας TIFF."]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":["Το EPSG: 3857 δεν είναι διαθέσιμο όταν έχει καθοριστεί AOI."]},"Show logo":{"msgid":"Show logo","msgstr":["Εμφάνιση λογότυπου"]},"Image format":{"msgid":"Image format","msgstr":["Μορφότυπος εικόνας"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Ανάλυση εικόνας"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Σύστημα συντεταγμένων"]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":["Προσθέστε κανάλι / μάσκα δεδομένων σε ακατέργαστα επίπεδα"]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":["Αποκοπή πρόσθετων καναλιών"]},"Layers":{"msgid":"Layers","msgstr":["Επίπεδα"]},"Visualized":{"msgid":"Visualized","msgstr":["Οπτικοποιημένα"]},"Raw":{"msgid":"Raw","msgstr":["Ακατέργαστα"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["Τα επίπεδα επικάλυψης του χάρτη (ετικέτες θέσης, οδικό δίκτυο και πολιτικά όρια) θα προστεθούν στην εικόνα."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Οι εξαγόμενες εικόνες θα περιλαμβάνουν την πηγή δεδομένων και την ημερομηνία, την κλίμακα εστίασης και την επωνυμία"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Προσθήκης σύντομης περιγραφής στην εξαγόμενη εικόνα"]},"Description":{"msgid":"Description","msgstr":["Περιγραφή"]},"Image format:":{"msgid":"Image format:","msgstr":["Μορφότυπος εικόνας:"]},"Basic":{"msgid":"Basic","msgstr":["Βασικό"]},"Analytical":{"msgid":"Analytical","msgstr":["Αναλυτικό"]},"High-res print":{"msgid":"High-res print","msgstr":["Εκτύπωση υψηλής ανάλυσης"]},"Download image":{"msgid":"Download image","msgstr":["Λήψη εικόνας"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["Παρουσιάστηκε σφάλμα κατά τη λήψη ορισμένων εικόνων:"]},"min/px":{"msgid":"min/px","msgstr":["λεπτά/px"]},"sec/px":{"msgid":"sec/px","msgstr":["δευτερόλεπτα/px"]},"Resolution":{"msgid":"Resolution","msgstr":["Ανάλυση"]},"lat.":{"msgid":"lat.","msgstr":["πλάτος"]},"deg/px":{"msgid":"deg/px","msgstr":["μοίρες/px"]},"long.":{"msgid":"long.","msgstr":["μήκος"]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Προβλεπόμενη ανάλυση: $ {formattedResolution} μ / px"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Σφάλμα: Η συγχώνευση δεδομένων δεν υποστηρίζει μορφές KMZ / JPG και KMZ / PNG."]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":["Σφάλμα: Μπορείτε να κάνετε λήψη οπτικοποίησης με εφέ μόνο σε μορφές JPEG ή PNG."]},"Image download":{"msgid":"Image download","msgstr":["Λήψη εικόνας"]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":["Προειδοποίηση: Τα ακόλουθα επίπεδα χρησιμοποιούν προϊόντα δεδομένων, επομένως ο επιθυμητός τύπος δεδομένων ενδέχεται να μην οριστεί:"]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":["Προειδοποίηση: Το Evalscript δεν είναι σε τυπική μορφή V3 και δεν ήταν δυνατή η ρύθμιση του επιθυμητού τύπου δεδομένων για:"]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":["Αυτό σημαίνει ότι η παράμετρος \"sampleType\" ενδέχεται να έχει οριστεί ως προεπιλεγμένη (AUTO). Μπορείτε να το επιδιορθώσετε με την επεξεργασία του evalscript. Μάθετε περισσότερα για το \"sampleType\" στην τεκμηρίωση"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Πλάτος εικόνας (ίντσες)"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Μήκος εικόνας (ίντσες)"]},"DPI:":{"msgid":"DPI:","msgstr":["DPI:"]},"5 years":{"msgid":"5 years","msgstr":["5 χρόνια"]},"2 years":{"msgid":"2 years","msgstr":["2 χρόνια"]},"1 year":{"msgid":"1 year","msgstr":["1 χρόνος"]},"6 months":{"msgid":"6 months","msgstr":["6 μήνες"]},"3 months":{"msgid":"3 months","msgstr":["3 μήνες"]},"1 month":{"msgid":"1 month","msgstr":["1 μήνας"]},"Retry":{"msgid":"Retry","msgstr":["Δοκιμάστε ξανά"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Φόρτωνει, παρακαλώ περιμένετε"]},"mean":{"msgid":"mean","msgstr":["μέση τιμή"]},"median":{"msgid":"median","msgstr":["διάμεσος"]},"st. dev.":{"msgid":"st. dev.","msgstr":["τυπική απόκλιση"]},"min / max":{"msgid":"min / max","msgstr":["ελάχιστο/μέγιστο"]},"Export CSV":{"msgid":"Export CSV","msgstr":["Εξαγωγή CSV"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Χρονικό διάστημα:"]},"Date:":{"msgid":"Date:","msgstr":["Ημερομηνία:"]},"Single date":{"msgid":"Single date","msgstr":["Μοναδική ημερομηνία"]},"Timespan":{"msgid":"Timespan","msgstr":["Χρονικό διάστημα"]},"hh":{"msgid":"hh","msgstr":["ωω"]},"mm":{"msgid":"mm","msgstr":["λλ"]},"From:":{"msgid":"From:","msgstr":["Από:"]},"Until:":{"msgid":"Until:","msgstr":["Μέχρι:"]},"Apply":{"msgid":"Apply","msgstr":["Εφαρμογή"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Κοινή χρήση στο Facebook"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Κοινή χρήση στο Twitter"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Κοιτάξτε αυτό "]},"Logout":{"msgid":"Logout","msgstr":["Αποσύνδεση"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["Συνδεθείτε για να ξεκλειδώσετε προηγμένες λειτουργίες όπως χρονομέτρηση, αναλυτική λήψη, δικές σας διαμορφώσεις και άλλα."]},"Login":{"msgid":"Login","msgstr":["Σύνδεση"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Παρακολούθηση της Γης από το Διάστημα"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Γεωργία"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Ατμόσφαιρα και ατμοσφαιρική ρύπανση"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Ανίχνευση αλλαγών με το πέρας του χρόνου"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Πλημμύρα και ξηρασία"]},"Geology":{"msgid":"Geology","msgstr":["Γεωλογία"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Ωκεάνια και υδάτινα σώματα"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Χιόνια και Παγετώνες"]},"Urban":{"msgid":"Urban","msgstr":["Αστικός"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Βλάστηση και δασοκομία"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Ηφαίστεια"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Πυρκαγιές"]},"Default":{"msgid":"Default","msgstr":["Προκαθορισμένο"]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":["Το πρόγραμμα περιήγησης ιστού δεν υποστηρίζει δυνατότητες 3D, οι οποίες απαιτούνται για την εμφάνιση αυτού του περιεχομένου."]},"More information":{"msgid":"More information","msgstr":["Περισσότερες πληροφορίες"]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":["Δεν είναι δυνατή η σύνδεση στην υπηρεσία 3D! Θα ξαναδοκιμάσετε;"]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":["Η εικόνα είναι πολύ μεγάλη για αυτήν τη συσκευή!\nΜέγεθος εικόνας: {0} x {1}, μέγιστο: {2}"]},"Home":{"msgid":"Home","msgstr":["Αρχική"]},"Shading":{"msgid":"Shading","msgstr":["Σκίαση"]},"Sphere mode":{"msgid":"Sphere mode","msgstr":["Λειτουργία σφαίρας"]},"Eye height":{"msgid":"Eye height","msgstr":["Ύψος ματιών"]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":["Δεν είναι δυνατή η φόρτωση της εικόνας"]},"Geometries":{"msgid":"Geometries","msgstr":["Γεωμετρίες"]},"Now":{"msgid":"Now","msgstr":["Τώρα"]},"Terrain":{"msgid":"Terrain","msgstr":["Έδαφος"]},"Time":{"msgid":"Time","msgstr":["Χρόνος"]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":["# Καλώς ήλθατε στον EO Browser!!\n\nΈνα πλήρες αρχείο Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P,\nLandsat 5, 7 και 8 της ESA, Landsat 8 με παγκόσμια κάλυψη και Envisat Meris,\nMODIS, Proba-V και GIBS προϊόντα σε ένα μοναδικό σημείο πρόσβασης.\n\n[Σελίδα παρουσίασης του EO Browser](https://www.sentinel-hub.com/explore/eobrowser/)\n[Οδηγός χρήσης του EO Browser](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":["#### Γρήγορη επισκόπηση των λειτουργιών του EO Browser\n\nΤο EO Browser περιλαμβάνει ένα πλήρες αρχείο Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, Landsat 5, 7 και 8 της ESA, Landsat 8 με παγκόσμια κάλυψη και Envisat Meris, MODIS, Proba-V και GIBS προϊόντα σε ένα μοναδικό σημείο πρόσβασης και καθιστά δυνατή την περιήγηση και τη σύγκριση εικόνων πλήρους ανάλυσης από αυτές τις πηγές. Για να το πετύχετε αυτό, μεταβαίνετε στην περιοχή που σας ενδιαφέρει, επιλέγετε πηγές δεδομένων, χρονικό διάστημα και νεφοκάλυψη και ελέγχετε τα δεδομένα που προκύπτουν.\n\nΜπορείτε να συνεχίσετε αυτόν τον οδηγό εκμάθησης κάνοντας κλικ στο κουμπί \"Επόμενο\" ή μπορείτε να τον τερματίσετε. Κάνοντας κλικ στο εικονίδιο πληροφοριών στην επάνω δεξιά γωνία μπορείτε πάντα να συνεχίσετε τον οδηγό εκμάθησης σε περίπτωση που το κλείσατε κατά λάθος ή επιθυμείτε να κάνετε κάποιες δοκιμές."]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":["** Οι συνδεδεμένοι χρήστες ** μπορούν να χρησιμοποιήσουν τα προσαρμοσμένα θέματα τους,\nνα αποθηκεύσουν και να φορτώσουν πινέζες, να δημιουργήσουν μια ιστορία με πινέζες, \nνα μετρήσουν τις αποστάσεις, να δημιουργήσουν timelapse και\nνα χρησιμοποιήσουν τη σύνθετη λήψη εικόνων.\n\nΓια να δημιουργήσετε έναν δωρεάν λογαριασμό, απλώς κάντε κλικ [εδώ]\nή εντός της εφαρμογής ** Σύνδεση ** και στη συνέχεια \\\"Εγγραφή\\\"."]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":["Στην καρτέλα ** Εξερευνηση ** μπορείτε να:\n\n- Επιλέξετε ένα θέμα **. **\n- ** Αναζητήσετε ** δεδομένα.\n- Προβάλετε ** Στιγμιότυπα ** του θέματος\n\nΤο αναπτυσσόμενο μενού ** Θέμα ** σας προσφέρει διαφορετικά προκαθορισμένα θέματα, καθώς και δικά σας διαμορφωμένα στιγμιότυπα, εάν είστε συνδεδεμένοι. Για να δημιουργήσετε ένα στιγμιότυπο, κάντε κλικ στο\nτο εικονίδιο των ρυθμίσεων και συνδεθείτε με τα ίδια διαπιστευτήρια που χρησιμοποιήσατε για το EO Browser.\n\nΣτην ενότητα ** Αναζήτηση ** μπορείτε να ορίσετε κριτήρια αναζήτησης:\n - Επιλέξτε από ποιους δορυφόρους θέλετε να λαμβάνετε τα δεδομένα επιλέγοντας τα κατάλληλα πλαίσια ελέγχου.\n - Ορίστε πρόσθετες επιλογές όπου απαιτείται, για παράδειγμα νεφοκάλυψη, με τη μπάρα ρύθμισης.\n - Επιλέξτε το χρονικό εύρος πληκτρολογώντας την ημερομηνία ή επιλέξτε την ημερομηνία από το ημερολόγιο.\n\nΜπορείτε να διαβάσετε για τους δορυφόρους κάνοντας κλικ στο εικονίδιο της ερώτησης\n δίπλα στο όνομα της πηγής δεδομένων.\n\nΜόλις πατήσετε Αναζήτηση θα λάβετε μια λίστα αποτελεσμάτων. Για κάθε αποτέλεσμα εμφανίζεται \nμια εικόνα προεπισκόπησης και πληροφορίες σχετικά με την πηγή δεδομένων. Για ορισμένες πηγές δεδομένων το εικονίδιο του συνδέσμου είναι επίσης ορατό για κάθε αποτέλεσμα.\nΚάνοντας κλικ σε αυτό εμφανίζεται σύνδεσμος προς την αρχική εικόνα στο EO Cloud ή το SciHub. Κάνοντας κλικ στο Οπτικοποιήση θα ανοίξει η καρτέλα ** Οπτικοποιήση ** για το επιλεγμένο αποτέλεσμα.\n\nΣτην ενότητα ** Επισήμανση ** θα βρείτε προεπιλεγμένες τοποθεσίες οι οποίες συνδέονται με το επιλεγμένο θέμα."]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":["Στην καρτέλα Οπτικοποίηση μπορείτε να επιλέξετε διαφορετικούς προκαθορισμένους ή προσαρμοσμένους συνδυασμούς καναλιών για να οπτικοποιήσετε δεδομένα με το επιθυμητό αποτέλεσμα.\n\nΜερικές από τις συνήθεις επιλογές είναι:\n- ** True Colour ** - Οπτική ερμηνεία της κάλυψης γης.\n- ** False Color ** - Οπτική ερμηνεία της βλάστησης.\n- ** NDVI ** - Κανονικοποιημένος δείκτης βλάστησης.\n- ** Moisture index ** - Δείκτης υγρασίας\n- ** SWIR ** - Δείκτης υπερύθρων μικρού κύματος.\n- ** NDWI ** - Κανονικοποιημένος δείκτης νερού.\n- ** NDSI ** - Κανονικοποιημένος δείκτης χιονιού.\n\nΟι περισσότερες απεικονίσεις συνοδεύονται από μια περιγραφή και υπόμνημα, που μπορείτε να δείτε κάνοντας κλικ στο εικονίδιο\nτης επέκτασης .\n \nΓια τις περισσότερες πηγές δεδομένων είναι διαθέσιμη η επιλογή ** Προσαρμοσμένος αλγόριθμος **. Κάντε κλικ σε αυτό για να επιλέξετε προσαρμοσμένους\nσυνδυασμούς καναλιών, συνδυασμούς δεικτών ή να γράψτε το δικό σας αλγόριθμο ταξινόμησης για την οπτικοποίηση των δεδομένων. Μπορείτε επίσης να\nχρησιμοποιήστε προσαρμοσμένους αλγόριθμους, οι οποίοι είναι αποθηκευμένοι, είτε στο Google Drive, είτε στο GitHub είτε στο [Αποθετήριο προσαρμοσμένων αλγορίθμων](https://custom-scripts.sentinel-hub.com/).\nΕπικολλήστε τη διεύθυνση URL του αλγορίθμου στο πλαίσιο κειμένου στο πεδίο της σύνθετης επεξεργασίας αλγορίθμου και κάντε κλικ στην επιλογή Ανανέωση.\n \nΜπορείτε να αλλάξετε την ημερομηνία απευθείας στην καρτέλα Οπτικοποίηση , χωρίς να επιστρέψετε στην καρτέλα ** Εξερεύνηση **. Πληκτρολογήστε ή επιλέξτε την επιθυμητή ημερομηνία από το ημερολόγιο .\n\nΠάνω από τις απεικονίσεις που δημιουργείτε εμφανίζεται γραμμή πρόσθετων εργαλείων. Σημειώστε ότι η διαθεσιμότητά τους εξαρτάται από την πηγή δεδομένων.\n- ** Επίπεδο πινεζών ** για να το αποθηκεύσετε στην εφαρμογή για μελλοντική χρήση - κάνοντας κλικ στο εικονίδιο με τις πινέζες .\n- Επιλέξτε ** προχωρημένες επιλογές ** όπως τη μέθοδο δειγματοληψίας ή εφαρμόστε διαφορετικά ** εφέ ** όπως αντίθεση (gain) και φωτεινότητα (gamma) - κάνοντας κλικ στο εικονίδιο των ρυθμίσεων εφέ .\n- Προσθέστε ένα επίπεδο στην καρτέλα ** Σύγκριση ** για μελλοντική σύγκριση - κάνοντας κλικ στο εικονίδιο σύγκρισης .\n- ** Εστίαση ** στο κέντρο της πινακίδας - κάνοντας κλικ στο σταυρόνημα .\n- Εναλλαγή ** ορατότητας επιπέδου ** - κάνοντας κλικ στο εικονίδιο ορατότητας .\n- ** Μοιραστείτε ** την οπτικοποίησή σας στα μέσα κοινωνικής δικτύωσης - κάνοντας κλικ στο εικονίδιο κοινής χρήσης ."]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":["Στην καρτέλα ** Σύγκριση ** θα βρείτε όλες τις απεικονίσεις που προσθέσατε μέσω του στη ** Σύγκριση **.\n\nΥπάρχουν δύο συγκριτικές λειτουργίες:\n - ** Αδιαφάνεια ** (Σύρετε τη μπάρα ρύθμισης αριστερά ή δεξιά για να δημιουργήσετε διαφάνεια μεταξύ των συγκρινόμενων εικόνων)\n - ** Διαχωρισμός ** (Σύρετε τη μπάρα ρύθμισης αριστερά ή δεξιά για να μετακινήσετε το όριο μεταξύ των συγκρινόμενων εικόνων)\n\nΜπορείτε να προσθέσετε όλες τις πινέζες στο εργαλείο της σύγκρισης σύγκρισης χρησιμοποιώντας ** Προσθήκη όλων των πινέζων ** ή να καταργήσετε όλες τις απεικονίσεις\nαπό την καρτέλα ** Σύγκριση ** με το ** Κατάργηση όλων **."]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":["Η καρτέλα ** Πινέζες ** περιέχει τα καρφιτσωμένα (αγαπημένα / αποθηκευμένα) αντικείμενά σας. Τα αντικείμενα αυτά περιέχουν πληροφορίες\nσχετικά με την τοποθεσία, την πηγή δεδομένων και το συγκεκριμένο επίπεδο, την εστίαση και το χρονικό προσδιορισμό.\n\nΓια κάθε πινέζα έχετε αρκετές επιλογές αλληλεπίδρασης:\n\n- Αλλαγή ** παραγγελίας ** - κάνοντας κλικ στο εικονίδιο μετακίνησης\n\n \n \n\nστην επάνω αριστερή γωνία της πινέζας, σύροντας προς τα πάνω ή προς τα κάτω στη λίστα.\n- ** Μετονομασία ** - κάνοντας κλικ στο εικονίδιο με το μολύβι δίπλα στο όνομα του πινέλου.\n- Προσθήκη στην καρτέλα ** Σύγκριση ** - κάνοντας κλικ στο εικονίδιο σύγκρισης \n- Εισαγάγετε μια περιγραφή ** ** - κάνοντας κλικ στο εικονίδιο επέκτασης .\n- ** Κατάργηση ** - κάνοντας κλικ στο εικονίδιο κατάργησης .\n- ** Ζουμ ** στην τοποθεσία του πείρου - κάνοντας κλικ στο Lat / Lon.\n\nΣτη γραμμή πάνω από όλες τις πινέζες έχετε διαφορετικές επιλογές που ισχύουν:\n- Δημιουργήστε τη δική σας ιστορία από καρφίτσες - κάνοντας κλικ στο ** Story **.\n- Μοιραστείτε τις πινέζες σας με άλλους μέσω ενός συνδέσμου - κάνοντας κλικ στο ** Κοινή χρήση **.\n- Εξαγωγή πινεζών ως αρχείο JSON - κάνοντας κλικ στο ** Εξαγωγή **.\n- Εισαγωγή πινεζών από ένα αρχείο JSON - κάνοντας κλικ στο ** Εισαγωγή **.\n- Διαγράψτε όλες τις πινέζες - κάνοντας κλικ στο ** Διαγραφή **."]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":["Αναζητήστε μια τοποθεσία είτε μετακινώντας το χάρτη με το ποντίκι είτε πληκτρολογώντας την τοποθεσία στο πεδίο της\nαναζήτησης."]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":["Εδώ μπορείτε να επιλέξετε το βασικό επίπεδο και τα συμπληρωματικά επίπεδα (οδικό δίκτυο, όρια, ετικέτες) που εμφανίζονται στο χάρτη."]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":["Στο σημείο αυτό μπορείτε να κάνετε εναλλαγή μεταξύ της λειτουργίας ** κανονική ** και ** εκπαίδευση **. Η λειτουργία ** Εκπαίδευση ** προσφέρει μια ελαφρώς απλοποιημένη έκδοση της εφαρμογής.\nΕίναι επίσης προσβάσιμο απευθείας μέσω του [αποκλειστικού URL](https://apps.sentinel-hub.com/eo-browser-education/)."]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":["Μπορείτε να δείτε τον οδηγό εκμάθησης ανά πάσα στιγμή κάνοντας κλικ σε αυτό το εικονίδιο πληροφοριών\n\n\n."]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":["Αυτό το εργαλείο σας επιτρέπει να σχεδιάσετε ένα πολύγωνο στο χάρτη και να εμφανίσετε την έκτασή του.\n\nΌλα τα επίπεδα που επιστρέφουν μία τιμή (όπως NDVI, Moisture index, NDWI,…) υποστηρίζουν την προβολή του\nδείκτη για την επιλεγμένη περιοχή στο βάθος του χρόνου. Κάνοντας κλικ στο εικονίδιο γραφήματος θα\nεμφανίστε τα διαγράμματα. Μπορείτε να καταργήσετε το πολύγωνο κάνοντας κλικ στο εικονίδιο κατάργησης .\n\nΜπορείτε επίσης να ανεβάσετε ένα αρχείο KML / KMZ, GPX ή GEOJSON / JSON με γεωμετρία πολυγώνου.\n\nΤο εικονίδιο δύο φύλλων σας επιτρέπει να αντιγράψετε τις συντεταγμένες πολυγώνου ως GEOJSON. Το σταυρόνημα \nκεντράρει το χάρτη στο πολύγωνο που σχεδιάστηκε.\n\nΟι εξαγόμενες εικόνες θα περικοπούν στην περιοχή ενδιαφέροντος για αναλυτικούς σκοπούς."]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":["Με αυτό το εργαλείο μπορείτε να επισημάνετε ένα σημείο στο χάρτη.\n\nΜπορείτε επίσης να δείτε στατιστικά δεδομένα για ορισμένα επίπεδα κάνοντας κλικ στο εικονίδιο γραφήματος\n.\nΜπορείτε να καταργήσετε την επισήμανση του σημείου κάνοντας κλικ στο εικονίδιο κατάργησης .\n"]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":["Με αυτό το εργαλείο μπορείτε να μετρήσετε αποστάσεις και εμβαδά στο χάρτη.\n\nΚάθε κλικ του ποντικιού δημιουργεί ένα νέο σημείο στη διαδρομή. Για να σταματήσετε να προσθέτετε σημεία, πατήστε το πλήκτρο Esc
\nή κάντε διπλό κλικ στο χάρτη.\nΜπορείτε να καταργήσετε τη μέτρηση κάνοντας κλικ στο εικονίδιο κατάργησης ."]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":["Με αυτό το εργαλείο μπορείτε να πραγματοποιήσετε τη λήψη μιας εικόνας οπτικοποιημένων δεδομένων για την εμφανιζόμενη τοποθεσία. Μπορείτε να διαλέξετε\nνα εμφανίσετε το υπόμνημα καθώς και να προσθέσετε περιγραφή.\nΕνεργοποιώντας την αναλυτική λειτουργία, μπορείτε να επιλέξετε ανάμεσα σε διάφορους μορφότυπους εικόνας, αναλύσεις και\nσυστήματα συντεταγμένων. Μπορείτε επίσης να επιλέξετε πολλά επίπεδα και να τα μεταφορτώσετε σε ένα αρχείο .zip
.\n\nΚάντε κλικ στο κουμπί λήψης\n Λήψη \nκαι θα αρχίσει η λήψη των εικόνων σας. Η διαδικασία μπορεί να διαρκέσει μερικά δευτερόλεπτα, ανάλογα με την επιλεγμένη\nανάλυση και τον αριθμό των επιλεγμένων επιπέδων.\n\nΠριν από τη λήψη, μπορείτε να ορίσετε μια περιοχή ενδιαφέροντος (AOI) κάνοντας κλικ στο εργαλείο επιλογής περιοχής.\nΤα δεδομένα σας θα περικοπούν για να ταιριάζουν με αυτήν την περιοχή."]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":["Φτάσατε στο τέλος του οδηγού εκμάθησης. Εάν έχετε οποιαδήποτε άλλη ερώτηση, μη διστάσετε να μας ρωτήσετε στο [φόρουμ](https://forum.sentinel-hub.com/)\nή επικοινωνήστε μαζί μας [μέσω email](mailto: info@sentinel-hub.com? Subject = EO% 20Browser% 20Feedback).\n\n\nΕάν θέλετε να δείτε τον οδηγό στο μέλλον, μπορείτε να κάνετε κλικ στο εικονίδιο πληροφοριών\n\n\n\nστην επάνω δεξιά γωνία."]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":["#### Γρήγορη επισκόπηση των λειτουργιών του EO Browser\n\nΕάν έχετε μικρή οθόνη, μεταβείτε [εδώ](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) για να δείτε τον οδηγό χρήσης.\n\nΜπορείτε ανά πάσα στιγμή να δείτε ξανά αυτές τις πληροφορίες κάνοντας κλικ στο εικονίδιο πληροφοριών\n\n\n\nστην επάνω δεξιά γωνία.\n\n#### Άλλοι πόροι\n- [Σελίδα παρουσίασης EO Browser](https://www.sentinel-hub.com/explore/eobrowser/)\n- [Αναβαθμίσεις του EO Browser από το καλοκαίρι του 2018 - βίντεο](https://www.youtube.com/embed/m3pron0C0kE)"]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":["Τι είναι ο EO Browser;"]},"User Account":{"msgid":"User Account","msgstr":["Λογαριασμός χρήστη"]},"Discover Tab":{"msgid":"Discover Tab","msgstr":["Καρτέλα Εξερεύνησης"]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":["Καρτέλα Οπτικοποίησης"]},"Compare Tab":{"msgid":"Compare Tab","msgstr":["Καρτέλα Συγκριτικής Ανάλυσης"]},"Pins Tab":{"msgid":"Pins Tab","msgstr":["Καρτέλα με Πινέζες"]},"Search Places":{"msgid":"Search Places","msgstr":["Αναζήτηση Τοποθεσιών"]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":["Επίπεδα και Υπόβαθρα"]},"Education Mode":{"msgid":"Education Mode","msgstr":["Λειτουργία Εκπαίδευσης"]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":["Πληροφορίες και Οδηγός Εκμάθησης"]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":["Σχεδίαση Περιοχής Ενδιαφέροντος (AOI)"]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":["Επισήμανση Σημείου Ενδιαφέροντος"]},"Measure Distances":{"msgid":"Measure Distances","msgstr":["Μέτρηση Αποστάσεων"]},"Download Image":{"msgid":"Download Image","msgstr":["Λήψη Εικόνας"]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":["Δημιουργία Κινούμενης Εικόνας Timelapse"]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":["Καλή Περιήγηση!"]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":["Καλώς ορίσατε στον EO Browser!"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":["Κανάλι 1 - Κίτρινη ουσία και χρωστικές ουσίες - 412,5 nm"]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":["Κανάλι 3 - Χλωροφύλλη και άλλες χρωστικές - 490 nm"]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":["Κανάλι 4 - Αιωρούμενο ίζημα, κόκκινη παλίρροια - 510 nm"]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":["Κανάλι 5 - Ελάχιστη απορρόφηση χλωροφύλλης - 560 nm"]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":["Κανάλι 6 - Αιωρούμενο ίζημα - 620 nm"]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":["Κανάλι 7 - Απορρόφηση χλωροφύλλης και βάση αναφοράς φθορισμού - 665 nm"]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":["Κανάλι 8 - Μέγιστος φθορισμός χλωροφύλλης - 681 nm"]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":["Κανάλι 9 - Βάση αναφοράς φθορισμού, Ατμοσφαιρική διόρθωση - 709 nm"]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":["Κανάλι 10 - Βλάστηση, σύννεφα - 753 nm"]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":["Κανάλι 12 - Διορθώσεις ατμόσφαιρας - 779 nm"]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":["Κανάλι 13 - Βλάστηση, υδρατμοί - 865 nm"]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":["Κανάλι 14 - Διορθώσεις ατμόσφαιρας - 779 nm"]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":["Κανάλι 15 - Υδρατμοί, ξηρά - 900 nm"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Κανάλι 1 - Μπλέ - 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Κανάλι 2 - Πράσινο - 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Κανάλι 3 - Κόκκινο - 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Κανάλι 4 - Εγγύς Υπέρυθρο NIR - 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Κανάλι 5 - Μικροκυματικό Υπέρυθρο SWIR-1 - 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Κανάλι 7 - Μικροκυματικό Υπέρυθρο SWIR-2 - 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Κανάλι 8 - Παγχρωματικό - 520-900 nm"]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":["Κανάλι 1 - Παράκτια Ζώνη/Αερολύματα - 433-453 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Κανάλι 2 - Μπλε - 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Κανάλι 3 - Πράσινο - 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Κανάλι 4 - Κόκκινο - 630-680 nm"]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":["Κανάλι 5 - Εγγύς Υπέρυθρο NIR - 845-885 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Κανάλι 6 - Μικροκυματικό Υπέρυθρο SWIR-1 - 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Κανάλι 7 - Μικροκυματικό Υπέρυθρο SWIR-2 - 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Κανάλι 8 - Παγχρωματικό - 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Κανάλι 9 - Νέφος Cirrus - 1360-1390 nm"]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":["Landsat 5 (αρχείο ESA)"]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":["Landsat 7 (αρχείο ESA)"]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":["Landsat 8 (αρχείο ESA)"]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":["Landsat 8 (αρχείο USGS )"]},"Red band":{"msgid":"Red band","msgstr":["Κόκκινο κανάλι"]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":["841 - 876 nm (NIR)"]},"Blue band":{"msgid":"Blue band","msgstr":["Μπλε κανάλι"]},"Green band":{"msgid":"Green band","msgstr":["Πράσινο κανάλι"]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":["1230 - 1250 nm"]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":["1628 - 1652 nm"]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":["2105 - 2155 nm"]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":["Κανάλι 1 - Παράκτια ζώνη αερολύματος - 443 nm"]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":["Κανάλι 2 - Μπλε - 490 nm"]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":["Κανάλι 3 - Πράσινο - 560 nm"]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":["Κανάλι 4 - Κόκκινο - 665 nm"]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":["Κανάλι 5 - Βλάστηση Red Edge - 705 nm"]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":["Κανάλι 6 - Βλάστηση Red Edge - 740 nm"]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":["Κανάλι 7 - Βλάστηση Red Edge - 783 nm"]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":["Κανάλι 8 - Εγγύς Υπέρυθρο NIR - 842 nm"]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":["Κανάλι 9 - Υδρατμοί - 945 nm"]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":["Κανάλι 10 - Μικροκυματικό υπέρυθρο SWIR - Νέφος Cirrus - 1375 nm"]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":["Κανάλι 11 - Μικροκυματικό υπέρυθρο SWIR - 1610 nm"]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":["Κανάλι 12 - Μικροκυματικό υπέρυθρο SWIR - 2190 nm"]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":["Κανάλι 8A - Βλάστηση Red Edge - 865 nm"]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (με ατμοσφαιρική διόρθωση)"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":["Κανάλι 1 - Διόρθωση αερολύματος, βελτιωμένη ανάκτηση συστατικών νερού - 400 nm"]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":["Κανάλι 2 - Κίτρινη ουσία και χρωστικές ουσίες (θολερότητα) -412 nm"]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":["Κανάλι 3 - Μέγιστη απορρόφηση Chl, βιογεωχημεία, βλάστηση - 442,5 nm"]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":["Κανάλι 4 - Υψηλή συγκέντρωση Chl, άλλες χρωστικές - 490 nm"]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":["Κανάλι 5 - Χλωροφύλλη, ιζήματα, θολερότητα, κόκκινη παλίρροια - 510 nm"]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":["Κανάλι 6 - Σημείο αναφοράς για την ύπαρξη χλωροφύλλης (ελάχιστη χλωροφύλλη) - 560 nm"]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":["Κανάλι 7 - Ίζημα - 620 nm"]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":["Κανάλι 8 - Chl (2η απόλυτη μέγιστη τιμή), Ίζημα, κίτρινη ουσία / βλάστηση - 665 nm"]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":["Κανάλι 9 - Για τη βέλτιστη εκτίμηση του φθορισμού και την εξαλειψη της επιδρασης του φασματικού χαμόγελου σε συνδυασμό με το κανάλι 8 (665 nm) και το κανάλι 10 (681,25 nm) - 673,75 nm"]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":["Κανάλι 10 - Κορυφή φθορισμού Chl, Red Edge - 681,25 nm"]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":["Κανάλι 11 - Βασική γραμμή φθορισμού Chl, μετάβαση κόκκινου άκρου - 708,75 nm"]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":["Κανάλι 12 - απορρόφηση O2 / σύννεφα , βλάστηση - 753,75 nm"]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":["Κανάλι 13 - Απορρόφηση O2 / Διόρθωση αερολυμάτων - 761,25 nm"]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":["Κανάλι 14 - Ατμοσφαιρική διόρθωση - 764.375 nm"]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":["Κανάλι 15 - O2A που χρησιμοποιείται για πίεση στην κορυφή του νέφος, φθορισμός στην ξηρά - 767,5 nm"]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":["Κανάλι 16 -Ατμοσφαιρική διόρθωση/διόρθωση αερολυμάτων. - 778.75 nm"]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":["Κανάλι 17 - Ατμοσφαιρική διόρθωση/διόρθωση αερολυμάτων, σύννεφα, συμπροσαρμογή εικόνας βάσει εικονοστοιχείων. - 778.75 nm"]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":["Κανάλι 18 - Ζώνη αναφοράς απορρόφησης υδρατμών. Κοινή ζώνη αναφοράς με όργανο SLSTR. Παρακολούθηση βλάστησης - 885 nm"]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":["Κανάλι 19 - Απορρόφηση υδρατμών απορρόφησης / παρακολούθηση βλάστησης (μέγιστη ανάκλαση) - 900 nm"]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":["Κανάλι 20 - Απορρόφηση υδρατμών, ατμοσφαιρική διόρθωση/διόρθωση υδρατμών. - 940 nm"]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":["Κανάλι 21 - Ατμοσφαιρική διόρθωση/διόρθωση υδρατμών - 1020 nm"]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":["Κανάλι F1 - Εκπομπές πυρκαγιάς στο Θερμικό IR - Ενεργή πυρκαγιά - 3742,00 nm"]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":["Κανάλι F2 - Εκπομπές πυρκαγιάς στο Θερμικό IR - Ενεργή πυρκαγιά - 10854.00 nm"]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":["Κανάλι S1 - VNIR - Νέφος, παρακολούθηση βλάστησης, αερολύματα - 554,27 nm"]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":["Κανάλι S2 - VNIR - NDVI, παρακολούθηση βλάστησης, αεροζόλ - 659,47 nm"]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":["Κανάλι S3 - VNIR - NDVI, επισήμανση νέφους, συμπροσαρμογή εικόνας βάσει εικονοστοιχείων - 868.00 nm"]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":["Κανάλι S4 - SWIR - Ανίχνευση νέφους Cirrus στην ξηρά - 1374,80 nm"]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":["Κανάλι S5 - SWIR - Νέφος, πάγος, χιόνι, παρακολούθηση βλάστησης - 1613,40 nm"]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":["Κανάλι S6 - SWIR - Κατάσταση βλάστησης και νέφους - 2255,70 nm"]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":["Κανάλι S7 - Θερμικό υπέρυθρο IR - SST, LST, ενεργή φωτιά - 3742,00 nm"]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":["Κανάλι S8 - Θερμικό υπέρυθρο IR - SST, LST, ενεργή φωτιά - 10854.00 nm"]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":["Κανάλι S9 - Θερμικό υπέρυθρο IR - SST, LST - 12022.50 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Ανακλαστικότητα"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Θερμοκρασία φωτεινότητας"]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":["Με βάση το συνδυασμό των καναλιών 4, 3, 2"]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":["Με βάση το συνδυασμό καναλιών (B04-B03) / (B04 + B03)"]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":["Με βάση το συνδυασμό των καναλιών 5, 4, 3"]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":["Με βάση τα κανάλια του φυσικού έγχρωμου σύνθετου 4, 3, 2 και το παγχρωματικό κανάλι 8"]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":["Με βάση το συνδυασμό καναλιών (B05-B04) / (B05 + B04)"]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":["VV - γραμμικό gamma0 - ορθοανηγμένο"]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":["VV - γραμμικό gamma0 - μη-ορθοανηγμένο"]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":["VH - γραμμικό gamma0 - ορθοανηγμένο"]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":["Με βάση το συνδυασμό των καναλιών 3, 2, 1"]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":["VH - γραμμικό gamma 0 - - μη-ορθοανηγμένο"]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":["Έγχρωμη εικόνα με την αντιστοίχιση των αρχικών καναλιών στα κανάλια RGB. Τιμή [RGB] = [VV, 2 VH, VV / VH / 100.0] - γραμμικό gamma0 - ορθοανηγμένο"]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":["VV - decibel gamma0 [-20,0] - ορθοανηγμένο"]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":["VH - decibel gamma0 [-20,0] - ορθοανηγμένο"]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":["Επιστρέφει ένα σύνθετο (VH, VV, VH-VV)"]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":["HH - γραμμικό gamma0 - ορθοανηγμένο"]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":["HV - γραμμικό gamma0 - ορθοανηγμένο"]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":["Έγχρωμη εικόνα με την αντιστοίχιση των αρχικών καναλιών στα κανάλια RGB. Τιμή [RGB] = [HH, 2 HV, HH / HV / 100.0] - γραμμικό gamma0 - ορθοανηγμένο"]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":["HH - decibel gamma0 [-20,0] - ορθοανηγμένο"]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":["HV - decibel gamma0 [-20,0] - ορθοανηγμένο"]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":["HH - linear gamma0 - μη-ορθοανηγμένο"]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":["Με βάση τα κανάλια 4,3,2"]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":["Με βάση τα κανάλια 8,4,3"]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":["Με βάση τα κανάλια 12,11,4"]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Με βάση το συνδυασμό καναλιών (B8 - B4) / (B8 + B4)"]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":["Με βάση το συνδυασμό καναλιών (B8A - B11) / (B8A + B11)"]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":["Με βάση τα κανάλια 12,8A, 4"]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":["Με βάση το συνδυασμό καναλιών (B3 - B8) / (B3 + B8)"]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":["Με βάση το συνδυασμό καναλιών (B3 - B11) / (B3 + B11). Τιμές άνω των 0,42 υποδεικνύουν την ύπαρξη χιονιού"]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":["Ταξινόμηση δεδομένων Sentinel 2 ως αποτέλεσμα του αλγορίθμου ταξινόμησης της ESA."]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":["Δείκτης Αερολυμάτων UV από 380 και 340 nm"]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":["Με βάση το συνδυασμό καναλιών (B3 - B11) / (B3 + B11)"]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":["Δείκτης OLCI Terrestrial Chlorophyll, βάσει συνδυασμού καναλιών (B12 - B11) / (B11 - B10)"]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":["Δείκτης Αερολυμάτων UV από 388 και 354 nm"]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":["Μέση τιμή του λόγου ανάμιξης ξηρού αέρα με στήλη μεθανίου"]},"Cloud base height":{"msgid":"Cloud base height","msgstr":["Ύψος βάσης νέφους"]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":["Πίεση βάσης νέφους"]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":["Αποτελεσματικό ραδιομετρικό κλάσμα νέφους"]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":["Οπτικό πάχος νέφους"]},"Cloud top height":{"msgid":"Cloud top height","msgstr":["Ύψος κορυφής νέφους"]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":["Πίεση κορυφής νέφους"]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":["Συνολική στήλη μονοξειδίου του άνθρακα"]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":["Τροποσφαιρική κάθετη στήλη φορμαλδεΰδης"]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":["Τροποσφαιρική στήλη διοξειδίου του αζώτου"]},"Ozone total column":{"msgid":"Ozone total column","msgstr":["Συνολική στήλη όζοντος"]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":["Συνολική στήλη διοξειδίου του θείου"]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":["Με βάση τα κανάλια 2, 1, 4"]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":["Με βάση το συνδυασμό καναλιών (B02 - B01) / (B02 + B01)"]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":["Με βάση το συνδυασμό καναλιών (B02 - B05) / (B02 + B05)"]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":["Με βάση το συνδυασμό καναλιών (B06 - B07) / (B06 + B07)"]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":["Με βάση τα κανάλια 1, 4, 3"]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":["Με βάση το συνδυασμό των καναλιών 7, 5, 3"]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":["(B09 - B08)/(B09 + B08)"]},"B09 / B08":{"msgid":"B09 / B08","msgstr":["B09 / B08"]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":["Με βάση το συνδυασμό των καναλιών 13, 5, 2"]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":["Με βάση τα κανάλια 13, 4, 1"]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":["Με βάση τα κανάλια 13, 5, 2"]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":["Με βάση το συνδυασμό καναλιών (B13-B07) / (B13 + B07)"]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":["Δείκτης χερσαίας χλωροφύλλης"]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V 10-ημερήσια σύνθεση\nΚορυφή κομοστέγης (Ατμοσφαιρικά διορθωμένη)\nχρονική ανάλυση: 10-ημερησίως\nΑνάλυση: 333M (μέγεθος εικονοστοιχείου)"]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V ημερήσια σύνθεση\nΚορυφή της ατμόσφαιρας\nχρονική ανάλυση: καθημερινά\nΑνάλυση: 333M (μέγεθος pixel)"]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-ημερήσια σύνθεση\nΚορυφή της ατμόσφαιρας\nχρονική ανάλυση: 5-ημέρες\nΑνάλυση: 100M (μέγεθος pixel)"]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V Καθημερινή σύνθεση\nΚορυφή κομοστέγης (Ατμοσφαιρικά διορθωμένη)\nχρονική ανάλυση: καθημερινά\nΑνάλυση: 333M (μέγεθος pixel)"]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":["OrbitTracks_Aqua_Descending"]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-ημερήσια σύνθεση\nΚορυφή κομοστέγης (Ατμοσφαιρικά διορθωμένη)\nχρονική ανάλυση: 5-ημέρες\nΑνάλυση: 100M (μέγεθος εικονοστοιχείου)"]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":["OrbitTracks_Aqua_Ascending"]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":["OrbitTracks_Aura_Ascending"]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":["OrbitTracks_Aura_Descending"]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":["OrbitTracks_CloudSat_Ascending"]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":["OrbitTracks_Calipso_Ascending"]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":["OrbitTracks_Calipso_Descending"]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":["OrbitTracks_CloudSat_Descending"]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":["OrbitTracks_CYGNSS_Ascending"]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":["OrbitTracks_CYGNSS_Descending"]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":["OrbitTracks_GCOM-C_Ascending"]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":["OrbitTracks_GCOM-C_Descending"]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":["OrbitTracks_GCOM-W1_Ascending"]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":["OrbitTracks_GCOM-W1_Descending"]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":["OrbitTracks_GOSAT-2_Ascending"]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":["OrbitTracks_GOSAT-2_Descending"]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":["OrbitTracks_GOSAT_Ascending"]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":["OrbitTracks_GOSAT_Descending"]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":["OrbitTracks_GPM_Ascending"]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":["OrbitTracks_GPM_Descending"]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":["OrbitTracks_ICESAT-2_Ascending"]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":["OrbitTracks_ICESAT-2_Descending"]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":["OrbitTracks_ISS_Ascending"]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":["OrbitTracks_ISS_Descending"]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":["OrbitTracks_Landsat-7_Ascending"]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":["OrbitTracks_Landsat-7_Descending"]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":["OrbitTracks_Landsat-8_Ascending"]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":["OrbitTracks_METOP-A_Ascending"]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":["OrbitTracks_METOP-B_Descending"]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":["OrbitTracks_Landsat-8_Descending"]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":["OrbitTracks_METOP-C_Ascending"]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":["OrbitTracks_METOP-C_Descending"]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":["OrbitTracks_NOAA-20_Ascending"]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":["OrbitTracks_NOAA-20_Descending"]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":["OrbitTracks_METOP-B_Ascending"]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":["OrbitTracks_OCO-2_Ascending"]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":["OrbitTracks_OCO-2_Descending"]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":["OrbitTracks_SAOCOM1-A_Ascending"]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":["OrbitTracks_SAOCOM1-A_Descending"]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":["OrbitTracks_Sentinel-1A_Ascending"]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":["OrbitTracks_Sentinel-1B_Ascending"]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":["OrbitTracks_Sentinel-1A_Descending"]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":["OrbitTracks_METOP-A_Descending"]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":["OrbitTracks_Sentinel-1B_Descending"]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":["OrbitTracks_Sentinel-2A_Ascending"]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":["OrbitTracks_Sentinel-2A_Descending"]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":["OrbitTracks_Sentinel-2B_Ascending"]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":["OrbitTracks_Sentinel-2B_Descending"]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":["OrbitTracks_Sentinel-5P_Ascending"]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":["OrbitTracks_Sentinel-5P_Descending"]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":["OrbitTracks_SMAP_Ascending"]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":["OrbitTracks_SMAP_Descending"]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":["OrbitTracks_Suomi_NPP_Descending"]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":["OrbitTracks_Suomi_NPP_Ascending"]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":["OrbitTracks_Terra_Descending"]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":["OrbitTracks_Terra_Ascending"]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":["Με βάση τα κανάλια 4, 3, 2, ενισχυμένα από τα κανάλια 12 και 11."]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":["Με βάση τα κανάλια B07, B06, B04"]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Με βάση το συνδυασμό καναλιών (B8 - B4) / (B8 + B4)"]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":["Με βάση το θερμικό κανάλι 10"]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":["Με βάση τα κανάλια B12, B11, B8A"]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":["Με βάση το συνδυασμό καναλιών (B08 - B12) / (B08 + B12)"]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":["Ενισχυμένη απεικόνιση φυσικού χρώματος"]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":["Με βάση το συνδυασμό των καναλιών 8, 6, 4"]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":["Ενισχυμένος δείκτης βλάστησης"]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":["Με βάση το συνδυασμό: BSI, B08, B11"]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":["Ταξινόμηση NDMI για άρδευση"]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":["Με βάση τα κανάλια B11, B08, B02"]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":["Ψευδο-έγχρωμο σύνθετο 13, 5, 2"]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":["Με βάση τον συνδυασμό καναλιών (B13 - B07) / (B13 + B07)"]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":["Με βάση τα κανάλια 12,8,2"]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":["Με βάση τα κανάλια 4, 3, 2"]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":["Με βάση τα κανάλια 8, 4, 3"]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":["Με βάση τα κανάλια 12, 8, 2"]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":["Με βάση τα κανάλια 8, 11, 12"]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":["Ιζηματοποίηση νερού και περιεκτικότητα σε χλωροφύλλη"]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":["Με βάση τα κανάλια 12, 8A, 4"]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":["Με βάση το NDSI"]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":["Με βάση το συνδυασμό των καναλιών 11, 8, 2"]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":["Με βάση τα κανάλια B12, B11, B04"]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":["Με βάση τα κανάλια 4, 3, 2"]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":["Ατμοσφαιρικά ανθεκτικός δείκτης βλάστησης"]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":["Δείκτης βλάστησης προσαρμοσμένος στο έδαφος"]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":["# Θερμικό υπέρυθρο κανάλι εκπομπής φωτιάς IR\n\nΤο Sentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) διαθέτει δύο ειδικά κανάλια (F1 και F2) που στοχεύουν στην ανίχνευση θερμοκρασίας στην επιφάνεια της γης (LST). Το κανάλι F2, με κεντρικό μήκος κύματος 10854 nm καταγράφει μετρήσεις στο θερμικό υπέρυθρο, ή TIR. Είναι πολύ χρήσιμο για παρακολούθηση συμβάντων πυρκαγιάς και υψηλής θερμοκρασίας σε ανάλυση 1 km.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)"]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":["# Μεθάνιο (CH4)\n\n\n\nΤο μεθάνιο είναι, μετά το διοξείδιο του άνθρακα, ο πιο σημαντικός παράγοντας για την ανθρωπογενή (προκαλούμενη από ανθρώπινη δραστηριότητα) επίδραση του θερμοκηπίου. Οι μετρήσεις παρέχονται σε μέρη ανά δισεκατομμύριο (ppb) με χωρική ανάλυση 7 km x 3,5 km.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/methane)"]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":["# Φορμαλδεΰδη (HCHO)\n\n\n\nΟι μακροχρόνιες δορυφορικές παρατηρήσεις της τροποσφαιρικής φορμαλδεΰδης (HCHO) είναι απαραίτητες στην εκτίμηση της ποιότητας του αέρα και γενικότερα στις χημικών-κλιματικών μελέτες, τόσο σε τοπική, όσο και σε παγκόσμια κλίμακα. Οι εποχιακές και διετείς παραλλαγές της κατανομής της φορμαλδεΰδης σχετίζονται κυρίως με αλλαγές θερμοκρασίας και πυρκαγιές, αλλά και με αλλαγές σε ανθρωπογενείς δραστηριότητες. Η διάρκεια ζωής της είναι της τάξης μερικών ωρών, ενώ οι συγκεντρώσεις HCHO στο οριακό στρώμα είναι πιθανό να σχετίζονται άμεσα με την απελευθέρωση βραχείας ζωής υδρογονανθράκων, οι οποίοι ως επί το πλείστον δε μπορούν να παρατηρηθούν απευθείας από το διάστημα. Οι μετρήσεις γίνονται σε mol ανά τετραγωνικό μέτρο (mol / m ^ 2).\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/formaldehyde)"]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":["# Διοξείδιο του θείου (SO2)\n\n\n\nΤο διοξείδιο του θείου εισέρχεται στην ατμόσφαιρα της Γης μέσω τόσο φυσικών όσο και ανθρωπογενών διεργασιών. Η ύπαρξη και μελέτη του παίζει σημαντικό ρόλο στη χημεία σε τοπική και παγκόσμια κλίμακα και μπορεί να προκαλέσει βραχυπρόθεσμη ρύπανση έως σημαντικές επιπτώσεις στο κλίμα. Περίπου το 30% του εκπεμπόμενου SO2 προέρχεται από φυσικές πηγές. Το μεγαλύτερο ποσοστό της εμφάνισης του διοξειδίου οφείλεται σε ανθρωπογενείς δραστηριότητες. Ο αισθητήρας Sentinel-5P / TROPOMI καταγράφει την επιφάνεια της Γης με χρόνο επίσκεψης μίας ημέρας, με χωρική ανάλυση 3,5 x 7 km που επιτρέπει την καταγραφή λεπτομερειών, συμπεριλαμβανομένης της ανίχνευσης μικρότερων εκπομπών SO2. Οι μετρήσεις γίνονται σε mol ανά τετραγωνικό μέτρο (mol / m ^ 2).\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/sulphur-dioxide)"]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":["# Όζον (O3)\n\n\n\nΤο όζον είναι ζωτικής σημασίας για την ισορροπία της γήινης ατμόσφαιρας. Στη στρατόσφαιρα, το στρώμα του όζοντος προστατεύει τη βιόσφαιρα από την επικίνδυνη ηλιακή υπεριώδη ακτινοβολία. Στην τροπόσφαιρα, δρα ως καθαριστικός παράγοντας, αλλά σε υψηλή συγκέντρωση καθίσταται επίσης επιβλαβής για την υγεία των ανθρώπων, των ζώων και της βλάστησης. Το όζον επιδρά επίσης και στην τρέχουσα κλιματική αλλαγή. Από την ανακάλυψη της τρύπας του όζοντος στην Ανταρκτική τη δεκαετία του 1980 και το επακόλουθο πρωτόκολλο του Μόντρεαλ το οποίο ρυθμίζει την παραγωγή ουσιών που περιέχουν χλώριο και καταστρέφουν το όζον, η ανίχνευση και παρακολούθησή του πραγματοποιείται τακτικά από το έδαφος και από το διάστημα. Οι μετρήσεις είναι σε mol ανά τετραγωνικό μέτρο (mol / m ^ 2)\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/total-ozone-column)"]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":["# Διοξείδιο του αζώτου (NO2)\n\n\n\nΤο διοξείδιο του αζώτου (NO2) και το οξείδιο του αζώτου (NO) αναφέρονται συνήθως ως οξείδια του αζώτου. Είναι σημαντικά στοιχεία στην ατμόσφαιρα της Γης, τα οποία υπάρχουν τόσο στην τροπόσφαιρα όσο και στη στρατόσφαιρα. Εισέρχονται στην ατμόσφαιρα ως αποτέλεσμα ανθρωπογενών δραστηριοτήτων (ιδίως καύσης ορυκτών καυσίμων και καύσης βιομάζας) αλλά και φυσικών διεργασιών (όπως μικροβιολογικές διεργασίες σε εδάφη, πυρκαγιές και κεραυνούς). Οι μετρήσεις γίνονται σε mol ανά τετραγωνικό μέτρο (mol / m ^ 2).\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/nitrogen-dioxide)"]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":["# Μονοξείδιο του άνθρακα (CO)\n\n\n\nΤο μονοξείδιο του άνθρακα (CO) είναι ένα σημαντικό ατμοσφαιρικό στοιχείο. Σε ορισμένες αστικές περιοχές αποτελεί σημαντικό ατμοσφαιρικό ρύπο. Οι κύριες πηγές CO είναι η καύση ορυκτών καυσίμων, η καύση βιομάζας και η ατμοσφαιρική οξείδωση μεθανίου και άλλων υδρογονανθράκων. Η συνολική στήλη μονοξειδίου του άνθρακα μετράται σε mol ανά τετραγωνικό μέτρο (mol / m ^ 2).\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/carbon-monoxide)"]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":["# Δείκτης αερολύματος\n\nΟ δείκτης αερολύματος (AI) είναι ένας ποιοτικός δείκτης που δείχνει την παρουσία υψηλών στρωμάτων αερολυμάτων στην ατμόσφαιρα. Μπορεί να χρησιμοποιηθεί για την ανίχνευση της παρουσίας αερολυμάτων που απορροφούν την υπεριώδη ακτινοβολία, όπως η σκόνη της ερήμου και τα ηφαιστειακά τέφρα. Οι θετικές τιμές (από ανοιχτό μπλε έως κόκκινο) υποδεικνύουν την παρουσία αερολυμάτων απορρόφησης UV. Αυτός ο δείκτης υπολογίζεται για δύο ζεύγη μήκους κύματος: 340/380 nm και 354/388 nm.\n\nΠερισσότερες πληροφορίες [εδώ.](Https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)"]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":["# Βάση ύψους νέφους\n\nΒάση ύψους νέφους σε μέτρα (m)."]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":["# Βάση πίεσης νέφους\n\nΗ βάση πίεσης του νέφους σε Pascal (Pa)."]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":["# Οπτικό πάχος νέφους\n\nΤο πάχος του νέφους είναι μια βασική παράμετρος για τον χαρακτηρισμό των οπτικών ιδιοτήτων των νεφών. Είναι ένα μέτρο της ποσότητας του φωτός του ήλιου που περνά μέσα από το σύννεφο για να φτάσει στην επιφάνεια της Γης. Όσο υψηλότερο είναι το οπτικό πάχος του σύννεφου, τόσο περισσότερο ηλιακό φως διασκορπίζεται και αντανακλάται. Το σκούρο μπλε υποδεικνύει την ύπαρξη τιμές χαμηλού οπτικού πάχους του νέφους και το κόκκινο δείχνει μεγαλύτερο οπτικό πάχος."]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":["# Ύψος κορυφής νέφους\n\nΤο ύψος του νέφους μετράται σε μέτρα (m)."]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":["# Πίεση κορυφής νέφος\n\nΗ πίεση που μετράται στην κορυφή του νέφους σε Pascal (Pa)."]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":["# Κανονικοποιημένος δείκτης βλάστησης (NDVI)\n\nΟ κανονικοποιημένος δείκτης βλάστησης είναι ένας απλός, αλλά αποτελεσματικός δείκτης για τον ποσοτικό προσδιορισμό της πράσινης βλάστησης. Είναι ένα μέτρο της κατάστασης της βλάστησης που βασίζεται στον τρόπο με τον οποίο τα φυτά αντανακλούν το φως σε ορισμένα μήκη κύματος. Το εύρος τιμών του NDVI είναι -1 έως 1. Οι αρνητικές τιμές του NDVI (τιμές που πλησιάζουν στο -1) αντιστοιχούν στο νερό. Οι τιμές κοντά στο μηδέν (-0,1 έως 0,1) αντιστοιχούν γενικά σε άγονες περιοχές, με στοιχεία βράχων, άμμου ή χιονιού. Οι χαμηλές, θετικές τιμές αντιπροσωπεύουν θάμνους και λιβάδια (περίπου 0,2 έως 0,4), ενώ οι υψηλές τιμές υποδεικνύουν την ύπαρξη εύκρατων και τροπικών δασών (τιμές πλησιάζουν το 1).\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) και [εδώ.](Https://eos.com/ndvi/)"]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":["# Ενισχυμένος δείκτης βλάστησης (EVI)\n\nΟ ενισχυμένος δείκτης βλάστησης (EVI) είναι ένας «βελτιστοποιημένος» δείκτης βλάστησης καθώς διορθώνει τα σήματα του θορύβου του εδάφους και τις ατμοσφαιρικές επιδράσεις. Είναι πολύ χρήσιμος σε περιοχές με πυκνή κομοστέγη. Το εύρος τιμών για EVI είναι -1 έως 1, με υγιή βλάστηση γενικά περίπου 0,20 έως 0,80.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel"]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Ατμοσφαιρικά Ανθεκτικός Δείκτης Βλάστησης (ARVI)\n\nΟ ατμοσφαιρικά ανθεκτικός δείκτης βλάστησης(ARVI) είναι ένας δείκτης βλάστησης που ελαχιστοποιεί τις επιπτώσεις της ατμοσφαιρικής σκέδασης. Είναι χρήσιμος για περιοχές με υψηλή περιεκτικότητα σε αερολύματα (ομίχλη, σκόνη, καπνός, ατμοσφαιρική ρύπανση). Το εύρος για ένα ARVI είναι -1 έως 1, ενώ η πράσινη βλάστηση γενικά κυμαίνεται μεταξύ τιμών 0,20 έως 0,80.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) και [εδώ.](Https://eos.com/blog/6-spectral-indexes-on -τοπ-of-ndvi-to-make-your-vegetation-analysis-complete /)"]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Εδαφικά προσαρμοσμένος δείκτης βλάστησης (SAVI)\n\nΟ εδαφικά προσαρμοσμένος δείκτης βλάστησης είναι παρόμοιος με το δείκτη βλάστησης Normalized Difference (NDVI), αλλά χρησιμοποιείται σε περιοχές όπου η βλάστηση είναι χαμηλή (<40%). Ο δείκτης αποτελεί μια τεχνική μετασχηματισμού που ελαχιστοποιεί τις επιδράσεις της φωτεινότητας του εδάφους, σε σχέση με τους δείκτες φασματικής βλάστησης που περιλαμβάνουν μήκη κύματος κόκκινου και εγγύς υπέρυθρου (NIR). Ο δείκτης είναι χρήσιμος για την ανάλυση του εδάφους, νέων καλλιεργειών και ξηρών περιοχών με αραιά βλάστηση\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) και [εδώ.](Https://eos.com/blog/6-spectral-indexes-on -τοπ-of-ndvi-to-make-your-vegetation-analysis-complete /)"]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":["# Τροποποιημένος δείκτης ανθοκυανίνης (mARI / ARI2)\n\nΟι ανθοκυανίνες είναι χρωστικές που είναι κοινές στα φυτά και προκαλούν τον κόκκινο, μπλε και μωβ χρωματισμό τους. Παρέχουν πολύτιμες πληροφορίες σχετικά με τη φυσική κατάσταση των φυτών, καθώς θεωρούνται δείκτες διαφόρων τύπων ασθενειών. Η ανάκλαση της ανθοκυανίνης είναι υψηλότερη γύρω στα 550nm. Ωστόσο, στα ίδια μήκη κύματος ανακλά και από η χλωροφύλλη. Για την απομόνωση των ανθοκυανινών, αφαιρείται το φασματικό κανάλι των 700nm, που αντανακλά μόνο τη χλωροφύλλη και όχι τις ανθοκυανίνες.\n\nΓια τη διόρθωση της πυκνότητας και του πάχους των φύλλων, η εγγύς υπέρυθρη φασματική ακτινοβολία (στα συνιστώμενα μήκη κύματος 760-800nm), η οποία σχετίζεται με τη σκέδαση των φύλλων, προστίθεται στον βασικό δείκτη ARI. Ο νέος δείκτης ονομάζεται τροποποιημένος ARI ή mARI (επίσης ARI2).\n\nΟι τιμές mARI για τα υπό εξέταση δέντρα σε [αυτό το αρχικό άρθρο](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) κυμαίνονταν από 0 έως 8."]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":["# Αλγόριθμος Πράσινης Πόλης\n\nΟ αλγόριθμος Green City στοχεύει στην ευαισθητοποίηση σχετικά με τις πράσινες περιοχές σε πόλεις σε όλο τον κόσμο. Συγκεκριμένα, λαμβάνει υπόψη τον κανονικοποιημένο δείκτη βλάστησης (NDVI) και τα πραγματικά μήκη κύματος χρώματος. Διαχωρίζει τις συσσωρευμένες περιοχές από τις βλαστημένες, καθιστώντας το χρήσιμο για τον εντοπισμό αστικών περιοχών. Οι χτισμένες περιοχές εμφανίζονται με γκρι χρώμα και η βλάστηση εμφανίζεται με πράσινο χρώμα.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":["#Αλγόριθμος Αστικής Ταξινόμησης\n\nΟ αλγόριθμος Urban Classified αποσκοπεί στον εντοπισμό αστικών περιοχών διαχωρίζοντας τες από άγονο έδαφος, βλάστηση και νερό. Οι περιοχές με υψηλή περιεκτικότητα σε υγρασία παρουσιάζονται με μπλε χρώμα. Περιοχές που υποδηλώνουν την ύπαρξη αστικών περιοχών παρουσιάζονται με λευκά χρώματα και η βλάστηση με πράσινο. Οτιδήποτε άλλο δείχνει άγονο έδαφος και εμφανίζεται σε καφέ χρώματα.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)"]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":["# Αλγόριθμος Έγχρωμου Υπέρυθρου Αστικών Περιοχών\n\nΑυτός ο αλγόριθμος, που δημιουργήθηκε από τον Leo Tolari, συνδυάζει την φυσική έγχρωμη απεικόνιση με μήκη κύματος υπέρυθρων (NIR) και μικροκυματικών υπέρυθρων (SWIR). Ο αλγόριθμος επισημαίνει αστικές περιοχές καλύτερα από το φυσικό χρώμα.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)"]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":["# NDMI για την έλλειψη υγρασίας\n\nΟ κανονικοποιημένος δείκτης υγρασίας μπορεί να χρησιμοποιηθεί για την παρακολούθηση της άρδευσης. Για όλες τις τιμές δείκτη άνω του 0, λαμβάνοντας υπόψη τη χρήση και κάλυψη γης, είναι δυνατόν να προσδιοριστεί εάν έχει γίνει άρδευση. Γνωρίζοντας τον τύπο της καλλιέργειας που καλλιεργείται (π.χ. εσπεριδοειδή), είναι δυνατό να προσδιοριστεί εάν η άρδευση είναι αποτελεσματική ή όχι κατά τη διάρκεια της κρίσιμης καλλιεργητικής θερινής περιόδου, καθώς και εάν ορισμένα τμήματα της καλλιέργειας υπόκεινται σε υπερθέρμανση.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)"]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":["# Κανονικοποιημένος δείκτης υγρασίας (NDMI)\n\nΟ κανονικοποιημένος δείκτης υγρασίας (NDMI) χρησιμοποιείται για τον προσδιορισμό της περιεκτικότητας σε νερό και την παρακολούθηση της ξηρασίας. Το εύρος τιμών του NDMI είναι -1 έως 1. Οι αρνητικές τιμές του NDMI (τιμές που πλησιάζουν στο -1) αντιστοιχούν σε άγονο έδαφος. Οι τιμές γύρω στο μηδέν (-0,2 έως 0,4) αντιστοιχούν γενικά στην έλλειψη νερού. Υψηλές, θετικές τιμές αντιπροσωπεύουν υψηλή κομοστέγη με επάρκεια νερού (περίπου 0,4 έως 1).\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":["# Κανονικοποιημένος δείκτης νερού (NDWI)\n\nΟ κανονικοποιημένος δείκτης νερού είναι ο πλέον κατάλληλος για τη χαρτογράφηση υδάτινων σωμάτων. Οι τιμές των υδάτινων σωμάτων είναι μεγαλύτερες από 0,5. Η βλάστηση έχει μικρότερες τιμές. Ο αστικός ιστός έχει θετικές τιμές μεταξύ μηδέν και 0,2.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":["# Κανονικοποιημένος δείκτης νερού (NDWI)\n\nΟ κανονικοποιημένος δείκτης νερού είναι ο πλέον κατάλληλος για τη χαρτογράφηση υδάτινων σωμάτων. Οι τιμές των υδάτινων σωμάτων είναι μεγαλύτερες από 0,5. Η βλάστηση έχει μικρότερες τιμές. Ο αστικός ιστός έχει θετικές τιμές μεταξύ μηδέν και 0,2."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Ψευδο-έγχρωμο σύνθετο\n\nΈνα ψευδο-έγχρωμο σύνθετο βασίζεται σε τουλάχιστον ένα μη ορατό μήκος κύματος για την οπτικοποίηση της Γης. Το σύνθετο που χρησιμοποιεί το εγγύς υπέρυθρο, κόκκινο και πράσινο κανάλι είναι πολύ δημοφιλές (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το ψευδο-έγχρωμο σύνθετο συνήθως χρησιμοποιείται για την εκτίμηση της πυκνότητας και της υγείας των φυτών, καθώς τα φυτά αντανακλούν κοντά στο υπέρυθρο και πράσινο φως, ενώ απορροφούν το κόκκινο. Οι πόλεις και το έδαφος εμφανίζονται γκρι ή μαύρα και το νερό εμφανίζεται μπλε ή μαύρο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) και [εδώ.](Https://earthobservatory.nasa.gov/features/FalseColor/page6.php )"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Ψευδο-έγχρωμο σύνθετο\n\nΈνα ψευδο-έγχρωμο σύνθετο βασίζεται σε τουλάχιστον ένα μη ορατό μήκος κύματος για την οπτικοποίηση της Γης. Το σύνθετο που χρησιμοποιεί το εγγύς υπέρυθρο, κόκκινο και πράσινο κανάλι είναι πολύ δημοφιλές (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το ψευδο-έγχρωμο σύνθετο συνήθως χρησιμοποιείται για την εκτίμηση της πυκνότητας και της υγείας των φυτών, καθώς τα φυτά αντανακλούν κοντά στο υπέρυθρο και πράσινο φως, ενώ απορροφούν το κόκκινο. Οι πόλεις και το έδαφος εμφανίζονται γκρι ή μαύρα και το νερό εμφανίζεται μπλε ή μαύρο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) και [εδώ.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":["# Ψευδο-έγχρωμο σύνθετο\n\nΈνα ψευδο-έγχρωμο σύνθετο βασίζεται σε τουλάχιστον ένα μη ορατό μήκος κύματος για την οπτικοποίηση της Γης. Το σύνθετο που χρησιμοποιεί το εγγύς υπέρυθρο, κόκκινο και πράσινο κανάλι είναι πολύ δημοφιλές (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το ψευδο-έγχρωμο σύνθετο συνήθως χρησιμοποιείται για την εκτίμηση της πυκνότητας και της υγείας των φυτών, καθώς τα φυτά αντανακλούν κοντά στο υπέρυθρο και πράσινο φως, ενώ απορροφούν το κόκκινο. Οι πόλεις και το έδαφος εμφανίζονται γκρι ή μαύρα και το νερό εμφανίζεται μπλε ή μαύρο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) και [εδώ.](https://earthobservatory.nasa.gov/features/FalseColor)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Ψευδο-έγχρωμο σύνθετο\n\nΈνα ψευδο-έγχρωμο σύνθετο βασίζεται σε τουλάχιστον ένα μη ορατό μήκος κύματος για την οπτικοποίηση της Γης. Το σύνθετο που χρησιμοποιεί το εγγύς υπέρυθρο, κόκκινο και πράσινο κανάλι είναι πολύ δημοφιλές (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το ψευδο-έγχρωμο σύνθετο συνήθως χρησιμοποιείται για την εκτίμηση της πυκνότητας και της υγείας των φυτών, καθώς τα φυτά αντανακλούν κοντά στο υπέρυθρο και πράσινο φως, ενώ απορροφούν το κόκκινο. Οι πόλεις και το έδαφος εμφανίζονται γκρι ή μαύρα και το νερό εμφανίζεται μπλε ή μαύρο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) και [εδώ.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy)."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) και [εδώ.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) και [εδώ.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) και [εδώ.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\nΠερισσότερες πληροφορίες [εδώ](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\nΠερισσότερες πληροφορίες [εδώ](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)"]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":["# Ενισχυμένο φυσικό έγχρωμο σύνθετο\n\nΑυτός ο αλγόριθμος χρησιμοποιεί βελτιστοποίησες για να κρύψει τα καμένα εικονοστοιχεία και να εξομαλύνει την υπερέκθεση της εικόνας. Με τον τρόπο αυτό τα σύννεφα φαίνονται φυσικά και διατηρούν όσο το δυνατόν περισσότερες πληροφορίες. Οι εικόνες Sentinel-3 OLCI καλύπτουν μεγάλες περιοχές, καθιστώντας δυνατή την παρατήρηση μεγάλων σχηματισμών νέφους, όπως συμβαίνει στους τυφώνες.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)"]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Παγχρωματικό φυσικό έγχρωμο σύνθετο \n\nΤο παγχρωματικό φυσικό έγχρωμο σύνθετο δημιουργείται χρησιμοποιώντας τα συνηθισμένα κανάλια φυσικού χρώματος (κόκκινο, πράσινο και μπλε (RGB)), στα οποία η ανάλυση βελτιώνεται με τη χρήση του παγχρωματικού καναλιού (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Μια εικόνα από το παγχρωματικό κανάλι είναι παρόμοια με μια ασπρόμαυρη μεμβράνη: συνδυάζει το φως από τα κόκκινα, πράσινα και μπλε μέρη του φάσματος και υπολογίζει μια συνολική τιμή ανακλαστικότητας. Οι παγχρωματικές εικόνες έχουν τέσσερις φορές την ανάλυση ενός φυσικού σύνθετου φυσικού χρώματος, ενισχύοντας σημαντικά τη χρησιμότητα των εικόνων Landsat.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) και [εδώ.](Https://landsat.gsfc.nasa.gov/ landsat-8 / landsat-8-ζώνες /)"]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":["# Ψευδο-έγχρωμο σύνθετο για αστικές περιοχές\n\nΤο σύνθετο αυτό χρησιμοποιείται για την απεικόνιση των αστικών περιοχών. Η βλάστηση απεικονίζεται σε αποχρώσεις του πράσινου, ενώ οι αστικές περιοχές απεικονίζονται με λευκό, γκρι ή μωβ χρώμα. Το έδαφος, η άμμος και τα ορυκτά εμφανίζονται με μια ποικιλία χρωμάτων. Το χιόνι και ο πάγος εμφανίζονται με σκούρο μπλε και το νερό με μαύρο ή μπλε. Οι πλημμυρισμένες περιοχές εμφανίζονται με πολύ σκούρες μπλε αποχρώσεις, σχεδόν μαύρες. Το σύνθετο είναι χρήσιμο στην ανίχνευση πυρκαγιών και ηφαιστειακών σχηματισμών, καθώς εμφανίζονται σε αποχρώσεις του κόκκινου και του κίτρινου.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) και [εδώ.](Https://eos.com/false-color/)"]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Ψευδο-έγχρωμο σύνθετο για αστικές περιοχές\n\nΑυτό το σύνθετο χρησιμοποιεί έναν συνδυασμό καναλιών στο ορατό και στο μικροκυματικό υπέρυθρο φάσμα (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Η βλάστηση απεικονίζεται με αποχρώσεις του πράσινου. Οι πιο σκούρες αποχρώσεις του πράσινου δείχνουν πυκνότερη βλάστηση, ενώ η αραιή βλάστηση έχει ανοιχτότερες αποχρώσεις. Οι αστικές περιοχές εμφανίζονται μπλε και τα εδάφη απεικονίζονται με διαφορετικές αποχρώσεις του καφέ.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://gisgeography.com/landsat-8-bands-combinations/)"]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":["# Σύνθετο Καλλιέργειας\n\nΑυτό το σύνθετο χρησιμοποιεί το μικροκυματικό και εγγύς υπέρυθρο και το μπλε κανάλι με στόχο την παρακολούθηση της υγείας των καλλιεργειών (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Τα υπέρυθρα κανάλια συμβάλουν στην ανάδειξη της πυκνής βλάστησης, η οποία εμφανίζεται σκούρο πράσινο στο σύνθετο. Οι καλλιέργειες εμφανίζονται σε ένα ζωντανό πράσινο και το γυμνό έδαφος με ματζέντα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) και [εδώ.](Https://gisgeography.com/sentinel-2-bands-combinations/)"]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":["# Ταξινόμηση Χιονιού\n\nΟ αλγόριθμος Snow Classifier στοχεύει στην ανίχνευση χιονιού μέσω της ταξινόμησης των εικονοστοιχείων βάσει του δείκτη Normalized Difference Snow Index (NDSI) Οι τιμές που ταξινομούνται ως χιόνι επιστρέφονται με έντονο μπλε χρώμα. Ο αλγόριθμος είναι πιθανό να υπερεκτιμά τις περιοχές χιονιού πάνω από σύννεφα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":["# Ulyssys Water Quality Viewer (UWQV)\n\nΟ αλγόριθμος στοχεύει να απεικονίσει δυναμικά την περιεκτικότητα των υδάτινων σωμάτων σε χλωροφύλλη και ιζήματα, η οποία αποτελεί σημαντικό δείκτη της ποιότητας του νερού. Η περιεκτικότητα σε χλωροφύλλη απεικονίζεται με χρώματα από σκούρο μπλε (χαμηλή περιεκτικότητα σε χλωροφύλλη) έως πράσινο και κόκκινο (υψηλή περιεκτικότητα σε χλωροφύλλη). Οι συγκεντρώσεις ιζημάτων είναι χρώματος καφέ. Το σκούρο καφέ δείχνει υψηλή περιεκτικότητα σε ιζήματα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)"]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":["# Βελτιστοποιημένο Φυσικό Έγχρωμο Σύνθετο\n\nΑυτός ο αλγόριθμος στοχεύει στην απεικόνισης της Γης σε ένα όμορφο φυσικό έγχρωμο σύνθετο. Χρησιμοποιεί βελτιστοποίησες για να κρύψει τα καμένα εικονοστοιχεία και να εξομαλύνει την υπερέκθεση της εικόνας.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)"]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":["# Γεωλογικό Σύνθετο 12, 8, 2 \n\nΑυτό το σύνθετο χρησιμοποιεί το βραχυκυματικό υπέρυθρο κανάλι 12 για να ξεχωρίσει διαφορετικούς τύπους πετρωμάτων (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού κύματος: ένας δορυφόρος μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Κάθε πέτρωμα και ορυκτό ανακλά διαφορετικά το βραχυκυματικό υπέρυθρο, καθιστώντας δυνατή τη γεωλογική χαρτογράφηση με τη σύγκριση της ανακλώμενης ακτινοβολίας SWIR. Το εγγύς υπέρυθρο (NIR) κανάλι 8 απεικονίζει τη βλάστηση και το 2 την υγρασία, συμβάλλοντας περαιτέρω στο διαχωρισμό των διαφορετικών τύπων εδάφους. Το σύνθετο είναι χρήσιμο για εντοπισμό γεωλογικών σχηματισμών και χαρακτηριστικών (π.χ. ρήγματα, κατάγματα), λιθολογία (π.χ. γρανίτης, βασάλτης κ.λπ.) και εφαρμογές εξόρυξης \n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)"]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":["# Γεωλογικό Σύνθετο 8, 11, 12\n\nΑυτό το σύνθετο χρησιμοποιεί τα δυο βραχυκυματικά υπέρυθρα κανάλια 11 και 12 με στόχο των διαχωρισμό των διαφορετικών τύπων πετρωμάτων (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού κύματος: ένας δορυφόρος μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια)). Κάθε πέτρωμα και ορυκτό ανακλά διαφορετικά το βραχυκυματικό υπέρυθρο, καθιστώντας δυνατή τη γεωλογική χαρτογράφηση με τη σύγκριση της ανακλώμενης ακτινοβολίας SWIR. Το εγγύς υπέρυθρο (NIR) κανάλι 8 απεικονίζει τη βλάστηση, βοηθώντας περαιτέρω στον διαχωρισμό των διαφορετικών τύπων εδάφους. Η βλάστηση απεικονίζεται κόκκινη. Το σύνθετο είναι χρήσιμο για τη διαφοροποίηση της βλάστησης και του εδάφους, ιδιαίτερα των γεωλογικών χαρακτηριστικών που μπορεί να είναι χρήσιμα για εξόρυξη.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://earthobservatory.nasa.gov/χαρακτηριστικά/FalseColor/σελίδα5.php) και [εδώ.](http://murphygeological.com/new---sentinel-2.html#)"]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":["# Πυρκαγιές\n\nΟ αλγόριθμος που αναπτύχθηκε από τον Pierre Markuse, , απεικονίζει πυρκαγιές σε δεδομένα Sentinel-2. Συνδυάζει το φυσικό έγχρωμο σύνθετο με κάποια δεδομένα NIR/SWIR για την απεικόνιση περιοχών με καπνό και την ανάδειξη κάποιων λεπτομερειών, από τα κανάλια B11 και B12. Η πυρκαγιά εμφανίζεται με κόκκινο και πορτοκαλί.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)"]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":["# Ενισχυμένο Φυσικό Έγχρωμο Σύνθετο\n\nΟ αλγόριθμος που αναπτύχθηκε από τον Pierre Markuse, χρησιμοποιεί πολλαπλά κανάλια (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού κύματος: ένας δορυφόρος μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια) και εφαρμόζει παραμέτρους κορεσμού και φωτεινότητας για να ενισχύσει το φυσικό έγχρωμο σύνθετο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)"]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":["# Δείκτης Καμένης Έκτασης\n\nΟ Δείκτης καμένης έκτασης χρησιμοποιεί το ευρύτερο φάσμα των καναλιών του ορατού, Red-Edge, NIR και SWIR.\n\nΠεριγραφή τιμών:()=> Το εύρος τιμών του δείκτη είναι από '-1' έως '1' για καμένες περιοχές και από '1' - '6' για ενεργές πυρκαγιές. Διαφορετικές εντάσεις πυρκαγιάς μπορεί να οδηγήσουν σε διαφορετικά κατώφλια: το εύρος τιμών προέκυψε μετά από βαθμονόμηση του αρχικού συντάκτη σε μεσογειακές κυρίως περιοχές.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)"]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":["# Κανονικοποιημένος Δείκτης Καμένης Έκτασης (NBR)\n\nΟ κανονικοποιημένος δείκτης καμένης έκτασης χρησιμοποιείται συχνά για την εκτίμηση της έντασης της καταστροφής του εδάφους. Χρησιμοποιεί εγγύς (NIR) και βραχυκυματικά (SWIR) υπέρυθρα. Η υγιής βλάστηση έχει υψηλή ανακλαστικότητα στο εγγύς υπέρυθρο τμήμα του φάσματος και χαμηλή στο βραχυκυματικό. Αντίθετα, οι καμένες περιοχές έχουν υψηλή ανακλαστικότητα στο βραχυκυματικό υπέρυθρο, και χαμηλή στο εγγύς. Τα σκούρα εικονοστοιχεία υποδεικνύουν καμένες περιοχές.\n\n\n\nΠερισσότερες πληροφορίες [εδώ]](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.htmlκαι [εδώ.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":["# Ατμοσφαιρική διείσδυση\n\nΑυτό το σύνθετο χρησιμοποιεί διαφορετικά κανάλια (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού κύματος: ένας δορυφόρος μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια) στο μη ορατό τμήμα του ηλεκτρομαγνητικού φάσματος για να μειώσει την επίδραση της ατμόσφαιρας στην εικόνα. Τα βραχυκυματικά υπέρυθρα κανάλια 11 και 12 αντανακλώνται έντονα από θερμές περιοχές, κάνοντάς τα κατάλληλα για τη χαρτογράφηση πυρκαγιών και καμένων περιοχών. Το βραχυκυματικό υπέρυθρο κανάλι 8, αντιθέτως, ανακλάται ιδιαίτερα από τη βλάστηση, κάτι το οποίο καταδεικνύει την απουσία πυρκαγιάς. Η βλάστηση εμφανίζεται μπλε, εμφανίζοντας λεπτομέρειες που σχετίζονται με την κατάσταση της βλάστησης. Η υγιής βλάστηση εμφανίζεται με γαλάζιο χρώμα ενώ η αραιή ή/και άνυδρη βλάστηση εμφανίζεται με θαμπό μπλε. Ο αστικός ιστός εμφανίζεται με λευκά, γκρίζα, κυανά ή μωβ.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://eos.com/atmospheric-penetration/)"]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":["# Απεικόνιση Γυμνού Εδάφους\n\nΗ απεικόνιση του γυμνού εδάφους μπορεί να φανεί χρήσιμη για τη χαρτογράφηση του χώματος, τον εντοπισμό κατολισθήσεων ή την έκταση της διάβρωσης σε περιοχές χωρίς βλάστηση. Αυτή η απεικόνιση δείχνει τη βλάστηση σε πράσινο και το άγονο έδαφος σε κόκκινο χρώμα. Το νερό εμφανίζεται σε μαύρο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) και [εδώ](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)"]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":["# Φυσικό Έγχρωμο σύνθετο με Υπέρυθρες Επισημάνσεις\n\nΑυτό το σύνθετο βελτιώνει την φυσική χρωματική απεικόνιση προσθέτοντας το βραχυκυματικό υπέρυθρο για να ενισχύσει τις λεπτομέρειες. Εμφανίζει θερμές περιοχές σε κόκκινο/πορτοκαλί χρώμα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)"]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":["# Ανίχνευση Καμένων Περιοχών\n\nΑυτός ο αλγόριθμος χρησιμοποιείται για τον εντοπισμό μεγάλων περιοχών που έχουν καεί πρόσφατα. Τα κόκκινα εικονοστοιχεία απεικονίζουν καμένες περιοχές και όλα τα άλλα εικονοστοιχεία απεικονίζονται σε φυσικό χρώμα. Ο αλγόριθμος μερικές φορές υπερεκτιμά τις καμένες περιοχές πάνω από νερά και σύννεφα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":["# Δείκτης Χερσαίας Χλωροφύλλης (OTCI)\n\n\n\nΟ δείκτης χερσαίας χλωροφύλλης (OTCI) εκτιμάται με βάση την περιεκτικότητα σε χλωροφύλλη στη χερσαία βλάστηση και μπορεί να χρησιμοποιηθεί για την παρακολούθηση της κατάστασης και της υγείας της βλάστησης. Οι χαμηλές τιμές OTCI συνήθως υποδηλώνουν την ύπαρξη νερού, άμμου ή χιονιού. Πολύ υψηλές τιμές, που απεικονίζονται με λευκό, επίσης υποδηλώνουν την απουσία χλωροφύλλης κατά πάσα πιθανότητα. Γενικά οι υψηλές τιμές αντιστοιχούν σε έδαφος, βραχώδες έδαφος ή σύννεφα. Οι τιμές χλωροφύλλης που κυμαίνονται από κόκκινο (χαμηλές τιμές χλωροφύλλης) έως σκούρο πράσινο (υψηλές τιμές χλωροφύλλης) μπορούν να χρησιμοποιηθούν για τον προσδιορισμό της υγείας της βλάστησης.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":["# Κανονικοποιημένος Δείκτης Αλατότητας\n\nΟ δείκτης απεικονίζει την ποσότητα αλατιού που υπάρχει στο έδαφος. Η αλατοποίηση του εδάφους είναι μία από τις πιο συνήθεις διαδικασίες αποδόμησης του εδάφους, ειδικά σε άνυδρες και ημι-άνυδρες περιοχές, όπου τα ποσά βροχόπτωσης υπερβαίνουν αυτά της εξάτμισης\n\nΟι υψηλές τιμές δείχνουν υψηλή αλατότητα και οι χαμηλές τιμές δείχνουν χαμηλή αλατότητα .\n\nΔιαβάστε περισσότερα [εδώ,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [εδώ](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) και [εδώ.](https://www.indexdatabase.de/db/i-single.php?id=57)"]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":["Με αυτό το εργαλείο μπορείτε να δημιουργήσετε μια κινούμενη εικόνα timelapse του οπτικοποιημένου επιπέδου και της τοποθεσίας που εμφανίζεται.\n\nΑρχικά, επιλέξτε ένα χρονικό εύρος. Μπορείτε να περιορίσετε τα αποτελέσματα αναζήτησης εφαρμόζοντας φίλτρα κατά μήνες\n(πλαίσιο ελέγχου με το φίλτρο ανά μήνες) ή / και επιλέγοντας μία εικόνα ανά καθορισμένη περίοδο (τροχιά, ημέρα, εβδομάδα, μήνα,\nέτος).\n\nΣτη συνέχεια, πατήστε Αναζήτηση και επιλέξτε τις εικόνες σας.\nΜπορείτε να επιλέξετε όλα τα αποτελέσματα από το πλαίσιο ελέγχου ή να φιλτράρετε τις εικόνες με την επιθυμητή νεφοκάλυψη, μετακινώντας τη μπάρα ρύθμισης. Επίσης μπορείτε να επιλέξετε εικόνες μία προς μία\nΜέσω του πλαισίου ελέγχου ** Όρια ** μπορείτε να ενεργοποιήσετε / απενεργοποιήσετε τα όρια στην εικόνα σας.\n\nΜπορείτε να κάνετε προεπισκόπηση του timelapse πατώντας το κουμπί αναπαραγωγής στο κάτω μέρος. Μπορείτε επίσης να ρυθμίσετε την ταχύτητα αναπαραγωγής\n(καρέ ανά δευτερόλεπτο).\n\nΌταν είστε ικανοποιημένοι με το αποτέλεσμα, κάντε κλικ στο κουμπί λήψης και το timelapse θα\nμεταφορτωθεί ως αρχείο .gif
."]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":["Τα στιγμιότυπα του χρήστης δεν ήταν δυνατό να φορτωθούν, καθώς ο λογαριασμός σας Sentinel Hub δεν είχε ρυθμιστεί ή έχει λήξει. Μπορείτε ακόμα να χρησιμοποιήσετε τον EO Browser, αλλά δεν θα μπορείτε να χρησιμοποιήσετε τα ιδιοποιημένα στιγμιότυπά σας. Για να μπορείτε να καθορίσετε τα δικά στιγμιότυπα της υπηρεσίας, μπορείτε να υποβάλετε αίτηση για δωρεάν δοκιμαστική περίοδο 30 ημερών ή να εγγραφείτε σε ένα από τα σχέδια: "]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":["Αυτά είναι τμήματα θεμάτων τα οποία περιέχουν μη διαθέσιμες πηγές δεδομένων:"]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":["Κανάλι 2 - Μέγιστη απορρόφηση χλωροφύλλης - 442 nm"]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":["Κανάλι 11 - ζώνη απορρόφησης O2 R-στελέχους - 761 nm"]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":["Το ** DEM ** (Digital Elevation Model - Ψηφιακό Μοντέλο Εδάφους) είναι μια ψηφιακή αναπαράσταση του εδάφους (συνήθως της επιφάνειας της Γης). Δημιουργείται με τη διαίρεση όλου του κόσμου σε κελιά πλέγματος, καθένα από τα οποία έχει μια τιμή υψομέτρου σε μέτρα. Ανάλογα με το μέγεθος του κελιού του πλέγματος, ένα DEM μπορεί να είναι πιο λεπτομερές (υψηλή ανάλυση) ή λιγότερο λεπτομερές (χαμηλή ανάλυση). Οι συλλογές δεδομένων Sentinel Hub DEM (Mapzen και Copernicus) είναι στατικές (ανεξάρτητες από την ημερομηνία) και είναι παγκοσμίως διαθέσιμες.\n\n** Συνήθης χρήση: ** Μοντελοποίηση ροών νερού, ορθοαναγωγή εικόνων Sentinel-1."]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["Το ** Copernicus DEM ** αναπαριστά την επιφάνεια της Γης, συμπεριλαμβανομένων κτιρίων, υποδομών και βλάστησης. Ομοίως με το Mapzen DEM, βασίζεται σε συνδυασμό διαφορετικών DEM (βασίζεται στο [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Είναι μια στατική συλλογή (ανεξάρτητη από την ημερομηνία) με παγκόσμια κάλυψη.\n\n** Χωρική ανάλυση: ** 30 m και σε κάποιες περιπτώσεις 90 m (όπου δεν είναι διαθέσιμα οι πινακίδες με τα 30 m).\n\nΣυντελεστές: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["Το ** Copernicus DEM ** αναπαριστά την επιφάνεια της Γης, συμπεριλαμβανομένων κτιρίων, υποδομών και βλάστησης. Παρόμοια με το Mapzen DEM, βασίζεται σε συνδυασμό διαφορετικών DEM (βάση [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Είναι μια στατική συλλογή (ανεξάρτητη από την ημερομηνία) με παγκόσμια κάλυψη.\n\n** Χωρική ανάλυση: ** 90 μ\n\nΣυντελεστές: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"User Instances":{"msgid":"User Instances","msgstr":["Στιγμιότυπα Χρήστη"]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":["Πλοήγηση ποντικιού"]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":["Προηγμένα εφέ RGB"]},"Disabled":{"msgid":"Disabled","msgstr":["Απενεργοποιημένο"]},"Yes":{"msgid":"Yes","msgstr":["Ναί"]},"Orthorectification":{"msgid":"Orthorectification","msgstr":["Ορθοαναγωγή"]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":["Πρωτεύον σύνολο δεδομένων:"]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":["Ψευδώνυμο (alias) της πηγής δεδομένων:"]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":["Συμπληρωματικά δεδομένα:"]},"Cancel":{"msgid":"Cancel","msgstr":["Απόρριψη"]},"Error":{"msgid":"Error","msgstr":["Σφάλμα"]},"Help":{"msgid":"Help","msgstr":["Βοήθεια"]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":["Τοποθετήστε την κάμερα 3D βάσει του χάρτη 2D"]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":["Τροποποιημένος δείκτης ανάκλασης ανθοκυανίνης"]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":["# Αποτελεσματικό κλάσμα ραδιομετρικού νέφους\n\nΤο αποτελεσματικό κλάσμα του ραδιομετρικού νέφους αντιπροσωπεύει το τμήμα της επιφάνειας της Γης που καλύπτεται από σύννεφα, διαιρούμενο με τη συνολική επιφάνεια. Τα σύννεφα προκαλούν θωράκιση, λευκαύγεια (albedo) και απορρόφηση της ακτινοβολίας. Το αποτελεσματικό κλάσμα του ραδιομετρικού νέφους είναι μια σημαντική παράμετρος για τη διόρθωση αυτών των εφέ."]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Θερμικό κανάλι 10\n\nΑυτή η θερμική απεικόνιση βασίζεται στο κανάλι 10 (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος · Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Στο κεντρικό μήκος κύματος των 10895 nm μετρά στο θερμικό υπέρυθρο, ή TIR. Αντί να μετρά τη θερμοκρασία του αέρα, όπως κάνουν οι μετεωρολογικοί σταθμοί, το κανάλι 10 παρέχει πληροφορίες στο ίδιο το έδαφος, το οποίο συχνά είναι πολύ πιο ζεστό. Το θερμικό κανάλι 10 είναι χρήσιμο για την παροχή επιφανειακών θερμοκρασιών και συλλέγεται με ανάλυση 100 μέτρων.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":["# Σύνθετο του μικροκυματικού υπέρυθρου (SWIR)\n\nΟι μετρήσεις υπερύθρων μικρού κύματος (SWIR) μπορούν να βοηθήσουν τους επιστήμονες να εκτιμήσουν πόσο νερό υπάρχει στα φυτά και το έδαφος, καθώς το νερό απορροφά μήκη κύματος SWIR. Τα υπέρυθρα κανάλια μικρού κύματος (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος · Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια) είναι επίσης χρήσιμα για τη διάκριση μεταξύ διαφορετικών τύπων σύννεφων (σύννεφα νερού έναντι σύννεφων πάγου), χιονιού και πάγου, που εμφανίζονται λευκά σε ορατό φως. Σε αυτό το σύνθετο η βλάστηση εμφανίζεται σε αποχρώσεις του πράσινου, τα εδάφη και οι τεχνητές περιοχές βρίσκονται σε διάφορες αποχρώσεις του καφέ και το νερό εμφανίζεται μαύρο. Η πρόσφατα καμένη γη αντανακλά έντονα τις ζώνες SWIR, καθιστώντας τις πολύτιμες για τη χαρτογράφηση των ζημιών από φωτιά. Κάθε τύπος βραχώδους εδάφους αντανακλά το υπέρυθρο φως μικρού κύματος, με αποτέλεσμα να καθίσταται δυνατή η χαρτογράφηση της γεωλογίας.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/composites/)"]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":["# Κανονικοποιημένος Δείκτης Χιονιού(NDSI)\n\nΟ κανονικοποιημένος δείκτης χιονιού Sentinel-2 μπορεί να χρησιμοποιηθεί για τη διάκριση μεταξύ σύννεφου και χιονιού καθώς το χιόνι απορροφά το υπέρυθρο φως μικρού κύματος, αλλά αντανακλά το ορατό φως, ενώ το σύννεφο είναι γενικά ανακλαστικό και στα δύο μήκη κύματος. Το χιόνι απεικονίζεται με έντονο μπλε χρώμα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/)."]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":["# Ταξινόμηση σκηνής\n\n\n\nΗ ταξινόμηση σκηνής αναπτύχθηκε για τη διάκριση μεταξύ θολών pixel, καθαρών pixel και pixel νερού από δεδομένα Sentinel-2 και είναι αποτέλεσμα του αλγορίθμου ταξινόμησης σκηνής της ESA. Παρέχονται δώδεκα διαφορετικές ταξινομήσεις, συμπεραλμβανομένης της τυπολογίας των σύννεφων, βλάστησης, εδαφών / ερήμου, νερού και χιονιού. Δεν αποτελεί χάρτη κάλυψης γης με αυστηρή έννοια.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/)."]},"Zoom to location":{"msgid":"Zoom to location","msgstr":["Εστίαση στην περιοχή"]},"Remove layer":{"msgid":"Remove layer","msgstr":["Αφαίρεση επιπέδου"]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":["Κανάλι 10 - Θερμικό Υπέρυθρο (TIRS) - 10895 nm"]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":["Κανάλι 11 - Θερμικό Υπέρυθρο (TIRS) - 12005 nm"]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":["Το αποθετήριο ** CORINE Land Cover (CLC) ** είναι ένα σύνολο δεδομένων βασισμένο σε φορέα που αποτελείται από 44 κατηγορίες κάλυψης γης και χρήσης γης, που προέρχονται από μια σειρά δορυφορικών αποστολών. Στην πλειονότητα των ευρωπαϊκών χωρών, το CLC παράγεται χρησιμοποιώντας οπτική ερμηνεία δορυφορικών εικόνων υψηλής ανάλυσης. Σε μερικές χώρες εφαρμόζονται ημι-αυτόματες λύσεις, χρησιμοποιώντας εθνικά δεδομένα in situ, επεξεργασία δορυφορικών εικόνων και χρήση εργαλείων GIS. Περισσότερες πληροφορίες [εδώ] (https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover).\n\n** Κάλυψη **: Το μεγαλύτερο μέρος της Ευρώπης.\n\n** Διαθεσιμότητα δεδομένων **:\nΤα δεδομένα CLC ενημερώνονται κάθε 6 χρόνια. Στο πρόγραμμα περιήγησης EO, τα δεδομένα είναι διαθέσιμα για τις ακόλουθες ημερομηνίες:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n** Κοινή χρήση **:\nΠαρακολούθηση της χρήσης γης και κάλυψη γης, ανάλυση και πρόβλεψη αλλαγών για διάφορες εφαρμογές, όπως περιβάλλον, γεωργία, μεταφορές και χωρικός σχεδιασμός."]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":["** Τα προϊόντα Global Land Cover ** παρέχουν έναν ξεχωριστό χάρτη ταξινόμησης κάλυψης γης σύμφωνα με το Σύστημα Ταξινόμησης Κάλυψης Γης UN-FAO. Πρόσθετα συνεχή κλασματικά στρώματα για όλες τις βασικές κατηγορίες κάλυψης γης περιλαμβάνονται ως ζώνες, για να παρέχουν πιο λεπτομερείς πληροφορίες για κάθε κατηγορία κάλυψης γης Περισσότερες πληροφορίες [εδώ] (https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover).\n\n** Κάλυψη **: Παγκόσμια.\n\n** Διαθεσιμότητα δεδομένων **:\nΕνημερώνεται σε ετήσια βάση. Στο πρόγραμμα περιήγησης EO, τα δεδομένα είναι διαθέσιμα για τις ακόλουθες ημερομηνίες:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n** Κοινή χρήση **:\nΠαρακολούθηση της χρήσης γης και της κάλυψης της γης, που χρησιμοποιείται για να βοηθήσει τη λήψη αποφάσεων σε διάφορα θέματα, όπως η γεωργία και η επισιτιστική ασφάλεια, η βιοποικιλότητα, η κλιματική αλλαγή, οι δασικοί και υδάτινοι πόροι, η υποβάθμιση και η ερημοποίηση της γης και η αγροτική ανάπτυξη."]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":["Κύρια διακριτή ταξινόμηση γης σύμφωνα με το σχήμα FAO LCCS"]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":["Πιθανότητα ταξινόμησης, δείκτης ποιότητας για τη διακριτή ταξινόμηση"]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":["Τύπος δάσους για όλα τα εικονοστοιχεία στα οποία το κλάσμα κάλυψης δέντρων είναι μεγαλύτερο από 1%"]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":["Κλασματική κάλυψη (%) για την κατηγορία γυμνού εδάφους και αραιής βλάστησης"]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":["Κλασματική κάλυψη (%) για την κατηγορία του καλλιεργήσιμου εδάφους"]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":["Κλασματική κάλυψη (%) για την κατηγορία ποώδους βλάστησης"]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":["Κλασματικό κάλυμμα (%) για την κατηγορία βρύα & λειχήνες"]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":["Κλασματικό κάλυμμα (%) για την κατηγορία των θάμνων"]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":["Κλασματικό κάλυμμα (%) για την κατηγορία χιονιού και πάγου"]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":["Κλασματική κάλυψη (%) για την δασική κατηγορία"]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":["Κλασματικό κάλυμμα (%) για την κατηγορία των ανθρωπογενών κατασκευών"]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":["Κλασματική κάλυψη (%) για τη κατηγορία των μόνιμων υδατικών συστημάτων της ενδοχώρας"]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":["Κλασματική κάλυψη (%) για την κατηγορία εποχιακών υδάτινων σωμάτων"]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":["Δείκτης πυκνότητας δεδομένων που δείχνει την ποιότητα των δεδομένων παρατήρησης Γης (0 = κακή, 100 = τέλεια δεδομένα)"]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":["Επίπεδο ποιότητας σχετικά με τον εντοπισμό αλλαγών του τρέχοντος χαρτογραφημένου έτους στο προηγούμενο χαρτογραφημένο έτος. Αποτελεί ένα σύνολο 3 επιπέδων εμπιστοσύνης για όλους τους χάρτες CONSO και NRT με τιμών όπως:\n0 = Καμία αλλαγή.\n1 - Πιθανή εμπιστοσύνη.\n2 - Μεσαία εμπιστοσύνη.\n3 = Υψηλή αυτοπεποίθηση.\nΣΗΜΕΙΩΣΗ: Οι τιμές του καναλιού Change_Confidence_layer στα δεδομένα του 2015 δεν εμφανίζονται σωστά, επομένως τα δεδομένα αυτής της ζώνης δεν πρέπει να χρησιμοποιούνται για το συγκεκριμένο έτος."]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":["Σύρετε τις κατηγορίες στα πεδία RGB."]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":["# Χάρτης ταξινόμησης διακριτών τιμών\n\n\n\nΑυτό το επίπεδο απεικονίζει τον παγκόσμιο χάρτη κάλυψης γης με 23 διακριτές κατηγορίες που ορίζονται χρησιμοποιώντας το Σύστημα Ταξινόμησης Κάλυψης Γης UN-FAO (LCCS) και με συνδυασμό χρωμάτων που ορίζονται στο Εγχειρίδιο χρήστη προϊόντος του χάρτη [εδώ.] (Https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)"]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":["# Τύποι δασών\n\n\n\nΟπτικοποιημένοι τύποι δασών βάσει των 6 κατηγοριών, που ορίζονται στο Σύστημα Ταξινόμησης Κάλυψης Γης του UN-FAO (LCCS). Περισσότερα [εδώ.] (Https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)."]},"File upload":{"msgid":"File upload","msgstr":["Μεταφόρτωση αρχείου"]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Ανεβάστε ένα αρχείο KML / KMZ, GPX ή GEOJSON / JSON για να δημιουργήσετε μια περιοχή ενδιαφέροντος. Η περιοχή θα χρησιμοποιηθεί για αποκοπή κατά την εξαγωγή μιας εικόνας."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":["Αποθέστε το αρχείο KML / KMZ, GPX, GEOJSON / JSON ή αναζητήστε τον υπολογιστή σας"]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":["Επίπεδο ανίχνευσης κύριων υδάτινων σωμάτων που δείχνει εικονοστοιχεία με νερό και εικονοστοιχεία χωρίς νερό\n0 = Θάλασσα\n70 = Νερό\n251 = Δεν υπάρχουν δεδομένα\n255 = Χωρίς νερό"]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":["Ποιοτικό επίπεδο πληροφορίας σχετικά με την παρουσία των υδάτινων σωμάτων\n0 = Θάλασσα\n71 = Πολύ χαμηλή παρουσία\n72 = Χαμηλή παρουσία\n73 = Μεσαία παρουσία\n74 = Υψηλή παρουσία\n75 = Πολύ υψηλή παρουσία\n76 = Μόνιμη παρουσία\n251 = Δεν υπάρχουν δεδομένα\n252 = Σύννεφο\n255 = Όχι νερό"]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":["# Υδάτινα Σώματα\n\n\n\nΑυτό το επίπεδο πληροφορίας απεικονίζει την ανίχνευση υδάτινων σωμάτων (WB), η οποία δείχνει τα υδάτινα σώματα που εντοπίστηκαν χρησιμοποιώντας τον Τροποποιημένο Δείκτη Νερού Κανονικοποιημένης Διαφοράς (MNDWI) που προέρχεται από δεδομένα Sentinel-2 Επιπέδου 1C. Περισσότερες πληροφορίες [εδώ](https://collections.sentinel-hub.com/water-bodies/readme.html) και [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/)."]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":["Το προϊόν ** Υδάτινα σώματα ** απεικονίζει την έκταση της επιφάνειας που καλύπτεται από τα εσωτερικά ύδατα σε μόνιμη, εποχιακή ή περιστασιακή βάση σε παγκόσμια κλίμακα. Περιέχει ένα κύριο επίπεδο ανίχνευσης σώματος νερού (WB) και ένα επίπεδο ποιοτικής πληροφορίας (QUAL), σχετικά με την εποχιακή δυναμική των ανιχνευόμενων υδάτινων σωμάτων. Περισσότερες πληροφορίες [εδώ](https://collections.sentinel-hub.com/water-bodies/).\n\n**Κάλυψη**:\nΠαγκόσμια κάλυψη από μήκος -180 ° Α έως + 180 ° Δ και πλάτος + 80 ° Β έως -60 ° Ν. Ανάλογα με το μήνα, ορισμένες περιοχές μεγάλου γεωγραφικού πλάτους δεν καλύπτονται από τους δορυφόρους Sentinel-2.\n\n** Διαθεσιμότητα δεδομένων **:\nΑπό τον Οκτώβριο του 2020, το αρχείο ενημερώνεται κάθε μήνα.\n\n** Κοινή χρήση **\nΠαρακολούθηση υδάτινων σωμάτων, ξηρασίας, πλημμυρών και κλιματικής αλλαγής."]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Κάλυψη γης Corine (CLC)\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται και οι 44 κατηγορίες. Μάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης [εδώ ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Τεχνητές επιφάνειες\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται μόνο οι 11 κατηγορίες τεχνητής επιφάνειας, με βάση την ταξινόμηση [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature- οδηγίες / html). Μάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης με όλες τις κατηγορίες [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Γεωργικές περιοχές\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται μόνο οι 11 γεωργικές κατηγορίες, με βάση την ταξινόμηση [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines / html). Μάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης με όλες τις κατηγορίες [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Δασικές και ημιφυσικές περιοχές\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται μόνο οι 12 κατηγορίες δασικών και ημιφυσικών περιοχών, με βάση την ταξινόμηση [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Μάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης με όλες τις κατηγορίες [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Υγρότοποι\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται μόνο οι 5 κατηγορίες υγροτόπων, με βάση την ταξινόμηση [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html).\nΜάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης με όλες τις κατηγορίες [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Υδάτινα σώματα\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται μόνο οι 6 κατηγορίες υδατικών σωμάτων, με βάση την ταξινόμηση [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/ html). Μάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης με όλες τις κατηγορίες [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":["# Υδάτινα σώματα - Παρουσία\n\n\n\nΑυτό το επίπεδο πληροφορίας απεικονίζει τα 6 επίπεδα της Ποιότητας (QUAL), παρέχοντας πληροφορίες για την εποχική δυναμική των ανιχνευόμενων υδάτινων σωμάτων. Το QUAL παράγεται από στατιστικά στοιχεία παρουσίας υδάτινων σωμάτων που υπολογίστηκαν από προηγούμενα μηνιαία προϊόντα υδάτινων σωμάτων. Τα στατιστικά στοιχεία ταξινομούνται από χαμηλή παρουσία έως μόνιμη παρουσία. Περισσότερες πληροφορίες [εδώ](https://collections.sentinel-hub.com/water-bodies/readme.html) και [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#)."]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":["Landsat 8 L1"]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":["Landsat 8 L2"]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":["Πολύ Μπλε (443 nm)"]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":["Μπλε (482 nm)"]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":["Πράσινο (561.5 nm)"]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":["Κόκκινο (654.5 nm)"]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":["Εγγύς Υπέρυθρο (NIR) (865 nm)"]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":["Μικροκυματικό Υπέρυθρο (SWIR) 1 (1608.5 nm)"]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":["Μικροκυματικό Υπέρυθρο (SWIR) 2 (2200.5 nm)"]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":["Θερμικό Υπέρυθρο (TIRS) 1(10895 nm)"]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":["** Τα δεδομένα Επιπέδου 1 ** (από το ** Landsat Collection 2 **) παρέχουν προϊόντα ανακλαστικότητας και θερμοκρασίας εκτός της ατμόσφαιρας (top of atmosphere-TOA), με παγκόσμια κάλυψη.\n\nΤα δεδομένα έχουν υποβληθεί σε διάφορα στάδια επεξεργασίας, συμπεριλαμβανομένων γεωμετρικών και ραδιομετρικών βελτιώσεων.\n\nΠερισσότερες πληροφορίες σχετικά με τα δεδομένα Επιπέδου-1 [εδώ](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt -science_support_page_related_con) και [εδώ](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)"]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":["** Τα δεδομένα Επιπέδου-2 ** (από το ** Landsat Collection 2 **) παρέχουν προϊόντα επιφανειακής ανακλαστικότητας και θερμοκρασίας (CEOS Analysis Ready Data), με παγκόσμια κάλυψη\n\nΤα προϊόντα δεδομένων δημιουργούνται από δεδομένα Επιπέδου-1 που πληρούν τους περιορισμούς της Ηλιακής Γωνίας Ζενίθ, μικρότερης των 76 μοιρών και περιλαμβάνουν τα απαιτούμενα βοηθητικά σύνολα δεδομένων για τη δημιουργία ενός επιστημονικά βιώσιμου προϊόντος.\n\nΜάθετε περισσότερα για τα δεδομένα Επιπέδου-2 [εδώ](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) και [εδώ]( https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/)."]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["** Ο Landsat 8 ** είναι ο πιο πρόσφατα εκτοξευμένος δορυφόρος Landsat (παρέχεται από τη NASA / USGS) και φέρει τα όργανα Operational Land Imager (OLI) και τους θερμικά υπέρυθρους αισθητήρες (TIRS), με 9 οπτικά και 2 θερμικά κανάλια. Αυτοί οι δύο αισθητήρες παρέχουν εποχιακή παγκόσμια κάλυψη της γης.\n\n** Χωρική ανάλυση: ** 15 m για το παγχρωματικό κανάλι και 30 m για τα υπόλοιπα (τα θερμικά κανάλια προκύπτουν ύστερα απο ανασύσταση εικόνας από τα 100 m).\n\n** Χρόνος επίσκεψης: ** 16 ημέρες\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Φεβρουάριο του 2013\n\n** Κοινή χρήση: ** Παρακολούθηση βλάστησης, χρήση γης, χάρτες κάλυψης γης, παρακολούθηση αλλαγών κ.λπ."]},"Please select a layer":{"msgid":"Please select a layer","msgstr":["Παρακαλώ επιλέξτε ένα επίπεδο"]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":["Το ιστόγραμμα μπορεί να εμφανιστεί μόνο κατά την οπτικοποίηση"]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":["Το ιστόγραμμα δεν είναι διαθέσιμο για "]},"Recalculate":{"msgid":"Recalculate","msgstr":["Επανυπολογισμός"]},"Histogram":{"msgid":"Histogram","msgstr":["Ιστογράμμα"]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":["Αλλαγές στην εμφάνιση νερού μεταξύ δύο εποχών, η πρώτη που κυμαίνεται από το 1984 έως το 1999 και η δεύτερη που καλύπτει το χρονικό εύρος από το 2000 έως το 2019."]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":["Μέγιστη έκταση των επιφανειακών υδάτων στο χρονικό εύρος των 36 ετών."]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":["Διαχρονική συχνότητα παρουσίας επιφανειακών υδάτων στο χρονικό διάστημα μεταξύ 1984 και 2019."]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":["Διαχρονική μεταβλητότητα της παρουσίας επιφανειακών υδάτων σε καθορισμένη περίοδο νερού εντός του χρονικού διαστήματος από το 1984 έως το 2019."]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":["Διαχρονική κατανομή επιφανειακών υδάτων το 2019."]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":["Οπτικοποιεί τις αλλαγές στις τρεις κατηγορίες επιφανειακών υδάτων (1) όχι νερό, (2) εποχιακό νερό και (3) μόνιμο νερό μεταξύ του πρώτου και του περασμένου έτους στην 36ετή χρονική περίοδο."]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":["# Παγκόσμιο Επιφανειακό Νερό - Παρουσία\n\n\n\nΤο επίπεδο πληροφορίας δείχνει τις διαχρονικές και μέσα στο έτος διακυμάνσεις της παρουσίας επιφανειακών υδάτων στο χρονικό διάστημα μεταξύ Μαρτίου 1984 και Δεκεμβρίου 2019. Οι μόνιμες περιοχές νερού με 100% εμφάνιση κατά τη διάρκεια των 36 ετών εμφανίζονται με μπλε χρώμα, ενώ οι πιο ανοιχτές αποχρώσεις του ροζ και μωβ υποδεικνύουν χαμηλότερες βαθμίδες παρουσίας νερού. Μάθετε περισσότερα [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/)."]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":["# Παγκόσμιο Επιφανειακό Νερό - Ένταση αλλαγής της παρουσίας\n\n\n\nΤο επίπεδο πληροφορίας απεικονίζει τις αλλαγές στην παρουσία του νερού μεταξύ δύο διαφορετικών εποχών, η πρώτη που κυμαίνεται από τον Μάρτιο του 1984 έως τον Δεκέμβριο του 1999 και η άλλη που καλύπτει την περίοδο από τον Ιανουάριο του 2000 έως τον Δεκέμβριο του 2019. Οι περιοχές με αύξηση της εμφάνισης του νερού απεικονίζονται σε διαφορετικές αποχρώσεις του πράσινου, περιοχές χωρίς αλλαγή απεικονίζονται με μαύρο χρώμα και οι περιοχές με μείωση εμφανίζονται σε αποχρώσεις του κόκκινου. Μάθετε περισσότερα [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/)."]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":["# Παγκόσμιο Επιφανειακό Νερό - Εποχικότητα\n\n\n\nΤο επίπεδο πληροφορίας της εποχικότητας παρέχει πληροφορίες σχετικά με την κατανομή των επιφανειακών υδάτων το 2019. Τα μόνιμα υδάτινα σώματα (το νερό ήταν παρόν για 12 μήνες) χρωματίζονται σε σκούρο μπλε και το εποχικό νερό (το νερό ήταν παρόν για λιγότερο από 12 μήνες) σε βαθμιαία φωτεινότερες αποχρώσεις του μπλε, με τις πιο ανοιχτές μπλε περιοχές όπου το νερό υπήρχε μόνο για 1 μήνα. Μάθετε περισσότερα [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#)."]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":["# Παγκόσμιο Επιφανειακό Νερό - Μετατροπές\n\n\n\nΤο επίπεδο των μετατροπών προέρχεται από μια σύγκριση μεταξύ του πρώτου και του τελευταίου έτους στην 36ετή χρονική περίοδο. Απεικονίζει τις αλλαγές μεταξύ εποχιακού και μόνιμου νερού. Για παράδειγμα, το \"χαμένο εποχιακό\" σημαίνει, ότι στο παρελθόν το εποχικό νερό μετατράπηκε σε γη, το \"νέο εποχιακό\" σημαίνει ότι η γη έχει μετατραπεί σε εποχικά νερά και ούτω καθεξής. Μάθετε περισσότερα [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) και μάθετε τι σημαίνει κάθε κατηγορία [εδώ](https://global-surface-water.appspot.com/faq )."]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":["# Παγκόσμιο Επιφανειακό Νερό - Έκταση\n\n\n\nΑυτό το επίπεδο πληροφορίας απεικονίζει το νερό με μπλε χρώμα. Συνδυάζει όλα τα άλλα επίπεδα πληροφορίας και απεικονίζει όλες τις τοποθεσίες για τις οποίες έχει εντοπιστεί παρουσία νερού στο διάστημα 36 ετών. Μάθετε περισσότερα [εδώ] (https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/)."]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":["# Παγκόσμιο επιφανειακό νερό - Επανεμφάνιση\n\n\n\nΤο επίπεδο της επανεμφάνισης δείχνει πόσο συχνά το νερό επέστρεψε σε μια συγκεκριμένη τοποθεσία σε μια καθορισμένη περίοδο νερού μεταξύ 1984 και 2019. Το πορτοκαλί χρώμα υποδηλώνει χαμηλή επανεμφάνιση (το νερό επιστρέφει στην περιοχή σπάνια) και το ανοιχτό μπλε χρώμα δείχνει υψηλή επανεμφάνιση (το νερό επιστρέφει συχνά στην περιοχή ). Μάθετε περισσότερα [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/)."]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":["Μπλε (450-520 nm))"]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":["Πράσινο (520-600 nm)"]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":["Κόκκινο (630-690 nm)"]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":["Εγγύς Υπέρυθρο (NIR) (760-900 nm)"]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":["Μικροκυματικό Υπέρυθρο (SWIR) (1550-1750 nm)1"]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":["Θερμικό Υπέρυθρο (10400-12500 nm)"]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":["Μικροκυματικό Υπέρυθρο (SWIR) 2 (2080-2350 nm)"]},"Level 1":{"msgid":"Level 1","msgstr":["Επίπεδο 1"]},"Level 2":{"msgid":"Level 2","msgstr":["Επίπεδο 2"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Ψευδοέγχρωμο σύνθετο\n\nΈνα ψευδοέγχρωμο σύνθετο χρησιμοποιεί τουλάχιστον ένα μη ορατό μήκος κύματος για την απεικόνιση ης Γης. Συγκεκριμένα, το σύνθετο που χρησιμοποιεί υπέρυθρα, κόκκινα και πράσινα κανάλια είναι πολύ δημοφιλές (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος · ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το ψευδοέγχρωμο σύνθετο συνήθως χρησιμοποιείται για την αξιολόγηση της πυκνότητας και της υγείας των φυτών, καθώς τα φυτά ανακλούν το φως κοντά στο υπέρυθρο και το πράσινο, ενώ απορροφούν το κόκκινο. Οι πόλεις και το γυμνό έδαφος εμφανίζεται σε αποχρώσεις του γκρι και το νερό εμφανίζεται μπλε ή μαύρο."]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":["Η συλλογή ** Landsat 4-5 TM ** περιλαμβάνει εικόνες που παράγονται με τον αισθητήρα Thematic Mapper (TM), ο οποίος μεταφέρθηκε στους δορυφόρους Landsat 4 και 5. Υπάρχουν 6 οπτικά και ένα θερμικό υπέρυθρο κανάλι, με χωρική ανάλυση 30 μέτρων. Τα δεδομένα διαθέτουν παγκόσμια κάλυψη στη γη και είναι διαθέσιμα για τα έτη από το 1982 έως το 2012. Παρέχονται προϊόντα Επιπέδου-1 εκτός της ατμόσφαιρας (top of atmosphere - TOA) και Επιπέδου-2 επιφανειακής ανακλαστικότητας.\n\n** Χωρική ανάλυση **: 30 μέτρα\n\n** Χρόνος επίσκεψης ** 16 ημέρες\n\n** Διαθεσιμότητα δεδομένων **: παγκόσμια κάλυψη, Επίπεδο-1 από τον Αύγουστο του 1982 έως τον Μάιο του 2012, Επίπεδο-2 από τον Ιούλιο του 1984 έως τον Μάιο του 2012.\n\n** Κοινή χρήση **: Παρακολούθηση βλάστησης, πάγου και υδάτινων πόρων, ανίχνευση αλλαγών και δημιουργία χρήσεων γης - χαρτών κάλυψης γης."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":["# Φυσικό Έγχρωμο Σύνθετο\n\nΟι αισθητήρες που διαθέτουν οι δορυφόροι μπορούν να απεικονίσουν τη Γη σε διάφορες περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα ονομάζεται κανάλι. Το Landsat 4-5 TM έχει 7 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί ορατές λωρίδες φωτός κόκκινου, πράσινου και μπλε στα αντίστοιχα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα τη δημιουργία ενός προϊόντος με φυσικό χρώμα, που είναι μια καλή αναπαράσταση της Γης όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites)."]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":["# Θερμικό κανάλι 6\n\nΑυτή η θερμική οπτικοποίηση βασίζεται στο κανάλι 6 (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος · ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το κεντρικό μήκος κύματος των 11040 nm αντιστοιχεί στο θερμικό υπέρυθρο, ή TIR. Αντί να μετρά τη θερμοκρασία του αέρα, όπως κάνουν οι μετεωρολογικοί σταθμοί, το κανάλι 6 έχει σημείο αναφοράς το ίδιο το έδαφος., το οποίο είναι συχνά πολύ πιο ζεστό. Το θερμικό κανάλι 6 είναι χρήσιμο για την παροχή επιφανειακών θερμοκρασιών, συλλέγεται με ανάλυση 120 μέτρων και μετά την ανασύσταση της εικόνας προκύπτει ανάλυση 30 μέτρων.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites)."]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":["Η συνάρτηση αρχικοποίησης (setup function) στο evalscript δεν περιέχει το σωστό αποτέλεσμα εξόδου. Το αποτέλεσμα πρέπει να περιλαμβάνει:"]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":["Προσπαθήσατε να οπτικοποιήσετε ένα επίπεδο που δεν αντιστοιχεί σε κάποιο δείκτη. Η λειτουργία ιστογράμματος προς το παρόν λειτουργεί μόνο για δείκτες (π.χ. NDVI).\n\nΕπιλέξτε ένα επίπεδο δείκτη για να χρησιμοποιήσετε αυτήν τη δυνατότητα."]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["Το προϊόν Landsat 4-5 TM Επιπέδου-1 ** παρέχει εικόνες ανακλαστικότητας εκτός της ατμόσφαιρας (top of atmosphere-TOA). Τα δεδομένα Επιπέδου-1 παράγονται με επεξεργασία δεδομένων Landsat TM με τυπικές παραμέτρους επεξεργασίας, όπως κυβική συνέλιξη και διόρθωση εδάφους. Μάθετε περισσότερα [εδώ](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) και [εδώ](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects)."]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["Το προϊόν ** Landsat 4-5 TM Επιπέδου-2 ** παράγεται από την επεξεργασία δεδομένων Επιπέδου-1 για τη μετατροπή τους σε επιφανειακή ανακλαστικότητα - μια εκτίμηση της φασματικής ανάκλασης της επιφάνειας στο επίπεδο του εδάφους με την απουσία ατμοσφαιρικής σκέδασης και απορρόφησης. Μάθετε περισσότερα [εδώ](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) και [εδώ](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects)."]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":["Η συλλογή ** Global Surface Water ** προέρχεται από εικόνες Landsat 5, 7 και 8 και δείχνει διάφορες πτυχές της χωροχρονικής κατανομής των επιφανειακών υδάτων μεταξύ 1984 και 2020 (με ετήσιες αναθεωρήσεις) σε παγκόσμια κλίμακα σε έξι διαφορετικά επίπεδα. Ως επιφανειακό νερό θεωρείται κάθε ακάλυπτη έκταση νερού (περιοχές γλυκού και θαλασσινού νερού) μεγαλύτερη από 30m² ορατή από το διάστημα, συμπεριλαμβανομένων των φυσικών και τεχνητών υδάτινων σωμάτων. Περισσότερες πληροφορίες [εδώ] (https://collections.sentinel-hub.com/global-surface-water/).\n\n** Κάλυψη **: Παγκόσμια κάλυψη από μήκος 170 ° E έως 180 ° W και γεωγραφικό πλάτος 80 ° N έως 50 ° S.\n\n** Διαθεσιμότητα δεδομένων **: 1984 - 2019, 1984 - 2020.\n\n** Χωρική ανάλυση **: 30 μέτρα.\n\n** Κοινή χρήση **: Παρακολούθηση υδατικών συστημάτων για διαχείριση υδατικών πόρων, μοντελοποίηση κλίματος, διατήρηση της βιοποικιλότητας και επισιτιστική ασφάλεια."]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":["Το ** Mapzen DEM ** βασίζεται στο SRTM30 (Shuttle Radar Topography Mission) και [άλλες πηγές](https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). Τα δεδομένα της βαθυμετρίας λαμβάνονται από το [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). Είναι μια στατική συλλογή (ανεξάρτητη από την ημερομηνία) με παγκόσμια κάλυψη.\n\n** Χωρική ανάλυση: ** Κυρίως 90 m, σε ορισμένες περιοχές έως 10 m.\n\nΣυντελεστές: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)"]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Μέγιστη νεφοκάλυψη:"]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Μεταφόρτωση δεδομένων"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":[""]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]}}}}
\ No newline at end of file
+{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","plural-forms":"nplurals=2; plural=(n != 1);","mime-version":"1.0","content-transfer-encoding":"8bit","project-id-version":"","pot-creation-date":"","po-revision-date":"","language-team":"","x-generator":"Poedit 3.0","last-translator":"","language":"el"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nPlural-Forms: nplurals=2; plural=(n != 1);\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\nProject-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLanguage-Team: \nx-generator: Poedit 3.0\nLast-Translator: \nLanguage: el\n"]},"Education":{"msgid":"Education","msgstr":["Εκπαίδευση"]},"Normal":{"msgid":"Normal","msgstr":["Κανονικό"]},"Close":{"msgid":"Close","msgstr":["Κλείσιμο"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Να κλείσει και να μην εμφανιστεί ξανά"]},"Previous":{"msgid":"Previous","msgstr":["Προηγούμενο"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Τερματισμός εκμάθησης"]},"Next":{"msgid":"Next","msgstr":["Επόμενο"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Συνεχίστε με το πρόγραμμα εκμάθησης"]},"Don't show again":{"msgid":"Don't show again","msgstr":["Να μην εμφανιστεί ξανά"]},"Show info":{"msgid":"Show info","msgstr":["Εμφάνιση πληροφοριών"]},"Discover":{"msgid":"Discover","msgstr":["Εξερευνήστε"]},"Visualize":{"msgid":"Visualize","msgstr":["Οπτικοποιήστε"]},"Compare":{"msgid":"Compare","msgstr":["Συγκρίνετε"]},"Pins":{"msgid":"Pins","msgstr":["Πινέζες"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Παρουσιάστηκε σφάλμα κατά τη λήψη εικόνων:"]},"No tile found":{"msgid":"No tile found","msgstr":["Δε βρέθηκε πινακίδα"]},"Dataset":{"msgid":"Dataset","msgstr":["Σύνολο δεδομένων"]},"Show":{"msgid":"Show","msgstr":["Εμφάνιση"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Εμφανίση εφέ και προηγμένων επιλογών"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Εμφάνιση οπτικοποίησης"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Προσθήκη στις πινέζες"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Προσθήκη στη σύγκριση"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Εστίαση στην πινακίδα"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Απόκρυψη επιπέδου"]},"Show layer":{"msgid":"Show layer","msgstr":["Εμφάνιση επιπέδου"]},"Share":{"msgid":"Share","msgstr":["Διαμοιρασμός"]},"Custom":{"msgid":"Custom","msgstr":["Προσαρμοσμένο"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Δημιουργία προσαρμοσμένης οπτικοποίησης"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Εστίαση για προβολή δεδομένων"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Δωρεάν εγγραφή"]},"for all features":{"msgid":"for all features","msgstr":["για όλα τα χαρακτηριστικά"]},"Powered by":{"msgid":"Powered by","msgstr":["Δημιουργήθηκε από"]},"with contributions by":{"msgid":"with contributions by","msgstr":["με τη συμβολή των"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["Παρακαλώ επιλέξτε πηγή(ές) δεδομένων!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["Μη έγκυρο χρονικό εύρος!"]},"No results found":{"msgid":"No results found","msgstr":["Δε βρέθηκαν αποτελέσματα"]},"Theme":{"msgid":"Theme","msgstr":["Θέμα"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Διαχείριση διαμόρφωσης στιγμιότυπων"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Συνδεθείτε για να χρησιμοποιήσετε προσαρμοσμένα στιγμιότυπα που έχετε διαμορφώσει."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["Σφάλμα κατά την ανάκτηση συμπληρωματικών δεδομένων!"]},"Search":{"msgid":"Search","msgstr":["Αναζήτηση"]},"Highlights":{"msgid":"Highlights","msgstr":["Επισημάνσεις"]},"Data sources":{"msgid":"Data sources","msgstr":["Πηγές δεδομένων"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Παρακαλώ επιλέξτε θέμα"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Χρονικό εύρος (UTC)"]},"Date":{"msgid":"Date","msgstr":["Ημερομηνία"]},"Hide description":{"msgid":"Hide description","msgstr":["Απόκρυψη περιγραφής"]},"Show description":{"msgid":"Show description","msgstr":["Εμφάνιση περιγραφής"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Το θέμα δεν περιλαμβάνει επισημάνσεις"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Βασίζεται σε: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["Μέρα 1 (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["Μέρα 5 (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["Μέρα 10 (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["Ο3 (Όζον)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (διοξείδιο του αζώτου)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (διοξείδιο του θείου)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (μονοξείδιο του άνθρακα)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (Φορμαλδεΰδη)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (μεθάνιο)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER AI (δείκτης αερολύματος)"]},"Cloud":{"msgid":"Cloud","msgstr":["Σύννεφα"]},"Other":{"msgid":"Other","msgstr":["Άλλο"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Μέγιστη νεφοκάλυψη"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Προηγμένη αναζήτηση"]},"Data location":{"msgid":"Data location","msgstr":["Τοποθεσία δεδομένων"]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":["Οι υπηρεσίες Sentinel-1 είναι διαθέσιμες τόσο στο EOCloud όσο και στο AWS. Οι δυνατότητες της κάθε\nυπηρεσίας διαφέρουν. Περισσότερες πληροφορίες στο"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Παρακαλώ επιλέξτε τουλάχιστον μια τοποθεσία!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Λειτουργία λήψης"]},"Polarization":{"msgid":"Polarization","msgstr":["Πόλωση"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Παρακαλώ επιλέξτε τουλάχιστον μία λειτουργία λήψης δεδομένων!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Επιλέξτε τουλάχιστον ένα είδος πόλωσης!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Κατεύθυνση τροχιάς"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Επιλέξτε τουλάχιστον μία κατεύθυνση τροχιάς!"]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["** Το MERIS ** (φασματόμετρο μέσης ανάλυσης) ήταν ένας αισθητήρας του δορυφόρου [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) με πρωταρχικό στόχο την παρατήρηση του εδάφους, των ωκεανών και της ατμόσφαιρας. Δεν είναι πλέον εν ενεργεία και ο Sentinel-3 έχει διαδεχτεί το MERIS.\n\n** Χωρική ανάλυση: ** Πλήρης ανάλυση εδάφους και ακτής: 260m x 290m (που μπορεί να δει μόνο λεπτομέρειες μεγαλύτερες από 260m x 290m).\n\n** Χρόνος επίσκεψης: ** το πολύ 3 ημέρες για επίσκεψη στην ίδια περιοχή.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Ιούνιο του 2002 έως τον Απρίλιο του 2012.\n\n** Κοινή χρήση: ** Παρακολούθηση ωκεανών (φυτοπλαγκτόν, αιωρούμενη ύλη), ατμόσφαιρα (υδρατμοί, CO2, σύννεφα, αερολύματα) και γη (δείκτης βλάστησης, παγκόσμια κάλυψη, υγρασία)."]},"Credits:":{"msgid":"Credits:","msgstr":["Συντελεστές:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["** Το GIBS ** (Παγκόσμιες υπηρεσίες αναζήτησης εικόνων) παρέχει γρήγορη πρόσβαση σε περισσότερες από 600 δορυφορικές εικόνες\nκαι προϊόντα, με παγκόσμια κάλυψη. Οι περισσότερες εικόνες είναι διαθέσιμες μέσα σε λίγες ώρες μετά\nτη λήψη και ορισμένα προϊόντα διαρκούν σχεδόν 30 χρόνια."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["Οι δορυφόροι ** Landsat ** της NASA / U.S. Geological Survey είναι παρόμοιοι με τους Sentinel-2 (δηλαδή καταγράφουν ορατά και υπέρυθρα μήκη κύματος)\nκαι επιπλέον μπορεί να καταγράψουν θερμικές υπέρυθρες ακτίνες (Landsat 8). Η σειρά Landsat έχει μακρά ιστορία που εκτείνεται σχεδόν σε πέντε δεκαετίες.\n Αυτή η πλατφόρμα παρέχει πρόσβαση σε εικόνες που αποκτήθηκαν από το Landsat 5, 7 και 8.\n\n** Χωρική ανάλυση: ** 15m, 30m και 100m (επανασύσταση στα 30m), ανάλογα με το μήκος κύματος (δηλαδή, μπορούν να φανούν μόνο λεπτομέρειες μεγαλύτερες από 10m και 30m). Περισσότερες πληροφορίες [εδώ](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n** Χρόνος επίσκεψης: ** Μέγιστος χρόνος 8 ημερών για κάλυψη της ίδιας περιοχή με τη χρήση των δύο λειτουργικών δορυφόρων Landsat 7 και Landsat 8.\n\n** Διαθεσιμότητα δεδομένων: ** Ευρώπη και Βόρεια Αφρική από το 1984 έως το 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 έως σήμερα (Landsat 8) από το αρχείο ESA. Το παγκόσμιο αρχείο Γεωλογικής Έρευνας των ΗΠΑ (USGS) από τον Απρίλιο του 2013 έως σήμερα (μόνο για το Landsat 8).\n\n** Κοινή χρήση: ** Παρακολούθηση βλάστησης, χρήσεις γης, χάρτες κάλυψης γης, παρακολούθηση αλλαγών κ.λπ."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["Ο δορυφόρος ** MODIS ** της Nasa - (Spectroradiometer Imaging Moderate Resolution) αποκτά δεδομένα με στόχο\nτην καταγραφή των διεργασιών που συμβαίνουν στην ξηρά. Το πρόγραμμα περιήγησης EO παρέχει δεδομένα για\nπαρατήρηση της γης (κανάλια 1-7).\n\n** Χωρική ανάλυση: ** 250m (ζώνες 1-2), 500m (ζώνες 3-7), 1000m (ζώνες 8-36).\n\n** Χρόνος επίσκεψης: ** Παγκόσμια κάλυψη σε 1-2 ημέρες με τους δορυφόρους Aqua και Terra.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Ιανουάριο του 2013.\n\n** Κοινή χρήση: ** Παρακολούθηση γης, νεφών, ωκεανών σε παγκόσμια κλίμακα."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["Ο δορυφόρος ** Proba-V ** είναι ένας μικρός δορυφόρος σχεδιασμένος για να χαρτογραφήσει την κάλυψη της γης και την ανάπτυξη της βλάστησης,\nσε ολόκληρο τον κόσμο, κάθε δύο ημέρες. Το EO Browser παρέχει παράγωγα προϊόντα που ελαχιστοποιούν τη νεφοκάλυψη\nσυνδυάζοντας λήψεις χωρίς σύννεφα εντός χρονικής περιόδου 1 ημέρας (S1), 5 ημερών (S5) και 10 ημερών (S10).\n\n** Χωρική ανάλυση: ** 100m για S1 και S5, 333m για S1 και S10, 1000m για S1 και S10.\n\n** Χρόνος επίσκεψης: ** 1 ημέρα για γεωγραφικά πλάτη 35-75 ° Β και 35-56 ° Ν, 2 ημέρες για γεωγραφικά πλάτη μεταξύ 35 ° Β\nκαι 35 ° Ν.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Οκτώβριο του 2013.\n\n** Κοινή χρήση: ** Η παρατήρηση της κάλυψης της γης, η ανάπτυξη της βλάστησης, η αξιολόγηση των κλιματικών επιπτώσεων,\nη διαχείριση των υδατικών πόρων, παρακολούθηση γεωργικών εκτάσεων και εκτίμηση της επισιτιστικής ασφάλειας, εσωτερικά ύδατα\nπαρακολούθηση των φυσικών πόρων και ανίχνευση της σταθερής εξάπλωσης των ερήμων και της αποψίλωσης των δασών."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":["Ο δορυφόρος ** Sentinel-1 ** παρέχει εικόνες ραντάρ παντός καιρού, την ημέρα και τη νύχτα, για χερσαίες και ωκεάνιες περιοχές. Ο \nEOBrowser παρέχει δεδομένα που αποκτήθηκαν σε λειτουργίες Interferometric Wide Swath (IW) και Extra Wide Swath (EW)\nκαι έχουν μετατραπεί σε προϊόντα Level-1 Ground Range Detected (GRD).\n\n** Μέγεθος εικονοψηφίδας: ** 10m (IW), 40m (EW).\n\n** Χρόνος επίσκεψης: ** <= 5 ημέρες με την αξιοποίηση και των δύο δορυφόρων.\n\n** Χρόνος επίσκεψης ** (για αύξουσα και φθίνουσα τροχιά, με την αξιοποίηση και των δύο δορυφόρων): <= 3 ημέρες, δείτε το [σενάριο παρατήρησης](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation -σενάριο)\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Οκτώβριο του 2014.\n\n** Κοινή χρήση: ** Παρακολούθηση θαλάσσιων και χερσαίων περιοχών, διαχείριση έκτακτων αναγκών, κλιματική αλλαγή."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["Ο δορυφόρος ** Sentinel-2 ** παρέχει εικόνες υψηλής ανάλυσης στα ορατά και υπέρυθρα μήκη κύματος, για την παρακολούθηση της βλάστησης, του εδάφους και του νερού, των εσωτερικών υδάτων και των παράκτιων περιοχών. .\n\n** Χωρική ανάλυση: ** 10m, 20m και 60m, ανάλογα με το μήκος κύματος (δηλαδή, εμφανίζονται μόνο λεπτομέρειες μεγαλύτερες από 10m, 20m και 60m). Περισσότερες πληροφορίες [εδώ](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial).\n\n** Χρόνος επίσκεψης: ** το πολύ 5 ημέρες για επίσκεψη της ίδιας περιοχή, με την αξιοποίηση και των δύο δορυφόρων.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Ιούνιο του 2015. Πλήρης παγκόσμια κάλυψη από το Μάρτιο του 2017.\n\n** Κοινή χρήση: ** χάρτες κάλυψης γης, ανίχνευσης αλλαγών, παρακολούθηση βλάστησης, παρακολούθηση καμένων περιοχών."]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Τα δεδομένα επιπέδου 2Α είναι δεδομένα υψηλής ποιότητας στα οποία έχει εφαρμοστεί ατμοσφαιρική διόρθωση. Τα δεδομένα είναι διαθέσιμα παγκοσμίως από τον Μάρτιο του 2017.\n\nΠερισσότερες πληροφορίες σχετικά με την ατμοσφαιρική διόρθωση [εδώ](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction)."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Τα δεδομένα επιπέδου 1C είναι δεδομένα επαρκούς ποιότητας για τις περισσότερες έρευνες. Σε αυτά έχουν εφαρμοστεί όλες οι διορθώσεις εικόνας εκτός από την ατμοσφαιρική. Τα δεδομένα είναι διαθέσιμα παγκοσμίως από τον Ιούνιο του 2015 και μετά."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["** Κύριος στόχος της αποστολής Sentinel-3 ** είναι η μέτρηση της τοπογραφίας της επιφάνειας της θάλασσας, της θερμοκρασίας της επιφάνειας της θάλασσας και της ξηράς, του χρώματος της επιφάνειας του ωκεανού και της ξηράς. Το Sentinel-3 διαθέτει τέσσερις αισθητήρες. Τα δεδομένα που αποκτήθηκαν από το Ocean and Land Color Instrument (OLCI) και το Sea and Land Surface Temperature Instrument (SLSTR) είναι διαθέσιμα σε αυτήν την πλατφόρμα.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Μάιο του 2016 και μετά."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["Ο αισθητήρας ** Sea and Land Surface Temperature (SLSTR) ** του δορυφόρου Sentinel-3 καταγράφει τη θερμοκρασία της επιφάνειας της θάλασσας και της ξηράς\nσε παγκόσμιο επίπεδο. Το SLSTR καταγράφει τα ορατά μήκη κύματος, το βραχυκυματικό και θερμικό υπέρυθρο του ηλεκτρομαγνητικού φάσματος.\n\n** Χωρική ανάλυση: ** 500 μέτρα για ορατά μήκη κύματος και το εγγύς και βραχυκυματικό υπέρυθρο και 1 χλμ για το θερμικό κανάλι\n(δηλαδή, εμφανίζονται μόνο λεπτομέρειες μεγαλύτερες από 500m και 1km αντίστοιχα).\n\n** Χρόνος επίσκεψης: ** Μέγιστη 1 ημέρα για επίσκεψη στην ίδια περιοχή, χρησιμοποιώντας και τους δύο δορυφόρους.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Μάιο του 2016 και μετά.\n\n** Κοινή χρήση: ** Παρακολούθηση της αλλαγής του κλίματος, παρακολούθηση της βλάστησης, ενεργός εντοπισμός πυρκαγιάς, παρακολούθηση της θερμοκρασίας της ξηράς και της θάλασσας."]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["Ο αισθητήρας ** Ocean and Land Color Instrument (OLCI) ** του δορυφόρου Sentinel-3 είναι ένα φασματόμετρο που\nμετρά την ηλιακή ακτινοβολία που αντανακλάται από τη Γη και παρακολουθεί τον ωκεανό, το περιβάλλον,\nκαι το κλίμα. Παρέχει συχνότερες λήψεις στο ορατό φάσμα από το Sentinel-2 αλλά σε χαμηλότερη ανάλυση\nκαι με περισσότερα μήκη κύματος. Ο Sentinel-3 OLCI αποτελεί διάδοχο του αισθητήρα MERIS στο δορυφόρο Envisat, του οποίου η αποστολή ολοκληρώθηκε.\n\n** Χωρική ανάλυση: ** 300 μέτρα (δηλαδή, μπορούν να καταγραφούν λεπτομέρειες μεγαλύτερες από 300 μέτρα).\n\n** Χρόνος επίσκεψης: ** Μέγιστο 2 ημέρες για επίσκεψη της ίδιας περιοχή, με την αξιοποίηση και των δύο δορυφόρων.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Μάιο του 2016 και μετά.\n\n** Κοινή χρήση: ** Παρακολούθηση της τοπογραφίας και του χρώματος της ξηράς και της θάλασσας."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["Ο ** Sentinel-5P ** είναι ένας δορυφόρος που παρέχει ατμοσφαιρικές μετρήσεις που χρησιμοποιούνται για την εκτίμηση της ποιότητας του αέρα, την παρακολούθηση του όζοντος, την υπεριώδη ακτινοβολία,\nκαι κλιματική παρακολούθηση και πρόβλεψη.\n\n** Χωρική ανάλυση: ** 7 x 3,5 χιλιόμετρα (δηλαδή, εμφανίζονται μόνο λεπτομέρειες μεγαλύτερες από 7 x 3,5 χιλιόμετρα).\n\n** Ώρα επίσκεψης: ** Μέγιστη 1 ημέρα για επίσκεψη στην ίδια περιοχή.\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Απρίλιο του 2018 και μετά.\n\n** Κοινή χρήση: ** Παρακολούθηση της συγκέντρωσης του μονοξειδίου του άνθρακα (CO), του διοξειδίου του αζώτου (NO2) και του όζοντος (O3) στον αέρα. Παρακολούθηση του δείκτη UV αερολύματος (AER_AI) και διαφόρων γεωφυσικών παραμέτρων των νεφών (Cloud)."]},"Copied":{"msgid":"Copied","msgstr":["Αντιγράφηκε"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["Αντιγραφή στο πρόχειρο"]},"Data source name":{"msgid":"Data source name","msgstr":["Ονομασία πηγής δεδομένων"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Χρόνος λήψης"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Νεφοκάλυψη"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Υψόμετρο του ήλιου"]},"MGRS location":{"msgid":"MGRS location","msgstr":["Τοποθεσία MGRS"]},"AWS path":{"msgid":"AWS path","msgstr":["Διαδρομή AWS"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["Διαδρομή EO Cloud"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["Διαδρομή CreoDIAS"]},"SciHub link":{"msgid":"SciHub link","msgstr":["Σύνδεσμος SciHub"]},"Back to search":{"msgid":"Back to search","msgstr":["Πίσω στην αναζήτηση"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Εμφάνιση αποτελέσματος ${ this.state.results.length }","Εμφάνιση αποτελεσμάτων ${ this.state.results.length }"]},"Load more":{"msgid":"Load more","msgstr":["Φόρτωσε περισσότερα"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Φόρτωση περισσότερων αποτελεσμάτων ..."]},"Results":{"msgid":"Results","msgstr":["Αποτελέσματα"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Εμφάνιση αποτελέσματος ${ this.state.selectedTiles.length }.","Εμφάνιση αποτελεσμάτων ${ this.state.selectedTiles.length }"]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Επεξεργασία περιγραφής πινέζας"]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":["Αυτή η πινέζα προς το παρόν δεν έχει περιγραφή."]},"Reject changes":{"msgid":"Reject changes","msgstr":["Απόρριψη αλλαγών"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Αποδοχή αλλαγών"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Μετονομασία πινέζας"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Αφαίρεση πινέζας"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Εστίαση στην τοποθεσία της πινέζας"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Πλάτος/Μήκος"]},"Zoom":{"msgid":"Zoom","msgstr":["Εστίαση"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["Πρόκειται να προσθέσετε ${ N_PINS } πινέζα(ες) στη συλλογή σας. Θέλετε να συνεχίσετε;"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["ΠΡΟΕΙΔΟΠΟΙΗΣΗ: Πρόκειται να διαγράψετε μια πινέζα. Θέλετε να συνεχίσετε;"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":["ΠΡΟΕΙΔΟΠΟΙΗΣΗ: Πρόκειται να διαγράψετε όλες τις πινέζες. Θέλετε να συνεχίσετε;"]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["Χωρίς πινέζες. Μεταβείτε στην καρτέλα Οπτικοποίηση για να αποθηκεύσετε μια πινέζα ή να ανεβάσετε ένα αρχείο JSON με αποθηκευμένες πινέζες."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Να σημειωθεί ότι οι πινέζες θα αποθηκευτούν μόνο αν συνδεθείτε. Διαφορετικά, οι πινέζες θα χαθούν μόλις κλείσει η εφαρμογή."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Αποεπιλογή όλων"]},"Select all":{"msgid":"Select all","msgstr":["Επιλογή όλων"]},"No pins.":{"msgid":"No pins.","msgstr":["Καμία πινέζα."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Δημιουργία συνδέσμου (Επιλογή πινέζας ${ selectedPins.length })","Δημιουργία συνδέσμου (Επιλογή πινεζών ${ selectedPins.length })"]},"File type not supported":{"msgid":"File type not supported","msgstr":["Ο τύπος του αρχείου δεν υποστηρίζεται"]},"not supported":{"msgid":"not supported","msgstr":["δεν υποστηρίζεται"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["Καμία πινέζα δε βρέθηκε."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Σφάλμα κατά τη συντακτική ανάλυση του αρχείου:"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":["Μεταφόρτωση αρχείου JSON με αποθηκευμένες πινέζες."]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["Αποθέστε αρχείο JSON ή αναζητήστε στον υπολογιστή"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Διατηρήστε τις υπάρχουσες πινέζες"]},"Share pins":{"msgid":"Share pins","msgstr":["Κοινή χρήση πινέζων"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Δημιουργήστε μια ιστορία απο τις πινέζες"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Εξάγετε τις πινέζες στον υπολογιστή σας"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Εισάγετε πινέζες απο αποθηκευμένο αρχείο"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Διαγράψτε όλες τις πινέζες"]},"Story":{"msgid":"Story","msgstr":["Ιστορία"]},"Export":{"msgid":"Export","msgstr":["Εξαγωγή"]},"Import":{"msgid":"Import","msgstr":["Εισαγωγή"]},"Clear":{"msgid":"Clear","msgstr":["Καθαρισμός"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Κοινή χρήση του συνδέσμου με τις πινέζες"]},"Creating link...":{"msgid":"Creating link...","msgstr":["Δημιουργία συνδέσμου..."]},"OK":{"msgid":"OK","msgstr":["Εντάξει"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Ενημέρωση συλλογής με τις πινέζες."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Παρουσιάστηκε ένα πρόβλημα κατά την ενημέρωση της συλλογής με τις πινέζες: $ {updatingPinsError}."]},"Hello,":{"msgid":"Hello,","msgstr":["Γειά,"]},"Opacity":{"msgid":"Opacity","msgstr":["Αδιαφάνεια"]},"Split position":{"msgid":"Split position","msgstr":["Θέση διαχωριστικού"]},"split":{"msgid":"split","msgstr":["διαχωρισμός"]},"opacity":{"msgid":"opacity","msgstr":["αδιαφάνεια"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["Δεν υπάρχουν επίπεδα για σύγκριση."]},"Remove all":{"msgid":"Remove all","msgstr":["Αφαίρεση όλων"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Προσθήκη όλων των πινέζων"]},"Split":{"msgid":"Split","msgstr":["Διαχωρισμός"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Παρουσιάστηκε σφάλμα κατά τη λήψη των στιγμιοτύπων σας"]},"Download":{"msgid":"Download","msgstr":["Λήψη"]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Οπτικοποίηση εδάφους σε 3D"]},"Left button":{"msgid":"Left button","msgstr":["Αριστερό πλήκτρο"]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":["Κάντε κλικ και σύρετε χρησιμοποιώντας το αριστερό πλήκτρο του ποντικιού για να μετακινηθείτε στο χάρτη σε σταθερό ύψος. Χρησιμοποιήστε το συνδυασμό πλήκτρων SHIFT + αριστερό κλικ για περιστροφή."]},"Right button":{"msgid":"Right button","msgstr":["Δεξί πλήκτρο"]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":["Κάντε δεξί κλικ και σύρετε προς τα πάνω / κάτω για να αλλάξετε το ύψος της κάμερας. Κάντε δεξί κλικ και\nσύρετε αριστερά / δεξιά για περιστροφή του οπτικού πεδίου της κάμερας."]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":["Μεσαίο πλήκτρο / τροχός"]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":["Χρησιμοποιήστε τον τροχό κύλισης για να αλλάξετε το ύψος της κάμερας (όπως το δεξί κλικ + σύρετε\nπάνω κάτω). Κάντε κλικ και σύρετε το κουμπί τροχού για να αλλάξετε τη γωνία της κάμερας."]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":["Πλοήγηση με το πληκτρολόγιο"]},"Arrow keys":{"msgid":"Arrow keys","msgstr":["Πλήκτρα με βέλη"]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":["Χρησιμοποιήστε τα πλήκτρα με τα βέλη για να μετακινηθείτε στο χάρτη σε σταθερό ύψος."]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":["SHIFT + πλήκτρα με βέλη"]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":["Κρατήστε πατημένο το πλήκτρο SHIFT ενώ πατάτε τα πλήκτρα με τα βέλη για να αλλάξετε την προβολή της κάμερας."]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":["Σελίδα πάνω/Σελίδα κάτω"]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":["Χρησιμοποιήστε τα πλήκτρα PG UP ή PG DN για να αλλάξετε το υψόμετρο της κάμερας."]},"Map navigation":{"msgid":"Map navigation","msgstr":["Πλοήγηση στο χάρτη"]},"Pan console":{"msgid":"Pan console","msgstr":["Έλεγχος κίνησης στην ίδια κλίμακα (pan)"]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":["Ο έλεγχος κίνησης στην ίδια κλίμακα σας επιτρέπει να μετακινηθείτε στο χάρτη σε σταθερή κλίμακα/εστίαση. Κάντε κλικ και σύρετε για μετακίνηση\nσυνεχώς. Όσο πιο μακριά σύρετε από το κέντρο, τόσο πιο γρήγορα θα κινηθείτε."]},"Camera console":{"msgid":"Camera console","msgstr":["Κονσόλα κάμερας"]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":["Η κονσόλα κάμερας μετακινεί μόνο την προβολή της κάμερας. Κάντε κλικ και σύρετε για να αλλάξετε την προβολή της κάμερας.\nΌσο πιο μακριά σύρετε από το κέντρο, τόσο πιο γρήγορα θα αλλάξετε την προβολή."]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":["Πλήκτρα εστίασης"]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":["Κάνοντας κλικ σε αυτά θα αλλάξει το ύψος της κάμερας. Το πλήκτρο συν θα μετακινήσει την κάμερα\nπιο κοντά στη γη, το πλήκτρο μείον θα μετακινήσει την κάμερα πιο μακριά."]},"Go to Place":{"msgid":"Go to Place","msgstr":["Μετάβαση σε Τοποθεσία"]},"Labels":{"msgid":"Labels","msgstr":["Ετικέτες"]},"Borders":{"msgid":"Borders","msgstr":["Όρια"]},"Roads":{"msgid":"Roads","msgstr":["Οδικό δίκτυο"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Εστίαση"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Αποεστίαση"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["Σχετικά με το EO Browser"]},"Contact us":{"msgid":"Contact us","msgstr":["Επικοινωνήστε μαζί μας"]},"Get data":{"msgid":"Get data","msgstr":["Λήψη δεδομένων"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Πρέπει να συνδεθείτε για να χρησιμοποιήσετε αυτήν τη λειτουργία."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Παρακαλώ επιλέξτε ένα επίπεδο."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["Δεν είναι δυνατή η λήψη εικόνας στη λειτουργία σύγκρισης."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["Αυτή η πηγή δεδομένων δεν υποστηρίζεται."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":["Γράφημα υπηρεσίας Statistical Info / Feature Info"]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Γράφημα υπηρεσίας Statistical Info / Feature Info - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["παρακαλώ επιλέξτε ένα επίπεδο"]},"not available for ":{"msgid":"not available for ","msgstr":["δεν είναι διαθέσιμο για "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["δεν είναι διαθέσιμο για ${ props.presetLayerName }\" (το επίπεδο με τιμή δεν έχει καθοριστεί)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Πρώτα αναζητήστε δεδομένα."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Δημιουργήστε κινούμενη εικόνα σε timelapse"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Επισήμανση σημείου ενδιαφέροντος"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Κεντράρετε το χάρτη στο χαρακτηριστικό"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Αφαίρεση γεωμετρίας"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Περιοχή ενδιαφέροντος"]},"Select mode":{"msgid":"Select mode","msgstr":["Λειτουργία επιλογής"]},"Mode:":{"msgid":"Mode:","msgstr":["Λειτουργία:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Αφαίρεση μέτρησης"]},"Measure":{"msgid":"Measure","msgstr":["Μέτρηση"]},"km":{"msgid":"km","msgstr":["χλμ"]},"m":{"msgid":"m","msgstr":["μ"]},"Gain":{"msgid":"Gain","msgstr":["Ενίσχυση"]},"Gamma":{"msgid":"Gamma","msgstr":["Γάμμα"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Ελάχιστη ποιότητα δεδομένων"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Κλιμάκωση προς τα επάνω"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Κλιμάκωση προς τα κάτω"]},"Reset all":{"msgid":"Reset all","msgstr":["Επαναφορά όλων"]},"filter by months":{"msgid":"filter by months","msgstr":["φιλτράρισμα κατά μήνες"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Αντιγραφή γεωμετρίας στο πρόχειρο"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Απόρριψη επεξεργασίας."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Σχεδιάστε την περιοχή ενδιαφέροντος"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Λιγότερη νεφοκάλυψη"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Χρησιμοποιήστε πρόσθετα σύνολα δεδομένων (για προχωρημένους)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Σειρά προβολής τμημάτων μωσαϊκού"]},"Most recent":{"msgid":"Most recent","msgstr":["Τα πιο πρόσφατα"]},"Least recent":{"msgid":"Least recent","msgstr":["Λιγότερο πρόσφατα"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Προσαρμόστε το χρονικό διάστημα"]},"Back":{"msgid":"Back","msgstr":["Πίσω"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Σφάλμα κατά τη φόρτωση αλγορίθμου. Ελέγξτε τη διεύθυνση URL σας."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Καταργήστε την επιλογή Φόρτωση αλγορίθμου από τη διεύθυνση URL για επεξεργασία του κώδικα"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Φόρτωση αλγορίθμου από διεύθυνση URL"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["Εισάγετε σύνδεσμο URL προς τον αλγόριθμο"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Ο αλγόριθμος φορτώθηκε."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Επιτρέπονται μόνο τομείς HTTPS."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Φόρτωση σεναρίου στον επεξεργαστή κώδικα"]},"Refresh":{"msgid":"Refresh","msgstr":["Ανανέωση"]},"orbit":{"msgid":"orbit","msgstr":["τροχιά"]},"day":{"msgid":"day","msgstr":["ημέρα"]},"week":{"msgid":"week","msgstr":["εβδομάδα"]},"month":{"msgid":"month","msgstr":["μήνας"]},"year":{"msgid":"year","msgstr":["έτος"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Επιλέξτε 1 εικόνα ανά:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Τεχνική Timelapse"]},"Select All":{"msgid":"Select All","msgstr":["Επιλογή όλων"]},"Speed:":{"msgid":"Speed:","msgstr":["Ταχύτητα:"]},"frames / s":{"msgid":"frames / s","msgstr":["καρέ / δευτερόλεπτο"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Προετοιμασία..."]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Δεν είναι δυνατή η λήψη των αρχείων:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Δεν είναι δυνατή η λήψη μέσω χάρτη"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["Δεν είναι δυνατή η συμπίεση των αρχείων:"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Παρουσιάστηκε πρόβλημα κατά τη λήψη της εικόνας"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Σφάλμα κατά τη λήψη της εικόνας: η διεύθυνση url είναι άδεια!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Σφάλμα κατά την ανάκτηση εικόνας:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["Δεν ήταν δυνατή η φόρτωση της εικόνας από blob"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Σύρετε τα κανάλια στα πεδία RGB."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Σύρετε κανάλια στην εξίσωση του δείκτη"]},"Index ":{"msgid":"Index ","msgstr":["Δείκτης "]},"Threshold":{"msgid":"Threshold","msgstr":["Κατώφλι"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Αφαίρεση εργαλείου επιλογής χρωμάτων"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Προσθήκη εργαλείου επιλογής χρωμάτων"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Κάντε κλικ για να τοποθετήσετε το δείκτη"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Κάντε κλικ για να τοποθετήσετε την πρώτη κορυφή"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Κάντε κλικ για να συνεχίσετε τη σχεδίαση"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Κάντε κλικ στο πρώτο σημείο για να ολοκληρώσετε τη σχεδίαση"]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":["Δημιουργία timelapse αυτής της περιοχής"]},"Show captions":{"msgid":"Show captions","msgstr":["Εμφάνιση υπότιτλων"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Εμφάνιση τίτλου διαφάνειας"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Προσθήκη επικαλύψεων χάρτη"]},"Show legend":{"msgid":"Show legend","msgstr":["Εμφάνιση υπομνήματος"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["Δεν βρέθηκαν πινέζες στο τρέχον οπτικό πεδίο."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Ορισμένες πινέζες ($ {N_PINS_OUTSIDE_BOUNDS}) αγνοούνται επειδή δε βρίσκονται στην επιλεγμένη περιοχή."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["Για να δημιουργήσετε μια ιστορία με τις πινέζες, μεταβείτε στην επιθυμητή θέση στον χάρτη.\n\nΌλες οι πινέζες στο τρέχον οπτικό πεδίο θα χρησιμοποιηθούν για τη δημιουργία της ιστορίας, ενώ οι υπόλοιπες θα αγνοηθούν."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["Το αρχείο θα έχει συνημμένο το λογότυπο."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["Ένα κανάλι-μάσκα δεδομένων θα συμπεριληφθεί στα ληφθέντα ακατέργαστα κανάλια ως δεύτερο κανάλι."]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":["Ο μορφότυπος αρχείου Tagged Image File Format (TIFF) μπορεί να περιέχει μεγάλο αριθμό καναλιών, ωστόσο συνήθεις εφαρμογές θέασης εικόνων (π.χ. Windows Photo Viewer) δεν μπορούν να εμφανίσουν εικόνες TIFF με περισσότερες από 3 κανάλια.\nΕάν αυτή η επιλογή είναι ενεργοποιημένη, μόνο τα τρία πρώτα κανάλια θα συμπεριληφθούν στην εικόνα.\nΕάν αυτή η επιλογή είναι απενεργοποιημένη, όλα τα κανάλια θα συμπεριληφθούν, αλλά θα πρέπει να χρησιμοποιήσετε μια εφαρμογή που υποστηρίζει εικόνες με περισσότερα από 3 κανάλια (π.χ. QGIS) για την εμφάνιση της εικόνας TIFF."]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":["Το EPSG: 3857 δεν είναι διαθέσιμο όταν έχει καθοριστεί AOI."]},"Show logo":{"msgid":"Show logo","msgstr":["Εμφάνιση λογότυπου"]},"Image format":{"msgid":"Image format","msgstr":["Μορφότυπος εικόνας"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Ανάλυση εικόνας"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Σύστημα συντεταγμένων"]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":["Προσθέστε κανάλι / μάσκα δεδομένων σε ακατέργαστα επίπεδα"]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":["Αποκοπή πρόσθετων καναλιών"]},"Layers":{"msgid":"Layers","msgstr":["Επίπεδα"]},"Visualized":{"msgid":"Visualized","msgstr":["Οπτικοποιημένα"]},"Raw":{"msgid":"Raw","msgstr":["Ακατέργαστα"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["Τα επίπεδα επικάλυψης του χάρτη (ετικέτες θέσης, οδικό δίκτυο και πολιτικά όρια) θα προστεθούν στην εικόνα."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Οι εξαγόμενες εικόνες θα περιλαμβάνουν την πηγή δεδομένων και την ημερομηνία, την κλίμακα εστίασης και την επωνυμία"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Προσθήκης σύντομης περιγραφής στην εξαγόμενη εικόνα"]},"Description":{"msgid":"Description","msgstr":["Περιγραφή"]},"Image format:":{"msgid":"Image format:","msgstr":["Μορφότυπος εικόνας:"]},"Basic":{"msgid":"Basic","msgstr":["Βασικό"]},"Analytical":{"msgid":"Analytical","msgstr":["Αναλυτικό"]},"High-res print":{"msgid":"High-res print","msgstr":["Εκτύπωση υψηλής ανάλυσης"]},"Download image":{"msgid":"Download image","msgstr":["Λήψη εικόνας"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["Παρουσιάστηκε σφάλμα κατά τη λήψη ορισμένων εικόνων:"]},"min/px":{"msgid":"min/px","msgstr":["λεπτά/px"]},"sec/px":{"msgid":"sec/px","msgstr":["δευτερόλεπτα/px"]},"Resolution":{"msgid":"Resolution","msgstr":["Ανάλυση"]},"lat.":{"msgid":"lat.","msgstr":["πλάτος"]},"deg/px":{"msgid":"deg/px","msgstr":["μοίρες/px"]},"long.":{"msgid":"long.","msgstr":["μήκος"]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Προβλεπόμενη ανάλυση: $ {formattedResolution} μ / px"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Σφάλμα: Η συγχώνευση δεδομένων δεν υποστηρίζει μορφές KMZ / JPG και KMZ / PNG."]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":["Σφάλμα: Μπορείτε να κάνετε λήψη οπτικοποίησης με εφέ μόνο σε μορφές JPEG ή PNG."]},"Image download":{"msgid":"Image download","msgstr":["Λήψη εικόνας"]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":["Προειδοποίηση: Τα ακόλουθα επίπεδα χρησιμοποιούν προϊόντα δεδομένων, επομένως ο επιθυμητός τύπος δεδομένων ενδέχεται να μην οριστεί:"]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":["Προειδοποίηση: Το Evalscript δεν είναι σε τυπική μορφή V3 και δεν ήταν δυνατή η ρύθμιση του επιθυμητού τύπου δεδομένων για:"]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":["Αυτό σημαίνει ότι η παράμετρος \"sampleType\" ενδέχεται να έχει οριστεί ως προεπιλεγμένη (AUTO). Μπορείτε να το επιδιορθώσετε με την επεξεργασία του evalscript. Μάθετε περισσότερα για το \"sampleType\" στην τεκμηρίωση"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Πλάτος εικόνας (ίντσες)"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Μήκος εικόνας (ίντσες)"]},"DPI:":{"msgid":"DPI:","msgstr":["DPI:"]},"5 years":{"msgid":"5 years","msgstr":["5 χρόνια"]},"2 years":{"msgid":"2 years","msgstr":["2 χρόνια"]},"1 year":{"msgid":"1 year","msgstr":["1 χρόνος"]},"6 months":{"msgid":"6 months","msgstr":["6 μήνες"]},"3 months":{"msgid":"3 months","msgstr":["3 μήνες"]},"1 month":{"msgid":"1 month","msgstr":["1 μήνας"]},"Retry":{"msgid":"Retry","msgstr":["Δοκιμάστε ξανά"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Φόρτωνει, παρακαλώ περιμένετε"]},"mean":{"msgid":"mean","msgstr":["μέση τιμή"]},"median":{"msgid":"median","msgstr":["διάμεσος"]},"st. dev.":{"msgid":"st. dev.","msgstr":["τυπική απόκλιση"]},"min / max":{"msgid":"min / max","msgstr":["ελάχιστο/μέγιστο"]},"Export CSV":{"msgid":"Export CSV","msgstr":["Εξαγωγή CSV"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Χρονικό διάστημα:"]},"Date:":{"msgid":"Date:","msgstr":["Ημερομηνία:"]},"Single date":{"msgid":"Single date","msgstr":["Μοναδική ημερομηνία"]},"Timespan":{"msgid":"Timespan","msgstr":["Χρονικό διάστημα"]},"hh":{"msgid":"hh","msgstr":["ωω"]},"mm":{"msgid":"mm","msgstr":["λλ"]},"From:":{"msgid":"From:","msgstr":["Από:"]},"Until:":{"msgid":"Until:","msgstr":["Μέχρι:"]},"Apply":{"msgid":"Apply","msgstr":["Εφαρμογή"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Κοινή χρήση στο Facebook"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Κοινή χρήση στο Twitter"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Κοιτάξτε αυτό "]},"Logout":{"msgid":"Logout","msgstr":["Αποσύνδεση"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["Συνδεθείτε για να ξεκλειδώσετε προηγμένες λειτουργίες όπως χρονομέτρηση, αναλυτική λήψη, δικές σας διαμορφώσεις και άλλα."]},"Login":{"msgid":"Login","msgstr":["Σύνδεση"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Παρακολούθηση της Γης από το Διάστημα"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Γεωργία"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Ατμόσφαιρα και ατμοσφαιρική ρύπανση"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Ανίχνευση αλλαγών με το πέρας του χρόνου"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Πλημμύρα και ξηρασία"]},"Geology":{"msgid":"Geology","msgstr":["Γεωλογία"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Ωκεάνια και υδάτινα σώματα"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Χιόνια και Παγετώνες"]},"Urban":{"msgid":"Urban","msgstr":["Αστικός"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Βλάστηση και δασοκομία"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Ηφαίστεια"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Πυρκαγιές"]},"Default":{"msgid":"Default","msgstr":["Προκαθορισμένο"]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":["Το πρόγραμμα περιήγησης ιστού δεν υποστηρίζει δυνατότητες 3D, οι οποίες απαιτούνται για την εμφάνιση αυτού του περιεχομένου."]},"More information":{"msgid":"More information","msgstr":["Περισσότερες πληροφορίες"]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":["Δεν είναι δυνατή η σύνδεση στην υπηρεσία 3D! Θα ξαναδοκιμάσετε;"]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":["Η εικόνα είναι πολύ μεγάλη για αυτήν τη συσκευή!\nΜέγεθος εικόνας: {0} x {1}, μέγιστο: {2}"]},"Home":{"msgid":"Home","msgstr":["Αρχική"]},"Shading":{"msgid":"Shading","msgstr":["Σκίαση"]},"Sphere mode":{"msgid":"Sphere mode","msgstr":["Λειτουργία σφαίρας"]},"Eye height":{"msgid":"Eye height","msgstr":["Ύψος ματιών"]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":["Δεν είναι δυνατή η φόρτωση της εικόνας"]},"Geometries":{"msgid":"Geometries","msgstr":["Γεωμετρίες"]},"Now":{"msgid":"Now","msgstr":["Τώρα"]},"Terrain":{"msgid":"Terrain","msgstr":["Έδαφος"]},"Time":{"msgid":"Time","msgstr":["Χρόνος"]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":["# Καλώς ήλθατε στον EO Browser!!\n\nΈνα πλήρες αρχείο Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P,\nLandsat 5, 7 και 8 της ESA, Landsat 8 με παγκόσμια κάλυψη και Envisat Meris,\nMODIS, Proba-V και GIBS προϊόντα σε ένα μοναδικό σημείο πρόσβασης.\n\n[Σελίδα παρουσίασης του EO Browser](https://www.sentinel-hub.com/explore/eobrowser/)\n[Οδηγός χρήσης του EO Browser](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":["#### Γρήγορη επισκόπηση των λειτουργιών του EO Browser\n\nΤο EO Browser περιλαμβάνει ένα πλήρες αρχείο Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, Landsat 5, 7 και 8 της ESA, Landsat 8 με παγκόσμια κάλυψη και Envisat Meris, MODIS, Proba-V και GIBS προϊόντα σε ένα μοναδικό σημείο πρόσβασης και καθιστά δυνατή την περιήγηση και τη σύγκριση εικόνων πλήρους ανάλυσης από αυτές τις πηγές. Για να το πετύχετε αυτό, μεταβαίνετε στην περιοχή που σας ενδιαφέρει, επιλέγετε πηγές δεδομένων, χρονικό διάστημα και νεφοκάλυψη και ελέγχετε τα δεδομένα που προκύπτουν.\n\nΜπορείτε να συνεχίσετε αυτόν τον οδηγό εκμάθησης κάνοντας κλικ στο κουμπί \"Επόμενο\" ή μπορείτε να τον τερματίσετε. Κάνοντας κλικ στο εικονίδιο πληροφοριών στην επάνω δεξιά γωνία μπορείτε πάντα να συνεχίσετε τον οδηγό εκμάθησης σε περίπτωση που το κλείσατε κατά λάθος ή επιθυμείτε να κάνετε κάποιες δοκιμές."]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":["** Οι συνδεδεμένοι χρήστες ** μπορούν να χρησιμοποιήσουν τα προσαρμοσμένα θέματα τους,\nνα αποθηκεύσουν και να φορτώσουν πινέζες, να δημιουργήσουν μια ιστορία με πινέζες, \nνα μετρήσουν τις αποστάσεις, να δημιουργήσουν timelapse και\nνα χρησιμοποιήσουν τη σύνθετη λήψη εικόνων.\n\nΓια να δημιουργήσετε έναν δωρεάν λογαριασμό, απλώς κάντε κλικ [εδώ]\nή εντός της εφαρμογής ** Σύνδεση ** και στη συνέχεια \\\"Εγγραφή\\\"."]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":["Στην καρτέλα ** Εξερευνηση ** μπορείτε να:\n\n- Επιλέξετε ένα θέμα **. **\n- ** Αναζητήσετε ** δεδομένα.\n- Προβάλετε ** Στιγμιότυπα ** του θέματος\n\nΤο αναπτυσσόμενο μενού ** Θέμα ** σας προσφέρει διαφορετικά προκαθορισμένα θέματα, καθώς και δικά σας διαμορφωμένα στιγμιότυπα, εάν είστε συνδεδεμένοι. Για να δημιουργήσετε ένα στιγμιότυπο, κάντε κλικ στο\nτο εικονίδιο των ρυθμίσεων και συνδεθείτε με τα ίδια διαπιστευτήρια που χρησιμοποιήσατε για το EO Browser.\n\nΣτην ενότητα ** Αναζήτηση ** μπορείτε να ορίσετε κριτήρια αναζήτησης:\n - Επιλέξτε από ποιους δορυφόρους θέλετε να λαμβάνετε τα δεδομένα επιλέγοντας τα κατάλληλα πλαίσια ελέγχου.\n - Ορίστε πρόσθετες επιλογές όπου απαιτείται, για παράδειγμα νεφοκάλυψη, με τη μπάρα ρύθμισης.\n - Επιλέξτε το χρονικό εύρος πληκτρολογώντας την ημερομηνία ή επιλέξτε την ημερομηνία από το ημερολόγιο.\n\nΜπορείτε να διαβάσετε για τους δορυφόρους κάνοντας κλικ στο εικονίδιο της ερώτησης\n δίπλα στο όνομα της πηγής δεδομένων.\n\nΜόλις πατήσετε Αναζήτηση θα λάβετε μια λίστα αποτελεσμάτων. Για κάθε αποτέλεσμα εμφανίζεται \nμια εικόνα προεπισκόπησης και πληροφορίες σχετικά με την πηγή δεδομένων. Για ορισμένες πηγές δεδομένων το εικονίδιο του συνδέσμου είναι επίσης ορατό για κάθε αποτέλεσμα.\nΚάνοντας κλικ σε αυτό εμφανίζεται σύνδεσμος προς την αρχική εικόνα στο EO Cloud ή το SciHub. Κάνοντας κλικ στο Οπτικοποιήση θα ανοίξει η καρτέλα ** Οπτικοποιήση ** για το επιλεγμένο αποτέλεσμα.\n\nΣτην ενότητα ** Επισήμανση ** θα βρείτε προεπιλεγμένες τοποθεσίες οι οποίες συνδέονται με το επιλεγμένο θέμα."]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":["Στην καρτέλα Οπτικοποίηση μπορείτε να επιλέξετε διαφορετικούς προκαθορισμένους ή προσαρμοσμένους συνδυασμούς καναλιών για να οπτικοποιήσετε δεδομένα με το επιθυμητό αποτέλεσμα.\n\nΜερικές από τις συνήθεις επιλογές είναι:\n- ** True Colour ** - Οπτική ερμηνεία της κάλυψης γης.\n- ** False Color ** - Οπτική ερμηνεία της βλάστησης.\n- ** NDVI ** - Κανονικοποιημένος δείκτης βλάστησης.\n- ** Moisture index ** - Δείκτης υγρασίας\n- ** SWIR ** - Δείκτης υπερύθρων μικρού κύματος.\n- ** NDWI ** - Κανονικοποιημένος δείκτης νερού.\n- ** NDSI ** - Κανονικοποιημένος δείκτης χιονιού.\n\nΟι περισσότερες απεικονίσεις συνοδεύονται από μια περιγραφή και υπόμνημα, που μπορείτε να δείτε κάνοντας κλικ στο εικονίδιο\nτης επέκτασης .\n \nΓια τις περισσότερες πηγές δεδομένων είναι διαθέσιμη η επιλογή ** Προσαρμοσμένος αλγόριθμος **. Κάντε κλικ σε αυτό για να επιλέξετε προσαρμοσμένους\nσυνδυασμούς καναλιών, συνδυασμούς δεικτών ή να γράψτε το δικό σας αλγόριθμο ταξινόμησης για την οπτικοποίηση των δεδομένων. Μπορείτε επίσης να\nχρησιμοποιήστε προσαρμοσμένους αλγόριθμους, οι οποίοι είναι αποθηκευμένοι, είτε στο Google Drive, είτε στο GitHub είτε στο [Αποθετήριο προσαρμοσμένων αλγορίθμων](https://custom-scripts.sentinel-hub.com/).\nΕπικολλήστε τη διεύθυνση URL του αλγορίθμου στο πλαίσιο κειμένου στο πεδίο της σύνθετης επεξεργασίας αλγορίθμου και κάντε κλικ στην επιλογή Ανανέωση.\n \nΜπορείτε να αλλάξετε την ημερομηνία απευθείας στην καρτέλα Οπτικοποίηση , χωρίς να επιστρέψετε στην καρτέλα ** Εξερεύνηση **. Πληκτρολογήστε ή επιλέξτε την επιθυμητή ημερομηνία από το ημερολόγιο .\n\nΠάνω από τις απεικονίσεις που δημιουργείτε εμφανίζεται γραμμή πρόσθετων εργαλείων. Σημειώστε ότι η διαθεσιμότητά τους εξαρτάται από την πηγή δεδομένων.\n- ** Επίπεδο πινεζών ** για να το αποθηκεύσετε στην εφαρμογή για μελλοντική χρήση - κάνοντας κλικ στο εικονίδιο με τις πινέζες .\n- Επιλέξτε ** προχωρημένες επιλογές ** όπως τη μέθοδο δειγματοληψίας ή εφαρμόστε διαφορετικά ** εφέ ** όπως αντίθεση (gain) και φωτεινότητα (gamma) - κάνοντας κλικ στο εικονίδιο των ρυθμίσεων εφέ .\n- Προσθέστε ένα επίπεδο στην καρτέλα ** Σύγκριση ** για μελλοντική σύγκριση - κάνοντας κλικ στο εικονίδιο σύγκρισης .\n- ** Εστίαση ** στο κέντρο της πινακίδας - κάνοντας κλικ στο σταυρόνημα .\n- Εναλλαγή ** ορατότητας επιπέδου ** - κάνοντας κλικ στο εικονίδιο ορατότητας .\n- ** Μοιραστείτε ** την οπτικοποίησή σας στα μέσα κοινωνικής δικτύωσης - κάνοντας κλικ στο εικονίδιο κοινής χρήσης ."]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":["Στην καρτέλα ** Σύγκριση ** θα βρείτε όλες τις απεικονίσεις που προσθέσατε μέσω του στη ** Σύγκριση **.\n\nΥπάρχουν δύο συγκριτικές λειτουργίες:\n - ** Αδιαφάνεια ** (Σύρετε τη μπάρα ρύθμισης αριστερά ή δεξιά για να δημιουργήσετε διαφάνεια μεταξύ των συγκρινόμενων εικόνων)\n - ** Διαχωρισμός ** (Σύρετε τη μπάρα ρύθμισης αριστερά ή δεξιά για να μετακινήσετε το όριο μεταξύ των συγκρινόμενων εικόνων)\n\nΜπορείτε να προσθέσετε όλες τις πινέζες στο εργαλείο της σύγκρισης σύγκρισης χρησιμοποιώντας ** Προσθήκη όλων των πινέζων ** ή να καταργήσετε όλες τις απεικονίσεις\nαπό την καρτέλα ** Σύγκριση ** με το ** Κατάργηση όλων **."]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":["Η καρτέλα ** Πινέζες ** περιέχει τα καρφιτσωμένα (αγαπημένα / αποθηκευμένα) αντικείμενά σας. Τα αντικείμενα αυτά περιέχουν πληροφορίες\nσχετικά με την τοποθεσία, την πηγή δεδομένων και το συγκεκριμένο επίπεδο, την εστίαση και το χρονικό προσδιορισμό.\n\nΓια κάθε πινέζα έχετε αρκετές επιλογές αλληλεπίδρασης:\n\n- Αλλαγή ** παραγγελίας ** - κάνοντας κλικ στο εικονίδιο μετακίνησης\n\n \n \n\nστην επάνω αριστερή γωνία της πινέζας, σύροντας προς τα πάνω ή προς τα κάτω στη λίστα.\n- ** Μετονομασία ** - κάνοντας κλικ στο εικονίδιο με το μολύβι δίπλα στο όνομα του πινέλου.\n- Προσθήκη στην καρτέλα ** Σύγκριση ** - κάνοντας κλικ στο εικονίδιο σύγκρισης \n- Εισαγάγετε μια περιγραφή ** ** - κάνοντας κλικ στο εικονίδιο επέκτασης .\n- ** Κατάργηση ** - κάνοντας κλικ στο εικονίδιο κατάργησης .\n- ** Ζουμ ** στην τοποθεσία του πείρου - κάνοντας κλικ στο Lat / Lon.\n\nΣτη γραμμή πάνω από όλες τις πινέζες έχετε διαφορετικές επιλογές που ισχύουν:\n- Δημιουργήστε τη δική σας ιστορία από καρφίτσες - κάνοντας κλικ στο ** Story **.\n- Μοιραστείτε τις πινέζες σας με άλλους μέσω ενός συνδέσμου - κάνοντας κλικ στο ** Κοινή χρήση **.\n- Εξαγωγή πινεζών ως αρχείο JSON - κάνοντας κλικ στο ** Εξαγωγή **.\n- Εισαγωγή πινεζών από ένα αρχείο JSON - κάνοντας κλικ στο ** Εισαγωγή **.\n- Διαγράψτε όλες τις πινέζες - κάνοντας κλικ στο ** Διαγραφή **."]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":["Αναζητήστε μια τοποθεσία είτε μετακινώντας το χάρτη με το ποντίκι είτε πληκτρολογώντας την τοποθεσία στο πεδίο της\nαναζήτησης."]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":["Εδώ μπορείτε να επιλέξετε το βασικό επίπεδο και τα συμπληρωματικά επίπεδα (οδικό δίκτυο, όρια, ετικέτες) που εμφανίζονται στο χάρτη."]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":["Στο σημείο αυτό μπορείτε να κάνετε εναλλαγή μεταξύ της λειτουργίας ** κανονική ** και ** εκπαίδευση **. Η λειτουργία ** Εκπαίδευση ** προσφέρει μια ελαφρώς απλοποιημένη έκδοση της εφαρμογής.\nΕίναι επίσης προσβάσιμο απευθείας μέσω του [αποκλειστικού URL](https://apps.sentinel-hub.com/eo-browser-education/)."]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":["Μπορείτε να δείτε τον οδηγό εκμάθησης ανά πάσα στιγμή κάνοντας κλικ σε αυτό το εικονίδιο πληροφοριών\n\n\n."]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":["Αυτό το εργαλείο σας επιτρέπει να σχεδιάσετε ένα πολύγωνο στο χάρτη και να εμφανίσετε την έκτασή του.\n\nΌλα τα επίπεδα που επιστρέφουν μία τιμή (όπως NDVI, Moisture index, NDWI,…) υποστηρίζουν την προβολή του\nδείκτη για την επιλεγμένη περιοχή στο βάθος του χρόνου. Κάνοντας κλικ στο εικονίδιο γραφήματος θα\nεμφανίστε τα διαγράμματα. Μπορείτε να καταργήσετε το πολύγωνο κάνοντας κλικ στο εικονίδιο κατάργησης .\n\nΜπορείτε επίσης να ανεβάσετε ένα αρχείο KML / KMZ, GPX ή GEOJSON / JSON με γεωμετρία πολυγώνου.\n\nΤο εικονίδιο δύο φύλλων σας επιτρέπει να αντιγράψετε τις συντεταγμένες πολυγώνου ως GEOJSON. Το σταυρόνημα \nκεντράρει το χάρτη στο πολύγωνο που σχεδιάστηκε.\n\nΟι εξαγόμενες εικόνες θα περικοπούν στην περιοχή ενδιαφέροντος για αναλυτικούς σκοπούς."]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":["Με αυτό το εργαλείο μπορείτε να επισημάνετε ένα σημείο στο χάρτη.\n\nΜπορείτε επίσης να δείτε στατιστικά δεδομένα για ορισμένα επίπεδα κάνοντας κλικ στο εικονίδιο γραφήματος\n.\nΜπορείτε να καταργήσετε την επισήμανση του σημείου κάνοντας κλικ στο εικονίδιο κατάργησης .\n"]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":["Με αυτό το εργαλείο μπορείτε να μετρήσετε αποστάσεις και εμβαδά στο χάρτη.\n\nΚάθε κλικ του ποντικιού δημιουργεί ένα νέο σημείο στη διαδρομή. Για να σταματήσετε να προσθέτετε σημεία, πατήστε το πλήκτρο Esc
\nή κάντε διπλό κλικ στο χάρτη.\nΜπορείτε να καταργήσετε τη μέτρηση κάνοντας κλικ στο εικονίδιο κατάργησης ."]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":["Με αυτό το εργαλείο μπορείτε να πραγματοποιήσετε τη λήψη μιας εικόνας οπτικοποιημένων δεδομένων για την εμφανιζόμενη τοποθεσία. Μπορείτε να διαλέξετε\nνα εμφανίσετε το υπόμνημα καθώς και να προσθέσετε περιγραφή.\nΕνεργοποιώντας την αναλυτική λειτουργία, μπορείτε να επιλέξετε ανάμεσα σε διάφορους μορφότυπους εικόνας, αναλύσεις και\nσυστήματα συντεταγμένων. Μπορείτε επίσης να επιλέξετε πολλά επίπεδα και να τα μεταφορτώσετε σε ένα αρχείο .zip
.\n\nΚάντε κλικ στο κουμπί λήψης\n Λήψη \nκαι θα αρχίσει η λήψη των εικόνων σας. Η διαδικασία μπορεί να διαρκέσει μερικά δευτερόλεπτα, ανάλογα με την επιλεγμένη\nανάλυση και τον αριθμό των επιλεγμένων επιπέδων.\n\nΠριν από τη λήψη, μπορείτε να ορίσετε μια περιοχή ενδιαφέροντος (AOI) κάνοντας κλικ στο εργαλείο επιλογής περιοχής.\nΤα δεδομένα σας θα περικοπούν για να ταιριάζουν με αυτήν την περιοχή."]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":["Φτάσατε στο τέλος του οδηγού εκμάθησης. Εάν έχετε οποιαδήποτε άλλη ερώτηση, μη διστάσετε να μας ρωτήσετε στο [φόρουμ](https://forum.sentinel-hub.com/)\nή επικοινωνήστε μαζί μας [μέσω email](mailto: info@sentinel-hub.com? Subject = EO% 20Browser% 20Feedback).\n\n\nΕάν θέλετε να δείτε τον οδηγό στο μέλλον, μπορείτε να κάνετε κλικ στο εικονίδιο πληροφοριών\n\n\n\nστην επάνω δεξιά γωνία."]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":["#### Γρήγορη επισκόπηση των λειτουργιών του EO Browser\n\nΕάν έχετε μικρή οθόνη, μεταβείτε [εδώ](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) για να δείτε τον οδηγό χρήσης.\n\nΜπορείτε ανά πάσα στιγμή να δείτε ξανά αυτές τις πληροφορίες κάνοντας κλικ στο εικονίδιο πληροφοριών\n\n\n\nστην επάνω δεξιά γωνία.\n\n#### Άλλοι πόροι\n- [Σελίδα παρουσίασης EO Browser](https://www.sentinel-hub.com/explore/eobrowser/)\n- [Αναβαθμίσεις του EO Browser από το καλοκαίρι του 2018 - βίντεο](https://www.youtube.com/embed/m3pron0C0kE)"]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":["Τι είναι ο EO Browser;"]},"User Account":{"msgid":"User Account","msgstr":["Λογαριασμός χρήστη"]},"Discover Tab":{"msgid":"Discover Tab","msgstr":["Καρτέλα Εξερεύνησης"]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":["Καρτέλα Οπτικοποίησης"]},"Compare Tab":{"msgid":"Compare Tab","msgstr":["Καρτέλα Συγκριτικής Ανάλυσης"]},"Pins Tab":{"msgid":"Pins Tab","msgstr":["Καρτέλα με Πινέζες"]},"Search Places":{"msgid":"Search Places","msgstr":["Αναζήτηση Τοποθεσιών"]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":["Επίπεδα και Υπόβαθρα"]},"Education Mode":{"msgid":"Education Mode","msgstr":["Λειτουργία Εκπαίδευσης"]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":["Πληροφορίες και Οδηγός Εκμάθησης"]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":["Σχεδίαση Περιοχής Ενδιαφέροντος (AOI)"]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":["Επισήμανση Σημείου Ενδιαφέροντος"]},"Measure Distances":{"msgid":"Measure Distances","msgstr":["Μέτρηση Αποστάσεων"]},"Download Image":{"msgid":"Download Image","msgstr":["Λήψη Εικόνας"]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":["Δημιουργία Κινούμενης Εικόνας Timelapse"]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":["Καλή Περιήγηση!"]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":["Καλώς ορίσατε στον EO Browser!"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":["Κανάλι 1 - Κίτρινη ουσία και χρωστικές ουσίες - 412,5 nm"]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":["Κανάλι 3 - Χλωροφύλλη και άλλες χρωστικές - 490 nm"]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":["Κανάλι 4 - Αιωρούμενο ίζημα, κόκκινη παλίρροια - 510 nm"]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":["Κανάλι 5 - Ελάχιστη απορρόφηση χλωροφύλλης - 560 nm"]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":["Κανάλι 6 - Αιωρούμενο ίζημα - 620 nm"]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":["Κανάλι 7 - Απορρόφηση χλωροφύλλης και βάση αναφοράς φθορισμού - 665 nm"]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":["Κανάλι 8 - Μέγιστος φθορισμός χλωροφύλλης - 681 nm"]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":["Κανάλι 9 - Βάση αναφοράς φθορισμού, Ατμοσφαιρική διόρθωση - 709 nm"]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":["Κανάλι 10 - Βλάστηση, σύννεφα - 753 nm"]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":["Κανάλι 12 - Διορθώσεις ατμόσφαιρας - 779 nm"]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":["Κανάλι 13 - Βλάστηση, υδρατμοί - 865 nm"]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":["Κανάλι 14 - Διορθώσεις ατμόσφαιρας - 779 nm"]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":["Κανάλι 15 - Υδρατμοί, ξηρά - 900 nm"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Κανάλι 1 - Μπλέ - 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Κανάλι 2 - Πράσινο - 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Κανάλι 3 - Κόκκινο - 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Κανάλι 4 - Εγγύς Υπέρυθρο NIR - 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Κανάλι 5 - Μικροκυματικό Υπέρυθρο SWIR-1 - 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Κανάλι 7 - Μικροκυματικό Υπέρυθρο SWIR-2 - 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Κανάλι 8 - Παγχρωματικό - 520-900 nm"]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":["Κανάλι 1 - Παράκτια Ζώνη/Αερολύματα - 433-453 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Κανάλι 2 - Μπλε - 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Κανάλι 3 - Πράσινο - 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Κανάλι 4 - Κόκκινο - 630-680 nm"]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":["Κανάλι 5 - Εγγύς Υπέρυθρο NIR - 845-885 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Κανάλι 6 - Μικροκυματικό Υπέρυθρο SWIR-1 - 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Κανάλι 7 - Μικροκυματικό Υπέρυθρο SWIR-2 - 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Κανάλι 8 - Παγχρωματικό - 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Κανάλι 9 - Νέφος Cirrus - 1360-1390 nm"]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":["Landsat 5 (αρχείο ESA)"]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":["Landsat 7 (αρχείο ESA)"]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":["Landsat 8 (αρχείο ESA)"]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":["Landsat 8 (αρχείο USGS )"]},"Red band":{"msgid":"Red band","msgstr":["Κόκκινο κανάλι"]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":["841 - 876 nm (NIR)"]},"Blue band":{"msgid":"Blue band","msgstr":["Μπλε κανάλι"]},"Green band":{"msgid":"Green band","msgstr":["Πράσινο κανάλι"]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":["1230 - 1250 nm"]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":["1628 - 1652 nm"]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":["2105 - 2155 nm"]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":["Κανάλι 1 - Παράκτια ζώνη αερολύματος - 443 nm"]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":["Κανάλι 2 - Μπλε - 490 nm"]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":["Κανάλι 3 - Πράσινο - 560 nm"]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":["Κανάλι 4 - Κόκκινο - 665 nm"]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":["Κανάλι 5 - Βλάστηση Red Edge - 705 nm"]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":["Κανάλι 6 - Βλάστηση Red Edge - 740 nm"]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":["Κανάλι 7 - Βλάστηση Red Edge - 783 nm"]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":["Κανάλι 8 - Εγγύς Υπέρυθρο NIR - 842 nm"]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":["Κανάλι 9 - Υδρατμοί - 945 nm"]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":["Κανάλι 10 - Μικροκυματικό υπέρυθρο SWIR - Νέφος Cirrus - 1375 nm"]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":["Κανάλι 11 - Μικροκυματικό υπέρυθρο SWIR - 1610 nm"]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":["Κανάλι 12 - Μικροκυματικό υπέρυθρο SWIR - 2190 nm"]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":["Κανάλι 8A - Βλάστηση Red Edge - 865 nm"]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (με ατμοσφαιρική διόρθωση)"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":["Κανάλι 1 - Διόρθωση αερολύματος, βελτιωμένη ανάκτηση συστατικών νερού - 400 nm"]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":["Κανάλι 2 - Κίτρινη ουσία και χρωστικές ουσίες (θολερότητα) -412 nm"]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":["Κανάλι 3 - Μέγιστη απορρόφηση Chl, βιογεωχημεία, βλάστηση - 442,5 nm"]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":["Κανάλι 4 - Υψηλή συγκέντρωση Chl, άλλες χρωστικές - 490 nm"]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":["Κανάλι 5 - Χλωροφύλλη, ιζήματα, θολερότητα, κόκκινη παλίρροια - 510 nm"]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":["Κανάλι 6 - Σημείο αναφοράς για την ύπαρξη χλωροφύλλης (ελάχιστη χλωροφύλλη) - 560 nm"]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":["Κανάλι 7 - Ίζημα - 620 nm"]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":["Κανάλι 8 - Chl (2η απόλυτη μέγιστη τιμή), Ίζημα, κίτρινη ουσία / βλάστηση - 665 nm"]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":["Κανάλι 9 - Για τη βέλτιστη εκτίμηση του φθορισμού και την εξαλειψη της επιδρασης του φασματικού χαμόγελου σε συνδυασμό με το κανάλι 8 (665 nm) και το κανάλι 10 (681,25 nm) - 673,75 nm"]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":["Κανάλι 10 - Κορυφή φθορισμού Chl, Red Edge - 681,25 nm"]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":["Κανάλι 11 - Βασική γραμμή φθορισμού Chl, μετάβαση κόκκινου άκρου - 708,75 nm"]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":["Κανάλι 12 - απορρόφηση O2 / σύννεφα , βλάστηση - 753,75 nm"]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":["Κανάλι 13 - Απορρόφηση O2 / Διόρθωση αερολυμάτων - 761,25 nm"]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":["Κανάλι 14 - Ατμοσφαιρική διόρθωση - 764.375 nm"]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":["Κανάλι 15 - O2A που χρησιμοποιείται για πίεση στην κορυφή του νέφος, φθορισμός στην ξηρά - 767,5 nm"]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":["Κανάλι 16 -Ατμοσφαιρική διόρθωση/διόρθωση αερολυμάτων. - 778.75 nm"]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":["Κανάλι 17 - Ατμοσφαιρική διόρθωση/διόρθωση αερολυμάτων, σύννεφα, συμπροσαρμογή εικόνας βάσει εικονοστοιχείων. - 778.75 nm"]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":["Κανάλι 18 - Ζώνη αναφοράς απορρόφησης υδρατμών. Κοινή ζώνη αναφοράς με όργανο SLSTR. Παρακολούθηση βλάστησης - 885 nm"]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":["Κανάλι 19 - Απορρόφηση υδρατμών απορρόφησης / παρακολούθηση βλάστησης (μέγιστη ανάκλαση) - 900 nm"]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":["Κανάλι 20 - Απορρόφηση υδρατμών, ατμοσφαιρική διόρθωση/διόρθωση υδρατμών. - 940 nm"]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":["Κανάλι 21 - Ατμοσφαιρική διόρθωση/διόρθωση υδρατμών - 1020 nm"]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":["Κανάλι F1 - Εκπομπές πυρκαγιάς στο Θερμικό IR - Ενεργή πυρκαγιά - 3742,00 nm"]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":["Κανάλι F2 - Εκπομπές πυρκαγιάς στο Θερμικό IR - Ενεργή πυρκαγιά - 10854.00 nm"]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":["Κανάλι S1 - VNIR - Νέφος, παρακολούθηση βλάστησης, αερολύματα - 554,27 nm"]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":["Κανάλι S2 - VNIR - NDVI, παρακολούθηση βλάστησης, αεροζόλ - 659,47 nm"]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":["Κανάλι S3 - VNIR - NDVI, επισήμανση νέφους, συμπροσαρμογή εικόνας βάσει εικονοστοιχείων - 868.00 nm"]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":["Κανάλι S4 - SWIR - Ανίχνευση νέφους Cirrus στην ξηρά - 1374,80 nm"]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":["Κανάλι S5 - SWIR - Νέφος, πάγος, χιόνι, παρακολούθηση βλάστησης - 1613,40 nm"]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":["Κανάλι S6 - SWIR - Κατάσταση βλάστησης και νέφους - 2255,70 nm"]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":["Κανάλι S7 - Θερμικό υπέρυθρο IR - SST, LST, ενεργή φωτιά - 3742,00 nm"]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":["Κανάλι S8 - Θερμικό υπέρυθρο IR - SST, LST, ενεργή φωτιά - 10854.00 nm"]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":["Κανάλι S9 - Θερμικό υπέρυθρο IR - SST, LST - 12022.50 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Ανακλαστικότητα"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Θερμοκρασία φωτεινότητας"]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":["Με βάση το συνδυασμό των καναλιών 4, 3, 2"]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":["Με βάση το συνδυασμό καναλιών (B04-B03) / (B04 + B03)"]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":["Με βάση το συνδυασμό των καναλιών 5, 4, 3"]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":["Με βάση τα κανάλια του φυσικού έγχρωμου σύνθετου 4, 3, 2 και το παγχρωματικό κανάλι 8"]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":["Με βάση το συνδυασμό καναλιών (B05-B04) / (B05 + B04)"]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":["VV - γραμμικό gamma0 - ορθοανηγμένο"]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":["VV - γραμμικό gamma0 - μη-ορθοανηγμένο"]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":["VH - γραμμικό gamma0 - ορθοανηγμένο"]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":["Με βάση το συνδυασμό των καναλιών 3, 2, 1"]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":["VH - γραμμικό gamma 0 - - μη-ορθοανηγμένο"]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":["Έγχρωμη εικόνα με την αντιστοίχιση των αρχικών καναλιών στα κανάλια RGB. Τιμή [RGB] = [VV, 2 VH, VV / VH / 100.0] - γραμμικό gamma0 - ορθοανηγμένο"]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":["VV - decibel gamma0 [-20,0] - ορθοανηγμένο"]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":["VH - decibel gamma0 [-20,0] - ορθοανηγμένο"]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":["Επιστρέφει ένα σύνθετο (VH, VV, VH-VV)"]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":["HH - γραμμικό gamma0 - ορθοανηγμένο"]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":["HV - γραμμικό gamma0 - ορθοανηγμένο"]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":["Έγχρωμη εικόνα με την αντιστοίχιση των αρχικών καναλιών στα κανάλια RGB. Τιμή [RGB] = [HH, 2 HV, HH / HV / 100.0] - γραμμικό gamma0 - ορθοανηγμένο"]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":["HH - decibel gamma0 [-20,0] - ορθοανηγμένο"]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":["HV - decibel gamma0 [-20,0] - ορθοανηγμένο"]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":["HH - linear gamma0 - μη-ορθοανηγμένο"]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":["Με βάση τα κανάλια 4,3,2"]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":["Με βάση τα κανάλια 8,4,3"]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":["Με βάση τα κανάλια 12,11,4"]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Με βάση το συνδυασμό καναλιών (B8 - B4) / (B8 + B4)"]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":["Με βάση το συνδυασμό καναλιών (B8A - B11) / (B8A + B11)"]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":["Με βάση τα κανάλια 12,8A, 4"]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":["Με βάση το συνδυασμό καναλιών (B3 - B8) / (B3 + B8)"]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":["Με βάση το συνδυασμό καναλιών (B3 - B11) / (B3 + B11). Τιμές άνω των 0,42 υποδεικνύουν την ύπαρξη χιονιού"]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":["Ταξινόμηση δεδομένων Sentinel 2 ως αποτέλεσμα του αλγορίθμου ταξινόμησης της ESA."]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":["Δείκτης Αερολυμάτων UV από 380 και 340 nm"]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":["Με βάση το συνδυασμό καναλιών (B3 - B11) / (B3 + B11)"]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":["Δείκτης OLCI Terrestrial Chlorophyll, βάσει συνδυασμού καναλιών (B12 - B11) / (B11 - B10)"]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":["Δείκτης Αερολυμάτων UV από 388 και 354 nm"]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":["Μέση τιμή του λόγου ανάμιξης ξηρού αέρα με στήλη μεθανίου"]},"Cloud base height":{"msgid":"Cloud base height","msgstr":["Ύψος βάσης νέφους"]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":["Πίεση βάσης νέφους"]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":["Αποτελεσματικό ραδιομετρικό κλάσμα νέφους"]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":["Οπτικό πάχος νέφους"]},"Cloud top height":{"msgid":"Cloud top height","msgstr":["Ύψος κορυφής νέφους"]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":["Πίεση κορυφής νέφους"]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":["Συνολική στήλη μονοξειδίου του άνθρακα"]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":["Τροποσφαιρική κάθετη στήλη φορμαλδεΰδης"]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":["Τροποσφαιρική στήλη διοξειδίου του αζώτου"]},"Ozone total column":{"msgid":"Ozone total column","msgstr":["Συνολική στήλη όζοντος"]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":["Συνολική στήλη διοξειδίου του θείου"]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":["Με βάση τα κανάλια 2, 1, 4"]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":["Με βάση το συνδυασμό καναλιών (B02 - B01) / (B02 + B01)"]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":["Με βάση το συνδυασμό καναλιών (B02 - B05) / (B02 + B05)"]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":["Με βάση το συνδυασμό καναλιών (B06 - B07) / (B06 + B07)"]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":["Με βάση τα κανάλια 1, 4, 3"]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":["Με βάση το συνδυασμό των καναλιών 7, 5, 3"]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":["(B09 - B08)/(B09 + B08)"]},"B09 / B08":{"msgid":"B09 / B08","msgstr":["B09 / B08"]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":["Με βάση το συνδυασμό των καναλιών 13, 5, 2"]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":["Με βάση τα κανάλια 13, 4, 1"]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":["Με βάση τα κανάλια 13, 5, 2"]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":["Με βάση το συνδυασμό καναλιών (B13-B07) / (B13 + B07)"]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":["Δείκτης χερσαίας χλωροφύλλης"]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V 10-ημερήσια σύνθεση\nΚορυφή κομοστέγης (Ατμοσφαιρικά διορθωμένη)\nχρονική ανάλυση: 10-ημερησίως\nΑνάλυση: 333M (μέγεθος εικονοστοιχείου)"]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V ημερήσια σύνθεση\nΚορυφή της ατμόσφαιρας\nχρονική ανάλυση: καθημερινά\nΑνάλυση: 333M (μέγεθος pixel)"]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-ημερήσια σύνθεση\nΚορυφή της ατμόσφαιρας\nχρονική ανάλυση: 5-ημέρες\nΑνάλυση: 100M (μέγεθος pixel)"]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V Καθημερινή σύνθεση\nΚορυφή κομοστέγης (Ατμοσφαιρικά διορθωμένη)\nχρονική ανάλυση: καθημερινά\nΑνάλυση: 333M (μέγεθος pixel)"]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":["OrbitTracks_Aqua_Descending"]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-ημερήσια σύνθεση\nΚορυφή κομοστέγης (Ατμοσφαιρικά διορθωμένη)\nχρονική ανάλυση: 5-ημέρες\nΑνάλυση: 100M (μέγεθος εικονοστοιχείου)"]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":["OrbitTracks_Aqua_Ascending"]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":["OrbitTracks_Aura_Ascending"]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":["OrbitTracks_Aura_Descending"]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":["OrbitTracks_CloudSat_Ascending"]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":["OrbitTracks_Calipso_Ascending"]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":["OrbitTracks_Calipso_Descending"]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":["OrbitTracks_CloudSat_Descending"]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":["OrbitTracks_CYGNSS_Ascending"]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":["OrbitTracks_CYGNSS_Descending"]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":["OrbitTracks_GCOM-C_Ascending"]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":["OrbitTracks_GCOM-C_Descending"]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":["OrbitTracks_GCOM-W1_Ascending"]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":["OrbitTracks_GCOM-W1_Descending"]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":["OrbitTracks_GOSAT-2_Ascending"]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":["OrbitTracks_GOSAT-2_Descending"]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":["OrbitTracks_GOSAT_Ascending"]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":["OrbitTracks_GOSAT_Descending"]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":["OrbitTracks_GPM_Ascending"]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":["OrbitTracks_GPM_Descending"]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":["OrbitTracks_ICESAT-2_Ascending"]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":["OrbitTracks_ICESAT-2_Descending"]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":["OrbitTracks_ISS_Ascending"]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":["OrbitTracks_ISS_Descending"]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":["OrbitTracks_Landsat-7_Ascending"]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":["OrbitTracks_Landsat-7_Descending"]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":["OrbitTracks_Landsat-8_Ascending"]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":["OrbitTracks_METOP-A_Ascending"]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":["OrbitTracks_METOP-B_Descending"]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":["OrbitTracks_Landsat-8_Descending"]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":["OrbitTracks_METOP-C_Ascending"]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":["OrbitTracks_METOP-C_Descending"]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":["OrbitTracks_NOAA-20_Ascending"]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":["OrbitTracks_NOAA-20_Descending"]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":["OrbitTracks_METOP-B_Ascending"]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":["OrbitTracks_OCO-2_Ascending"]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":["OrbitTracks_OCO-2_Descending"]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":["OrbitTracks_SAOCOM1-A_Ascending"]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":["OrbitTracks_SAOCOM1-A_Descending"]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":["OrbitTracks_Sentinel-1A_Ascending"]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":["OrbitTracks_Sentinel-1B_Ascending"]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":["OrbitTracks_Sentinel-1A_Descending"]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":["OrbitTracks_METOP-A_Descending"]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":["OrbitTracks_Sentinel-1B_Descending"]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":["OrbitTracks_Sentinel-2A_Ascending"]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":["OrbitTracks_Sentinel-2A_Descending"]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":["OrbitTracks_Sentinel-2B_Ascending"]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":["OrbitTracks_Sentinel-2B_Descending"]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":["OrbitTracks_Sentinel-5P_Ascending"]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":["OrbitTracks_Sentinel-5P_Descending"]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":["OrbitTracks_SMAP_Ascending"]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":["OrbitTracks_SMAP_Descending"]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":["OrbitTracks_Suomi_NPP_Descending"]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":["OrbitTracks_Suomi_NPP_Ascending"]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":["OrbitTracks_Terra_Descending"]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":["OrbitTracks_Terra_Ascending"]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":["Με βάση τα κανάλια 4, 3, 2, ενισχυμένα από τα κανάλια 12 και 11."]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":["Με βάση τα κανάλια B07, B06, B04"]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Με βάση το συνδυασμό καναλιών (B8 - B4) / (B8 + B4)"]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":["Με βάση το θερμικό κανάλι 10"]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":["Με βάση τα κανάλια B12, B11, B8A"]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":["Με βάση το συνδυασμό καναλιών (B08 - B12) / (B08 + B12)"]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":["Ενισχυμένη απεικόνιση φυσικού χρώματος"]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":["Με βάση το συνδυασμό των καναλιών 8, 6, 4"]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":["Ενισχυμένος δείκτης βλάστησης"]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":["Με βάση το συνδυασμό: BSI, B08, B11"]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":["Ταξινόμηση NDMI για άρδευση"]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":["Με βάση τα κανάλια B11, B08, B02"]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":["Ψευδο-έγχρωμο σύνθετο 13, 5, 2"]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":["Με βάση τον συνδυασμό καναλιών (B13 - B07) / (B13 + B07)"]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":["Με βάση τα κανάλια 12,8,2"]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":["Με βάση τα κανάλια 4, 3, 2"]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":["Με βάση τα κανάλια 8, 4, 3"]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":["Με βάση τα κανάλια 12, 8, 2"]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":["Με βάση τα κανάλια 8, 11, 12"]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":["Ιζηματοποίηση νερού και περιεκτικότητα σε χλωροφύλλη"]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":["Με βάση τα κανάλια 12, 8A, 4"]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":["Με βάση το NDSI"]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":["Με βάση το συνδυασμό των καναλιών 11, 8, 2"]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":["Με βάση τα κανάλια B12, B11, B04"]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":["Με βάση τα κανάλια 4, 3, 2"]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":["Ατμοσφαιρικά ανθεκτικός δείκτης βλάστησης"]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":["Δείκτης βλάστησης προσαρμοσμένος στο έδαφος"]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":["# Θερμικό υπέρυθρο κανάλι εκπομπής φωτιάς IR\n\nΤο Sentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) διαθέτει δύο ειδικά κανάλια (F1 και F2) που στοχεύουν στην ανίχνευση θερμοκρασίας στην επιφάνεια της γης (LST). Το κανάλι F2, με κεντρικό μήκος κύματος 10854 nm καταγράφει μετρήσεις στο θερμικό υπέρυθρο, ή TIR. Είναι πολύ χρήσιμο για παρακολούθηση συμβάντων πυρκαγιάς και υψηλής θερμοκρασίας σε ανάλυση 1 km.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)"]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":["# Μεθάνιο (CH4)\n\n\n\nΤο μεθάνιο είναι, μετά το διοξείδιο του άνθρακα, ο πιο σημαντικός παράγοντας για την ανθρωπογενή (προκαλούμενη από ανθρώπινη δραστηριότητα) επίδραση του θερμοκηπίου. Οι μετρήσεις παρέχονται σε μέρη ανά δισεκατομμύριο (ppb) με χωρική ανάλυση 7 km x 3,5 km.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/methane)"]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":["# Φορμαλδεΰδη (HCHO)\n\n\n\nΟι μακροχρόνιες δορυφορικές παρατηρήσεις της τροποσφαιρικής φορμαλδεΰδης (HCHO) είναι απαραίτητες στην εκτίμηση της ποιότητας του αέρα και γενικότερα στις χημικών-κλιματικών μελέτες, τόσο σε τοπική, όσο και σε παγκόσμια κλίμακα. Οι εποχιακές και διετείς παραλλαγές της κατανομής της φορμαλδεΰδης σχετίζονται κυρίως με αλλαγές θερμοκρασίας και πυρκαγιές, αλλά και με αλλαγές σε ανθρωπογενείς δραστηριότητες. Η διάρκεια ζωής της είναι της τάξης μερικών ωρών, ενώ οι συγκεντρώσεις HCHO στο οριακό στρώμα είναι πιθανό να σχετίζονται άμεσα με την απελευθέρωση βραχείας ζωής υδρογονανθράκων, οι οποίοι ως επί το πλείστον δε μπορούν να παρατηρηθούν απευθείας από το διάστημα. Οι μετρήσεις γίνονται σε mol ανά τετραγωνικό μέτρο (mol / m ^ 2).\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/formaldehyde)"]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":["# Διοξείδιο του θείου (SO2)\n\n\n\nΤο διοξείδιο του θείου εισέρχεται στην ατμόσφαιρα της Γης μέσω τόσο φυσικών όσο και ανθρωπογενών διεργασιών. Η ύπαρξη και μελέτη του παίζει σημαντικό ρόλο στη χημεία σε τοπική και παγκόσμια κλίμακα και μπορεί να προκαλέσει βραχυπρόθεσμη ρύπανση έως σημαντικές επιπτώσεις στο κλίμα. Περίπου το 30% του εκπεμπόμενου SO2 προέρχεται από φυσικές πηγές. Το μεγαλύτερο ποσοστό της εμφάνισης του διοξειδίου οφείλεται σε ανθρωπογενείς δραστηριότητες. Ο αισθητήρας Sentinel-5P / TROPOMI καταγράφει την επιφάνεια της Γης με χρόνο επίσκεψης μίας ημέρας, με χωρική ανάλυση 3,5 x 7 km που επιτρέπει την καταγραφή λεπτομερειών, συμπεριλαμβανομένης της ανίχνευσης μικρότερων εκπομπών SO2. Οι μετρήσεις γίνονται σε mol ανά τετραγωνικό μέτρο (mol / m ^ 2).\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/sulphur-dioxide)"]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":["# Όζον (O3)\n\n\n\nΤο όζον είναι ζωτικής σημασίας για την ισορροπία της γήινης ατμόσφαιρας. Στη στρατόσφαιρα, το στρώμα του όζοντος προστατεύει τη βιόσφαιρα από την επικίνδυνη ηλιακή υπεριώδη ακτινοβολία. Στην τροπόσφαιρα, δρα ως καθαριστικός παράγοντας, αλλά σε υψηλή συγκέντρωση καθίσταται επίσης επιβλαβής για την υγεία των ανθρώπων, των ζώων και της βλάστησης. Το όζον επιδρά επίσης και στην τρέχουσα κλιματική αλλαγή. Από την ανακάλυψη της τρύπας του όζοντος στην Ανταρκτική τη δεκαετία του 1980 και το επακόλουθο πρωτόκολλο του Μόντρεαλ το οποίο ρυθμίζει την παραγωγή ουσιών που περιέχουν χλώριο και καταστρέφουν το όζον, η ανίχνευση και παρακολούθησή του πραγματοποιείται τακτικά από το έδαφος και από το διάστημα. Οι μετρήσεις είναι σε mol ανά τετραγωνικό μέτρο (mol / m ^ 2)\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/total-ozone-column)"]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":["# Διοξείδιο του αζώτου (NO2)\n\n\n\nΤο διοξείδιο του αζώτου (NO2) και το οξείδιο του αζώτου (NO) αναφέρονται συνήθως ως οξείδια του αζώτου. Είναι σημαντικά στοιχεία στην ατμόσφαιρα της Γης, τα οποία υπάρχουν τόσο στην τροπόσφαιρα όσο και στη στρατόσφαιρα. Εισέρχονται στην ατμόσφαιρα ως αποτέλεσμα ανθρωπογενών δραστηριοτήτων (ιδίως καύσης ορυκτών καυσίμων και καύσης βιομάζας) αλλά και φυσικών διεργασιών (όπως μικροβιολογικές διεργασίες σε εδάφη, πυρκαγιές και κεραυνούς). Οι μετρήσεις γίνονται σε mol ανά τετραγωνικό μέτρο (mol / m ^ 2).\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/nitrogen-dioxide)"]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":["# Μονοξείδιο του άνθρακα (CO)\n\n\n\nΤο μονοξείδιο του άνθρακα (CO) είναι ένα σημαντικό ατμοσφαιρικό στοιχείο. Σε ορισμένες αστικές περιοχές αποτελεί σημαντικό ατμοσφαιρικό ρύπο. Οι κύριες πηγές CO είναι η καύση ορυκτών καυσίμων, η καύση βιομάζας και η ατμοσφαιρική οξείδωση μεθανίου και άλλων υδρογονανθράκων. Η συνολική στήλη μονοξειδίου του άνθρακα μετράται σε mol ανά τετραγωνικό μέτρο (mol / m ^ 2).\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Http://www.tropomi.eu/data-products/carbon-monoxide)"]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":["# Δείκτης αερολύματος\n\nΟ δείκτης αερολύματος (AI) είναι ένας ποιοτικός δείκτης που δείχνει την παρουσία υψηλών στρωμάτων αερολυμάτων στην ατμόσφαιρα. Μπορεί να χρησιμοποιηθεί για την ανίχνευση της παρουσίας αερολυμάτων που απορροφούν την υπεριώδη ακτινοβολία, όπως η σκόνη της ερήμου και τα ηφαιστειακά τέφρα. Οι θετικές τιμές (από ανοιχτό μπλε έως κόκκινο) υποδεικνύουν την παρουσία αερολυμάτων απορρόφησης UV. Αυτός ο δείκτης υπολογίζεται για δύο ζεύγη μήκους κύματος: 340/380 nm και 354/388 nm.\n\nΠερισσότερες πληροφορίες [εδώ.](Https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)"]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":["# Βάση ύψους νέφους\n\nΒάση ύψους νέφους σε μέτρα (m)."]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":["# Βάση πίεσης νέφους\n\nΗ βάση πίεσης του νέφους σε Pascal (Pa)."]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":["# Οπτικό πάχος νέφους\n\nΤο πάχος του νέφους είναι μια βασική παράμετρος για τον χαρακτηρισμό των οπτικών ιδιοτήτων των νεφών. Είναι ένα μέτρο της ποσότητας του φωτός του ήλιου που περνά μέσα από το σύννεφο για να φτάσει στην επιφάνεια της Γης. Όσο υψηλότερο είναι το οπτικό πάχος του σύννεφου, τόσο περισσότερο ηλιακό φως διασκορπίζεται και αντανακλάται. Το σκούρο μπλε υποδεικνύει την ύπαρξη τιμές χαμηλού οπτικού πάχους του νέφους και το κόκκινο δείχνει μεγαλύτερο οπτικό πάχος."]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":["# Ύψος κορυφής νέφους\n\nΤο ύψος του νέφους μετράται σε μέτρα (m)."]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":["# Πίεση κορυφής νέφος\n\nΗ πίεση που μετράται στην κορυφή του νέφους σε Pascal (Pa)."]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":["# Κανονικοποιημένος δείκτης βλάστησης (NDVI)\n\nΟ κανονικοποιημένος δείκτης βλάστησης είναι ένας απλός, αλλά αποτελεσματικός δείκτης για τον ποσοτικό προσδιορισμό της πράσινης βλάστησης. Είναι ένα μέτρο της κατάστασης της βλάστησης που βασίζεται στον τρόπο με τον οποίο τα φυτά αντανακλούν το φως σε ορισμένα μήκη κύματος. Το εύρος τιμών του NDVI είναι -1 έως 1. Οι αρνητικές τιμές του NDVI (τιμές που πλησιάζουν στο -1) αντιστοιχούν στο νερό. Οι τιμές κοντά στο μηδέν (-0,1 έως 0,1) αντιστοιχούν γενικά σε άγονες περιοχές, με στοιχεία βράχων, άμμου ή χιονιού. Οι χαμηλές, θετικές τιμές αντιπροσωπεύουν θάμνους και λιβάδια (περίπου 0,2 έως 0,4), ενώ οι υψηλές τιμές υποδεικνύουν την ύπαρξη εύκρατων και τροπικών δασών (τιμές πλησιάζουν το 1).\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) και [εδώ.](Https://eos.com/ndvi/)"]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":["# Ενισχυμένος δείκτης βλάστησης (EVI)\n\nΟ ενισχυμένος δείκτης βλάστησης (EVI) είναι ένας «βελτιστοποιημένος» δείκτης βλάστησης καθώς διορθώνει τα σήματα του θορύβου του εδάφους και τις ατμοσφαιρικές επιδράσεις. Είναι πολύ χρήσιμος σε περιοχές με πυκνή κομοστέγη. Το εύρος τιμών για EVI είναι -1 έως 1, με υγιή βλάστηση γενικά περίπου 0,20 έως 0,80.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel"]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Ατμοσφαιρικά Ανθεκτικός Δείκτης Βλάστησης (ARVI)\n\nΟ ατμοσφαιρικά ανθεκτικός δείκτης βλάστησης(ARVI) είναι ένας δείκτης βλάστησης που ελαχιστοποιεί τις επιπτώσεις της ατμοσφαιρικής σκέδασης. Είναι χρήσιμος για περιοχές με υψηλή περιεκτικότητα σε αερολύματα (ομίχλη, σκόνη, καπνός, ατμοσφαιρική ρύπανση). Το εύρος για ένα ARVI είναι -1 έως 1, ενώ η πράσινη βλάστηση γενικά κυμαίνεται μεταξύ τιμών 0,20 έως 0,80.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) και [εδώ.](Https://eos.com/blog/6-spectral-indexes-on -τοπ-of-ndvi-to-make-your-vegetation-analysis-complete /)"]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Εδαφικά προσαρμοσμένος δείκτης βλάστησης (SAVI)\n\nΟ εδαφικά προσαρμοσμένος δείκτης βλάστησης είναι παρόμοιος με το δείκτη βλάστησης Normalized Difference (NDVI), αλλά χρησιμοποιείται σε περιοχές όπου η βλάστηση είναι χαμηλή (<40%). Ο δείκτης αποτελεί μια τεχνική μετασχηματισμού που ελαχιστοποιεί τις επιδράσεις της φωτεινότητας του εδάφους, σε σχέση με τους δείκτες φασματικής βλάστησης που περιλαμβάνουν μήκη κύματος κόκκινου και εγγύς υπέρυθρου (NIR). Ο δείκτης είναι χρήσιμος για την ανάλυση του εδάφους, νέων καλλιεργειών και ξηρών περιοχών με αραιά βλάστηση\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) και [εδώ.](Https://eos.com/blog/6-spectral-indexes-on -τοπ-of-ndvi-to-make-your-vegetation-analysis-complete /)"]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":["# Τροποποιημένος δείκτης ανθοκυανίνης (mARI / ARI2)\n\nΟι ανθοκυανίνες είναι χρωστικές που είναι κοινές στα φυτά και προκαλούν τον κόκκινο, μπλε και μωβ χρωματισμό τους. Παρέχουν πολύτιμες πληροφορίες σχετικά με τη φυσική κατάσταση των φυτών, καθώς θεωρούνται δείκτες διαφόρων τύπων ασθενειών. Η ανάκλαση της ανθοκυανίνης είναι υψηλότερη γύρω στα 550nm. Ωστόσο, στα ίδια μήκη κύματος ανακλά και από η χλωροφύλλη. Για την απομόνωση των ανθοκυανινών, αφαιρείται το φασματικό κανάλι των 700nm, που αντανακλά μόνο τη χλωροφύλλη και όχι τις ανθοκυανίνες.\n\nΓια τη διόρθωση της πυκνότητας και του πάχους των φύλλων, η εγγύς υπέρυθρη φασματική ακτινοβολία (στα συνιστώμενα μήκη κύματος 760-800nm), η οποία σχετίζεται με τη σκέδαση των φύλλων, προστίθεται στον βασικό δείκτη ARI. Ο νέος δείκτης ονομάζεται τροποποιημένος ARI ή mARI (επίσης ARI2).\n\nΟι τιμές mARI για τα υπό εξέταση δέντρα σε [αυτό το αρχικό άρθρο](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) κυμαίνονταν από 0 έως 8."]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":["# Αλγόριθμος Πράσινης Πόλης\n\nΟ αλγόριθμος Green City στοχεύει στην ευαισθητοποίηση σχετικά με τις πράσινες περιοχές σε πόλεις σε όλο τον κόσμο. Συγκεκριμένα, λαμβάνει υπόψη τον κανονικοποιημένο δείκτη βλάστησης (NDVI) και τα πραγματικά μήκη κύματος χρώματος. Διαχωρίζει τις συσσωρευμένες περιοχές από τις βλαστημένες, καθιστώντας το χρήσιμο για τον εντοπισμό αστικών περιοχών. Οι χτισμένες περιοχές εμφανίζονται με γκρι χρώμα και η βλάστηση εμφανίζεται με πράσινο χρώμα.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":["#Αλγόριθμος Αστικής Ταξινόμησης\n\nΟ αλγόριθμος Urban Classified αποσκοπεί στον εντοπισμό αστικών περιοχών διαχωρίζοντας τες από άγονο έδαφος, βλάστηση και νερό. Οι περιοχές με υψηλή περιεκτικότητα σε υγρασία παρουσιάζονται με μπλε χρώμα. Περιοχές που υποδηλώνουν την ύπαρξη αστικών περιοχών παρουσιάζονται με λευκά χρώματα και η βλάστηση με πράσινο. Οτιδήποτε άλλο δείχνει άγονο έδαφος και εμφανίζεται σε καφέ χρώματα.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)"]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":["# Αλγόριθμος Έγχρωμου Υπέρυθρου Αστικών Περιοχών\n\nΑυτός ο αλγόριθμος, που δημιουργήθηκε από τον Leo Tolari, συνδυάζει την φυσική έγχρωμη απεικόνιση με μήκη κύματος υπέρυθρων (NIR) και μικροκυματικών υπέρυθρων (SWIR). Ο αλγόριθμος επισημαίνει αστικές περιοχές καλύτερα από το φυσικό χρώμα.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)"]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":["# NDMI για την έλλειψη υγρασίας\n\nΟ κανονικοποιημένος δείκτης υγρασίας μπορεί να χρησιμοποιηθεί για την παρακολούθηση της άρδευσης. Για όλες τις τιμές δείκτη άνω του 0, λαμβάνοντας υπόψη τη χρήση και κάλυψη γης, είναι δυνατόν να προσδιοριστεί εάν έχει γίνει άρδευση. Γνωρίζοντας τον τύπο της καλλιέργειας που καλλιεργείται (π.χ. εσπεριδοειδή), είναι δυνατό να προσδιοριστεί εάν η άρδευση είναι αποτελεσματική ή όχι κατά τη διάρκεια της κρίσιμης καλλιεργητικής θερινής περιόδου, καθώς και εάν ορισμένα τμήματα της καλλιέργειας υπόκεινται σε υπερθέρμανση.\n\n\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)"]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":["# Κανονικοποιημένος δείκτης υγρασίας (NDMI)\n\nΟ κανονικοποιημένος δείκτης υγρασίας (NDMI) χρησιμοποιείται για τον προσδιορισμό της περιεκτικότητας σε νερό και την παρακολούθηση της ξηρασίας. Το εύρος τιμών του NDMI είναι -1 έως 1. Οι αρνητικές τιμές του NDMI (τιμές που πλησιάζουν στο -1) αντιστοιχούν σε άγονο έδαφος. Οι τιμές γύρω στο μηδέν (-0,2 έως 0,4) αντιστοιχούν γενικά στην έλλειψη νερού. Υψηλές, θετικές τιμές αντιπροσωπεύουν υψηλή κομοστέγη με επάρκεια νερού (περίπου 0,4 έως 1).\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":["# Κανονικοποιημένος δείκτης νερού (NDWI)\n\nΟ κανονικοποιημένος δείκτης νερού είναι ο πλέον κατάλληλος για τη χαρτογράφηση υδάτινων σωμάτων. Οι τιμές των υδάτινων σωμάτων είναι μεγαλύτερες από 0,5. Η βλάστηση έχει μικρότερες τιμές. Ο αστικός ιστός έχει θετικές τιμές μεταξύ μηδέν και 0,2.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":["# Κανονικοποιημένος δείκτης νερού (NDWI)\n\nΟ κανονικοποιημένος δείκτης νερού είναι ο πλέον κατάλληλος για τη χαρτογράφηση υδάτινων σωμάτων. Οι τιμές των υδάτινων σωμάτων είναι μεγαλύτερες από 0,5. Η βλάστηση έχει μικρότερες τιμές. Ο αστικός ιστός έχει θετικές τιμές μεταξύ μηδέν και 0,2."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Ψευδο-έγχρωμο σύνθετο\n\nΈνα ψευδο-έγχρωμο σύνθετο βασίζεται σε τουλάχιστον ένα μη ορατό μήκος κύματος για την οπτικοποίηση της Γης. Το σύνθετο που χρησιμοποιεί το εγγύς υπέρυθρο, κόκκινο και πράσινο κανάλι είναι πολύ δημοφιλές (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το ψευδο-έγχρωμο σύνθετο συνήθως χρησιμοποιείται για την εκτίμηση της πυκνότητας και της υγείας των φυτών, καθώς τα φυτά αντανακλούν κοντά στο υπέρυθρο και πράσινο φως, ενώ απορροφούν το κόκκινο. Οι πόλεις και το έδαφος εμφανίζονται γκρι ή μαύρα και το νερό εμφανίζεται μπλε ή μαύρο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) και [εδώ.](Https://earthobservatory.nasa.gov/features/FalseColor/page6.php )"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Ψευδο-έγχρωμο σύνθετο\n\nΈνα ψευδο-έγχρωμο σύνθετο βασίζεται σε τουλάχιστον ένα μη ορατό μήκος κύματος για την οπτικοποίηση της Γης. Το σύνθετο που χρησιμοποιεί το εγγύς υπέρυθρο, κόκκινο και πράσινο κανάλι είναι πολύ δημοφιλές (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το ψευδο-έγχρωμο σύνθετο συνήθως χρησιμοποιείται για την εκτίμηση της πυκνότητας και της υγείας των φυτών, καθώς τα φυτά αντανακλούν κοντά στο υπέρυθρο και πράσινο φως, ενώ απορροφούν το κόκκινο. Οι πόλεις και το έδαφος εμφανίζονται γκρι ή μαύρα και το νερό εμφανίζεται μπλε ή μαύρο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) και [εδώ.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":["# Ψευδο-έγχρωμο σύνθετο\n\nΈνα ψευδο-έγχρωμο σύνθετο βασίζεται σε τουλάχιστον ένα μη ορατό μήκος κύματος για την οπτικοποίηση της Γης. Το σύνθετο που χρησιμοποιεί το εγγύς υπέρυθρο, κόκκινο και πράσινο κανάλι είναι πολύ δημοφιλές (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το ψευδο-έγχρωμο σύνθετο συνήθως χρησιμοποιείται για την εκτίμηση της πυκνότητας και της υγείας των φυτών, καθώς τα φυτά αντανακλούν κοντά στο υπέρυθρο και πράσινο φως, ενώ απορροφούν το κόκκινο. Οι πόλεις και το έδαφος εμφανίζονται γκρι ή μαύρα και το νερό εμφανίζεται μπλε ή μαύρο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) και [εδώ.](https://earthobservatory.nasa.gov/features/FalseColor)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Ψευδο-έγχρωμο σύνθετο\n\nΈνα ψευδο-έγχρωμο σύνθετο βασίζεται σε τουλάχιστον ένα μη ορατό μήκος κύματος για την οπτικοποίηση της Γης. Το σύνθετο που χρησιμοποιεί το εγγύς υπέρυθρο, κόκκινο και πράσινο κανάλι είναι πολύ δημοφιλές (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το ψευδο-έγχρωμο σύνθετο συνήθως χρησιμοποιείται για την εκτίμηση της πυκνότητας και της υγείας των φυτών, καθώς τα φυτά αντανακλούν κοντά στο υπέρυθρο και πράσινο φως, ενώ απορροφούν το κόκκινο. Οι πόλεις και το έδαφος εμφανίζονται γκρι ή μαύρα και το νερό εμφανίζεται μπλε ή μαύρο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) και [εδώ.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy)."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) και [εδώ.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) και [εδώ.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) και [εδώ.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\nΠερισσότερες πληροφορίες [εδώ](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":["# Φυσικό έγχρωμο σύνθετο\n\nΟι αισθητήρες των δορυφόρων μπορούν να απεικονίσουν τη Γη σε διαφορετικές περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα αποτελεί ένα κανάλι. Ο δορυφόρος Sentinel-2 έχει 13 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί τα κανάλια κόκκινο, πράσινο και μπλε, τα οποία αντιστοιχίζονται στα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα να δημιουργείται ένα προϊόν με φυσικό χρώμα, που αποτελεί μια αναπαράσταση της Γης, όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\nΠερισσότερες πληροφορίες [εδώ](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)"]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":["# Ενισχυμένο φυσικό έγχρωμο σύνθετο\n\nΑυτός ο αλγόριθμος χρησιμοποιεί βελτιστοποίησες για να κρύψει τα καμένα εικονοστοιχεία και να εξομαλύνει την υπερέκθεση της εικόνας. Με τον τρόπο αυτό τα σύννεφα φαίνονται φυσικά και διατηρούν όσο το δυνατόν περισσότερες πληροφορίες. Οι εικόνες Sentinel-3 OLCI καλύπτουν μεγάλες περιοχές, καθιστώντας δυνατή την παρατήρηση μεγάλων σχηματισμών νέφους, όπως συμβαίνει στους τυφώνες.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)"]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Παγχρωματικό φυσικό έγχρωμο σύνθετο \n\nΤο παγχρωματικό φυσικό έγχρωμο σύνθετο δημιουργείται χρησιμοποιώντας τα συνηθισμένα κανάλια φυσικού χρώματος (κόκκινο, πράσινο και μπλε (RGB)), στα οποία η ανάλυση βελτιώνεται με τη χρήση του παγχρωματικού καναλιού (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Μια εικόνα από το παγχρωματικό κανάλι είναι παρόμοια με μια ασπρόμαυρη μεμβράνη: συνδυάζει το φως από τα κόκκινα, πράσινα και μπλε μέρη του φάσματος και υπολογίζει μια συνολική τιμή ανακλαστικότητας. Οι παγχρωματικές εικόνες έχουν τέσσερις φορές την ανάλυση ενός φυσικού σύνθετου φυσικού χρώματος, ενισχύοντας σημαντικά τη χρησιμότητα των εικόνων Landsat.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) και [εδώ.](Https://landsat.gsfc.nasa.gov/ landsat-8 / landsat-8-ζώνες /)"]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":["# Ψευδο-έγχρωμο σύνθετο για αστικές περιοχές\n\nΤο σύνθετο αυτό χρησιμοποιείται για την απεικόνιση των αστικών περιοχών. Η βλάστηση απεικονίζεται σε αποχρώσεις του πράσινου, ενώ οι αστικές περιοχές απεικονίζονται με λευκό, γκρι ή μωβ χρώμα. Το έδαφος, η άμμος και τα ορυκτά εμφανίζονται με μια ποικιλία χρωμάτων. Το χιόνι και ο πάγος εμφανίζονται με σκούρο μπλε και το νερό με μαύρο ή μπλε. Οι πλημμυρισμένες περιοχές εμφανίζονται με πολύ σκούρες μπλε αποχρώσεις, σχεδόν μαύρες. Το σύνθετο είναι χρήσιμο στην ανίχνευση πυρκαγιών και ηφαιστειακών σχηματισμών, καθώς εμφανίζονται σε αποχρώσεις του κόκκινου και του κίτρινου.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) και [εδώ.](Https://eos.com/false-color/)"]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Ψευδο-έγχρωμο σύνθετο για αστικές περιοχές\n\nΑυτό το σύνθετο χρησιμοποιεί έναν συνδυασμό καναλιών στο ορατό και στο μικροκυματικό υπέρυθρο φάσμα (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Η βλάστηση απεικονίζεται με αποχρώσεις του πράσινου. Οι πιο σκούρες αποχρώσεις του πράσινου δείχνουν πυκνότερη βλάστηση, ενώ η αραιή βλάστηση έχει ανοιχτότερες αποχρώσεις. Οι αστικές περιοχές εμφανίζονται μπλε και τα εδάφη απεικονίζονται με διαφορετικές αποχρώσεις του καφέ.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://gisgeography.com/landsat-8-bands-combinations/)"]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":["# Σύνθετο Καλλιέργειας\n\nΑυτό το σύνθετο χρησιμοποιεί το μικροκυματικό και εγγύς υπέρυθρο και το μπλε κανάλι με στόχο την παρακολούθηση της υγείας των καλλιεργειών (Ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος. Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Τα υπέρυθρα κανάλια συμβάλουν στην ανάδειξη της πυκνής βλάστησης, η οποία εμφανίζεται σκούρο πράσινο στο σύνθετο. Οι καλλιέργειες εμφανίζονται σε ένα ζωντανό πράσινο και το γυμνό έδαφος με ματζέντα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) και [εδώ.](Https://gisgeography.com/sentinel-2-bands-combinations/)"]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":["# Ταξινόμηση Χιονιού\n\nΟ αλγόριθμος Snow Classifier στοχεύει στην ανίχνευση χιονιού μέσω της ταξινόμησης των εικονοστοιχείων βάσει του δείκτη Normalized Difference Snow Index (NDSI) Οι τιμές που ταξινομούνται ως χιόνι επιστρέφονται με έντονο μπλε χρώμα. Ο αλγόριθμος είναι πιθανό να υπερεκτιμά τις περιοχές χιονιού πάνω από σύννεφα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":["# Ulyssys Water Quality Viewer (UWQV)\n\nΟ αλγόριθμος στοχεύει να απεικονίσει δυναμικά την περιεκτικότητα των υδάτινων σωμάτων σε χλωροφύλλη και ιζήματα, η οποία αποτελεί σημαντικό δείκτη της ποιότητας του νερού. Η περιεκτικότητα σε χλωροφύλλη απεικονίζεται με χρώματα από σκούρο μπλε (χαμηλή περιεκτικότητα σε χλωροφύλλη) έως πράσινο και κόκκινο (υψηλή περιεκτικότητα σε χλωροφύλλη). Οι συγκεντρώσεις ιζημάτων είναι χρώματος καφέ. Το σκούρο καφέ δείχνει υψηλή περιεκτικότητα σε ιζήματα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)"]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":["# Βελτιστοποιημένο Φυσικό Έγχρωμο Σύνθετο\n\nΑυτός ο αλγόριθμος στοχεύει στην απεικόνισης της Γης σε ένα όμορφο φυσικό έγχρωμο σύνθετο. Χρησιμοποιεί βελτιστοποίησες για να κρύψει τα καμένα εικονοστοιχεία και να εξομαλύνει την υπερέκθεση της εικόνας.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)"]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":["# Γεωλογικό Σύνθετο 12, 8, 2 \n\nΑυτό το σύνθετο χρησιμοποιεί το βραχυκυματικό υπέρυθρο κανάλι 12 για να ξεχωρίσει διαφορετικούς τύπους πετρωμάτων (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού κύματος: ένας δορυφόρος μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Κάθε πέτρωμα και ορυκτό ανακλά διαφορετικά το βραχυκυματικό υπέρυθρο, καθιστώντας δυνατή τη γεωλογική χαρτογράφηση με τη σύγκριση της ανακλώμενης ακτινοβολίας SWIR. Το εγγύς υπέρυθρο (NIR) κανάλι 8 απεικονίζει τη βλάστηση και το 2 την υγρασία, συμβάλλοντας περαιτέρω στο διαχωρισμό των διαφορετικών τύπων εδάφους. Το σύνθετο είναι χρήσιμο για εντοπισμό γεωλογικών σχηματισμών και χαρακτηριστικών (π.χ. ρήγματα, κατάγματα), λιθολογία (π.χ. γρανίτης, βασάλτης κ.λπ.) και εφαρμογές εξόρυξης \n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)"]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":["# Γεωλογικό Σύνθετο 8, 11, 12\n\nΑυτό το σύνθετο χρησιμοποιεί τα δυο βραχυκυματικά υπέρυθρα κανάλια 11 και 12 με στόχο των διαχωρισμό των διαφορετικών τύπων πετρωμάτων (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού κύματος: ένας δορυφόρος μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια)). Κάθε πέτρωμα και ορυκτό ανακλά διαφορετικά το βραχυκυματικό υπέρυθρο, καθιστώντας δυνατή τη γεωλογική χαρτογράφηση με τη σύγκριση της ανακλώμενης ακτινοβολίας SWIR. Το εγγύς υπέρυθρο (NIR) κανάλι 8 απεικονίζει τη βλάστηση, βοηθώντας περαιτέρω στον διαχωρισμό των διαφορετικών τύπων εδάφους. Η βλάστηση απεικονίζεται κόκκινη. Το σύνθετο είναι χρήσιμο για τη διαφοροποίηση της βλάστησης και του εδάφους, ιδιαίτερα των γεωλογικών χαρακτηριστικών που μπορεί να είναι χρήσιμα για εξόρυξη.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://earthobservatory.nasa.gov/χαρακτηριστικά/FalseColor/σελίδα5.php) και [εδώ.](http://murphygeological.com/new---sentinel-2.html#)"]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":["# Πυρκαγιές\n\nΟ αλγόριθμος που αναπτύχθηκε από τον Pierre Markuse, , απεικονίζει πυρκαγιές σε δεδομένα Sentinel-2. Συνδυάζει το φυσικό έγχρωμο σύνθετο με κάποια δεδομένα NIR/SWIR για την απεικόνιση περιοχών με καπνό και την ανάδειξη κάποιων λεπτομερειών, από τα κανάλια B11 και B12. Η πυρκαγιά εμφανίζεται με κόκκινο και πορτοκαλί.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)"]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":["# Ενισχυμένο Φυσικό Έγχρωμο Σύνθετο\n\nΟ αλγόριθμος που αναπτύχθηκε από τον Pierre Markuse, χρησιμοποιεί πολλαπλά κανάλια (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού κύματος: ένας δορυφόρος μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια) και εφαρμόζει παραμέτρους κορεσμού και φωτεινότητας για να ενισχύσει το φυσικό έγχρωμο σύνθετο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)"]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":["# Δείκτης Καμένης Έκτασης\n\nΟ Δείκτης καμένης έκτασης χρησιμοποιεί το ευρύτερο φάσμα των καναλιών του ορατού, Red-Edge, NIR και SWIR.\n\nΠεριγραφή τιμών:()=> Το εύρος τιμών του δείκτη είναι από '-1' έως '1' για καμένες περιοχές και από '1' - '6' για ενεργές πυρκαγιές. Διαφορετικές εντάσεις πυρκαγιάς μπορεί να οδηγήσουν σε διαφορετικά κατώφλια: το εύρος τιμών προέκυψε μετά από βαθμονόμηση του αρχικού συντάκτη σε μεσογειακές κυρίως περιοχές.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)"]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":["# Κανονικοποιημένος Δείκτης Καμένης Έκτασης (NBR)\n\nΟ κανονικοποιημένος δείκτης καμένης έκτασης χρησιμοποιείται συχνά για την εκτίμηση της έντασης της καταστροφής του εδάφους. Χρησιμοποιεί εγγύς (NIR) και βραχυκυματικά (SWIR) υπέρυθρα. Η υγιής βλάστηση έχει υψηλή ανακλαστικότητα στο εγγύς υπέρυθρο τμήμα του φάσματος και χαμηλή στο βραχυκυματικό. Αντίθετα, οι καμένες περιοχές έχουν υψηλή ανακλαστικότητα στο βραχυκυματικό υπέρυθρο, και χαμηλή στο εγγύς. Τα σκούρα εικονοστοιχεία υποδεικνύουν καμένες περιοχές.\n\n\n\nΠερισσότερες πληροφορίες [εδώ]](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.htmlκαι [εδώ.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":["# Ατμοσφαιρική διείσδυση\n\nΑυτό το σύνθετο χρησιμοποιεί διαφορετικά κανάλια (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού κύματος: ένας δορυφόρος μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια) στο μη ορατό τμήμα του ηλεκτρομαγνητικού φάσματος για να μειώσει την επίδραση της ατμόσφαιρας στην εικόνα. Τα βραχυκυματικά υπέρυθρα κανάλια 11 και 12 αντανακλώνται έντονα από θερμές περιοχές, κάνοντάς τα κατάλληλα για τη χαρτογράφηση πυρκαγιών και καμένων περιοχών. Το βραχυκυματικό υπέρυθρο κανάλι 8, αντιθέτως, ανακλάται ιδιαίτερα από τη βλάστηση, κάτι το οποίο καταδεικνύει την απουσία πυρκαγιάς. Η βλάστηση εμφανίζεται μπλε, εμφανίζοντας λεπτομέρειες που σχετίζονται με την κατάσταση της βλάστησης. Η υγιής βλάστηση εμφανίζεται με γαλάζιο χρώμα ενώ η αραιή ή/και άνυδρη βλάστηση εμφανίζεται με θαμπό μπλε. Ο αστικός ιστός εμφανίζεται με λευκά, γκρίζα, κυανά ή μωβ.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://eos.com/atmospheric-penetration/)"]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":["# Απεικόνιση Γυμνού Εδάφους\n\nΗ απεικόνιση του γυμνού εδάφους μπορεί να φανεί χρήσιμη για τη χαρτογράφηση του χώματος, τον εντοπισμό κατολισθήσεων ή την έκταση της διάβρωσης σε περιοχές χωρίς βλάστηση. Αυτή η απεικόνιση δείχνει τη βλάστηση σε πράσινο και το άγονο έδαφος σε κόκκινο χρώμα. Το νερό εμφανίζεται σε μαύρο.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) και [εδώ](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)"]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":["# Φυσικό Έγχρωμο σύνθετο με Υπέρυθρες Επισημάνσεις\n\nΑυτό το σύνθετο βελτιώνει την φυσική χρωματική απεικόνιση προσθέτοντας το βραχυκυματικό υπέρυθρο για να ενισχύσει τις λεπτομέρειες. Εμφανίζει θερμές περιοχές σε κόκκινο/πορτοκαλί χρώμα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)"]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":["# Ανίχνευση Καμένων Περιοχών\n\nΑυτός ο αλγόριθμος χρησιμοποιείται για τον εντοπισμό μεγάλων περιοχών που έχουν καεί πρόσφατα. Τα κόκκινα εικονοστοιχεία απεικονίζουν καμένες περιοχές και όλα τα άλλα εικονοστοιχεία απεικονίζονται σε φυσικό χρώμα. Ο αλγόριθμος μερικές φορές υπερεκτιμά τις καμένες περιοχές πάνω από νερά και σύννεφα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":["# Δείκτης Χερσαίας Χλωροφύλλης (OTCI)\n\n\n\nΟ δείκτης χερσαίας χλωροφύλλης (OTCI) εκτιμάται με βάση την περιεκτικότητα σε χλωροφύλλη στη χερσαία βλάστηση και μπορεί να χρησιμοποιηθεί για την παρακολούθηση της κατάστασης και της υγείας της βλάστησης. Οι χαμηλές τιμές OTCI συνήθως υποδηλώνουν την ύπαρξη νερού, άμμου ή χιονιού. Πολύ υψηλές τιμές, που απεικονίζονται με λευκό, επίσης υποδηλώνουν την απουσία χλωροφύλλης κατά πάσα πιθανότητα. Γενικά οι υψηλές τιμές αντιστοιχούν σε έδαφος, βραχώδες έδαφος ή σύννεφα. Οι τιμές χλωροφύλλης που κυμαίνονται από κόκκινο (χαμηλές τιμές χλωροφύλλης) έως σκούρο πράσινο (υψηλές τιμές χλωροφύλλης) μπορούν να χρησιμοποιηθούν για τον προσδιορισμό της υγείας της βλάστησης.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":["# Κανονικοποιημένος Δείκτης Αλατότητας\n\nΟ δείκτης απεικονίζει την ποσότητα αλατιού που υπάρχει στο έδαφος. Η αλατοποίηση του εδάφους είναι μία από τις πιο συνήθεις διαδικασίες αποδόμησης του εδάφους, ειδικά σε άνυδρες και ημι-άνυδρες περιοχές, όπου τα ποσά βροχόπτωσης υπερβαίνουν αυτά της εξάτμισης\n\nΟι υψηλές τιμές δείχνουν υψηλή αλατότητα και οι χαμηλές τιμές δείχνουν χαμηλή αλατότητα .\n\nΔιαβάστε περισσότερα [εδώ,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [εδώ](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) και [εδώ.](https://www.indexdatabase.de/db/i-single.php?id=57)"]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":["Με αυτό το εργαλείο μπορείτε να δημιουργήσετε μια κινούμενη εικόνα timelapse του οπτικοποιημένου επιπέδου και της τοποθεσίας που εμφανίζεται.\n\nΑρχικά, επιλέξτε ένα χρονικό εύρος. Μπορείτε να περιορίσετε τα αποτελέσματα αναζήτησης εφαρμόζοντας φίλτρα κατά μήνες\n(πλαίσιο ελέγχου με το φίλτρο ανά μήνες) ή / και επιλέγοντας μία εικόνα ανά καθορισμένη περίοδο (τροχιά, ημέρα, εβδομάδα, μήνα,\nέτος).\n\nΣτη συνέχεια, πατήστε Αναζήτηση και επιλέξτε τις εικόνες σας.\nΜπορείτε να επιλέξετε όλα τα αποτελέσματα από το πλαίσιο ελέγχου ή να φιλτράρετε τις εικόνες με την επιθυμητή νεφοκάλυψη, μετακινώντας τη μπάρα ρύθμισης. Επίσης μπορείτε να επιλέξετε εικόνες μία προς μία\nΜέσω του πλαισίου ελέγχου ** Όρια ** μπορείτε να ενεργοποιήσετε / απενεργοποιήσετε τα όρια στην εικόνα σας.\n\nΜπορείτε να κάνετε προεπισκόπηση του timelapse πατώντας το κουμπί αναπαραγωγής στο κάτω μέρος. Μπορείτε επίσης να ρυθμίσετε την ταχύτητα αναπαραγωγής\n(καρέ ανά δευτερόλεπτο).\n\nΌταν είστε ικανοποιημένοι με το αποτέλεσμα, κάντε κλικ στο κουμπί λήψης και το timelapse θα\nμεταφορτωθεί ως αρχείο .gif
."]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":["Τα στιγμιότυπα του χρήστης δεν ήταν δυνατό να φορτωθούν, καθώς ο λογαριασμός σας Sentinel Hub δεν είχε ρυθμιστεί ή έχει λήξει. Μπορείτε ακόμα να χρησιμοποιήσετε τον EO Browser, αλλά δεν θα μπορείτε να χρησιμοποιήσετε τα ιδιοποιημένα στιγμιότυπά σας. Για να μπορείτε να καθορίσετε τα δικά στιγμιότυπα της υπηρεσίας, μπορείτε να υποβάλετε αίτηση για δωρεάν δοκιμαστική περίοδο 30 ημερών ή να εγγραφείτε σε ένα από τα σχέδια: "]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":["Αυτά είναι τμήματα θεμάτων τα οποία περιέχουν μη διαθέσιμες πηγές δεδομένων:"]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":["Κανάλι 2 - Μέγιστη απορρόφηση χλωροφύλλης - 442 nm"]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":["Κανάλι 11 - ζώνη απορρόφησης O2 R-στελέχους - 761 nm"]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":["Το ** DEM ** (Digital Elevation Model - Ψηφιακό Μοντέλο Εδάφους) είναι μια ψηφιακή αναπαράσταση του εδάφους (συνήθως της επιφάνειας της Γης). Δημιουργείται με τη διαίρεση όλου του κόσμου σε κελιά πλέγματος, καθένα από τα οποία έχει μια τιμή υψομέτρου σε μέτρα. Ανάλογα με το μέγεθος του κελιού του πλέγματος, ένα DEM μπορεί να είναι πιο λεπτομερές (υψηλή ανάλυση) ή λιγότερο λεπτομερές (χαμηλή ανάλυση). Οι συλλογές δεδομένων Sentinel Hub DEM (Mapzen και Copernicus) είναι στατικές (ανεξάρτητες από την ημερομηνία) και είναι παγκοσμίως διαθέσιμες.\n\n** Συνήθης χρήση: ** Μοντελοποίηση ροών νερού, ορθοαναγωγή εικόνων Sentinel-1."]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["Το ** Copernicus DEM ** αναπαριστά την επιφάνεια της Γης, συμπεριλαμβανομένων κτιρίων, υποδομών και βλάστησης. Ομοίως με το Mapzen DEM, βασίζεται σε συνδυασμό διαφορετικών DEM (βασίζεται στο [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Είναι μια στατική συλλογή (ανεξάρτητη από την ημερομηνία) με παγκόσμια κάλυψη.\n\n** Χωρική ανάλυση: ** 30 m και σε κάποιες περιπτώσεις 90 m (όπου δεν είναι διαθέσιμα οι πινακίδες με τα 30 m).\n\nΣυντελεστές: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["Το ** Copernicus DEM ** αναπαριστά την επιφάνεια της Γης, συμπεριλαμβανομένων κτιρίων, υποδομών και βλάστησης. Παρόμοια με το Mapzen DEM, βασίζεται σε συνδυασμό διαφορετικών DEM (βάση [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Είναι μια στατική συλλογή (ανεξάρτητη από την ημερομηνία) με παγκόσμια κάλυψη.\n\n** Χωρική ανάλυση: ** 90 μ\n\nΣυντελεστές: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"User Instances":{"msgid":"User Instances","msgstr":["Στιγμιότυπα Χρήστη"]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":["Πλοήγηση ποντικιού"]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":["Προηγμένα εφέ RGB"]},"Disabled":{"msgid":"Disabled","msgstr":["Απενεργοποιημένο"]},"Yes":{"msgid":"Yes","msgstr":["Ναί"]},"Orthorectification":{"msgid":"Orthorectification","msgstr":["Ορθοαναγωγή"]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":["Πρωτεύον σύνολο δεδομένων:"]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":["Ψευδώνυμο (alias) της πηγής δεδομένων:"]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":["Συμπληρωματικά δεδομένα:"]},"Cancel":{"msgid":"Cancel","msgstr":["Απόρριψη"]},"Error":{"msgid":"Error","msgstr":["Σφάλμα"]},"Help":{"msgid":"Help","msgstr":["Βοήθεια"]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":["Τοποθετήστε την κάμερα 3D βάσει του χάρτη 2D"]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":["Τροποποιημένος δείκτης ανάκλασης ανθοκυανίνης"]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":["# Αποτελεσματικό κλάσμα ραδιομετρικού νέφους\n\nΤο αποτελεσματικό κλάσμα του ραδιομετρικού νέφους αντιπροσωπεύει το τμήμα της επιφάνειας της Γης που καλύπτεται από σύννεφα, διαιρούμενο με τη συνολική επιφάνεια. Τα σύννεφα προκαλούν θωράκιση, λευκαύγεια (albedo) και απορρόφηση της ακτινοβολίας. Το αποτελεσματικό κλάσμα του ραδιομετρικού νέφους είναι μια σημαντική παράμετρος για τη διόρθωση αυτών των εφέ."]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Θερμικό κανάλι 10\n\nΑυτή η θερμική απεικόνιση βασίζεται στο κανάλι 10 (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος · Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Στο κεντρικό μήκος κύματος των 10895 nm μετρά στο θερμικό υπέρυθρο, ή TIR. Αντί να μετρά τη θερμοκρασία του αέρα, όπως κάνουν οι μετεωρολογικοί σταθμοί, το κανάλι 10 παρέχει πληροφορίες στο ίδιο το έδαφος, το οποίο συχνά είναι πολύ πιο ζεστό. Το θερμικό κανάλι 10 είναι χρήσιμο για την παροχή επιφανειακών θερμοκρασιών και συλλέγεται με ανάλυση 100 μέτρων.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":["# Σύνθετο του μικροκυματικού υπέρυθρου (SWIR)\n\nΟι μετρήσεις υπερύθρων μικρού κύματος (SWIR) μπορούν να βοηθήσουν τους επιστήμονες να εκτιμήσουν πόσο νερό υπάρχει στα φυτά και το έδαφος, καθώς το νερό απορροφά μήκη κύματος SWIR. Τα υπέρυθρα κανάλια μικρού κύματος (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος · Ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια) είναι επίσης χρήσιμα για τη διάκριση μεταξύ διαφορετικών τύπων σύννεφων (σύννεφα νερού έναντι σύννεφων πάγου), χιονιού και πάγου, που εμφανίζονται λευκά σε ορατό φως. Σε αυτό το σύνθετο η βλάστηση εμφανίζεται σε αποχρώσεις του πράσινου, τα εδάφη και οι τεχνητές περιοχές βρίσκονται σε διάφορες αποχρώσεις του καφέ και το νερό εμφανίζεται μαύρο. Η πρόσφατα καμένη γη αντανακλά έντονα τις ζώνες SWIR, καθιστώντας τις πολύτιμες για τη χαρτογράφηση των ζημιών από φωτιά. Κάθε τύπος βραχώδους εδάφους αντανακλά το υπέρυθρο φως μικρού κύματος, με αποτέλεσμα να καθίσταται δυνατή η χαρτογράφηση της γεωλογίας.\n\n\n\nΠερισσότερες πληροφορίες [εδώ.](Https://custom-scripts.sentinel-hub.com/sentinel-2/composites/)"]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":["# Κανονικοποιημένος Δείκτης Χιονιού(NDSI)\n\nΟ κανονικοποιημένος δείκτης χιονιού Sentinel-2 μπορεί να χρησιμοποιηθεί για τη διάκριση μεταξύ σύννεφου και χιονιού καθώς το χιόνι απορροφά το υπέρυθρο φως μικρού κύματος, αλλά αντανακλά το ορατό φως, ενώ το σύννεφο είναι γενικά ανακλαστικό και στα δύο μήκη κύματος. Το χιόνι απεικονίζεται με έντονο μπλε χρώμα.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/)."]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":["# Ταξινόμηση σκηνής\n\n\n\nΗ ταξινόμηση σκηνής αναπτύχθηκε για τη διάκριση μεταξύ θολών pixel, καθαρών pixel και pixel νερού από δεδομένα Sentinel-2 και είναι αποτέλεσμα του αλγορίθμου ταξινόμησης σκηνής της ESA. Παρέχονται δώδεκα διαφορετικές ταξινομήσεις, συμπεραλμβανομένης της τυπολογίας των σύννεφων, βλάστησης, εδαφών / ερήμου, νερού και χιονιού. Δεν αποτελεί χάρτη κάλυψης γης με αυστηρή έννοια.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/)."]},"Zoom to location":{"msgid":"Zoom to location","msgstr":["Εστίαση στην περιοχή"]},"Remove layer":{"msgid":"Remove layer","msgstr":["Αφαίρεση επιπέδου"]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":["Κανάλι 10 - Θερμικό Υπέρυθρο (TIRS) - 10895 nm"]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":["Κανάλι 11 - Θερμικό Υπέρυθρο (TIRS) - 12005 nm"]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":["Το αποθετήριο ** CORINE Land Cover (CLC) ** είναι ένα σύνολο δεδομένων βασισμένο σε φορέα που αποτελείται από 44 κατηγορίες κάλυψης γης και χρήσης γης, που προέρχονται από μια σειρά δορυφορικών αποστολών. Στην πλειονότητα των ευρωπαϊκών χωρών, το CLC παράγεται χρησιμοποιώντας οπτική ερμηνεία δορυφορικών εικόνων υψηλής ανάλυσης. Σε μερικές χώρες εφαρμόζονται ημι-αυτόματες λύσεις, χρησιμοποιώντας εθνικά δεδομένα in situ, επεξεργασία δορυφορικών εικόνων και χρήση εργαλείων GIS. Περισσότερες πληροφορίες [εδώ] (https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover).\n\n** Κάλυψη **: Το μεγαλύτερο μέρος της Ευρώπης.\n\n** Διαθεσιμότητα δεδομένων **:\nΤα δεδομένα CLC ενημερώνονται κάθε 6 χρόνια. Στο πρόγραμμα περιήγησης EO, τα δεδομένα είναι διαθέσιμα για τις ακόλουθες ημερομηνίες:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n** Κοινή χρήση **:\nΠαρακολούθηση της χρήσης γης και κάλυψη γης, ανάλυση και πρόβλεψη αλλαγών για διάφορες εφαρμογές, όπως περιβάλλον, γεωργία, μεταφορές και χωρικός σχεδιασμός."]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":["** Τα προϊόντα Global Land Cover ** παρέχουν έναν ξεχωριστό χάρτη ταξινόμησης κάλυψης γης σύμφωνα με το Σύστημα Ταξινόμησης Κάλυψης Γης UN-FAO. Πρόσθετα συνεχή κλασματικά στρώματα για όλες τις βασικές κατηγορίες κάλυψης γης περιλαμβάνονται ως ζώνες, για να παρέχουν πιο λεπτομερείς πληροφορίες για κάθε κατηγορία κάλυψης γης Περισσότερες πληροφορίες [εδώ] (https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover).\n\n** Κάλυψη **: Παγκόσμια.\n\n** Διαθεσιμότητα δεδομένων **:\nΕνημερώνεται σε ετήσια βάση. Στο πρόγραμμα περιήγησης EO, τα δεδομένα είναι διαθέσιμα για τις ακόλουθες ημερομηνίες:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n** Κοινή χρήση **:\nΠαρακολούθηση της χρήσης γης και της κάλυψης της γης, που χρησιμοποιείται για να βοηθήσει τη λήψη αποφάσεων σε διάφορα θέματα, όπως η γεωργία και η επισιτιστική ασφάλεια, η βιοποικιλότητα, η κλιματική αλλαγή, οι δασικοί και υδάτινοι πόροι, η υποβάθμιση και η ερημοποίηση της γης και η αγροτική ανάπτυξη."]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":["Κύρια διακριτή ταξινόμηση γης σύμφωνα με το σχήμα FAO LCCS"]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":["Πιθανότητα ταξινόμησης, δείκτης ποιότητας για τη διακριτή ταξινόμηση"]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":["Τύπος δάσους για όλα τα εικονοστοιχεία στα οποία το κλάσμα κάλυψης δέντρων είναι μεγαλύτερο από 1%"]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":["Κλασματική κάλυψη (%) για την κατηγορία γυμνού εδάφους και αραιής βλάστησης"]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":["Κλασματική κάλυψη (%) για την κατηγορία του καλλιεργήσιμου εδάφους"]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":["Κλασματική κάλυψη (%) για την κατηγορία ποώδους βλάστησης"]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":["Κλασματικό κάλυμμα (%) για την κατηγορία βρύα & λειχήνες"]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":["Κλασματικό κάλυμμα (%) για την κατηγορία των θάμνων"]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":["Κλασματικό κάλυμμα (%) για την κατηγορία χιονιού και πάγου"]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":["Κλασματική κάλυψη (%) για την δασική κατηγορία"]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":["Κλασματικό κάλυμμα (%) για την κατηγορία των ανθρωπογενών κατασκευών"]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":["Κλασματική κάλυψη (%) για τη κατηγορία των μόνιμων υδατικών συστημάτων της ενδοχώρας"]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":["Κλασματική κάλυψη (%) για την κατηγορία εποχιακών υδάτινων σωμάτων"]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":["Δείκτης πυκνότητας δεδομένων που δείχνει την ποιότητα των δεδομένων παρατήρησης Γης (0 = κακή, 100 = τέλεια δεδομένα)"]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":["Επίπεδο ποιότητας σχετικά με τον εντοπισμό αλλαγών του τρέχοντος χαρτογραφημένου έτους στο προηγούμενο χαρτογραφημένο έτος. Αποτελεί ένα σύνολο 3 επιπέδων εμπιστοσύνης για όλους τους χάρτες CONSO και NRT με τιμών όπως:\n0 = Καμία αλλαγή.\n1 - Πιθανή εμπιστοσύνη.\n2 - Μεσαία εμπιστοσύνη.\n3 = Υψηλή αυτοπεποίθηση.\nΣΗΜΕΙΩΣΗ: Οι τιμές του καναλιού Change_Confidence_layer στα δεδομένα του 2015 δεν εμφανίζονται σωστά, επομένως τα δεδομένα αυτής της ζώνης δεν πρέπει να χρησιμοποιούνται για το συγκεκριμένο έτος."]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":["Σύρετε τις κατηγορίες στα πεδία RGB."]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":["# Χάρτης ταξινόμησης διακριτών τιμών\n\n\n\nΑυτό το επίπεδο απεικονίζει τον παγκόσμιο χάρτη κάλυψης γης με 23 διακριτές κατηγορίες που ορίζονται χρησιμοποιώντας το Σύστημα Ταξινόμησης Κάλυψης Γης UN-FAO (LCCS) και με συνδυασμό χρωμάτων που ορίζονται στο Εγχειρίδιο χρήστη προϊόντος του χάρτη [εδώ.] (Https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)"]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":["# Τύποι δασών\n\n\n\nΟπτικοποιημένοι τύποι δασών βάσει των 6 κατηγοριών, που ορίζονται στο Σύστημα Ταξινόμησης Κάλυψης Γης του UN-FAO (LCCS). Περισσότερα [εδώ.] (Https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)."]},"File upload":{"msgid":"File upload","msgstr":["Μεταφόρτωση αρχείου"]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Ανεβάστε ένα αρχείο KML / KMZ, GPX ή GEOJSON / JSON για να δημιουργήσετε μια περιοχή ενδιαφέροντος. Η περιοχή θα χρησιμοποιηθεί για αποκοπή κατά την εξαγωγή μιας εικόνας."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":["Αποθέστε το αρχείο KML / KMZ, GPX, GEOJSON / JSON ή αναζητήστε τον υπολογιστή σας"]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":["Επίπεδο ανίχνευσης κύριων υδάτινων σωμάτων που δείχνει εικονοστοιχεία με νερό και εικονοστοιχεία χωρίς νερό\n0 = Θάλασσα\n70 = Νερό\n251 = Δεν υπάρχουν δεδομένα\n255 = Χωρίς νερό"]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":["Ποιοτικό επίπεδο πληροφορίας σχετικά με την παρουσία των υδάτινων σωμάτων\n0 = Θάλασσα\n71 = Πολύ χαμηλή παρουσία\n72 = Χαμηλή παρουσία\n73 = Μεσαία παρουσία\n74 = Υψηλή παρουσία\n75 = Πολύ υψηλή παρουσία\n76 = Μόνιμη παρουσία\n251 = Δεν υπάρχουν δεδομένα\n252 = Σύννεφο\n255 = Όχι νερό"]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":["# Υδάτινα Σώματα\n\n\n\nΑυτό το επίπεδο πληροφορίας απεικονίζει την ανίχνευση υδάτινων σωμάτων (WB), η οποία δείχνει τα υδάτινα σώματα που εντοπίστηκαν χρησιμοποιώντας τον Τροποποιημένο Δείκτη Νερού Κανονικοποιημένης Διαφοράς (MNDWI) που προέρχεται από δεδομένα Sentinel-2 Επιπέδου 1C. Περισσότερες πληροφορίες [εδώ](https://collections.sentinel-hub.com/water-bodies/readme.html) και [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/)."]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":["Το προϊόν ** Υδάτινα σώματα ** απεικονίζει την έκταση της επιφάνειας που καλύπτεται από τα εσωτερικά ύδατα σε μόνιμη, εποχιακή ή περιστασιακή βάση σε παγκόσμια κλίμακα. Περιέχει ένα κύριο επίπεδο ανίχνευσης σώματος νερού (WB) και ένα επίπεδο ποιοτικής πληροφορίας (QUAL), σχετικά με την εποχιακή δυναμική των ανιχνευόμενων υδάτινων σωμάτων. Περισσότερες πληροφορίες [εδώ](https://collections.sentinel-hub.com/water-bodies/).\n\n**Κάλυψη**:\nΠαγκόσμια κάλυψη από μήκος -180 ° Α έως + 180 ° Δ και πλάτος + 80 ° Β έως -60 ° Ν. Ανάλογα με το μήνα, ορισμένες περιοχές μεγάλου γεωγραφικού πλάτους δεν καλύπτονται από τους δορυφόρους Sentinel-2.\n\n** Διαθεσιμότητα δεδομένων **:\nΑπό τον Οκτώβριο του 2020, το αρχείο ενημερώνεται κάθε μήνα.\n\n** Κοινή χρήση **\nΠαρακολούθηση υδάτινων σωμάτων, ξηρασίας, πλημμυρών και κλιματικής αλλαγής."]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Κάλυψη γης Corine (CLC)\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται και οι 44 κατηγορίες. Μάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης [εδώ ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Τεχνητές επιφάνειες\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται μόνο οι 11 κατηγορίες τεχνητής επιφάνειας, με βάση την ταξινόμηση [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature- οδηγίες / html). Μάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης με όλες τις κατηγορίες [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Γεωργικές περιοχές\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται μόνο οι 11 γεωργικές κατηγορίες, με βάση την ταξινόμηση [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines / html). Μάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης με όλες τις κατηγορίες [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Δασικές και ημιφυσικές περιοχές\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται μόνο οι 12 κατηγορίες δασικών και ημιφυσικών περιοχών, με βάση την ταξινόμηση [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Μάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης με όλες τις κατηγορίες [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Υγρότοποι\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται μόνο οι 5 κατηγορίες υγροτόπων, με βάση την ταξινόμηση [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html).\nΜάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης με όλες τις κατηγορίες [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Υδάτινα σώματα\n\n\n\nΣε αυτό το επίπεδο Corine Land Cover, εμφανίζονται μόνο οι 6 κατηγορίες υδατικών σωμάτων, με βάση την ταξινόμηση [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/ html). Μάθετε για κάθε κατηγορία [εδώ](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) και δείτε τον αλγόριθμο απεικόνισης με όλες τις κατηγορίες [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":["# Υδάτινα σώματα - Παρουσία\n\n\n\nΑυτό το επίπεδο πληροφορίας απεικονίζει τα 6 επίπεδα της Ποιότητας (QUAL), παρέχοντας πληροφορίες για την εποχική δυναμική των ανιχνευόμενων υδάτινων σωμάτων. Το QUAL παράγεται από στατιστικά στοιχεία παρουσίας υδάτινων σωμάτων που υπολογίστηκαν από προηγούμενα μηνιαία προϊόντα υδάτινων σωμάτων. Τα στατιστικά στοιχεία ταξινομούνται από χαμηλή παρουσία έως μόνιμη παρουσία. Περισσότερες πληροφορίες [εδώ](https://collections.sentinel-hub.com/water-bodies/readme.html) και [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#)."]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":["Landsat 8 L1"]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":["Landsat 8 L2"]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":["Πολύ Μπλε (443 nm)"]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":["Μπλε (482 nm)"]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":["Πράσινο (561.5 nm)"]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":["Κόκκινο (654.5 nm)"]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":["Εγγύς Υπέρυθρο (NIR) (865 nm)"]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":["Μικροκυματικό Υπέρυθρο (SWIR) 1 (1608.5 nm)"]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":["Μικροκυματικό Υπέρυθρο (SWIR) 2 (2200.5 nm)"]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":["Θερμικό Υπέρυθρο (TIRS) 1(10895 nm)"]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":["** Τα δεδομένα Επιπέδου 1 ** (από το ** Landsat Collection 2 **) παρέχουν προϊόντα ανακλαστικότητας και θερμοκρασίας εκτός της ατμόσφαιρας (top of atmosphere-TOA), με παγκόσμια κάλυψη.\n\nΤα δεδομένα έχουν υποβληθεί σε διάφορα στάδια επεξεργασίας, συμπεριλαμβανομένων γεωμετρικών και ραδιομετρικών βελτιώσεων.\n\nΠερισσότερες πληροφορίες σχετικά με τα δεδομένα Επιπέδου-1 [εδώ](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt -science_support_page_related_con) και [εδώ](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)"]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":["** Τα δεδομένα Επιπέδου-2 ** (από το ** Landsat Collection 2 **) παρέχουν προϊόντα επιφανειακής ανακλαστικότητας και θερμοκρασίας (CEOS Analysis Ready Data), με παγκόσμια κάλυψη\n\nΤα προϊόντα δεδομένων δημιουργούνται από δεδομένα Επιπέδου-1 που πληρούν τους περιορισμούς της Ηλιακής Γωνίας Ζενίθ, μικρότερης των 76 μοιρών και περιλαμβάνουν τα απαιτούμενα βοηθητικά σύνολα δεδομένων για τη δημιουργία ενός επιστημονικά βιώσιμου προϊόντος.\n\nΜάθετε περισσότερα για τα δεδομένα Επιπέδου-2 [εδώ](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) και [εδώ]( https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/)."]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["** Ο Landsat 8 ** είναι ο πιο πρόσφατα εκτοξευμένος δορυφόρος Landsat (παρέχεται από τη NASA / USGS) και φέρει τα όργανα Operational Land Imager (OLI) και τους θερμικά υπέρυθρους αισθητήρες (TIRS), με 9 οπτικά και 2 θερμικά κανάλια. Αυτοί οι δύο αισθητήρες παρέχουν εποχιακή παγκόσμια κάλυψη της γης.\n\n** Χωρική ανάλυση: ** 15 m για το παγχρωματικό κανάλι και 30 m για τα υπόλοιπα (τα θερμικά κανάλια προκύπτουν ύστερα απο ανασύσταση εικόνας από τα 100 m).\n\n** Χρόνος επίσκεψης: ** 16 ημέρες\n\n** Διαθεσιμότητα δεδομένων: ** Από τον Φεβρουάριο του 2013\n\n** Κοινή χρήση: ** Παρακολούθηση βλάστησης, χρήση γης, χάρτες κάλυψης γης, παρακολούθηση αλλαγών κ.λπ."]},"Please select a layer":{"msgid":"Please select a layer","msgstr":["Παρακαλώ επιλέξτε ένα επίπεδο"]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":["Το ιστόγραμμα μπορεί να εμφανιστεί μόνο κατά την οπτικοποίηση"]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":["Το ιστόγραμμα δεν είναι διαθέσιμο για "]},"Recalculate":{"msgid":"Recalculate","msgstr":["Επανυπολογισμός"]},"Histogram":{"msgid":"Histogram","msgstr":["Ιστογράμμα"]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":["Αλλαγές στην εμφάνιση νερού μεταξύ δύο εποχών, η πρώτη που κυμαίνεται από το 1984 έως το 1999 και η δεύτερη που καλύπτει το χρονικό εύρος από το 2000 έως το 2019."]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":["Μέγιστη έκταση των επιφανειακών υδάτων στο χρονικό εύρος των 36 ετών."]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":["Διαχρονική συχνότητα παρουσίας επιφανειακών υδάτων στο χρονικό διάστημα μεταξύ 1984 και 2019."]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":["Διαχρονική μεταβλητότητα της παρουσίας επιφανειακών υδάτων σε καθορισμένη περίοδο νερού εντός του χρονικού διαστήματος από το 1984 έως το 2019."]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":["Διαχρονική κατανομή επιφανειακών υδάτων το 2019."]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":["Οπτικοποιεί τις αλλαγές στις τρεις κατηγορίες επιφανειακών υδάτων (1) όχι νερό, (2) εποχιακό νερό και (3) μόνιμο νερό μεταξύ του πρώτου και του περασμένου έτους στην 36ετή χρονική περίοδο."]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":["# Παγκόσμιο Επιφανειακό Νερό - Παρουσία\n\n\n\nΤο επίπεδο πληροφορίας δείχνει τις διαχρονικές και μέσα στο έτος διακυμάνσεις της παρουσίας επιφανειακών υδάτων στο χρονικό διάστημα μεταξύ Μαρτίου 1984 και Δεκεμβρίου 2019. Οι μόνιμες περιοχές νερού με 100% εμφάνιση κατά τη διάρκεια των 36 ετών εμφανίζονται με μπλε χρώμα, ενώ οι πιο ανοιχτές αποχρώσεις του ροζ και μωβ υποδεικνύουν χαμηλότερες βαθμίδες παρουσίας νερού. Μάθετε περισσότερα [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/)."]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":["# Παγκόσμιο Επιφανειακό Νερό - Ένταση αλλαγής της παρουσίας\n\n\n\nΤο επίπεδο πληροφορίας απεικονίζει τις αλλαγές στην παρουσία του νερού μεταξύ δύο διαφορετικών εποχών, η πρώτη που κυμαίνεται από τον Μάρτιο του 1984 έως τον Δεκέμβριο του 1999 και η άλλη που καλύπτει την περίοδο από τον Ιανουάριο του 2000 έως τον Δεκέμβριο του 2019. Οι περιοχές με αύξηση της εμφάνισης του νερού απεικονίζονται σε διαφορετικές αποχρώσεις του πράσινου, περιοχές χωρίς αλλαγή απεικονίζονται με μαύρο χρώμα και οι περιοχές με μείωση εμφανίζονται σε αποχρώσεις του κόκκινου. Μάθετε περισσότερα [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/)."]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":["# Παγκόσμιο Επιφανειακό Νερό - Εποχικότητα\n\n\n\nΤο επίπεδο πληροφορίας της εποχικότητας παρέχει πληροφορίες σχετικά με την κατανομή των επιφανειακών υδάτων το 2019. Τα μόνιμα υδάτινα σώματα (το νερό ήταν παρόν για 12 μήνες) χρωματίζονται σε σκούρο μπλε και το εποχικό νερό (το νερό ήταν παρόν για λιγότερο από 12 μήνες) σε βαθμιαία φωτεινότερες αποχρώσεις του μπλε, με τις πιο ανοιχτές μπλε περιοχές όπου το νερό υπήρχε μόνο για 1 μήνα. Μάθετε περισσότερα [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#)."]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":["# Παγκόσμιο Επιφανειακό Νερό - Μετατροπές\n\n\n\nΤο επίπεδο των μετατροπών προέρχεται από μια σύγκριση μεταξύ του πρώτου και του τελευταίου έτους στην 36ετή χρονική περίοδο. Απεικονίζει τις αλλαγές μεταξύ εποχιακού και μόνιμου νερού. Για παράδειγμα, το \"χαμένο εποχιακό\" σημαίνει, ότι στο παρελθόν το εποχικό νερό μετατράπηκε σε γη, το \"νέο εποχιακό\" σημαίνει ότι η γη έχει μετατραπεί σε εποχικά νερά και ούτω καθεξής. Μάθετε περισσότερα [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) και μάθετε τι σημαίνει κάθε κατηγορία [εδώ](https://global-surface-water.appspot.com/faq )."]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":["# Παγκόσμιο Επιφανειακό Νερό - Έκταση\n\n\n\nΑυτό το επίπεδο πληροφορίας απεικονίζει το νερό με μπλε χρώμα. Συνδυάζει όλα τα άλλα επίπεδα πληροφορίας και απεικονίζει όλες τις τοποθεσίες για τις οποίες έχει εντοπιστεί παρουσία νερού στο διάστημα 36 ετών. Μάθετε περισσότερα [εδώ] (https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/)."]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":["# Παγκόσμιο επιφανειακό νερό - Επανεμφάνιση\n\n\n\nΤο επίπεδο της επανεμφάνισης δείχνει πόσο συχνά το νερό επέστρεψε σε μια συγκεκριμένη τοποθεσία σε μια καθορισμένη περίοδο νερού μεταξύ 1984 και 2019. Το πορτοκαλί χρώμα υποδηλώνει χαμηλή επανεμφάνιση (το νερό επιστρέφει στην περιοχή σπάνια) και το ανοιχτό μπλε χρώμα δείχνει υψηλή επανεμφάνιση (το νερό επιστρέφει συχνά στην περιοχή ). Μάθετε περισσότερα [εδώ](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/)."]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":["Μπλε (450-520 nm))"]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":["Πράσινο (520-600 nm)"]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":["Κόκκινο (630-690 nm)"]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":["Εγγύς Υπέρυθρο (NIR) (760-900 nm)"]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":["Μικροκυματικό Υπέρυθρο (SWIR) (1550-1750 nm)1"]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":["Θερμικό Υπέρυθρο (10400-12500 nm)"]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":["Μικροκυματικό Υπέρυθρο (SWIR) 2 (2080-2350 nm)"]},"Level 1":{"msgid":"Level 1","msgstr":["Επίπεδο 1"]},"Level 2":{"msgid":"Level 2","msgstr":["Επίπεδο 2"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Ψευδοέγχρωμο σύνθετο\n\nΈνα ψευδοέγχρωμο σύνθετο χρησιμοποιεί τουλάχιστον ένα μη ορατό μήκος κύματος για την απεικόνιση ης Γης. Συγκεκριμένα, το σύνθετο που χρησιμοποιεί υπέρυθρα, κόκκινα και πράσινα κανάλια είναι πολύ δημοφιλές (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος · ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το ψευδοέγχρωμο σύνθετο συνήθως χρησιμοποιείται για την αξιολόγηση της πυκνότητας και της υγείας των φυτών, καθώς τα φυτά ανακλούν το φως κοντά στο υπέρυθρο και το πράσινο, ενώ απορροφούν το κόκκινο. Οι πόλεις και το γυμνό έδαφος εμφανίζεται σε αποχρώσεις του γκρι και το νερό εμφανίζεται μπλε ή μαύρο."]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":["Η συλλογή ** Landsat 4-5 TM ** περιλαμβάνει εικόνες που παράγονται με τον αισθητήρα Thematic Mapper (TM), ο οποίος μεταφέρθηκε στους δορυφόρους Landsat 4 και 5. Υπάρχουν 6 οπτικά και ένα θερμικό υπέρυθρο κανάλι, με χωρική ανάλυση 30 μέτρων. Τα δεδομένα διαθέτουν παγκόσμια κάλυψη στη γη και είναι διαθέσιμα για τα έτη από το 1982 έως το 2012. Παρέχονται προϊόντα Επιπέδου-1 εκτός της ατμόσφαιρας (top of atmosphere - TOA) και Επιπέδου-2 επιφανειακής ανακλαστικότητας.\n\n** Χωρική ανάλυση **: 30 μέτρα\n\n** Χρόνος επίσκεψης ** 16 ημέρες\n\n** Διαθεσιμότητα δεδομένων **: παγκόσμια κάλυψη, Επίπεδο-1 από τον Αύγουστο του 1982 έως τον Μάιο του 2012, Επίπεδο-2 από τον Ιούλιο του 1984 έως τον Μάιο του 2012.\n\n** Κοινή χρήση **: Παρακολούθηση βλάστησης, πάγου και υδάτινων πόρων, ανίχνευση αλλαγών και δημιουργία χρήσεων γης - χαρτών κάλυψης γης."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":["# Φυσικό Έγχρωμο Σύνθετο\n\nΟι αισθητήρες που διαθέτουν οι δορυφόροι μπορούν να απεικονίσουν τη Γη σε διάφορες περιοχές του ηλεκτρομαγνητικού φάσματος. Κάθε περιοχή στο φάσμα ονομάζεται κανάλι. Το Landsat 4-5 TM έχει 7 κανάλια. Το φυσικό έγχρωμο σύνθετο χρησιμοποιεί ορατές λωρίδες φωτός κόκκινου, πράσινου και μπλε στα αντίστοιχα κανάλια κόκκινου, πράσινου και μπλε χρώματος, με αποτέλεσμα τη δημιουργία ενός προϊόντος με φυσικό χρώμα, που είναι μια καλή αναπαράσταση της Γης όπως την αντιλαμβάνεται το ανθρώπινο μάτι.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites)."]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":["# Θερμικό κανάλι 6\n\nΑυτή η θερμική οπτικοποίηση βασίζεται στο κανάλι 6 (ένα κανάλι είναι μια περιοχή του ηλεκτρομαγνητικού φάσματος · ένας δορυφορικός αισθητήρας μπορεί να απεικονίσει τη Γη σε διαφορετικά κανάλια). Το κεντρικό μήκος κύματος των 11040 nm αντιστοιχεί στο θερμικό υπέρυθρο, ή TIR. Αντί να μετρά τη θερμοκρασία του αέρα, όπως κάνουν οι μετεωρολογικοί σταθμοί, το κανάλι 6 έχει σημείο αναφοράς το ίδιο το έδαφος., το οποίο είναι συχνά πολύ πιο ζεστό. Το θερμικό κανάλι 6 είναι χρήσιμο για την παροχή επιφανειακών θερμοκρασιών, συλλέγεται με ανάλυση 120 μέτρων και μετά την ανασύσταση της εικόνας προκύπτει ανάλυση 30 μέτρων.\n\n\n\nΠερισσότερες πληροφορίες [εδώ](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites)."]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":["Η συνάρτηση αρχικοποίησης (setup function) στο evalscript δεν περιέχει το σωστό αποτέλεσμα εξόδου. Το αποτέλεσμα πρέπει να περιλαμβάνει:"]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":["Προσπαθήσατε να οπτικοποιήσετε ένα επίπεδο που δεν αντιστοιχεί σε κάποιο δείκτη. Η λειτουργία ιστογράμματος προς το παρόν λειτουργεί μόνο για δείκτες (π.χ. NDVI).\n\nΕπιλέξτε ένα επίπεδο δείκτη για να χρησιμοποιήσετε αυτήν τη δυνατότητα."]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["Το προϊόν Landsat 4-5 TM Επιπέδου-1 ** παρέχει εικόνες ανακλαστικότητας εκτός της ατμόσφαιρας (top of atmosphere-TOA). Τα δεδομένα Επιπέδου-1 παράγονται με επεξεργασία δεδομένων Landsat TM με τυπικές παραμέτρους επεξεργασίας, όπως κυβική συνέλιξη και διόρθωση εδάφους. Μάθετε περισσότερα [εδώ](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) και [εδώ](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects)."]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["Το προϊόν ** Landsat 4-5 TM Επιπέδου-2 ** παράγεται από την επεξεργασία δεδομένων Επιπέδου-1 για τη μετατροπή τους σε επιφανειακή ανακλαστικότητα - μια εκτίμηση της φασματικής ανάκλασης της επιφάνειας στο επίπεδο του εδάφους με την απουσία ατμοσφαιρικής σκέδασης και απορρόφησης. Μάθετε περισσότερα [εδώ](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) και [εδώ](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects)."]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":["Η συλλογή ** Global Surface Water ** προέρχεται από εικόνες Landsat 5, 7 και 8 και δείχνει διάφορες πτυχές της χωροχρονικής κατανομής των επιφανειακών υδάτων μεταξύ 1984 και 2020 (με ετήσιες αναθεωρήσεις) σε παγκόσμια κλίμακα σε έξι διαφορετικά επίπεδα. Ως επιφανειακό νερό θεωρείται κάθε ακάλυπτη έκταση νερού (περιοχές γλυκού και θαλασσινού νερού) μεγαλύτερη από 30m² ορατή από το διάστημα, συμπεριλαμβανομένων των φυσικών και τεχνητών υδάτινων σωμάτων. Περισσότερες πληροφορίες [εδώ] (https://collections.sentinel-hub.com/global-surface-water/).\n\n** Κάλυψη **: Παγκόσμια κάλυψη από μήκος 170 ° E έως 180 ° W και γεωγραφικό πλάτος 80 ° N έως 50 ° S.\n\n** Διαθεσιμότητα δεδομένων **: 1984 - 2019, 1984 - 2020.\n\n** Χωρική ανάλυση **: 30 μέτρα.\n\n** Κοινή χρήση **: Παρακολούθηση υδατικών συστημάτων για διαχείριση υδατικών πόρων, μοντελοποίηση κλίματος, διατήρηση της βιοποικιλότητας και επισιτιστική ασφάλεια."]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":["Το ** Mapzen DEM ** βασίζεται στο SRTM30 (Shuttle Radar Topography Mission) και [άλλες πηγές](https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). Τα δεδομένα της βαθυμετρίας λαμβάνονται από το [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). Είναι μια στατική συλλογή (ανεξάρτητη από την ημερομηνία) με παγκόσμια κάλυψη.\n\n** Χωρική ανάλυση: ** Κυρίως 90 m, σε ορισμένες περιοχές έως 10 m.\n\nΣυντελεστές: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)"]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Μέγιστη νεφοκάλυψη:"]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Μεταφόρτωση δεδομένων"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":[""]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]},"Built-up probability [0 - 100 %] at 10 m spatial resolution":{"msgid":"Built-up probability [0 - 100 %] at 10 m spatial resolution","msgstr":[""]},"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).":{"msgid":"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).","msgstr":[""]},"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.":{"msgid":"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.","msgstr":[""]},"Resampling kernel":{"msgid":"Resampling kernel","msgstr":[""]},"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)":{"msgid":"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)","msgstr":[""]},"4x4 cubic convolution":{"msgid":"4x4 cubic convolution","msgstr":[""]},"nearest neighbour":{"msgid":"nearest neighbour","msgstr":[""]},"proprietary MTF kernel":{"msgid":"proprietary MTF kernel","msgstr":[""]},"Width":{"msgid":"Width","msgstr":[""]},"Height":{"msgid":"Height","msgstr":[""]},"Format":{"msgid":"Format","msgstr":[""]},"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.":{"msgid":"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.","msgstr":[""]},"Could not generate timelapse animation file. Try using lower resolution or fewer frames.":{"msgid":"Could not generate timelapse animation file. Try using lower resolution or fewer frames.","msgstr":[""]}}}}
\ No newline at end of file
diff --git a/src/translations/en.po b/src/translations/en.po
index 004a2a3c..ddc61ecf 100644
--- a/src/translations/en.po
+++ b/src/translations/en.po
@@ -5661,4 +5661,82 @@ msgid ""
msgstr ""
msgid "Currently only collections on services.sentinel-hub are supported."
+msgstr ""
+
+msgid "Built-up probability [0 - 100 %] at 10 m spatial resolution"
+msgstr ""
+
+msgid ""
+"# The Global Human Settlement Layer GHS-BUILT-S2 \n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"The Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up "
+"areas (expressed as probabilities from 0 to 100 %) at 10 m spatial "
+"resolution. It was derived from a Sentinel-2 global image composite for the "
+"reference year 2018 using Convolutional Neural Networks.\n"
+"\n"
+"This script visualises the built-up probabilities stretched to 0-255.\n"
+"\n"
+"For more information about the layer, visit [this "
+"website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-"
+"human-settlement-layer-ghs-built-s2/)."
+msgstr ""
+
+msgid ""
+"The **Global Human Settlement** (GHS) framework produces global maps of "
+"built-up areas, population density and settlements to monitor human "
+"presence on Earth over time.\n"
+"\n"
+"**Coverage**: Global coverage with longitude from 180°W to 180°E and "
+"latitude from 72°N to 56°S\n"
+"\n"
+"**Data Availability**: Reference year 2018\n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Knowledge of population distribution and density has a "
+"number of applications, including disaster risk management or the study and "
+"management of urbanisation processes, not only but also in relation to the "
+"challenges of climate change and environmental degradation."
+msgstr ""
+
+msgid "Resampling kernel"
+msgstr ""
+
+msgid ""
+"Select the resampling kernel to use: \n"
+"- 4x4 cubic convolution (CC), \n"
+"- nearest neighbour (NN), \n"
+"- or the proprietary MTF kernel (MTF)"
+msgstr ""
+
+msgid "4x4 cubic convolution"
+msgstr ""
+
+msgid "nearest neighbour"
+msgstr ""
+
+msgid "proprietary MTF kernel"
+msgstr ""
+
+msgid "Width"
+msgstr ""
+
+msgid "Height"
+msgstr ""
+
+msgid "Format"
+msgstr ""
+
+msgid ""
+"For optimisation reasons MPEG4 output format will always be used for "
+"generating a timelapse with transition \"fade\", even when GIF is selected."
+msgstr ""
+
+msgid ""
+"Could not generate timelapse animation file. Try using lower resolution or "
+"fewer frames."
msgstr ""
\ No newline at end of file
diff --git a/src/translations/en.po.json b/src/translations/en.po.json
index 2b89096f..c9c5d4f7 100644
--- a/src/translations/en.po.json
+++ b/src/translations/en.po.json
@@ -1 +1 @@
-{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","plural-forms":"nplurals = 2; plural = (n != 1);","language":"en","mime-version":"1.0","content-transfer-encoding":"8bit"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nPlural-Forms: nplurals = 2; plural = (n != 1);\nLanguage: en\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\n"]},"Education":{"msgid":"Education","msgstr":[""]},"Normal":{"msgid":"Normal","msgstr":[""]},"Close":{"msgid":"Close","msgstr":[""]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":[""]},"Previous":{"msgid":"Previous","msgstr":[""]},"End tutorial":{"msgid":"End tutorial","msgstr":[""]},"Next":{"msgid":"Next","msgstr":[""]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":[""]},"Don't show again":{"msgid":"Don't show again","msgstr":[""]},"Show info":{"msgid":"Show info","msgstr":[""]},"Discover":{"msgid":"Discover","msgstr":[""]},"Visualize":{"msgid":"Visualize","msgstr":[""]},"Compare":{"msgid":"Compare","msgstr":[""]},"Pins":{"msgid":"Pins","msgstr":[""]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":[""]},"No tile found":{"msgid":"No tile found","msgstr":[""]},"Dataset":{"msgid":"Dataset","msgstr":[""]},"Show":{"msgid":"Show","msgstr":[""]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":[""]},"Show visualization":{"msgid":"Show visualization","msgstr":[""]},"Add to Pins":{"msgid":"Add to Pins","msgstr":[""]},"Add to compare":{"msgid":"Add to compare","msgstr":[""]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":[""]},"Hide layer":{"msgid":"Hide layer","msgstr":[""]},"Show layer":{"msgid":"Show layer","msgstr":[""]},"Share":{"msgid":"Share","msgstr":[""]},"Custom":{"msgid":"Custom","msgstr":[""]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":[""]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":[""]},"Free sign up":{"msgid":"Free sign up","msgstr":[""]},"for all features":{"msgid":"for all features","msgstr":[""]},"Powered by":{"msgid":"Powered by","msgstr":[""]},"with contributions by":{"msgid":"with contributions by","msgstr":[""]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":[""]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":[""]},"No results found":{"msgid":"No results found","msgstr":[""]},"Theme":{"msgid":"Theme","msgstr":[""]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":[""]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":[""]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":[""]},"Search":{"msgid":"Search","msgstr":[""]},"Highlights":{"msgid":"Highlights","msgstr":[""]},"Data sources":{"msgid":"Data sources","msgstr":[""]},"Please select a theme":{"msgid":"Please select a theme","msgstr":[""]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":[""]},"Date":{"msgid":"Date","msgstr":[""]},"Hide description":{"msgid":"Hide description","msgstr":[""]},"Show description":{"msgid":"Show description","msgstr":[""]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":[""]},"Based on: ":{"msgid":"Based on: ","msgstr":[""]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":[""]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":[""]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":[""]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":[""]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":[""]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":[""]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":[""]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":[""]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":[""]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":[""]},"Cloud":{"msgid":"Cloud","msgstr":[""]},"Other":{"msgid":"Other","msgstr":[""]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":[""]},"Advanced search":{"msgid":"Advanced search","msgstr":[""]},"Data location":{"msgid":"Data location","msgstr":[""]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":[""]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":[""]},"Polarization":{"msgid":"Polarization","msgstr":[""]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":[""]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":[""]},"Orbit direction":{"msgid":"Orbit direction","msgstr":[""]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":[""]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":[""]},"Credits:":{"msgid":"Credits:","msgstr":[""]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":[""]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":[""]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":[""]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":[""]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":[""]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":[""]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":[""]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":[""]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":[""]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":[""]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":[""]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":[""]},"Copied":{"msgid":"Copied","msgstr":[""]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":[""]},"Data source name":{"msgid":"Data source name","msgstr":[""]},"Sensing time":{"msgid":"Sensing time","msgstr":[""]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":[""]},"Sun elevation":{"msgid":"Sun elevation","msgstr":[""]},"MGRS location":{"msgid":"MGRS location","msgstr":[""]},"AWS path":{"msgid":"AWS path","msgstr":[""]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":[""]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":[""]},"SciHub link":{"msgid":"SciHub link","msgstr":[""]},"Back to search":{"msgid":"Back to search","msgstr":[""]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["",""]},"Load more":{"msgid":"Load more","msgstr":[""]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":[""]},"Results":{"msgid":"Results","msgstr":[""]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["",""]},"Edit pin description":{"msgid":"Edit pin description","msgstr":[""]},"Reject changes":{"msgid":"Reject changes","msgstr":[""]},"Confirm changes":{"msgid":"Confirm changes","msgstr":[""]},"Rename pin":{"msgid":"Rename pin","msgstr":[""]},"Remove pin":{"msgid":"Remove pin","msgstr":[""]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":[""]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":[""]},"Zoom":{"msgid":"Zoom","msgstr":[""]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":[""]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":[""]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":[""]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":[""]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":[""]},"Deselect all":{"msgid":"Deselect all","msgstr":[""]},"Select all":{"msgid":"Select all","msgstr":[""]},"No pins.":{"msgid":"No pins.","msgstr":[""]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["",""]},"File type not supported":{"msgid":"File type not supported","msgstr":[""]},"not supported":{"msgid":"not supported","msgstr":[""]},"No pins were found.":{"msgid":"No pins were found.","msgstr":[""]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":[""]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":[""]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":[""]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":[""]},"Share pins":{"msgid":"Share pins","msgstr":[""]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":[""]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":[""]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":[""]},"Delete all pins":{"msgid":"Delete all pins","msgstr":[""]},"Story":{"msgid":"Story","msgstr":[""]},"Export":{"msgid":"Export","msgstr":[""]},"Import":{"msgid":"Import","msgstr":[""]},"Clear":{"msgid":"Clear","msgstr":[""]},"Share pins link":{"msgid":"Share pins link","msgstr":[""]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":[""]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":[""]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":[""]},"Opacity":{"msgid":"Opacity","msgstr":[""]},"Split position":{"msgid":"Split position","msgstr":[""]},"split":{"msgid":"split","msgstr":[""]},"opacity":{"msgid":"opacity","msgstr":[""]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":[""]},"Remove all":{"msgid":"Remove all","msgstr":[""]},"Add all pins":{"msgid":"Add all pins","msgstr":[""]},"Split":{"msgid":"Split","msgstr":[""]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":[""]},"Download":{"msgid":"Download","msgstr":[""]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":[""]},"Go to Place":{"msgid":"Go to Place","msgstr":[""]},"Labels":{"msgid":"Labels","msgstr":[""]},"Borders":{"msgid":"Borders","msgstr":[""]},"Roads":{"msgid":"Roads","msgstr":[""]},"Zoom in":{"msgid":"Zoom in","msgstr":[""]},"Zoom out":{"msgid":"Zoom out","msgstr":[""]},"About EO Browser":{"msgid":"About EO Browser","msgstr":[""]},"Contact us":{"msgid":"Contact us","msgstr":[""]},"Get data":{"msgid":"Get data","msgstr":[""]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":[""]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":[""]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":[""]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":[""]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":[""]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":[""]},"please select a layer":{"msgid":"please select a layer","msgstr":[""]},"not available for ":{"msgid":"not available for ","msgstr":[""]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":[""]},"Search for data first.":{"msgid":"Search for data first.","msgstr":[""]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":[""]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":[""]},"Center map on feature":{"msgid":"Center map on feature","msgstr":[""]},"Remove geometry":{"msgid":"Remove geometry","msgstr":[""]},"Area of interest":{"msgid":"Area of interest","msgstr":[""]},"Select mode":{"msgid":"Select mode","msgstr":[""]},"Mode:":{"msgid":"Mode:","msgstr":[""]},"Remove measurement":{"msgid":"Remove measurement","msgstr":[""]},"km":{"msgid":"km","msgstr":[""]},"m":{"msgid":"m","msgstr":[""]},"Gain":{"msgid":"Gain","msgstr":[""]},"Gamma":{"msgid":"Gamma","msgstr":[""]},"R":{"msgid":"R","msgstr":[""]},"G":{"msgid":"G","msgstr":[""]},"B":{"msgid":"B","msgstr":[""]},"Min. data quality":{"msgid":"Min. data quality","msgstr":[""]},"Upsampling":{"msgid":"Upsampling","msgstr":[""]},"Downsampling":{"msgid":"Downsampling","msgstr":[""]},"Reset all":{"msgid":"Reset all","msgstr":[""]},"filter by months":{"msgid":"filter by months","msgstr":[""]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":[""]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":[""]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":[""]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":[""]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":[""]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":[""]},"Most recent":{"msgid":"Most recent","msgstr":[""]},"Least recent":{"msgid":"Least recent","msgstr":[""]},"Customize timespan":{"msgid":"Customize timespan","msgstr":[""]},"Back":{"msgid":"Back","msgstr":[""]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":[""]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":[""]},"Load script from URL":{"msgid":"Load script from URL","msgstr":[""]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":[""]},"Script loaded.":{"msgid":"Script loaded.","msgstr":[""]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":[""]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":[""]},"Refresh":{"msgid":"Refresh","msgstr":[""]},"orbit":{"msgid":"orbit","msgstr":[""]},"day":{"msgid":"day","msgstr":[""]},"week":{"msgid":"week","msgstr":[""]},"month":{"msgid":"month","msgstr":[""]},"year":{"msgid":"year","msgstr":[""]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":[""]},"Timelapse":{"msgid":"Timelapse","msgstr":[""]},"Select All":{"msgid":"Select All","msgstr":[""]},"Speed:":{"msgid":"Speed:","msgstr":[""]},"frames / s":{"msgid":"frames / s","msgstr":[""]},"Preparing...":{"msgid":"Preparing...","msgstr":[""]},"Could not download files:":{"msgid":"Could not download files:","msgstr":[""]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":[""]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":[""]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":[""]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":[""]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":[""]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":[""]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":[""]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":[""]},"Index ":{"msgid":"Index ","msgstr":[""]},"Threshold":{"msgid":"Threshold","msgstr":[""]},"Remove color picker":{"msgid":"Remove color picker","msgstr":[""]},"Add color picker":{"msgid":"Add color picker","msgstr":[""]},"Click to place marker":{"msgid":"Click to place marker","msgstr":[""]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":[""]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":[""]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":[""]},"Show captions":{"msgid":"Show captions","msgstr":[""]},"Show slide title":{"msgid":"Show slide title","msgstr":[""]},"Add map overlays":{"msgid":"Add map overlays","msgstr":[""]},"Show legend":{"msgid":"Show legend","msgstr":[""]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":[""]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":[""]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":[""]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":[""]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":[""]},"Show logo":{"msgid":"Show logo","msgstr":[""]},"Image format":{"msgid":"Image format","msgstr":[""]},"Image resolution":{"msgid":"Image resolution","msgstr":[""]},"Coordinate system":{"msgid":"Coordinate system","msgstr":[""]},"Layers":{"msgid":"Layers","msgstr":[""]},"Visualized":{"msgid":"Visualized","msgstr":[""]},"Raw":{"msgid":"Raw","msgstr":[""]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":[""]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":[""]},"Description":{"msgid":"Description","msgstr":[""]},"Image format:":{"msgid":"Image format:","msgstr":[""]},"Basic":{"msgid":"Basic","msgstr":[""]},"Analytical":{"msgid":"Analytical","msgstr":[""]},"High-res print":{"msgid":"High-res print","msgstr":[""]},"Download image":{"msgid":"Download image","msgstr":[""]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":[""]},"min/px":{"msgid":"min/px","msgstr":[""]},"sec/px":{"msgid":"sec/px","msgstr":[""]},"Resolution":{"msgid":"Resolution","msgstr":[""]},"lat.":{"msgid":"lat.","msgstr":[""]},"deg/px":{"msgid":"deg/px","msgstr":[""]},"long.":{"msgid":"long.","msgstr":[""]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":[""]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":[""]},"Image download":{"msgid":"Image download","msgstr":[""]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":[""]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":[""]},"DPI:":{"msgid":"DPI:","msgstr":[""]},"5 years":{"msgid":"5 years","msgstr":[""]},"2 years":{"msgid":"2 years","msgstr":[""]},"1 year":{"msgid":"1 year","msgstr":[""]},"6 months":{"msgid":"6 months","msgstr":[""]},"3 months":{"msgid":"3 months","msgstr":[""]},"1 month":{"msgid":"1 month","msgstr":[""]},"Retry":{"msgid":"Retry","msgstr":[""]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":[""]},"mean":{"msgid":"mean","msgstr":[""]},"median":{"msgid":"median","msgstr":[""]},"st. dev.":{"msgid":"st. dev.","msgstr":[""]},"min / max":{"msgid":"min / max","msgstr":[""]},"Export CSV":{"msgid":"Export CSV","msgstr":[""]},"Timespan:":{"msgid":"Timespan:","msgstr":[""]},"Date:":{"msgid":"Date:","msgstr":[""]},"Single date":{"msgid":"Single date","msgstr":[""]},"Timespan":{"msgid":"Timespan","msgstr":[""]},"hh":{"msgid":"hh","msgstr":[""]},"mm":{"msgid":"mm","msgstr":[""]},"From:":{"msgid":"From:","msgstr":[""]},"Until:":{"msgid":"Until:","msgstr":[""]},"Apply":{"msgid":"Apply","msgstr":[""]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":[""]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":[""]},"Check this out ":{"msgid":"Check this out ","msgstr":[""]},"Logout":{"msgid":"Logout","msgstr":[""]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":[""]},"Login":{"msgid":"Login","msgstr":[""]},"Default":{"msgid":"Default","msgstr":[""]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":[""]},"Agriculture":{"msgid":"Agriculture","msgstr":[""]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":[""]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":[""]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":[""]},"Geology":{"msgid":"Geology","msgstr":[""]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":[""]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":[""]},"Urban":{"msgid":"Urban","msgstr":[""]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":[""]},"Volcanoes":{"msgid":"Volcanoes","msgstr":[""]},"Wildfires":{"msgid":"Wildfires","msgstr":[""]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":[""]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":[""]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":[""]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":[""]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":[""]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":[""]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":[""]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":[""]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":[""]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":[""]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":[""]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":[""]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":[""]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":[""]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":[""]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":[""]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":[""]},"User Account":{"msgid":"User Account","msgstr":[""]},"Discover Tab":{"msgid":"Discover Tab","msgstr":[""]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":[""]},"Compare Tab":{"msgid":"Compare Tab","msgstr":[""]},"Pins Tab":{"msgid":"Pins Tab","msgstr":[""]},"Search Places":{"msgid":"Search Places","msgstr":[""]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":[""]},"Education Mode":{"msgid":"Education Mode","msgstr":[""]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":[""]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":[""]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":[""]},"Measure Distances":{"msgid":"Measure Distances","msgstr":[""]},"Download Image":{"msgid":"Download Image","msgstr":[""]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":[""]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":[""]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":[""]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":[""]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":[""]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":[""]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":[""]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":[""]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":[""]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":[""]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":[""]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":[""]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":[""]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":[""]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":[""]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":[""]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":[""]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":[""]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":[""]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":[""]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":[""]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":[""]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":[""]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":[""]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":[""]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":[""]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":[""]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":[""]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":[""]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":[""]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":[""]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":[""]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":[""]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":[""]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":[""]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":[""]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":[""]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":[""]},"Red band":{"msgid":"Red band","msgstr":[""]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":[""]},"Blue band":{"msgid":"Blue band","msgstr":[""]},"Green band":{"msgid":"Green band","msgstr":[""]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":[""]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":[""]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":[""]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":[""]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":[""]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":[""]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":[""]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":[""]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":[""]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":[""]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":[""]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":[""]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":[""]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":[""]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":[""]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":[""]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":[""]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":[""]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":[""]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":[""]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":[""]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":[""]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":[""]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":[""]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":[""]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":[""]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":[""]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":[""]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":[""]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":[""]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":[""]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":[""]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":[""]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":[""]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":[""]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":[""]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":[""]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":[""]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":[""]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":[""]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":[""]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":[""]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":[""]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":[""]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":[""]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":[""]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":[""]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":[""]},"Reflectance":{"msgid":"Reflectance","msgstr":[""]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":[""]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":[""]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":[""]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":[""]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":[""]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":[""]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":[""]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":[""]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":[""]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":[""]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":[""]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":[""]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":[""]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":[""]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":[""]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":[""]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":[""]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":[""]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":[""]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":[""]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":[""]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":[""]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":[""]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":[""]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":[""]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":[""]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":[""]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":[""]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":[""]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":[""]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":[""]},"Cloud base height":{"msgid":"Cloud base height","msgstr":[""]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":[""]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":[""]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":[""]},"Cloud top height":{"msgid":"Cloud top height","msgstr":[""]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":[""]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":[""]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":[""]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":[""]},"Ozone total column":{"msgid":"Ozone total column","msgstr":[""]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":[""]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":[""]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":[""]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":[""]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":[""]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":[""]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":[""]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":[""]},"B09 / B08":{"msgid":"B09 / B08","msgstr":[""]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":[""]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":[""]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":[""]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":[""]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":[""]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":[""]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":[""]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":[""]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":[""]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":[""]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":[""]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":[""]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":[""]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":[""]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":[""]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":[""]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":[""]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":[""]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":[""]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":[""]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":[""]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":[""]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":[""]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":[""]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":[""]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":[""]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":[""]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":[""]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":[""]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":[""]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":[""]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":[""]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":[""]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":[""]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":[""]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":[""]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":[""]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":[""]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":[""]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":[""]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":[""]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":[""]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":[""]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":[""]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":[""]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":[""]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":[""]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":[""]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":[""]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":[""]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":[""]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":[""]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":[""]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":[""]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":[""]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":[""]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":[""]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":[""]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":[""]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":[""]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":[""]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":[""]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":[""]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":[""]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":[""]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":[""]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":[""]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":[""]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":[""]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":[""]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":[""]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":[""]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":[""]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":[""]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":[""]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":[""]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":[""]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":[""]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":[""]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":[""]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":[""]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":[""]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":[""]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":[""]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":[""]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":[""]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":[""]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":[""]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":[""]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":[""]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":[""]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":[""]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":[""]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":[""]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":[""]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":[""]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":[""]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":[""]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":[""]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":[""]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":[""]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":[""]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":[""]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":[""]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":[""]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":[""]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":[""]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":[""]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":[""]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":[""]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":[""]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":[""]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":[""]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":[""]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":[""]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":[""]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":[""]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":[""]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":[""]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":[""]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":[""]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":[""]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":[""]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":[""]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":[""]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":[""]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":[""]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":[""]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":[""]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":[""]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":[""]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":[""]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":[""]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":[""]},"Measure":{"msgid":"Measure","msgstr":[""]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":[""]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":[""]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":[""]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":[""]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":[""]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":[""]},"Creating link...":{"msgid":"Creating link...","msgstr":[""]},"OK":{"msgid":"OK","msgstr":[""]},"Hello,":{"msgid":"Hello,","msgstr":[""]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":[""]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":[""]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":[""]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":[""]},"More information":{"msgid":"More information","msgstr":[""]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":[""]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":[""]},"Home":{"msgid":"Home","msgstr":[""]},"Shading":{"msgid":"Shading","msgstr":[""]},"Sphere mode":{"msgid":"Sphere mode","msgstr":[""]},"Eye height":{"msgid":"Eye height","msgstr":[""]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":[""]},"Geometries":{"msgid":"Geometries","msgstr":[""]},"Now":{"msgid":"Now","msgstr":[""]},"Terrain":{"msgid":"Terrain","msgstr":[""]},"Time":{"msgid":"Time","msgstr":[""]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":[""]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":[""]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":[""]},"Left button":{"msgid":"Left button","msgstr":[""]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":[""]},"Right button":{"msgid":"Right button","msgstr":[""]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":[""]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":[""]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":[""]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":[""]},"Arrow keys":{"msgid":"Arrow keys","msgstr":[""]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":[""]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":[""]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":[""]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":[""]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":[""]},"Map navigation":{"msgid":"Map navigation","msgstr":[""]},"Pan console":{"msgid":"Pan console","msgstr":[""]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":[""]},"Camera console":{"msgid":"Camera console","msgstr":[""]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":[""]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":[""]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":[""]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":[""]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":[""]},"Histogram":{"msgid":"Histogram","msgstr":[""]},"Disabled":{"msgid":"Disabled","msgstr":[""]},"Yes":{"msgid":"Yes","msgstr":[""]},"Orthorectification":{"msgid":"Orthorectification","msgstr":[""]},"Cancel":{"msgid":"Cancel","msgstr":[""]},"Error":{"msgid":"Error","msgstr":[""]},"Help":{"msgid":"Help","msgstr":[""]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":[""]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":[""]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":[""]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":[""]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":[""]},"User Instances":{"msgid":"User Instances","msgstr":[""]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":[""]},"Recalculate":{"msgid":"Recalculate","msgstr":[""]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":[""]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":[""]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":[""]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":[""]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":[""]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":[""]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":[""]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":[""]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":[""]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":[""]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":[""]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":[""]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":[""]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":[""]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":[""]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":[""]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":[""]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":[""]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":[""]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":[""]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":[""]},"File upload":{"msgid":"File upload","msgstr":[""]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":[""]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":[""]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":[""]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":[""]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":[""]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":[""]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":[""]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":[""]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":[""]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":[""]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":[""]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":[""]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":[""]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":[""]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":[""]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":[""]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":[""]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":[""]},"Zoom to location":{"msgid":"Zoom to location","msgstr":[""]},"Remove layer":{"msgid":"Remove layer","msgstr":[""]},"Please select a layer":{"msgid":"Please select a layer","msgstr":[""]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":[""]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":[""]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":[""]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":[""]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":[""]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":[""]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":[""]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":[""]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":[""]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":[""]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":[""]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":[""]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":[""]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":[""]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":[""]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":[""]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":[""]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":[""]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":[""]},"Level 1":{"msgid":"Level 1","msgstr":[""]},"Level 2":{"msgid":"Level 2","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":[""]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":[""]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":[""]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":[""]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":[""]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]}}}}
\ No newline at end of file
+{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","plural-forms":"nplurals = 2; plural = (n != 1);","language":"en","mime-version":"1.0","content-transfer-encoding":"8bit"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nPlural-Forms: nplurals = 2; plural = (n != 1);\nLanguage: en\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\n"]},"Education":{"msgid":"Education","msgstr":[""]},"Normal":{"msgid":"Normal","msgstr":[""]},"Close":{"msgid":"Close","msgstr":[""]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":[""]},"Previous":{"msgid":"Previous","msgstr":[""]},"End tutorial":{"msgid":"End tutorial","msgstr":[""]},"Next":{"msgid":"Next","msgstr":[""]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":[""]},"Don't show again":{"msgid":"Don't show again","msgstr":[""]},"Show info":{"msgid":"Show info","msgstr":[""]},"Discover":{"msgid":"Discover","msgstr":[""]},"Visualize":{"msgid":"Visualize","msgstr":[""]},"Compare":{"msgid":"Compare","msgstr":[""]},"Pins":{"msgid":"Pins","msgstr":[""]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":[""]},"No tile found":{"msgid":"No tile found","msgstr":[""]},"Dataset":{"msgid":"Dataset","msgstr":[""]},"Show":{"msgid":"Show","msgstr":[""]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":[""]},"Show visualization":{"msgid":"Show visualization","msgstr":[""]},"Add to Pins":{"msgid":"Add to Pins","msgstr":[""]},"Add to compare":{"msgid":"Add to compare","msgstr":[""]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":[""]},"Hide layer":{"msgid":"Hide layer","msgstr":[""]},"Show layer":{"msgid":"Show layer","msgstr":[""]},"Share":{"msgid":"Share","msgstr":[""]},"Custom":{"msgid":"Custom","msgstr":[""]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":[""]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":[""]},"Free sign up":{"msgid":"Free sign up","msgstr":[""]},"for all features":{"msgid":"for all features","msgstr":[""]},"Powered by":{"msgid":"Powered by","msgstr":[""]},"with contributions by":{"msgid":"with contributions by","msgstr":[""]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":[""]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":[""]},"No results found":{"msgid":"No results found","msgstr":[""]},"Theme":{"msgid":"Theme","msgstr":[""]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":[""]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":[""]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":[""]},"Search":{"msgid":"Search","msgstr":[""]},"Highlights":{"msgid":"Highlights","msgstr":[""]},"Data sources":{"msgid":"Data sources","msgstr":[""]},"Please select a theme":{"msgid":"Please select a theme","msgstr":[""]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":[""]},"Date":{"msgid":"Date","msgstr":[""]},"Hide description":{"msgid":"Hide description","msgstr":[""]},"Show description":{"msgid":"Show description","msgstr":[""]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":[""]},"Based on: ":{"msgid":"Based on: ","msgstr":[""]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":[""]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":[""]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":[""]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":[""]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":[""]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":[""]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":[""]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":[""]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":[""]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":[""]},"Cloud":{"msgid":"Cloud","msgstr":[""]},"Other":{"msgid":"Other","msgstr":[""]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":[""]},"Advanced search":{"msgid":"Advanced search","msgstr":[""]},"Data location":{"msgid":"Data location","msgstr":[""]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":[""]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":[""]},"Polarization":{"msgid":"Polarization","msgstr":[""]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":[""]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":[""]},"Orbit direction":{"msgid":"Orbit direction","msgstr":[""]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":[""]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":[""]},"Credits:":{"msgid":"Credits:","msgstr":[""]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":[""]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":[""]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":[""]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":[""]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":[""]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":[""]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":[""]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":[""]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":[""]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":[""]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":[""]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":[""]},"Copied":{"msgid":"Copied","msgstr":[""]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":[""]},"Data source name":{"msgid":"Data source name","msgstr":[""]},"Sensing time":{"msgid":"Sensing time","msgstr":[""]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":[""]},"Sun elevation":{"msgid":"Sun elevation","msgstr":[""]},"MGRS location":{"msgid":"MGRS location","msgstr":[""]},"AWS path":{"msgid":"AWS path","msgstr":[""]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":[""]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":[""]},"SciHub link":{"msgid":"SciHub link","msgstr":[""]},"Back to search":{"msgid":"Back to search","msgstr":[""]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["",""]},"Load more":{"msgid":"Load more","msgstr":[""]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":[""]},"Results":{"msgid":"Results","msgstr":[""]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["",""]},"Edit pin description":{"msgid":"Edit pin description","msgstr":[""]},"Reject changes":{"msgid":"Reject changes","msgstr":[""]},"Confirm changes":{"msgid":"Confirm changes","msgstr":[""]},"Rename pin":{"msgid":"Rename pin","msgstr":[""]},"Remove pin":{"msgid":"Remove pin","msgstr":[""]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":[""]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":[""]},"Zoom":{"msgid":"Zoom","msgstr":[""]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":[""]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":[""]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":[""]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":[""]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":[""]},"Deselect all":{"msgid":"Deselect all","msgstr":[""]},"Select all":{"msgid":"Select all","msgstr":[""]},"No pins.":{"msgid":"No pins.","msgstr":[""]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["",""]},"File type not supported":{"msgid":"File type not supported","msgstr":[""]},"not supported":{"msgid":"not supported","msgstr":[""]},"No pins were found.":{"msgid":"No pins were found.","msgstr":[""]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":[""]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":[""]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":[""]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":[""]},"Share pins":{"msgid":"Share pins","msgstr":[""]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":[""]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":[""]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":[""]},"Delete all pins":{"msgid":"Delete all pins","msgstr":[""]},"Story":{"msgid":"Story","msgstr":[""]},"Export":{"msgid":"Export","msgstr":[""]},"Import":{"msgid":"Import","msgstr":[""]},"Clear":{"msgid":"Clear","msgstr":[""]},"Share pins link":{"msgid":"Share pins link","msgstr":[""]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":[""]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":[""]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":[""]},"Opacity":{"msgid":"Opacity","msgstr":[""]},"Split position":{"msgid":"Split position","msgstr":[""]},"split":{"msgid":"split","msgstr":[""]},"opacity":{"msgid":"opacity","msgstr":[""]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":[""]},"Remove all":{"msgid":"Remove all","msgstr":[""]},"Add all pins":{"msgid":"Add all pins","msgstr":[""]},"Split":{"msgid":"Split","msgstr":[""]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":[""]},"Download":{"msgid":"Download","msgstr":[""]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":[""]},"Go to Place":{"msgid":"Go to Place","msgstr":[""]},"Labels":{"msgid":"Labels","msgstr":[""]},"Borders":{"msgid":"Borders","msgstr":[""]},"Roads":{"msgid":"Roads","msgstr":[""]},"Zoom in":{"msgid":"Zoom in","msgstr":[""]},"Zoom out":{"msgid":"Zoom out","msgstr":[""]},"About EO Browser":{"msgid":"About EO Browser","msgstr":[""]},"Contact us":{"msgid":"Contact us","msgstr":[""]},"Get data":{"msgid":"Get data","msgstr":[""]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":[""]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":[""]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":[""]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":[""]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":[""]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":[""]},"please select a layer":{"msgid":"please select a layer","msgstr":[""]},"not available for ":{"msgid":"not available for ","msgstr":[""]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":[""]},"Search for data first.":{"msgid":"Search for data first.","msgstr":[""]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":[""]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":[""]},"Center map on feature":{"msgid":"Center map on feature","msgstr":[""]},"Remove geometry":{"msgid":"Remove geometry","msgstr":[""]},"Area of interest":{"msgid":"Area of interest","msgstr":[""]},"Select mode":{"msgid":"Select mode","msgstr":[""]},"Mode:":{"msgid":"Mode:","msgstr":[""]},"Remove measurement":{"msgid":"Remove measurement","msgstr":[""]},"km":{"msgid":"km","msgstr":[""]},"m":{"msgid":"m","msgstr":[""]},"Gain":{"msgid":"Gain","msgstr":[""]},"Gamma":{"msgid":"Gamma","msgstr":[""]},"R":{"msgid":"R","msgstr":[""]},"G":{"msgid":"G","msgstr":[""]},"B":{"msgid":"B","msgstr":[""]},"Min. data quality":{"msgid":"Min. data quality","msgstr":[""]},"Upsampling":{"msgid":"Upsampling","msgstr":[""]},"Downsampling":{"msgid":"Downsampling","msgstr":[""]},"Reset all":{"msgid":"Reset all","msgstr":[""]},"filter by months":{"msgid":"filter by months","msgstr":[""]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":[""]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":[""]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":[""]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":[""]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":[""]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":[""]},"Most recent":{"msgid":"Most recent","msgstr":[""]},"Least recent":{"msgid":"Least recent","msgstr":[""]},"Customize timespan":{"msgid":"Customize timespan","msgstr":[""]},"Back":{"msgid":"Back","msgstr":[""]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":[""]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":[""]},"Load script from URL":{"msgid":"Load script from URL","msgstr":[""]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":[""]},"Script loaded.":{"msgid":"Script loaded.","msgstr":[""]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":[""]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":[""]},"Refresh":{"msgid":"Refresh","msgstr":[""]},"orbit":{"msgid":"orbit","msgstr":[""]},"day":{"msgid":"day","msgstr":[""]},"week":{"msgid":"week","msgstr":[""]},"month":{"msgid":"month","msgstr":[""]},"year":{"msgid":"year","msgstr":[""]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":[""]},"Timelapse":{"msgid":"Timelapse","msgstr":[""]},"Select All":{"msgid":"Select All","msgstr":[""]},"Speed:":{"msgid":"Speed:","msgstr":[""]},"frames / s":{"msgid":"frames / s","msgstr":[""]},"Preparing...":{"msgid":"Preparing...","msgstr":[""]},"Could not download files:":{"msgid":"Could not download files:","msgstr":[""]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":[""]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":[""]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":[""]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":[""]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":[""]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":[""]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":[""]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":[""]},"Index ":{"msgid":"Index ","msgstr":[""]},"Threshold":{"msgid":"Threshold","msgstr":[""]},"Remove color picker":{"msgid":"Remove color picker","msgstr":[""]},"Add color picker":{"msgid":"Add color picker","msgstr":[""]},"Click to place marker":{"msgid":"Click to place marker","msgstr":[""]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":[""]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":[""]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":[""]},"Show captions":{"msgid":"Show captions","msgstr":[""]},"Show slide title":{"msgid":"Show slide title","msgstr":[""]},"Add map overlays":{"msgid":"Add map overlays","msgstr":[""]},"Show legend":{"msgid":"Show legend","msgstr":[""]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":[""]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":[""]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":[""]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":[""]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":[""]},"Show logo":{"msgid":"Show logo","msgstr":[""]},"Image format":{"msgid":"Image format","msgstr":[""]},"Image resolution":{"msgid":"Image resolution","msgstr":[""]},"Coordinate system":{"msgid":"Coordinate system","msgstr":[""]},"Layers":{"msgid":"Layers","msgstr":[""]},"Visualized":{"msgid":"Visualized","msgstr":[""]},"Raw":{"msgid":"Raw","msgstr":[""]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":[""]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":[""]},"Description":{"msgid":"Description","msgstr":[""]},"Image format:":{"msgid":"Image format:","msgstr":[""]},"Basic":{"msgid":"Basic","msgstr":[""]},"Analytical":{"msgid":"Analytical","msgstr":[""]},"High-res print":{"msgid":"High-res print","msgstr":[""]},"Download image":{"msgid":"Download image","msgstr":[""]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":[""]},"min/px":{"msgid":"min/px","msgstr":[""]},"sec/px":{"msgid":"sec/px","msgstr":[""]},"Resolution":{"msgid":"Resolution","msgstr":[""]},"lat.":{"msgid":"lat.","msgstr":[""]},"deg/px":{"msgid":"deg/px","msgstr":[""]},"long.":{"msgid":"long.","msgstr":[""]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":[""]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":[""]},"Image download":{"msgid":"Image download","msgstr":[""]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":[""]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":[""]},"DPI:":{"msgid":"DPI:","msgstr":[""]},"5 years":{"msgid":"5 years","msgstr":[""]},"2 years":{"msgid":"2 years","msgstr":[""]},"1 year":{"msgid":"1 year","msgstr":[""]},"6 months":{"msgid":"6 months","msgstr":[""]},"3 months":{"msgid":"3 months","msgstr":[""]},"1 month":{"msgid":"1 month","msgstr":[""]},"Retry":{"msgid":"Retry","msgstr":[""]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":[""]},"mean":{"msgid":"mean","msgstr":[""]},"median":{"msgid":"median","msgstr":[""]},"st. dev.":{"msgid":"st. dev.","msgstr":[""]},"min / max":{"msgid":"min / max","msgstr":[""]},"Export CSV":{"msgid":"Export CSV","msgstr":[""]},"Timespan:":{"msgid":"Timespan:","msgstr":[""]},"Date:":{"msgid":"Date:","msgstr":[""]},"Single date":{"msgid":"Single date","msgstr":[""]},"Timespan":{"msgid":"Timespan","msgstr":[""]},"hh":{"msgid":"hh","msgstr":[""]},"mm":{"msgid":"mm","msgstr":[""]},"From:":{"msgid":"From:","msgstr":[""]},"Until:":{"msgid":"Until:","msgstr":[""]},"Apply":{"msgid":"Apply","msgstr":[""]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":[""]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":[""]},"Check this out ":{"msgid":"Check this out ","msgstr":[""]},"Logout":{"msgid":"Logout","msgstr":[""]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":[""]},"Login":{"msgid":"Login","msgstr":[""]},"Default":{"msgid":"Default","msgstr":[""]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":[""]},"Agriculture":{"msgid":"Agriculture","msgstr":[""]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":[""]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":[""]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":[""]},"Geology":{"msgid":"Geology","msgstr":[""]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":[""]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":[""]},"Urban":{"msgid":"Urban","msgstr":[""]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":[""]},"Volcanoes":{"msgid":"Volcanoes","msgstr":[""]},"Wildfires":{"msgid":"Wildfires","msgstr":[""]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":[""]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":[""]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":[""]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":[""]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":[""]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":[""]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":[""]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":[""]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":[""]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":[""]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":[""]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":[""]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":[""]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":[""]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":[""]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":[""]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":[""]},"User Account":{"msgid":"User Account","msgstr":[""]},"Discover Tab":{"msgid":"Discover Tab","msgstr":[""]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":[""]},"Compare Tab":{"msgid":"Compare Tab","msgstr":[""]},"Pins Tab":{"msgid":"Pins Tab","msgstr":[""]},"Search Places":{"msgid":"Search Places","msgstr":[""]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":[""]},"Education Mode":{"msgid":"Education Mode","msgstr":[""]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":[""]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":[""]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":[""]},"Measure Distances":{"msgid":"Measure Distances","msgstr":[""]},"Download Image":{"msgid":"Download Image","msgstr":[""]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":[""]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":[""]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":[""]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":[""]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":[""]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":[""]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":[""]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":[""]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":[""]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":[""]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":[""]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":[""]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":[""]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":[""]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":[""]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":[""]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":[""]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":[""]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":[""]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":[""]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":[""]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":[""]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":[""]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":[""]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":[""]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":[""]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":[""]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":[""]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":[""]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":[""]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":[""]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":[""]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":[""]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":[""]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":[""]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":[""]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":[""]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":[""]},"Red band":{"msgid":"Red band","msgstr":[""]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":[""]},"Blue band":{"msgid":"Blue band","msgstr":[""]},"Green band":{"msgid":"Green band","msgstr":[""]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":[""]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":[""]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":[""]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":[""]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":[""]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":[""]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":[""]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":[""]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":[""]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":[""]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":[""]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":[""]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":[""]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":[""]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":[""]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":[""]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":[""]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":[""]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":[""]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":[""]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":[""]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":[""]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":[""]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":[""]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":[""]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":[""]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":[""]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":[""]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":[""]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":[""]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":[""]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":[""]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":[""]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":[""]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":[""]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":[""]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":[""]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":[""]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":[""]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":[""]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":[""]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":[""]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":[""]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":[""]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":[""]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":[""]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":[""]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":[""]},"Reflectance":{"msgid":"Reflectance","msgstr":[""]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":[""]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":[""]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":[""]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":[""]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":[""]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":[""]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":[""]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":[""]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":[""]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":[""]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":[""]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":[""]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":[""]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":[""]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":[""]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":[""]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":[""]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":[""]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":[""]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":[""]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":[""]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":[""]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":[""]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":[""]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":[""]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":[""]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":[""]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":[""]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":[""]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":[""]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":[""]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":[""]},"Cloud base height":{"msgid":"Cloud base height","msgstr":[""]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":[""]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":[""]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":[""]},"Cloud top height":{"msgid":"Cloud top height","msgstr":[""]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":[""]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":[""]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":[""]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":[""]},"Ozone total column":{"msgid":"Ozone total column","msgstr":[""]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":[""]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":[""]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":[""]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":[""]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":[""]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":[""]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":[""]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":[""]},"B09 / B08":{"msgid":"B09 / B08","msgstr":[""]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":[""]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":[""]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":[""]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":[""]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":[""]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":[""]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":[""]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":[""]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":[""]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":[""]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":[""]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":[""]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":[""]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":[""]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":[""]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":[""]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":[""]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":[""]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":[""]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":[""]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":[""]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":[""]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":[""]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":[""]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":[""]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":[""]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":[""]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":[""]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":[""]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":[""]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":[""]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":[""]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":[""]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":[""]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":[""]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":[""]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":[""]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":[""]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":[""]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":[""]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":[""]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":[""]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":[""]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":[""]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":[""]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":[""]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":[""]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":[""]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":[""]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":[""]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":[""]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":[""]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":[""]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":[""]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":[""]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":[""]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":[""]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":[""]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":[""]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":[""]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":[""]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":[""]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":[""]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":[""]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":[""]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":[""]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":[""]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":[""]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":[""]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":[""]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":[""]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":[""]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":[""]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":[""]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":[""]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":[""]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":[""]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":[""]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":[""]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":[""]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":[""]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":[""]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":[""]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":[""]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":[""]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":[""]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":[""]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":[""]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":[""]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":[""]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":[""]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":[""]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":[""]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":[""]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":[""]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":[""]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":[""]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":[""]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":[""]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":[""]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":[""]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":[""]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":[""]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":[""]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":[""]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":[""]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":[""]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":[""]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":[""]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":[""]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":[""]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":[""]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":[""]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":[""]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":[""]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":[""]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":[""]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":[""]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":[""]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":[""]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":[""]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":[""]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":[""]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":[""]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":[""]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":[""]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":[""]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":[""]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":[""]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":[""]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":[""]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":[""]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":[""]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":[""]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":[""]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":[""]},"Measure":{"msgid":"Measure","msgstr":[""]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":[""]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":[""]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":[""]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":[""]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":[""]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":[""]},"Creating link...":{"msgid":"Creating link...","msgstr":[""]},"OK":{"msgid":"OK","msgstr":[""]},"Hello,":{"msgid":"Hello,","msgstr":[""]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":[""]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":[""]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":[""]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":[""]},"More information":{"msgid":"More information","msgstr":[""]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":[""]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":[""]},"Home":{"msgid":"Home","msgstr":[""]},"Shading":{"msgid":"Shading","msgstr":[""]},"Sphere mode":{"msgid":"Sphere mode","msgstr":[""]},"Eye height":{"msgid":"Eye height","msgstr":[""]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":[""]},"Geometries":{"msgid":"Geometries","msgstr":[""]},"Now":{"msgid":"Now","msgstr":[""]},"Terrain":{"msgid":"Terrain","msgstr":[""]},"Time":{"msgid":"Time","msgstr":[""]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":[""]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":[""]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":[""]},"Left button":{"msgid":"Left button","msgstr":[""]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":[""]},"Right button":{"msgid":"Right button","msgstr":[""]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":[""]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":[""]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":[""]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":[""]},"Arrow keys":{"msgid":"Arrow keys","msgstr":[""]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":[""]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":[""]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":[""]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":[""]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":[""]},"Map navigation":{"msgid":"Map navigation","msgstr":[""]},"Pan console":{"msgid":"Pan console","msgstr":[""]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":[""]},"Camera console":{"msgid":"Camera console","msgstr":[""]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":[""]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":[""]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":[""]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":[""]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":[""]},"Histogram":{"msgid":"Histogram","msgstr":[""]},"Disabled":{"msgid":"Disabled","msgstr":[""]},"Yes":{"msgid":"Yes","msgstr":[""]},"Orthorectification":{"msgid":"Orthorectification","msgstr":[""]},"Cancel":{"msgid":"Cancel","msgstr":[""]},"Error":{"msgid":"Error","msgstr":[""]},"Help":{"msgid":"Help","msgstr":[""]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":[""]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":[""]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":[""]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":[""]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":[""]},"User Instances":{"msgid":"User Instances","msgstr":[""]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":[""]},"Recalculate":{"msgid":"Recalculate","msgstr":[""]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":[""]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":[""]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":[""]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":[""]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":[""]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":[""]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":[""]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":[""]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":[""]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":[""]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":[""]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":[""]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":[""]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":[""]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":[""]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":[""]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":[""]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":[""]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":[""]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":[""]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":[""]},"File upload":{"msgid":"File upload","msgstr":[""]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":[""]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":[""]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":[""]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":[""]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":[""]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":[""]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":[""]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":[""]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":[""]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":[""]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":[""]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":[""]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":[""]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":[""]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":[""]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":[""]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":[""]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":[""]},"Zoom to location":{"msgid":"Zoom to location","msgstr":[""]},"Remove layer":{"msgid":"Remove layer","msgstr":[""]},"Please select a layer":{"msgid":"Please select a layer","msgstr":[""]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":[""]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":[""]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":[""]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":[""]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":[""]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":[""]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":[""]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":[""]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":[""]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":[""]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":[""]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":[""]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":[""]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":[""]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":[""]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":[""]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":[""]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":[""]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":[""]},"Level 1":{"msgid":"Level 1","msgstr":[""]},"Level 2":{"msgid":"Level 2","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":[""]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":[""]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":[""]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":[""]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":[""]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]},"Built-up probability [0 - 100 %] at 10 m spatial resolution":{"msgid":"Built-up probability [0 - 100 %] at 10 m spatial resolution","msgstr":[""]},"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).":{"msgid":"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).","msgstr":[""]},"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.":{"msgid":"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.","msgstr":[""]},"Resampling kernel":{"msgid":"Resampling kernel","msgstr":[""]},"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)":{"msgid":"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)","msgstr":[""]},"4x4 cubic convolution":{"msgid":"4x4 cubic convolution","msgstr":[""]},"nearest neighbour":{"msgid":"nearest neighbour","msgstr":[""]},"proprietary MTF kernel":{"msgid":"proprietary MTF kernel","msgstr":[""]},"Width":{"msgid":"Width","msgstr":[""]},"Height":{"msgid":"Height","msgstr":[""]},"Format":{"msgid":"Format","msgstr":[""]},"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.":{"msgid":"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.","msgstr":[""]},"Could not generate timelapse animation file. Try using lower resolution or fewer frames.":{"msgid":"Could not generate timelapse animation file. Try using lower resolution or fewer frames.","msgstr":[""]}}}}
\ No newline at end of file
diff --git a/src/translations/es.po b/src/translations/es.po
index fec4d976..7bcfda42 100644
--- a/src/translations/es.po
+++ b/src/translations/es.po
@@ -7170,4 +7170,82 @@ msgid ""
msgstr ""
msgid "Currently only collections on services.sentinel-hub are supported."
+msgstr ""
+
+msgid "Built-up probability [0 - 100 %] at 10 m spatial resolution"
+msgstr ""
+
+msgid ""
+"# The Global Human Settlement Layer GHS-BUILT-S2 \n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"The Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up "
+"areas (expressed as probabilities from 0 to 100 %) at 10 m spatial "
+"resolution. It was derived from a Sentinel-2 global image composite for the "
+"reference year 2018 using Convolutional Neural Networks.\n"
+"\n"
+"This script visualises the built-up probabilities stretched to 0-255.\n"
+"\n"
+"For more information about the layer, visit [this "
+"website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-"
+"human-settlement-layer-ghs-built-s2/)."
+msgstr ""
+
+msgid ""
+"The **Global Human Settlement** (GHS) framework produces global maps of "
+"built-up areas, population density and settlements to monitor human "
+"presence on Earth over time.\n"
+"\n"
+"**Coverage**: Global coverage with longitude from 180°W to 180°E and "
+"latitude from 72°N to 56°S\n"
+"\n"
+"**Data Availability**: Reference year 2018\n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Knowledge of population distribution and density has a "
+"number of applications, including disaster risk management or the study and "
+"management of urbanisation processes, not only but also in relation to the "
+"challenges of climate change and environmental degradation."
+msgstr ""
+
+msgid "Resampling kernel"
+msgstr ""
+
+msgid ""
+"Select the resampling kernel to use: \n"
+"- 4x4 cubic convolution (CC), \n"
+"- nearest neighbour (NN), \n"
+"- or the proprietary MTF kernel (MTF)"
+msgstr ""
+
+msgid "4x4 cubic convolution"
+msgstr ""
+
+msgid "nearest neighbour"
+msgstr ""
+
+msgid "proprietary MTF kernel"
+msgstr ""
+
+msgid "Width"
+msgstr ""
+
+msgid "Height"
+msgstr ""
+
+msgid "Format"
+msgstr ""
+
+msgid ""
+"For optimisation reasons MPEG4 output format will always be used for "
+"generating a timelapse with transition \"fade\", even when GIF is selected."
+msgstr ""
+
+msgid ""
+"Could not generate timelapse animation file. Try using lower resolution or "
+"fewer frames."
msgstr ""
\ No newline at end of file
diff --git a/src/translations/es.po.json b/src/translations/es.po.json
index d1854240..4c8da279 100644
--- a/src/translations/es.po.json
+++ b/src/translations/es.po.json
@@ -1 +1 @@
-{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","plural-forms":"nplurals=2; plural=(n != 1);","language":"es","mime-version":"1.0","content-transfer-encoding":"8bit","project-id-version":"","pot-creation-date":"","po-revision-date":"","last-translator":"","language-team":"","x-generator":"Poedit 2.4.2"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nPlural-Forms: nplurals=2; plural=(n != 1);\nLanguage: es\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\nProject-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLast-Translator: \nLanguage-Team: \nx-generator: Poedit 2.4.2\n"]},"Education":{"msgid":"Education","msgstr":["Educación"]},"Normal":{"msgid":"Normal","msgstr":["Normal"]},"Close":{"msgid":"Close","msgstr":["Cerrar"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Cerrar y no mostrar más"]},"Previous":{"msgid":"Previous","msgstr":["Anterior"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Terminar el tutorial"]},"Next":{"msgid":"Next","msgstr":["Siguiente"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Acceder al tutorial"]},"Don't show again":{"msgid":"Don't show again","msgstr":["No mostrar más"]},"Show info":{"msgid":"Show info","msgstr":["Mostrar información"]},"Discover":{"msgid":"Discover","msgstr":["Descubrir"]},"Visualize":{"msgid":"Visualize","msgstr":["Visualizar"]},"Compare":{"msgid":"Compare","msgstr":["Comparar"]},"Pins":{"msgid":"Pins","msgstr":["Marcadores"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Error al extraer las imágenes:"]},"No tile found":{"msgid":"No tile found","msgstr":["Tesela no encontrada"]},"Dataset":{"msgid":"Dataset","msgstr":["Conjunto de datos"]},"Show":{"msgid":"Show","msgstr":["Mostrar"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Mostrar efectos y opciones avanzadas"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Mostrar visualización"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Añadir a marcadores"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Añadir a comparar"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Ajustar a la tesela"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Ocultar capa"]},"Show layer":{"msgid":"Show layer","msgstr":["Mostrar capa"]},"Share":{"msgid":"Share","msgstr":["Compartir"]},"Custom":{"msgid":"Custom","msgstr":["Personalizar"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Crear visualización personalizada"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Ampliar para ver los datos"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Regístrese gratis"]},"for all features":{"msgid":"for all features","msgstr":["para acceder a todas las prestaciones"]},"Powered by":{"msgid":"Powered by","msgstr":["Desarrollado por"]},"with contributions by":{"msgid":"with contributions by","msgstr":["con aportaciones de"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["¡Seleccione la(s) fuente(s) de datos!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["¡Intervalo temporal no válido!"]},"No results found":{"msgid":"No results found","msgstr":["Sin resultados"]},"Theme":{"msgid":"Theme","msgstr":["Tema"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Definir la configuración"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Acceda para aplicar su configuración personalizada."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["¡Error al extraer datos adicionales!"]},"Search":{"msgid":"Search","msgstr":["Buscar"]},"Highlights":{"msgid":"Highlights","msgstr":["Destacados"]},"Data sources":{"msgid":"Data sources","msgstr":["Fuentes de datos"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Por favor, elija un tema"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Intervalo temporal [UTC]"]},"Date":{"msgid":"Date","msgstr":["Fecha"]},"Hide description":{"msgid":"Hide description","msgstr":["Ocultar descripción"]},"Show description":{"msgid":"Show description","msgstr":["Mostrar descripción"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Este tema carece de destacados"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Basado en: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["1 día (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["5 días (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["10 días (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["O3 (ozono)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (dióxido de nitrógeno)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (dióxido de azufre)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (monóxido de carbono)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (formaldehído)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (metano)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER AI (índice de aerosoles)"]},"Cloud":{"msgid":"Cloud","msgstr":["Nubosidad"]},"Other":{"msgid":"Other","msgstr":["Otros"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Máx. cobertura nubosa"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Búsqueda avanzada"]},"Data location":{"msgid":"Data location","msgstr":["Ubicación de los datos"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Por favor, ¡seleccione al menos una ubicación!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Modo de adquisición"]},"Polarization":{"msgid":"Polarization","msgstr":["Polarización"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Por favor, ¡seleccione al menos un modo de adquisición!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Por favor, ¡seleccione al menos un modo de polarización!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Dirección orbital"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Por favor, ¡seleccione al menos una dirección orbital!"]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["**MERIS** (espectrómetro de resolución intermedia) era un sensor a bordo del satélite [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) con la misión principal de observar el color del territorio y los océanos, así como la atmósfera. Ya no está activo y lo sustituye Sentinel-3.\n\n**Resolución espacial:** Resolución máxima en terreno y costas: 260 m x 290 m (solo pueden verse detalles mayores que 260 m x 290 m).\n\n**Tiempo de revisita:** pasa un máximo de 3 días entre dos visitas a la misma zona.\n\n**Disponibilidad de datos:** de junio de 2002 hasta abril de 2012.\n\n**Uso habitual:** Monitorización del océano (fitopancton, materia en suspensión), de la atmósfera (vapor de agua, CO2, nubosidad, aerosoles) y del territorio (índice de vegetación, cobertura global, humedad)."]},"Credits:":{"msgid":"Credits:","msgstr":["Créditos:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["**GIBS** (Global Imagery Browse Services, Servicios de Búsqueda Global de Imágenes) proporciona\nun acceso rápido a más de 600 productos de imágenes satelitales que cubren todo el mundo. La mayoría\nde las imágenes está disponible pocas horas tras el paso del satélite y hay productos que abarcan casi\n30 años."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["Los satélites **Landsat** de la NASA y el Servicio Geológico de EE. UU. son similares a Sentinel-2 (captan longitudes de onda tanto visibles como infrarrojas)\ny en algunos casos detectan el infrarrojo térmico (Landsat 8). La serie Landsat tiene una larga historia de captación de imágenes que abarca casi cinco décadas.\nEsta plataforma da acceso a imágenes de los Landsat 5, 7 y 8.\n\n**Resolución espacial:** 15 m, 30 m y 100 m remuestreado a 30 m, según la longitud de onda (es decir, solo pueden verse detalles mayores que 10 m o 30 m). Más información [aquí](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Tiempo de revisita:** pasa un máximo de 8 días entre dos visitas a la misma zona mediante alguno de los dos satélites operativos, Landsat 7 u 8.\n\n**Disponibilidad de datos:** \n Europa y norte de África 1984-2011 (Landsat 5), 1999-2003 (Landsat 7), 2013 hasta hoy (Landsat 8), en el archivo de ESA. El archivo global del Servicio Geológico de EE. UU. (USGS) abarca desde abril de 2013 hasta hoy (solo para Landsat 8).\n\n**Uso habitual:** Monitorización de la vegetación, mapas de cobertura y uso del suelo, monitorización de cambios, etc."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["**MODIS** (Espectrorradiómetro de Imágenes de Resolución Moderada), de la NASA, toma datos con el fin de\nmejorar el conocimiento de los procesos globales que afectan al territorio. EO Browser proporciona datos de\nobservación del territorio (bandas 1-7).\n\n**Resolución espacial:** 250 m (bandas 1-2), 500 m (bandas 3-7), 1000 m (bandas 8-36).\n\n**Tiempo de revisita:** Cobertura global en 1-2 días con ambos satélites, Aqua y Terra.\n\n**Disponibilidad de datos:** desde enero de 2013.\n\n**Uso habitual:** monitorización del territorio, nubosidad y color del océano a escala global."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["El pequeño satélite **Proba-V** está diseñado para cartografiar la cobertura del suelo y el crecimiento\nde la vegetación en todo el globo cada dos días. EO Browser proporciona sus productos derivados\nminimizando la nubosidad al combinar las medidas libres de nubes tomadas dentro de un periodo de\n1 día (S1), 5 días (S5) o 10 días (S10).\n\n**Resolución espacial:** 100 m para S1 y S5, 333 m para S1 y S10, 1000 m para S1 y S10.\n\n**Tiempo de revisita:** 1 día para latitudes 35-75°N y 35-56°S, 2 días para latitudes entre 35°N\ny 35°S.\n\n**Disponibilidad de datos:** desde octubre de 2013.\n\n**Uso habitual:** observación de la cobertura del suelo, crecimiento de la vegetación, diagnóstico del impacto\nclimático, gestión de recursos hídricos, monitorización agrícola y estimaciones de seguridad alimentaria, monitorización\nde los recursos hídricos continentales, seguimiento del avance de los desiertos y la deforestación."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":["**Sentinel-1** proporciona imágenes de radar obtenidas en cualesquiera condiciones meteorológicas, de día\no de noche, de tierra y mar. EO Browser da acceso a los datos tomados tanto en el modo IW (interferometric wide swath,\nbarrido interferométrico ancho) como EW (extra-wide swath, barrido extra-ancho), procesados al Nivel 1 GRD (ground\nrange detected, detección de la distancia al suelo).\n\n**Pixel spacing:** 10 m (IW), 40 m (EW).\n\n**Tiempo de revisita:** <= 5 días si se usan ambos satélites.\n\n**Tiempo de revisita:** (combinando pasos ascendentes y descendentes y superposiciones): <= 3 días, véase la\n[estrategia de observación](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n \n**Disponibilidad de datos:** desde octubre de 2014.\n\n**Uso habitual:** monitorización del territorio y de los mares, respuesta a emergencias, cambio climático."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["**Sentinel-2** obtiene imágenes de alta resolución en longitudes de onda visibles e infrarrojas con el fin de monitorizar la vegetación, el suelo y la cobertura del territorio, los cursos de agua continentales y las zonas costeras.\n\n**Resolución espacial:** 10 m, 20 m y 60 m, según la longitud de onda (es decir, solo llegan a distinguirse los detalles mayores que 10 m, 20 m o 60 m). Más información [aquí](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Tiempo de revisita:** hay un máximo de 5 días entre visitas consecutivas a la misma zona por medio de los dos satélites.\n\n**Disponibilidad de datos:** desde junio de 2015. Cobertura global completa desde marzo de 2017.\n\n**Uso habitual:** mapas de cobertura del suelo, mapas de detección de cambios en el territorio, monitorización de la vegetación, monitorización de áreas quemadas."]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Los datos de Nivel 2A son aquellos de gran calidad en los que se corrigen los efectos de la atmósfera sobre la luz reflejada por la superficie de la Tierra. Estos datos están disponibles para todo el globo desde marzo de 2017.\n\nMás información sobre la corrección de efectos atmosféricos [aquí](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction)."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Los datos de Nivel 1C poseen calidad suficiente para la mayoría de investigaciones y tienen aplicadas todas las correcciones excepto las de efectos debidos a la atmósfera. Estos datos están disponibles para todo el globo desde junio de 2015."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["El objetivo principal de la misión **Sentinel-3** consiste en medir la topografía de la superficie, la temperatura de los mares y del terreno, así como el color de los océanos y del territorio. Sentinel-3 porta cuatro instrumentos distintos. Esta plataforma ofrece los datos del Instrumento para el Color del Océano y del Suelo (OLCI, Ocean and Land Colour Instrument) y del Instrumento para la Temperatura del Mar y de la Superficie del Terreno (SLSTR, Sean and Land Surface Temperature Instrument).\n\n**Disponibilidad de datos:** desde mayo de 2016."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["El **Instrumento para la Temperatura del Mar y de la Superficie del Terreno (SLSTR, Sean and Land Surface Temperature Instrument)** a bordo de \nSentinel-3 mide la temperatura del suelo y de los mares a escala tanto regional como global. SLSTR abarca longitudes de onda visibles, infrarrojo cercano \ne infrarrojo térmico. \n\n**Resolución espacial:** 500 m para las longitudes de onda visibles y del infrarrojo cercano, 1 km para el infrarrojo térmico (es decir, solo se distinguen\ndetalles mayores que 500 m o 1 km, respectivamente).\n\n**Tiempo de revisita:** 1 día como máximo entre visitas consecutivas a la misma área, usando los dos satélites.\n\n**Disponibilidad de datos:** desde mayo de 2016.\n\n**Uso habitual:** monitorización del cambio climático, monitorización de la vegetación, detección activa de incendios, monitorización de la temperatura superficial de los océanos y del terreno."]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["El **Instrumento para el Color del Océano y del Suelo (OLCI, Ocean and Land Colour Instrument)** a bordo de Sentinel-3 es un espectrómetro que \nmide la radiación solar reflejada por la Tierra y monitoriza los océanos, el medio ambiente y el clima. Proporciona imágenes con una frecuencia mayor\nque el Sentinel-2, pero con menos resolución espacial, si bien cubre más longitudes de onda. El instrumento OLCI del Sentinel-3 prolonga las medidas\nque antes tomaba el instrumento MERIS de Envisat, cuya misión ya finalizó.\n\n**Resolución espacial:** 300 m (es decir, solo se distinguen detalles mayores que 300 m).\n\n**Tiempo de revisita:** pasan como máximo 2 días entre visitas consecutivas a la misma zona, usando ambos satélites.\n\n**Disponibilidad de datos:** desde mayo de 2016.\n\n**Uso habitual:** topografía de la superficie, observación y monitorización del color de los mares y de la superficie del terreno."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["**Sentinel-5P** es un satélite que obtiene medidas atmosféricas orientadas a evaluar la calidad del aire, monitorizar el ozono, evaluar\nla radiación UV y monitorizar y pronosticar el estado del tiempo.\n\n**Resolución espacial:** 7 x 3.5 km (es decir, solo se distinguen detalles mayores que 7 x 3.5 km).\n\n**Tiempo de revisita:** como máximo pasa 1 día entre visitas consecutivas a la misma zona.\n\n**Disponibilidad de datos:** desde abril de 2018.\n\n**Uso habitual:** monitorización de la concentración de monóxido de carbono (CO), dióxido de nitrógeno (NO2) y ozono (O3). Monitorización\nde índice de aerosoles UV (AER_AI) y de otros parámetros geofísicos diversos de las nubes (Cloud)."]},"Copied":{"msgid":"Copied","msgstr":["Copiado"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["Copiar al portapapeles"]},"Data source name":{"msgid":"Data source name","msgstr":["Nombre de la fuente de datos"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Hora de la toma"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Cobertura nubosa"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Altura del Sol"]},"MGRS location":{"msgid":"MGRS location","msgstr":["Posición MGRS"]},"AWS path":{"msgid":"AWS path","msgstr":["Banda en AWS"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["Banda en EO Cloud"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["Banda en CreoDIAS"]},"SciHub link":{"msgid":"SciHub link","msgstr":["Enlace a SciHub"]},"Back to search":{"msgid":"Back to search","msgstr":["Volver a buscar"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Mostrando ${this.state.results.length} resultado","Mostrando ${this.state.results.length} resultados"]},"Load more":{"msgid":"Load more","msgstr":["Cargar más"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Cargar más resultados..."]},"Results":{"msgid":"Results","msgstr":["Resultados"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Mostrando ${ this.state.selectedTiles.length } resultado.","Mostrando ${ this.state.selectedTiles.length } resultados."]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Modificar la descripción del marcador"]},"Reject changes":{"msgid":"Reject changes","msgstr":["Descartar cambios"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Confirmar cambios"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Renombrar marcador"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Eliminar marcador"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Ampliación sobre el marcador"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Lat/Lon"]},"Zoom":{"msgid":"Zoom","msgstr":["Ampliación"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["Vd. va a añadir ${ N_PINS } marcador(es) a su colección. ¿Quiere continuar?"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["ADVERTENCIA: Vd. va a eliminar un marcador. ¿Quiere continuar?"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":["ADVERTENCIA: Vd. va a eliminar todos los marcadores. ¿Quiere continuar?"]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["No hay marcadores. Vaya a la pestaña Visualizar para guardar alguno, o cargue un fichero JSON con marcadores guardados."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Tenga en cuenta que los marcadores se guardarán solo si Vd. ha accedido como usuario registrado. De otro modo los marcadores se perderán al cerrar el programa."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Desmarcar todo"]},"Select all":{"msgid":"Select all","msgstr":["Marcar todo"]},"No pins.":{"msgid":"No pins.","msgstr":["No hay marcadores."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Crear enlace (${ selectedPins.length } marcador seleccionado)","Crear enlace (${ selectedPins.length } marcadores seleccionados)"]},"File type not supported":{"msgid":"File type not supported","msgstr":["Tipo de archivo no admitido"]},"not supported":{"msgid":"not supported","msgstr":["no admitido"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["No se encontraron marcadores."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Error al procesar el archivo:"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":["Cargar un fichero JSON con marcadores guardados."]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["Suelte un fichero JSON o busque en su computadora"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Conservar los marcadores existentes"]},"Share pins":{"msgid":"Share pins","msgstr":["Compartir marcadores"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Crear una secuencia a partir de marcadores"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Exportar marcadores a la computadora"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Importar marcadores de un fichero guardado"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Eliminar todos los marcadores"]},"Story":{"msgid":"Story","msgstr":["Secuencia"]},"Export":{"msgid":"Export","msgstr":["Exportar"]},"Import":{"msgid":"Import","msgstr":["Importar"]},"Clear":{"msgid":"Clear","msgstr":["Vaciar"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Compartir enlaces de marcadores"]},"Creating link...":{"msgid":"Creating link...","msgstr":["Creando enlace..."]},"OK":{"msgid":"OK","msgstr":["De acuerdo"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Actualizando la colección de marcadores."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Sucedió un problema al actualizar de manera permanente la colección de marcadores: ${ updatingPinsError }."]},"Hello,":{"msgid":"Hello,","msgstr":["Hola,"]},"Opacity":{"msgid":"Opacity","msgstr":["Opacidad"]},"Split position":{"msgid":"Split position","msgstr":["Posición de la cortina"]},"split":{"msgid":"split","msgstr":["cortina"]},"opacity":{"msgid":"opacity","msgstr":["opacidad"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["No hay capas que comparar."]},"Remove all":{"msgid":"Remove all","msgstr":["Eliminar todo"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Añadir todos los marcadores"]},"Split":{"msgid":"Split","msgstr":["Cortina"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Sucedió un problema durante la descarga de sus parámetros"]},"Download":{"msgid":"Download","msgstr":["Descargar"]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Visualizar el terreno en 3D"]},"Go to Place":{"msgid":"Go to Place","msgstr":["Ir al lugar"]},"Labels":{"msgid":"Labels","msgstr":["Etiquetas"]},"Borders":{"msgid":"Borders","msgstr":["Fronteras"]},"Roads":{"msgid":"Roads","msgstr":["Carreteras"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Acercar"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Alejar"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["Acerca de EO Browser"]},"Contact us":{"msgid":"Contact us","msgstr":["Contacto"]},"Get data":{"msgid":"Get data","msgstr":["Obtener datos"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Función accesible solo a usuarios registrados."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Por favor, seleccione una capa."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["No es posible descargar imágenes en modo de comparación."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["No se admite esta fuente de datos."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":["Información estadística / Ficha de información"]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Información estadística / Ficha de información - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["por favor, seleccione una capa"]},"not available for ":{"msgid":"not available for ","msgstr":["no disponible para "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["no disponible para \"${ props.presetLayerName }\" (la capa carece de ese valor)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Primero busque datos."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Crear una animación"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Marcar un punto de interés"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Centrar el mapa en este detalle"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Eliminar la forma"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Área de interés"]},"Select mode":{"msgid":"Select mode","msgstr":["Elija el modo"]},"Mode:":{"msgid":"Mode:","msgstr":["Modo:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Eliminar medida"]},"km":{"msgid":"km","msgstr":["km"]},"m":{"msgid":"m","msgstr":["m"]},"Gain":{"msgid":"Gain","msgstr":["Ganancia"]},"Gamma":{"msgid":"Gamma","msgstr":["Gamma"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Calidad mínima"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Incremento de resolución"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Reducción de resolución"]},"Reset all":{"msgid":"Reset all","msgstr":["Reiniciar todo"]},"filter by months":{"msgid":"filter by months","msgstr":["filtro por meses"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Copiar la forma geométrica al portapapeles"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Cancelar la modificación."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Trazar el área de interés"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Cobertura nubosa mínima"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Usar conjuntos de datos adicionales (avanzado)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Orden del mosaico"]},"Most recent":{"msgid":"Most recent","msgstr":["Más reciente"]},"Least recent":{"msgid":"Least recent","msgstr":["Menos reciente"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Personalizar el intervalo temporal"]},"Back":{"msgid":"Back","msgstr":["Atrás"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Error al cargar el script. Compruebe la URL."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Desmarcar la carga del script desde la URL para modificar el código"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Cargar script desde URL"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["Introduzca la URL de su script"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Script cargado."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Solo se permiten dominios HTTPS."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Cargar el script en el editor de código"]},"Refresh":{"msgid":"Refresh","msgstr":["Refrescar"]},"orbit":{"msgid":"orbit","msgstr":["órbita"]},"day":{"msgid":"day","msgstr":["día"]},"week":{"msgid":"week","msgstr":["semana"]},"month":{"msgid":"month","msgstr":["mes"]},"year":{"msgid":"year","msgstr":["año"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Seleccione una imagen por:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Animación"]},"Select All":{"msgid":"Select All","msgstr":["Seleccionar todo"]},"Speed:":{"msgid":"Speed:","msgstr":["Velocidad:"]},"frames / s":{"msgid":"frames / s","msgstr":["fotogramas / s"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Preparando..."]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Archivos no descargados:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Imposible descargar desde el lienzo"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["No se comprimieron en ZIP los ficheros:"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Sucedió un problema en la descarga de la imagen"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Error al trasladar la imagen: ¡la URL está vacía!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Error al trasladar la imagen:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["No se pudo cargar la imagen de la zona"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Trasladar las bandas a los campos RGB."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Trasladar las bandas a la ecuación de índice"]},"Index ":{"msgid":"Index ","msgstr":["Índice "]},"Threshold":{"msgid":"Threshold","msgstr":["Umbral"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Retirar el selector de color"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Añadir selector de color"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Pulse para colocar marcador"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Pulse para colocar el primer vértice"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Pulse para seguir trazando"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Pulse en el primer marcador para terminar"]},"Show captions":{"msgid":"Show captions","msgstr":["Mostrar pies de figura"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Mostrar el título de la diapositiva"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Añadir capas superpuestas"]},"Show legend":{"msgid":"Show legend","msgstr":["Mostrar leyenda"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["El campo de visión actual no contiene marcadores."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Se ignoran algunos marcadores (${ N_PINS_OUTSIDE_BOUNDS }) porque caen fuera del área seleccionada."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["Para crear una secuencia de marcadores, vaya al lugar deseado del mapa.\n\nLa secuencia de marcadores incluirá todos los marcadores que entren en el campo de visión, y se ignorarán los demás."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["El archivo portará un logotipo."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["A las bandas crudas que se descarguen se añadirá, como segunda banda, una máscara de datos (dataMask-band)."]},"Show logo":{"msgid":"Show logo","msgstr":["Mostrar el logotipo"]},"Image format":{"msgid":"Image format","msgstr":["Formato de imagen"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Resolución"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Sistema de coordenadas"]},"Layers":{"msgid":"Layers","msgstr":["Capas"]},"Visualized":{"msgid":"Visualized","msgstr":["Visibles"]},"Raw":{"msgid":"Raw","msgstr":["Crudas"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["La imagen incluirá las capas superpuestas (etiquetas de lugares, calles y fronteras administrativas)."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Las imágenes exportadas incluirán la fuente de los datos así como la fecha, el nivel de ampliación y marca de origen"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Añada una descripción breve de la imagen exportada"]},"Description":{"msgid":"Description","msgstr":["Descripción"]},"Image format:":{"msgid":"Image format:","msgstr":["Formato de imagen:"]},"Basic":{"msgid":"Basic","msgstr":["Básico"]},"Analytical":{"msgid":"Analytical","msgstr":["Analítico"]},"High-res print":{"msgid":"High-res print","msgstr":["Impresión en alta resolución"]},"Download image":{"msgid":"Download image","msgstr":["Descargar imagen"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["Ha ocurrido un error en el traslado de alguna imagen:"]},"min/px":{"msgid":"min/px","msgstr":["min/px"]},"sec/px":{"msgid":"sec/px","msgstr":["seg/px"]},"Resolution":{"msgid":"Resolution","msgstr":["Resolución"]},"lat.":{"msgid":"lat.","msgstr":["lat."]},"deg/px":{"msgid":"deg/px","msgstr":["grados/px"]},"long.":{"msgid":"long.","msgstr":["long."]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Resolución proyectada: ${ formattedResolution } m/px"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Error: la fusión de datos no admite los formatos KMZ/JPG ni KMZ/PNG."]},"Image download":{"msgid":"Image download","msgstr":["Descarga de imagen"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Anchura de la imagen [pulgadas]:"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Altura de la imagen [pulgadas]:"]},"DPI:":{"msgid":"DPI:","msgstr":["Puntos por pulgada:"]},"5 years":{"msgid":"5 years","msgstr":["5 años"]},"2 years":{"msgid":"2 years","msgstr":["2 años"]},"1 year":{"msgid":"1 year","msgstr":["1 año"]},"6 months":{"msgid":"6 months","msgstr":["6 meses"]},"3 months":{"msgid":"3 months","msgstr":["3 meses"]},"1 month":{"msgid":"1 month","msgstr":["1 mes"]},"Retry":{"msgid":"Retry","msgstr":["Reintentar"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Cargando, espere"]},"mean":{"msgid":"mean","msgstr":["media"]},"median":{"msgid":"median","msgstr":["mediana"]},"st. dev.":{"msgid":"st. dev.","msgstr":["desv. est."]},"min / max":{"msgid":"min / max","msgstr":["mín / máx"]},"Export CSV":{"msgid":"Export CSV","msgstr":["Exportar como CSV"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Intervalo:"]},"Date:":{"msgid":"Date:","msgstr":["Fecha:"]},"Single date":{"msgid":"Single date","msgstr":["Fecha única"]},"Timespan":{"msgid":"Timespan","msgstr":["Intervalo"]},"hh":{"msgid":"hh","msgstr":["hh"]},"mm":{"msgid":"mm","msgstr":["mm"]},"From:":{"msgid":"From:","msgstr":["Desde:"]},"Until:":{"msgid":"Until:","msgstr":["Hasta:"]},"Apply":{"msgid":"Apply","msgstr":["Aplicar"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Compartir en Facebook"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Compartir en Twitter"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Comprobar esto "]},"Logout":{"msgid":"Logout","msgstr":["Salir"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["Acceda como usuario registrado para disponer de prestaciones avanzadas como animaciones, descargas analíticas, configuración personalizada, y más."]},"Login":{"msgid":"Login","msgstr":["Acceder"]},"Default":{"msgid":"Default","msgstr":["Por defecto"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Monitorización de la Tierra desde el espacio"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Agricultura"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Atmósfera y contaminación del aire"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Detección de cambios"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Inundaciones y sequías"]},"Geology":{"msgid":"Geology","msgstr":["Geología"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Océanos y masas de agua"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Nieve y glaciares"]},"Urban":{"msgid":"Urban","msgstr":["Urbano"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Vegetación y bosques"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Volcanes"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Incendios forestales"]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":["# ¡Le damos la bienvenida a EO Browser!\n\nUn archivo completo de Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, así como\nel archivo de ESA de Landsat 5, 7 y 8, cobertura global de Landsat 8, MERIS en Envisat, \nMODIS, Proba-V y los productos de GIBS, todo en un solo sitio.\n\n[Página de presentación de EO Browser](https://www.sentinel-hub.com/explore/eobrowser/) \n[Guía de uso de EO Browser](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":["#### Resumen de las prestaciones de EO Browser\n\nEO Browser reúne un archivo completo de Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, así como el archivo de ESA de Landsat 5, 7 y 8, cobertura global de Landsat 8, MERIS en Envisat, MODIS, Proba-V y los productos de GIBS, todo en un solo sitio. Permite revisar y comparar imágenes de alta resolución procedentes de todas estas fuentes. Solo hay que ir al área de interés, seleccionar las fuentes de datos, así como el intervalo temporal y la cobertura nubosa, y consultar el resultado. \n\nAcceda al tutorial pulsando el botón \"Siguiente\", o ciérrelo. Siempre podrá retomar el tutorial a través del icono de información que hay en la esquina superior derecha, en caso de que lo cierre por error o porque quiera probar otras opciones."]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":["La pestaña **Descubrir** le permite:\n\n- Elegir un **Tema**.\n- **Buscar** datos.\n- Ver los **Destacados** del tema.\n\nEl menú **Tema** ofrece varios temas predefinidos, así como sus propios apartados personalizados si ha accedido como usuario registrado. Para crear un apartado pulse en\nel icono de ajustes y acceda con su usuario y contraseña de EO Browser.\n\nEn **Buscar** puede ajustar los criterios de búsqueda:\n- Elegir los satélites de los que desea recibir datos, marcando las casillas de selección.\n- Seleccionar las opciones adicionales permitidas como, por ejemplo, la cobertura nubosa, que se fija mediante una barra deslizable.\n- Seleccionar el intervalo temporal, bien tecleando las fechas, o bien eligiéndolas en el calendario.\n\nAcceda a explicaciones acerca de cada satélite pulsando el icono con la interrogación\n que hay junto al nombre de cada fuente de datos.\n\nAl pulsar Buscar se obtiene una lista de resultados, cada uno de los cuales \nse muestra con una vista previa acompañada de algunos datos relevantes sobre su procedencia. Con algunas fuentes de datos aparece también el icono junto a los resultados.\nAl pulsarlo se obtienen enlaces a las imágenes crudas en EO Cloud o en SciHub. El botón Visualizar abre la pestaña **Visualizar** para el resultado en cuestión.\n\n**Destacados** da acceso a ubicaciones interesantes, preseleccionadas por su relación con el tema elegido."]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":["La pestaña Visualizar permite elegir entre varias combinaciones de bandas espectrales predefinidas o personalizadas para visualizar los datos del resultado seleccionado.\n\nAlgunas opciones frecuentes:\n- **Color natural** - Interpretación visual de la cobertura del suelo.\n- **Color falso** - Interpretación visual de la vegetación.\n- **NDVI** - Índice normalizado de vegetación.\n- **NDMI** - Índice normalizado de humedad.\n- **SWIR** - Índice infrarrojo de onda corta.\n- **NDWI** - Índice normalizado de agua.\n- **NDSI** - Índice normalizado de nieve.\n\nLa mayoría de visualizaciones incluye una descripción y una leyenda que se pueden ver pulsando en el icono de desplegar .\n \nLa mayoría de las fuentes de datos dispone de la opción **Script personalizado**. Pulse para elegir combinaciones personalizadas de bandas espectrales o de índices, o para elaborar su propio script de clasificación para visualizar los datos. También puede acceder a scripts personalizados guardados en otros lugares, como en Google Drive, en GitHub o en nuestro [repositorio de scripts](https://custom-scripts.sentinel-hub.com/). \nCopie y pegue la dirección (URL) del script en una caja de texto en el panel de edición avanzada de scripts y pulse Refrescar.\n \nPuede modificar directamente los datos que haya en la pestaña Visualizar sin tener que volver a la pestaña **Descubrir**. Teclee una nueva fecha o selecciónela en el calendario .\n\nSobre las visualizaciones aparece la barra de herramientas adicionales. Observe que las herramientas disponibles cambian según la fuente de los datos.\n- **Añadir a marcadores** permite guardarla en la aplicación para su uso posterior: pulse en el icono de la chincheta .\n- Elija **opciones avanzadas**, como los métodos para alterar la resolución espacial o distintos **efectos**, como cambios de contraste (ganancia) o luminancia (gamma), mediante el icono con forma de barras deslizantes .\n- Añada una capa a la pestaña **Comparar** para cotejarla con otras más tarde, mediante el icono de comparar .\n- **Amplíe** al centro de la tesela pulsando en la cruceta .\n- Active o desactive la **visibilidad de la capa** mediante el icono de visibilidad .\n- Puede **compartir** la visualización en las redes sociales a través del icono correspondiente: ."]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":["En la pestaña **Comparar** encontrará todas las visualizaciones que haya añadido mediante . \n\nDispone de dos modos:\n - **Opacidad** (Mueva el deslizador a derecha o izquierda para inducir la transición entre las imágenes comparadas)\n - **Cortina** (Mueva el deslizador a derecha o izquierda para ubicar la frontera entre las imágenes comparadas)\n\nUsted puede añadir todos los marcadores al panel de comparación mediante **Añadir todos los marcadores** o eliminar todas las visualizaciones de la pestaña **Comparar** mediante el botón **Eliminar todo**."]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":["La pestaña **Marcadores** contiene tus visualizaciones favoritas. Cada marcador incluye información\nsobre la ubicación, la fuente de los datos y la capa concreta, nivel de ampliación y marca temporal.\n \n\nEs posible actuar sobre cada marcador de distintas maneras:\n\n- Alterar el **orden** - mediante el icono de mover\n\n \n \n \nque hay en la esquina superior izquierda del marcador: pulsa en él y arrastra arriba o abajo en la lista.\n- **Renombrar** - con el icono del lápiz que aparece junto al nombre.\n- Añadir a la pestaña **Comparar** - mediante el icono correspondiente \n- Aportar una **descripción** - mediante el icono de desplegar .\n- **Eliminar** - mediante el icono de la papelera .\n- **Ampliar** sobre el marcador - pulsando sobre los valores de longitud y latitud.\n\nEn la línea situada sobre los marcadores se ofrecen varias acciones que se pueden aplicar globalmente a todos:\n- Crear tu propia secuencia a partir de los marcadores mediante **Secuencia**.\n- Compartir tus marcadores mediante un enlace, pulsando en **Compartir**.\n- Exportar los marcadores a un fichero JSON mediante **Exportar**.\n- Importar marcadores de un fichero JSON mediante **Importar**.\n- Eliminar todos los marcadores mediante **Vaciar**."]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":["Busque una ubicación bien desplazando el mapa con el ratón o bien introduciendo el nombre de la misma en \nel campo de búsqueda."]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":["Seleccione la capa base y las superpuestas (carreteras, fronteras, leyendas) que aparecerán en el mapa."]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":["Alterne entre los modos **normal** y **educación**. El modo **educación** ofrece una versión simplificada de la aplicación.\nTambién está accesible en su [dirección específica en internet](https://apps.sentinel-hub.com/eo-browser-education/)."]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":["Acceda al tutorial en cualquier momento mediante el icono de información\n\n\n."]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":["Esta herramienta permite trazar un polígono sobre el mapa y mostrar su tamaño.\n\nTodas las capas que contienen valores individuales (como NDVI, NDMI, NDWI,...) permiten obtener el\nvalor del índice para el área seleccionada a lo largo del tiempo. Pulse en el icono de ficha \npara obtener la gráfica. Puede borrar el polígono con el icono .\n\nTambién es posible cargar un fichero KML/KMZ, GPX o GEOJSON/JSON con los datos del polígono.\n\nEl icono de las dos hojas permite copiar las coordenadas del polígono en formato GEOJSON; la cruceta \ncentra el mapa en el polígono trazado.\n\nLas imágenes exportadas se recortan al área de interés en las descargas analíticas."]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":["Esta herramienta sirve para marcar un punto en el mapa.\n\nPuede acceder también a datos estadísticos acerca de algunas capas si pulsa en el icono de ficha\n. \nElimine el marcador con el icono correspondiente .\n"]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":["Esta herramienta sirve para medir distancias y áreas sobre el mapa.\n\nCada pulsación del ratón inserta un punto más en la trayectoria. Para dejar de añadir puntos pulse la tecla\nEsc
key\no haga doble clic sobre el mapa.\nElimine la medida con el icono correspondiente ."]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":["Esta herramienta permite descargar una imagen con los datos visualizados de la ubicación mostrada. Puede elegir\nsi se muestra o no un encabezado, así como añadir su propia descripción.\nSi activa el modo analítico podrá optar entre varios formatos de imagen, resoluciones espaciales\ny sistemas de coordenadas. También es posible seleccionar varias capas y grabarlas en un fichero .zip
.\n\nPulse el botón de descargar\nDescargar\ny comenzará la descarga de sus imágenes. El proceso podría tardar unos segundos, dependiendo de la resolución\nelegida y del número de capas seleccionadas.\n\nAntes de descargar se puede definir un área de interés (ADI) pulsando en el icono de la herramienta\nde selección de áreas. Los datos se recortarán para ajustarse a esa área."]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":["Ha llegado al final del tutorial. Si tiene más preguntas puede plantearlas en el [foro](https://forum.sentinel-hub.com/)\no puede escribirnos por [correo electrónico](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nSi quiere repasar de nuevo el tutorial más adelante, siempre puede acceder a él con el icono de información\n\n\n\nsituado en la esquina superior derecha."]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":["#### Resumen de las prestaciones de EO Browser\n\nSi utiliza una pantalla pequeña, entonces vaya [aquí](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nPuede volver a ver esta información en cualquier momento si pulsa el icono de información\n\n\n\nque hay en la esquina superior derecha.\n\n#### Otros recursos\n- [Página de presentación de EO Browser](https://www.sentinel-hub.com/explore/eobrowser/)\n- [Actualizaciones de EO Browser verano 2018 - vídeo](https://www.youtube.com/embed/m3pron0C0kE)"]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":["¿Qué es EO Browser?"]},"User Account":{"msgid":"User Account","msgstr":["Cuenta de usuario"]},"Discover Tab":{"msgid":"Discover Tab","msgstr":["Pestaña Descubrir"]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":["Pestaña Visualizar"]},"Compare Tab":{"msgid":"Compare Tab","msgstr":["Pestaña Comparar"]},"Pins Tab":{"msgid":"Pins Tab","msgstr":["Pestaña de marcadores"]},"Search Places":{"msgid":"Search Places","msgstr":["Búsqueda de lugares"]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":["Capas y capas superpuestas"]},"Education Mode":{"msgid":"Education Mode","msgstr":["Modo educación"]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":["Información y tutorial"]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":["Trazar área de interés"]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":["Marcar punto de interés"]},"Measure Distances":{"msgid":"Measure Distances","msgstr":["Medida de distancias"]},"Download Image":{"msgid":"Download Image","msgstr":["Descargar imagen"]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":["Crear una animación"]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":["¡Feliz exploración!"]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":["¡Le damos la bienvenida a EO Browser!"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":["Banda 1 - Materia orgánica disuelta y pigmentos detríticos - 412.5 nm"]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":["Banda 3 - Clorofila y otros pigmentos - 490 nm"]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":["Banda 4 - Sedimentos en suspensión, mareas rojas - 510 nm"]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":["Banda 5 - Mínimo de absorción de la clorofila - 560 nm"]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":["Banda 6 - Sedimentos en suspensión - 620 nm"]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":["Banda 7 - Absorción de la clorofila y referencia para fluorescencia - 665 nm"]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":["Banda 8 - Pico de fluorescencia de la clorofila - 681 nm"]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":["Banda 9 - Referencia para fluorescencia, correcciones atmosféricas - 709 nm"]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":["Banda 10 - Vegetación, nubes - 753 nm"]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":["Banda 12 - Correcciones atmosféricas - 779 nm"]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":["Banda 13 - Vegetación, referencia de vapor de agua - 865 nm"]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":["Banda 14 - Correcciones atmosféricas - 885 nm"]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":["Banda 15 - Vapor de agua, tierra - 900 nm"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Banda 1 - Azul - 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Banda 2 - Verde - 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Banda 3 - Rojo - 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Banda 4 - NIR - 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Banda 5 - SWIR-1 - 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Banda 7 - SWIR-2 - 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Banda 8 - Pancromática - 520-900 nm"]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":["Banda 1 - Aerosoles costeros - 433-453 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Banda 2 - Azul - 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Banda 3 - Verde - 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Banda 4 - Rojo - 630-680 nm"]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":["Banda 5 - Infrarrojo cercano (NIR) - 845-885 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Banda 6 - SWIR-1 - 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Banda 7 - SWIR-2 - 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Banda 8 - Pancromática - 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Banda 9 - Cirros - 1360-1390 nm"]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":["Landsat 5 (archivo ESA)"]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":["Landsat 7 (Archivo ESA)"]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":["Landsat 8 (archivo ESA)"]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":["Landsat 8 (archivo USGS)"]},"Red band":{"msgid":"Red band","msgstr":["Banda roja"]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":["841 - 876 nm (NIR)"]},"Blue band":{"msgid":"Blue band","msgstr":["Banda azul"]},"Green band":{"msgid":"Green band","msgstr":["Banda verde"]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":["1230 - 1250 nm"]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":["1628 - 1652 nm"]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":["2105 - 2155 nm"]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":["Banda 1 - Aerosoles costeros - 443 nm"]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":["Banda 2 - Azul - 490 nm"]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":["Banda 3 - Verde - 560 nm"]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":["Banda 4 - Rojo - 665 nm"]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":["Banda 5 - Borde rojo de la vegetación - 705 nm"]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":["Banda 6 - Borde rojo de la vegetación - 740 nm"]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":["Banda 7 - Borde rojo de la vegetación - 783 nm"]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":["Banda 8 - Infrarrojo cercano (NIR) - 842 nm"]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":["Banda 9 - Vapor de agua - 945 nm"]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":["Banda 10 - Infrarrojo de onda corta (SWIR) - Cirros - 1375 nm"]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":["Banda 11 - Infrarrojo de onda corta (SWIR) - 1610 nm"]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":["Banda 12 - Infrarrojo de onda corta (SWIR) - 2190 nm"]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":["Banda 8A - Borde rojo de la vegetación - 865 nm"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":["Banda 1 - Corrección de aerosoles, recuperación mejorada de constituyentes del agua - 400 nm"]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":["Banda 2 - Materia orgánica en suspensión y pigmentos detríticos (turbidez) - 412 nm"]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":["Banda 3 - Máximo de absorción de la clorofila, biogeoquímica, vegetación - 442.5 nm"]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":["Banda 4 - Clorofila alta, otros pigmentos - 490 nm"]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":["Banda 5 - Clorofila, sedimentos, turbidez, marea roja - 510 nm"]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":["Banda 6 - Referencia para la clorofila (mínimo de la clorofila) - 560 nm"]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":["Banda 7 - Carga de sedimentos - 620 nm"]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":["Banda 8 - Segundo máximo de absorción de la clorofila, sedimentos, materia orgánica en suspensión/vegetación - 665 nm"]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":["Banda 10 - Borde rojo del pico de fluorescencia de la clorofila - 681.25 nm"]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":["Banda 11 - Borde rojo de la línea de base de fluorescencia de la clorofila - 708.75 nm"]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":["Banda 12 - Absorción de O2/nubes, vegetación - 753.75 nm"]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":["Banda 13 - Banda de absorción de O2/corrección de aerosoles - 761.25 nm"]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":["Banda 14 - Corrección atmosférica - 764.375 nm"]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":["Banda 15 - O2A para la presión en la cima nubosa, fluorescencia sobre tierra - 767.5 nm"]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":["Banda 16 - Corrección atmosférica/de aerosoles - 778.75 nm"]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":["Banda 17 - Corr. atmosf./corr. aerosoles, nubes, co-registro de píxeles - 865 nm"]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":["Banda 18 - Referencia para la absorción por vapor de agua, común con el instrumento SLSTR. Monitorización de vegetación - 885 nm"]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":["Banda 19 - Absorción por vapor de agua/monitorización de vegetación (reflectancia máxima) - 900 nm"]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":["Banda 20 - Absorción por vapor de agua, corrección atmosférica/de aerosoles - 940 nm"]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":["Banda 21 - Corrección atmosférica/de aerosoles - 1020 nm"]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":["Banda F1 - Emisión del fuego en el infrarrojo térmico (TIR) - Incendio activo - 3742.00 nm"]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":["Banda F2 - Emisión del fuego en el infrarrojo térmico (TIR) - Incendio activo - 10854.00 nm"]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":["Banda S1 - VNIR - Filtrado de nubes, monitorización de vegetación, aerosoles - 554.27 nm"]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":["Banda S2 - VNIR - Índice normalizado de vegetación (NDVI), monitorización de vegetación, aerosoles - 659.47 nm"]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":["Banda S3 - VNIR - Índice normalizado de vegetación (NDVI), marcado de nubes, co-registro de píxeles - 868.00 nm"]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":["Banda S4 - SWIR - Detección de cirros sobre tierra - 1374.80 nm"]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":["Banda S5 - SWIR - Corrección de nubes, hielo, nieve, monitorización de vegetación - 1613.40 nm"]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":["Banda S6 - SWIR - Estado de la vegetación y corrección de nubes - 2255.70 nm"]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":["Banda S7 - Infrarrojo térmico (TIR) ambiental - Temperatura superficial de mar y tierra, incendio activo - 3742.00 nm"]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":["Banda S8 - Infrarrojo térmico (TIR) ambiental - Temperatura superficial de mar y tierra, incendio activo - 10854.00 nm"]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":["Banda S9 - Infrarrojo térmico (TIR) ambiental - Temperatura superficial de mar y tierra - 12022.50 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Reflectancia"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Temperatura de brillo"]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":["A partir de la combinación de las bandas 4, 3, 2"]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":["A partir de la combinación de bandas (B04-B03)/(B04+B03)"]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":["A partir de la combinación de las bandas 5, 4, 3"]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":["Basado en las bandas de color natural 4, 3, 2, y en la pancromática 8"]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":["A partir de la combinación de bandas (B05-B04)/(B05+B04)"]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":["VV - lineal gamma0 - ortocorregido"]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":["VV - gamma0 lineal - no ortocorregido"]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":["VH - gamma0 lineal - ortocorregido"]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":["A partir de la combinación de las bandas 3, 2, 1"]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":["VH - gamma0 lineal - no ortocorregido"]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":["Imagen en color tras combinar las bandas de entrada. Valor [RGB] = [VV, 2 VH, VV / VH / 100.0] - gamma0 lineal - ortocorregida"]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":["VV - gamma0 decibélico [-20,0] - ortocorregido"]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":["VH - gamma0 decibélico [-20,0] - ortocorregido"]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":["Devuelve una composición de (VH, VV, VH-VV)"]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":["HH - gamma0 lineal - ortocorregido"]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":["HV - gamma0 lineal - ortocorregido"]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":["Imagen en color tras combinar las bandas de entrada. Valor [RGB] = [HH, 2 HV, HH / HV / 100.0] - gamma0 lineal - ortocorregida"]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":["HH - gamma0 decibélico [-20,0] - ortocorregido"]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":["HV - gamma0 decibélico [-20,0] - ortocorregido"]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":["HH - gamma0 lineal - no ortocorregido"]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":["A partir de las bandas 4, 3, 2"]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":["A partir de las bandas 8, 4, 3"]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":["A partir de las bandas 12, 11, 4"]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":["A partir de la combinación de bandas (B8 - B4)/(B8 + B4)"]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":["A partir de la combinación de bandas (B8A - B11)/(B8A + B11)"]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":["A partir de las bandas 12, 8A, 4"]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":["A partir de la combinación de bandas (B3 - B8)/(B3 + B8)"]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":["A partir de la combinación de bandas (B3 - B11)/(B3 + B11); los valores superiores a 0.42 indican nieve"]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":["Clasificación de los datos de Sentinel2 por el algoritmo de clasificación de escenas de la ESA."]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":["Índice UV de aerosoles (380 y 340 nm)"]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":["A partir de la combinación de bandas (B3 - B11)/(B3 + B11)"]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":["Índice OLCI de clorofila terrestre, basado en la combinación de bandas (B12 - B11)/(B11 - B10)"]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":["Índice UV de aerosoles (388 y 354 nm)"]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":["Razón de mezcla del metano promediada en la columna de aire seco"]},"Cloud base height":{"msgid":"Cloud base height","msgstr":["Altitud de la base de las nubes"]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":["Presión en la base de las nubes"]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":["Fracción de nubes radiométrica efectiva"]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":["Espesor óptico de las nubes"]},"Cloud top height":{"msgid":"Cloud top height","msgstr":["Altitud de la cima de las nubes"]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":["Presión en la cima de las nubes"]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":["Columna total de monóxido de carbono"]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":["Columna vertical troposférica de formaldehído"]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":["Columna troposférica de dióxido de nitrógeno"]},"Ozone total column":{"msgid":"Ozone total column","msgstr":["Columna total de ozono"]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":["Columna total de dióxido de azufre"]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":["A partir de las bandas 2, 1, 4"]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":["A partir de la combinación de bandas (B02 - B01)/(B02 + B01)"]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":["A partir de la combinación de bandas (B02 - B05)/(B02 + B05)"]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":["A partir de la combinación de bandas (B06 - B07)/(B06 + B07)"]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":["A partir de las bandas 1, 4, 3"]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":["A partir de la combinación de las bandas 7, 5, 3"]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":["(B09 - B08)/(B09 + B08)"]},"B09 / B08":{"msgid":"B09 / B08","msgstr":["B09 / B08"]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":["A partir de la combinación de las bandas 13, 5, 2"]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":["A partir de las bandas 13, 4, 1"]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":["A partir de las bandas 13, 5, 2"]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":["A partir de la combinación de bandas (B13-B07) / (B13+B07)"]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":["Índice de clorofila terrestre"]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V: síntesis de 10 días\nDosel vegetal (corregido de atmósfera)\nResolución temporal: 10 días\nResolución: 333 m (tamaño del píxel)"]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V: síntesis diaria\nCima de la atmósfera\nResolución temporal: diaria\nResolución: 333 m (tamaño del píxel)"]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V: síntesis de 5 días\nCima de la atmósfera\nResolución temporal: 5 días\nResolución: 100 m (tamaño del píxel)"]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V: síntesis diaria\nDosel vegetal (corregido de atmósfera)\nResolución temporal: diaria\nResolución: 333 m (tamaño del píxel)"]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":["OrbitTracks_Aqua_Descending"]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V: síntesis de 5 días\nDosel vegetal (corregido de atmósfera)\nResolución temporal: 5 días\nResolución: 100 m (tamaño del píxel)"]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":["OrbitTracks_Aqua_Ascending"]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":["OrbitTracks_Aura_Ascending"]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":["OrbitTracks_Aura_Descending"]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":["OrbitTracks_CloudSat_Ascending"]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":["OrbitTracks_Calipso_Ascending"]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":["OrbitTracks_Calipso_Descending"]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":["OrbitTracks_CloudSat_Descending"]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":["OrbitTracks_CYGNSS_Ascending"]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":["OrbitTracks_CYGNSS_Descending"]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":["OrbitTracks_GCOM-C_Ascending"]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":["OrbitTracks_GCOM-C_Descending"]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":["OrbitTracks_GCOM-W1_Ascending"]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":["OrbitTracks_GCOM-W1_Descending"]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":["OrbitTracks_GOSAT-2_Ascending"]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":["OrbitTracks_GOSAT-2_Descending"]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":["OrbitTracks_GOSAT_Ascending"]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":["OrbitTracks_GOSAT_Descending"]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":["OrbitTracks_GPM_Ascending"]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":["OrbitTracks_GPM_Descending"]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":["OrbitTracks_ICESAT-2_Ascending"]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":["OrbitTracks_ICESAT-2_Descending"]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":["OrbitTracks_ISS_Ascending"]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":["OrbitTracks_ISS_Descending"]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":["OrbitTracks_Landsat-7_Ascending"]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":["OrbitTracks_Landsat-7_Descending"]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":["OrbitTracks_Landsat-8_Ascending"]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":["OrbitTracks_METOP-A_Ascending"]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":["OrbitTracks_METOP-B_Descending"]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":["OrbitTracks_Landsat-8_Descending"]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":["OrbitTracks_METOP-C_Ascending"]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":["OrbitTracks_METOP-C_Descending"]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":["OrbitTracks_NOAA-20_Ascending"]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":["OrbitTracks_NOAA-20_Descending"]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":["OrbitTracks_METOP-B_Ascending"]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":["OrbitTracks_OCO-2_Ascending"]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":["OrbitTracks_OCO-2_Descending"]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":["OrbitTracks_SAOCOM1-A_Ascending"]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":["OrbitTracks_SAOCOM1-A_Descending"]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":["OrbitTracks_Sentinel-1A_Ascending"]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":["OrbitTracks_Sentinel-1B_Ascending"]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":["OrbitTracks_Sentinel-1A_Descending"]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":["OrbitTracks_METOP-A_Descending"]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":["OrbitTracks_Sentinel-1B_Descending"]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":["OrbitTracks_Sentinel-2A_Ascending"]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":["OrbitTracks_Sentinel-2A_Descending"]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":["OrbitTracks_Sentinel-2B_Ascending"]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":["OrbitTracks_Sentinel-2B_Descending"]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":["OrbitTracks_Sentinel-5P_Ascending"]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":["OrbitTracks_Sentinel-5P_Descending"]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":["OrbitTracks_SMAP_Ascending"]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":["OrbitTracks_SMAP_Descending"]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":["OrbitTracks_Suomi_NPP_Descending"]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":["OrbitTracks_Suomi_NPP_Ascending"]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":["OrbitTracks_Terra_Descending"]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":["OrbitTracks_Terra_Ascending"]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":["A partir de las bandas 4, 3, 2 y con realce mediante las bandas 12 y 11."]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":["A partir de las bandas B07, B06, B04"]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":["A partir de la combinación de bandas (B8 - B4)/(B8 + B4)"]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":["A partir de la banda térmica 10"]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":["A partir de las bandas B12, B11, B8A"]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":["A partir de la combinación de bandas (B08 - B12)/(B08 + B12)"]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":["Visualización en color natural realzado"]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":["A partir de la combinación de las bandas 8, 6, 4"]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":["Índice de vegetación realzado"]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":["A partir de la combinación de las bandas BSI, B08, B11"]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":["Índice normalizado de humedad (NDMI) clasificado para riego"]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":["A partir de las bandas B11, B08, B02"]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":["Color falso 13, 5, 2"]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":["A partir de la combinación de bandas (B13 - B07) / (B13 + B07)"]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":["A partir de las bandas 12, 8, 2"]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":["A partir de las bandas 4, 3, 2"]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":["A partir de las bandas 8, 4, 3"]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":["A partir de las bandas 12, 8, 2"]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":["A partir de las bandas 8, 11, 12"]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":["Contenido de sedimentos y clorofila del agua"]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":["A partir de las bandas 12, 8A, 4"]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":["Basado en el índice normalizado de nieve (NDSI)"]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":["A partir de la combinación de las bandas 11, 8, 2"]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":["A partir de las bandas B12, B11, B04"]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":["A partir de las bandas 4, 3, 2"]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":["Índice de vegetación atmosféricamente resistente"]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":["Índice de vegetación ajustado al suelo (SAVI)"]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":["# Bandas de emisión térmica infrarroja del fuego\n\nEl Instrumento de Temperatura Superficial del Mar y del Terreno (SLSTR, \"Sea and Land Surface Temperature Instrument\") de Sentinel-3 cuenta con dos canales (F1 y F2) dedicados a medir la temperatura de la superficie del terreno (LST, «Land Surface Temperature\"). El canal F2 tiene la longitud de onda central en 10854 nm, en el infrarrojo térmico, o TIR, y resulta muy útil para monitorizar incendios o sucesos de altas temperaturas con una resolución espacial de 1 km. \n\n\n\nMás información [aquí.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)"]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":["# Metano (CH4)\n\n\n\nTras el dióxido de carbono, el metano constituye la principal aportación al incremento antropogénico (causado por el ser humano) del efecto invernadero. Las medidas se dan en partes por mil millones (ppb) con una resolución espacial de 7 km x 3.5 km.\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/methane)"]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":["# Formaldehído (HCHO)\n\n\n\nLas observaciones satelitales a largo plazo del formaldehído (HCHO) son esenciales para los estudios de la calidad del aire y de la interdependencia entre la química y el clima, tanto a escala regional como global. Las variaciones estacionales e interanuales de la distribución de formaldehído se relacionan, sobre todo, con cambios de temperatura y con incendios, pero también con cambios antropogénicos (debidos a la actividad humana). La vida del formaldehído en la atmósfera asciende a apenas unas cuantas horas, por lo que cabe relacionar directamente su concentración en la capa límite con la emisión de hidrocarburos de vida corta, los cuales no suelen ser detectables desde el espacio. Las medidas se dan en moles por metro cuadrado (mol/ m^2).\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/formaldehyde)"]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":["# Dióxido de azufre (SO2)\n\n\n\nEl dióxido de azufre accede a la atmósfera terrestre mediante procesos naturales y antropogénicos (causados por el ser humano). Tiene relevancia química a nivel local y global y su incidencia abarca desde contaminación de corta duración hasta efectos en el clima. Solo alrededor del 30% de las emisiones de SO2 procede de fuentes naturales; la mayoría es de origen antropogénico. El instrumento TROPOMI de Sentinel-5P muestrea la superficie terrestre a intervalos de un día con una resolución espacial de 3.5 x 7 km, lo que permite distinguir detalles finos que incluyen los penachos más reducidos de SO2. Las medidas se dan en moles por metro cuadrado (mol/ m^2).\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/sulphur-dioxide)"]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":["# Ozono (O3)\n\n\n\nEl ozono es trascendental para el equilibrio de la atmósfera terrestre. La capa de ozono de la estratosfera protege la biosfera de la peligrosa radiación ultravioleta. En la troposfera, el ozono actúa como un agente limpiador muy eficaz, pero en concentraciones elevadas resulta perjudicial para la salud humana, de los animales y de las plantas. El ozono es, además, uno de los gases con una contribución significativa al efecto invernadero responsable del cambio climático en curso. Desde el descubrimiento del agujero de ozono antártico en la década de 1980 y el posterior protocolo de Montreal que regula la producción de sustancias cloradas destructoras de ozono, este gas se ha venido monitorizando de manera rutinaria tanto desde el suelo como desde el espacio. Las medidas se dan en moles por metro cuadrado (mol/ m^2)\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/total-ozone-column)"]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":["# Dióxido de nitrógeno (NO2)\n\n\n\nEl dióxido de nitrógeno (NO2) y el monóxido de nitrógeno (NO) suelen recibir la denominación conjunta de óxidos de nitrógeno. Son gases traza importantes en la atmósfera terrestre y se encuentran tanto en la troposfera como en la estratosfera. Aparecen en la atmósfera debido a actividades antropogénicas (en especial por la quema de combustibles fósiles y de biomasa), pero también por procesos naturales como ciertas actividades microbianas en los suelos, incendios forestales y caída de rayos. Las medidas se dan en moles por metro cuadrado (mol/ m^2).\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/nitrogen-dioxide)"]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":["# Monóxido de carbono (CO)\n\n\n\nEl monóxido de carbono (CO) es un gas traza muy importante. Actúa como uno de los contaminantes principales en las zonas urbanas. Sus fuentes principales son la quema de combustibles fósiles, la combustión de biomasa y la oxidación atmosférica de metano y otros hidrocarburos. La columna total de monóxido de carbono se mide en moles por metro cuadrado (mol/ m^2).\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/carbon-monoxide)"]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":["# Índice de aerosoles\n\nEl índice de aerosoles (AI, Aerosol Index) es un indicador cualitativo que evalúa la presencia de capas de aerosoles suspendidos en la atmósfera. Puede usarse para detectar aerosoles absorbentes de la radiación UV, como el polvo del desierto o los penachos de cenizas volcánicas. Adopta valores positivos (del azul claro al rojo) cuando hay aerosoles que absorben UV. El índice se calcula a partir de dos parejas de longitudes de onda: 340/380 nm y 354/388 nm.\n\nMás información [aquí.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)"]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":["# Altitud de la base de las nubes\n\nAltitud de la base de las nubes medida en metros (m)."]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":["# Presión en la base de las nubes\n\nPresión en la base de las nubes medida en pascales (Pa)."]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":["# Espesor óptico de las nubes\n\nEl espesor óptico es un parámetro crucial para caracterizar las propiedades ópticas de las nubes. Constituye una medida de la cantidad de luz solar que logra atravesarlas hasta alcanzar la superficie terrestre. Cuanto mayor sea el espesor óptico de una nube, mayor es la cantidad de luz solar esparcida y reflejada por ella. El azul oscuro indica valores bajos de espesor óptico, mientras que el rojo corresponde a espesores mayores."]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":["# Altitud de la cima de las nubes\n\nAltitud de la cima de las nubes medida en metros (m)."]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":["# Presión en la cima de las nubes\n\nPresión en la cima de las nubes medida en pascales (Pa)."]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":["# Índice normalizado de vegetación (NDVI)\n\nEl índice normalizado de vegetación (NDVI, Normalized Difference Vegetation Index) es un índice simple, pero efectivo, para cuantificar la vegetación verde. Ofrece una medida del estado de salud de la vegetación, basado en el modo en que las plantas reflejan la luz de ciertas longitudes de onda. NDVI adopta valores entre -1 y +1. Los valores negativos (los más cercanos a -1) corresponden a agua. Valores en torno a cero (entre -0.1 y +0.1) suelen indicar zonas sin vegetación como roca, arena o nieve. Los valores positivos pero pequeños se corresponden con arbustos y campos cubiertos de hierba (aproximadamente entre +0.2 y +0.4), en tanto que cifras más elevadas describen los bosques templados y las selvas tropicales (valores cercanos a +1).\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) y [aquí.](https://eos.com/ndvi/)"]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":["# Índice realzado de vegetación (EVI)\n\nEl índice realzado de vegetación (EVI, Enhanced Vegetation Index\") es un índice de vegetación optimizado que corrige las señales de fondo debidas al terreno y a efectos atmosféricos. Resulta muy útil en áreas de cobertura boscosa densa. El EVI adopta valores entre -1 y +1, y la vegetación sana suele caer entre +0.20 y +0.80.\n\n\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) y [aquí.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)"]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Índice de vegetación resistente a la atmósfera (ARVI)\n\nEl índice de vegetación resistente a la atmósfera (ARVI, Atmospherically Resistant Vegetation Index) minimiza el efecto del esparcimiento atmosférico. Resulta especialmente útil en regiones donde la atmósfera presenta un contenido elevado de aerosoles (niebla, polvo, humo, contaminación del aire). El ARVI adopta valores entre -1 y +1, donde la vegetación verde suele aparecer entre 0.10 y 0.80.\n\n\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) y [aquí.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Índice de vegetación ajustado al suelo (SAVI)\n\nEl índice de vegetación ajustado al suelo (SAVI, Soil Adjusted Vegetation Index) es similar al índice normalizado de vegetación (NDVI), pero resulta más adecuado en zonas con cobertura vegetal pobre (< 40 %). Este índice incorpora una técnica de transformación para minimizar el brillo del suelo en el cálculo de los índices de vegetación que emplean longitudes de onda rojas y en el infrarrojo cercano (NIR). Este índice es útil para analizar cultivos tempranos, regiones áridas con vegetación dispersa y superficies con suelo desnudo.\n\n\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) y [aquí.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":["# Índice de reflectancia de la antocianina modificado (mARI/ARI2)\n\nLas antocianinas son pigmentos frecuentes en las plantas superiores, responsables de sus colores rojos, azules y morados. Proporcionan información valiosa sobre el estado fisiológico de las plantas, puesto que se consideran indicadores de varios tipos de estrés vegetal. La reflectancia de las antocianinas es máxima en torno a 550 nm. Sin embargo, también la clorofila refleja esas longitudes de onda. Por eso, para aislar las antocianinas se sustrae la banda espectral de 700 nm, reflejada por la clorofila pero no por las antocianinas.\n\nEn este índice modificado (mARI, o bien ARI2) se añade al índice básico ARI una banda espectral del infrarrojo cercano (en las longitudes de onda recomendadas de 760-800 nm) para aplicar una corrección que tiene en cuenta la densidad y el grosor de las hojas, dado que esta banda está relacionada con la luz esparcida por las hojas. \n\nLos valores de mARI van desde 0 hasta 8 para la muestra de árboles examinada en este [artículo original.](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/)\n\n\n\n\n\nMás información [aquí.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)"]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":["# Script de ciudad verde\n\nEl script de ciudad verde pretende promover la concienciación sobre las zonas vedes en las ciudades de todo el mundo. El script tiene en cuenta el índice normalizado de vegetación (NDVI) y las longitudes de onda del color natural para distinguir entre áreas edificadas y zonas verdes, lo que lo hace útil para identificar zonas urbanas. Las áreas edificadas se muestran en gris, y la vegetación, en verde.\n\n\n\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":["# Script de clasificación urbana\n\nEl script de clasificación urbana detecta áreas edificadas y las distingue del suelo desnudo, de la vegetación y del agua. Las zonas con niveles elevados de humedad se muestran en azul; las zonas clasificadas como edificadas aparecen en blanco; las superficies cubiertas de vegetación se plasman en verde, y todo lo demás corresponde a suelo desnudo y se codifica con tonos pardos.\n\n\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)"]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":["# Script de suelo urbano en colores infrarrojos\n\nEste script, obra de Leo Tolari, combina la visualización en color natural con longitudes de onda en el infrarrojo cercano (NIR) y en el infrarrojo de onda corta (SWIR). Logra una apariencia realista, pero realza las zonas urbanas mejor que el color natural.\n\n\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)"]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":["# NDMI para el estrés hídrico\n\nEl índice normalizado de humedad (NDMI, Normalized Difference Moisture Index) aplicado al estrés hídrico detecta el riego. Para cualquier valor de este índice por encima de 0, y conocido el uso y la cobertura del suelo, es posible detectar si se ha producido riego. Si además se conoce el tipo de cultivo (por ejemplo, cítricos), cabe identificar si el riego está siendo o no eficaz durante la fase crucial de crecimiento en el estío, así como esclarecer si hay porciones de las explotaciones agrarias con riego insuficiente o excesivo.\n\n\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)"]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":["# Índice normalizado de humedad (NDMI)\n\nEl índice normalizado de humedad (NDMI, Normalized Difference Moisture Index) se emplea para determinar el contenido de agua de la vegetación y para monitorizar sequías. NDMI adopta valores entre -1 y +1. Los valores negativos (cercanos a -1) corresponden a suelo desnudo. Valores alrededor de cero (de -0.2 a +0.4) suelen indicar estrés hídrico. Los valores positivos más altos representan cubiertas vegetales elevadas y sin estrés hídrico (aproximadamente desde +0.4 hasta +1).\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":["# Índice normalizado de agua (NDWI)\n\nEl índice normalizado de agua (NDWI, Normalized Difference Water Index\") resulta adecuado para cartografiar masas de agua, que presentan valores de este índice superiores a +0.5. La vegetación ostenta valores menores. Las edificaciones tienen valores positivos entre cero y +0.2.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":["# Índice normalizado de agua (NDWI)\n\nEl índice normalizado de agua (NDWI, Normalized Difference Water Index) resulta adecuado para cartografiar masas de agua, que presentan valores de este índice superiores a +0.5. La vegetación ostenta valores menores. Las edificaciones tienen valores positivos entre cero y +0.2."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Composición en color falso\n\nUna composición en color falso recurre al menos a una imagen obtenida en longitudes de onda no visibles. Es frecuente la composición en color falso que utiliza las bandas del infrarrojo cercano, el rojo y el verde (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas). Estas composiciones en color falso se suelen emplear para diagnosticar la densidad y la salud de la vegetación, porque las plantas reflejan las bandas infrarroja y verde, mientras que absorben el color rojo. Las ciudades y el suelo desnudo aparecen grises o en tonos pardos, mientras que el agua se muestra azul o negra.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) y [aquí.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Composición en color falso\n\nUna composición en color falso recurre al menos a una imagen obtenida en longitudes de onda no visibles. Es frecuente la composición en color falso que utiliza las bandas del infrarrojo cercano, el rojo y el verde (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas). Estas composiciones en color falso se suelen emplear para diagnosticar la densidad y la salud de la vegetación, porque las plantas reflejan las bandas infrarroja y verde, mientras que absorben el color rojo. Las ciudades y el suelo desnudo aparecen grises o en tonos pardos, mientras que el agua se muestra azul o negra.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) y [aquí.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":["# Composición en color falso\n\nUna composición en color falso recurre al menos a una imagen obtenida en longitudes de onda no visibles. Es frecuente la composición en color falso que utiliza las bandas del infrarrojo cercano, el rojo y el verde (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas). Estas composiciones en color falso se suelen emplear para diagnosticar la densidad y la salud de la vegetación, porque las plantas reflejan las bandas infrarroja y verde, mientras que absorben el color rojo. Las ciudades y el suelo desnudo aparecen grises o en tonos pardos, mientras que el agua se muestra azul o negra.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) y [aquí.](https://earthobservatory.nasa.gov/features/FalseColor)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Composición en color falso\n\nUna composición en color falso recurre al menos a una imagen obtenida en longitudes de onda no visibles. Es frecuente la composición en color falso que utiliza las bandas del infrarrojo cercano, el rojo y el verde (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas). Estas composiciones en color falso se suelen emplear para diagnosticar la densidad y la salud de la vegetación, porque las plantas reflejan las bandas infrarroja y verde, mientras que absorben el color rojo. Las ciudades y el suelo desnudo aparecen grises o en tonos pardos, mientras que el agua se muestra azul o negra.\n\n\n\nMás información [aquí.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":["# Composición en color natural\n\nLos sensores satelitales captan imágenes de la Tierra en diferentes regiones del espectro electromagnético, cada una de las cuales se conoce como una banda. La composición en color natural introduce las bandas de luz visible roja, verde y azul en los tres canales correspondientes, rojo, verde y azul, para ofrecer un producto en color que constituye una buena representación de la Tierra tal y como la vería el ser humano de manera natural.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) y [aquí.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy)."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Composición en color natural\n\nLos sensores satelitales captan imágenes de la Tierra en diferentes regiones del espectro electromagnético, cada una de las cuales se conoce como una banda. La composición en color natural introduce las bandas de luz visible roja, verde y azul en los tres canales correspondientes, rojo, verde y azul, para ofrecer un producto en color que constituye una buena representación de la Tierra tal y como la vería el ser humano de manera natural.\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) y [aquí.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Composición en color natural\n\nLos sensores satelitales captan imágenes de la Tierra en diferentes regiones del espectro electromagnético, cada una de las cuales se conoce como una banda. La composición en color natural introduce las bandas de luz visible roja, verde y azul en los tres canales correspondientes, rojo, verde y azul, para ofrecer un producto en color que constituye una buena representación de la Tierra tal y como la vería el ser humano de manera natural.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) y [aquí.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Composición en color natural\n\nLos sensores satelitales captan imágenes de la Tierra en diferentes regiones del espectro electromagnético, cada una de las cuales se conoce como una banda. La composición en color natural introduce las bandas de luz visible roja, verde y azul en los tres canales correspondientes, rojo, verde y azul, para ofrecer un producto en color que constituye una buena representación de la Tierra tal y como la vería el ser humano de manera natural.\n\n\n\nMás información [aquí.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":["# Composición en color natural\n\nLos sensores satelitales captan imágenes de la Tierra en diferentes regiones del espectro electromagnético, cada una de las cuales se conoce como una banda. La composición en color natural introduce las bandas de luz visible roja, verde y azul en los tres canales correspondientes, rojo, verde y azul, para ofrecer un producto en color que constituye una buena representación de la Tierra tal y como la vería el ser humano de manera natural.\n\n\n\nMás información [aquí.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)"]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":["# Visualización en color natural realzado\n\nEste script aplica técnicas de optimización para evitar la aparición de píxeles saturados y para equilibrar la exposición. Confiere una apariencia natural a las nubes y retiene toda la información visual posible. Las teselas de OLCI en Sentinel-3 cubren áreas muy amplias, lo que permite observar grandes formaciones nubosas como, por ejemplo, huracanes.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)"]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Refinado pancromático en color natural\n\nLa composición de refinado pancromático en color natural se obtiene a partir de los datos habituales de color natural (rojo, verde y azul, o RGB), los cuales se realzan mediante la banda pancromática 8 o con la banda pan (una banda es una región del espectro electromagnético; los sensores satelitales observan la Tierra en múltiples bandas). La imagen de la banda pan se parece a las que ofrecían las películas en blanco y negro: combina la luz de las regiones roja, verde y azul del espectro en una sola medida de la reflectancia general en luz visible. Las imágenes que resultan del refinado pancromático tienen el 4 veces la resolución que las composiciones normales en color natural, lo que incrementa mucho la utilidad de las imágenes Landsat.\n\n\n\nMás información [aquí](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) y [aquí.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":["# Composición urbana en color falso\n\nEsta composición se utiliza para visualizar mejor las zonas urbanizadas. La vegetación queda representada en tonos de verde, mientras que el terreno urbanizado aparece en blancos, grises y morados. El suelo, la arena y los minerales se revelan en toda una variedad de colores. La nieve y el hielo se muestran en azul oscuro, mientras que el agua se ve negra o azul. Las zonas inundadas se ven en azul muy oscuro, casi negro. Esta composición resulta útil para detectar incendios forestales y calderas volcánicas, que se reflejan en tonos rojos y amarillos.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) y [aquí.](https://eos.com/false-color/)"]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Composición urbana en color falso\n\nEsta composición recurre a una combinación de bandas en el rango visible y en el infrarrojo de onda corta (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas). La vegetación aparece en tonos verdes, más oscuros cuando es más densa, mientras que la vegetación dispersa adopta los tonos más claros. Las áreas urbanizadas se ven azules, mientras que el suelo se muestra en varios tonos pardos.\n\n\n\nMás información [aquí.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":["# Composición agrícola\n\nEsta composición emplea bandas de infrarrojo de onda corta, infrarrojo cercano y azul, para monitorizar la salud de los cultivos (una banda es un intervalo del espectro electromagnético; los sensores satelitales pueden fotografiar la Tierra en múltiples bandas). Las bandas de infrarrojo de onda corta y de infrarrojo cercano son especialmente adecuadas para realzar la vegetación densa, que se representa en verde oscuro en la composición. Los cultivos aparecen de un verde fosforescente, y el suelo desnudo se muestra en magenta. \n\n\n\nMás información [aquí](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) y [aquí.](https://gisgeography.com/sentinel-2-bands-combinations/)"]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":["# Clasificador de nieve\n\nEl algoritmo clasificador de nieve clasifica los píxeles a partir de umbrales tanto en diferencias de brillo como en el índice normalizado de nieve (NDSI, Normalized Difference Snow Index). Los valores considerados como nieve se representan en un azul claro llamativo. El script sobreestima la nieve cuando hay nubes.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":["# Visualizador Ulyssys de la calidad del agua (UWQV)\n\nEl script UWQV (Ulyssys Water Quality Viewer) permite visualizar de manera dinámica los contenidos de clorofila y sedimentos en las masas de agua, cantidades que suponen indicadores primarios de la calidad del agua. El contenido de clorofila se representa en colores desde el azul oscuro (bajo contenido de clorofila), pasando por el verde, hasta el rojo (valores elevados). La concentración de sedimentos se representa en tonos pardos; el marrón opaco indica un contenido de sedimentos elevado. \n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)"]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":["# Color natural optimizado\n\nEste script representa la Tierra en bellas imágenes en color natural. Aplica técnicas de optimización para evitar que aparezcan píxeles saturados, así como para equilibrar la exposición.\n\n\n\nMás información [aquí.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)"]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":["# Composición geológica 12, 8, 2\n\nEsta composición recurre a la banda 12 en el infrarrojo de onda corta (SWIR, Short-Wave InfraRed) para distinguir varios tipos de roca (una banda es una región del espectro electromagnético; los sensores satelitales observan la Tierra en múltiples bandas). Cada roca y mineral refleja de un modo distinto el infrarrojo de onda corta, lo que permite elaborar una cartografía geológica a través de la comparación de la luz infrarroja de onda corta reflejada. La banda 8 del infrarrojo cercano (NIR) realza la vegetación, mientras que la banda 2 es sensible a la humedad, lo que permite distinguir distintos materiales en el terreno. Esta composición resulta útil para localizar formaciones y estructuras geológicas como fallas y fracturas, estudiar la litología (distinguir granitos de basaltos, por ejemplo) o en aplicaciones mineras.\n\n\n\nMás información [aquí.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)"]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":["# Composición geológica 8, 11, 12\n\nEsta composición recurre a las bandas 11 y 12 del infrarrojo de onda corta (SWIR, Short-Wave InfraRed) para distinguir tipos de rocas (una banda es una región del espectro electromagnético; los sensores satelitales observan la Tierra en múltiples bandas). Cada roca y mineral refleja de un modo distinto la luz infrarroja de onda corta, lo que hace posible elaborar mapas geológicos comparando la luz infrarroja de onda corta reflejada. La banda 8 del infrarrojo cercano (NIR) realza la vegetación, lo que contribuye a distinguir los materiales superficiales. La vegetación aparece en color rojo en esta composición, que es útil para distinguir la vegetación del suelo, así como para identificar rasgos geológicos de interés en minería o para la explotación de minerales.\n\n\n\nMás información [aquí](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) y [aquí.](http://murphygeological.com/new---sentinel-2.html#)"]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":["# Incendios forestales\n\nEste script, desarrollado por Pierre Markuse, visualiza incendios forestales a partir de datos de Sentinel-2. Combina un fondo en color natural con algunos datos NIR/SWIR que permiten penetrar en el humo y añadir más detalles, a la vez que incorpora rasgos de B11 y B12 para representar los incendios en tonos rojos y naranjas.\n\n\n\nMás información [aquí.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)"]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":["# Color natural realzado\n\nEste script, desarrollado por Pierre Markuse, recurre a múltiples bandas (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas) y a controles de saturación y brillo para realzar la visualización en color natural.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)"]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":["# Índice de áreas quemadas\n\nEl indice de áreas quemadas aprovecha la gran anchura de las bandas visible, borde rojo, NIR y SWIR.\n\nDescripción de los valores:()=> El índice adopta valores desde `-1` hasta `1` para zonas quemadas, y de `1` hasta `6` para incendios activos. Distintos niveles corresponden a diferentes intensidades de fuego: el autor original calibró los valores actuales a partir, sobre todo, de las regiones mediterráneas.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)"]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":["# Fracción quemada normalizada (NBR)\n\nLa fracción quemada normalizada (NBR, Normalized Burn Ratio) se suele utilizar para estimar la gravedad de los incendios. Recurre a longitudes de onda en el infrarrojo cercano (NIR) y en el infrarrojo de onda corta (SWIR, Short-Wave InfraRed). La vegetación sana refleja mucho en la parte del espectro del infrarrojo cercano, pero poco en el infrarrojo de onda corta. En contraste, las zonas quemadas reflejan mucho en el infrarrojo de onda corta y poco en el infrarrojo cercano. Los píxeles más oscuros corresponden a áreas quemadas.\n\n\n\nMás información [aquí](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) y [aquí.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":["# Penetración atmosférica\n\nEsta composición utiliza varias bandas (una banda es un intervalo del espectro electromagnético; los sensores satelitales pueden fotografiar la Tierra en múltiples bandas) de la parte no visible del espectro electromagnético para reducir la influencia de la atmósfera sobre la imagen. Las áreas calientes reflejan mucho las bandas 11 y 12 del infrarrojo de onda corta, lo que las hace útiles para cartografiar incendios y áreas quemadas. Por el contrario, la banda 8 del infrarrojo de onda corta se refleja mucho en la vegetación, lo que indica ausencia de fuegos. La vegetación se representa en azul y muestra detalles relacionados con su vigor. La vegetación más sana aparece en azul claro, mientras que la vegetación sometida a estrés, dispersa o de zonas áridas figura en un azul más apagado. Los rasgos urbanos se ven en gris, cian o morado. \n\n\n\nMás información [aquí.](https://eos.com/atmospheric-penetration/)"]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":["# Visualización de suelo desnudo\n\nLa visualización de suelo desnudo puede ser útil en la cartografía de suelos, para investigar la ubicación de deslizamientos de tierra o el alcance de la erosión en áreas desprovistas de vegetación. Esta visualización muestra toda la vegetación en color verde y el suelo desnudo en rojo. El agua aparece en negro.\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) y [aquí.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)"]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":["# Composición en color natural con realce IR\n\nEsta composición realza la visualización en color natural mediante el añadido de información obtenida en el infrarrojo de onda corta para resaltar ciertos detalles. Las regiones calientes se muestran en rojo/anaranjado.\n\n\n\nMás información [aquí.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)"]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":["# Detección de zonas quemadas\n\nEste script detecta grandes zonas quemadas recientemente. Los píxeles que aparecen en rojo indican zonas quemadas, mientras que el resto de los píxeles se representa en color natural. El script tiende a sobrestimar las áreas quemadas sobre superficies de agua o sobre nubes.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":["# Índice de clorofila terrestre (OTCI)\n\n\n\nEl índice de clorofila terrestre (OTCI, Terrestrial Chlorophyill Index\") evalúa el contenido de clorofila de la vegetación terrestre y se aplica para monitorizar el estado y la salud de la vegetación. Valores bajos suelen significar agua, arena o nieve. Valores extremadamente elevados, que se muestran en color blanco, normalmente indican que tampoco hay clorofila y corresponden a suelo desnudo, rocas o nubes. Los valores codificados entre el rojo (valores pequeños) y el verde oscuro (valores elevados) pueden utilizarse para determinar la salud de la vegetación.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":["# Índice normalizado de salinidad\n\nEste índice valora la cantidad de sal que hay en los suelos. La salinización de los suelos supone uno de los procesos de degradación del territorio más frecuentes, sobre todo en regiones áridas y semiáridas, donde la precipitación es mayor que la evaporación. \n\nLos valores elevados indican mayor salinidad, mientras que los valores bajos apuntan a una salinidad menor. \n\nLea más al respecto [aquí,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf), [aquí](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) y [aquí.](https://www.indexdatabase.de/db/i-single.php?id=57)"]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":["Al entrar como **usuario registrado** podrá acceder a temas personalizados, guardar y cargar marcadores, crear \nsecuencias de marcadores, medir distancias, elaborar animaciones y utilizar la descarga avanzada de imágenes.\n\nCree una cuenta gratuita pulsando [aquí](https://services.sentinel-hub.com/oauth/subscription)\no, desde la aplicación, pulsando **Acceder** y, luego, \"Regístrese gratis\"."]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":["Esta herramienta permite crear una animación a partir de la capa visualizada y el emplazamiento mostrado.\n\nElija en primer lugar un intervalo temporal. Es posible refinar más los resultados si se filtran por meses\n(marque la casilla correspondiente) y/o seleccionando imágenes de un periodo definido (órbita, día, semana,\nmes, año).\n\nPulse luego Buscar y seleccione ahí las imágenes.\nPuede elegirlas todas con la casilla correspondiente, o filtrar las imágenes según la cobertura nubosa a través del mando deslizable. También es posible elegir las imágenes marcando todas de una en una en la lista. La casilla de **Bordes** activa o desactiva los bordes de la imagen. \n\nAcceda a la vista previa de la animación con el botón de reproducción situado debajo. También es posible\najustar la velocidad (fotogramas por segundo).\n\nCuando obtenga un resultado satisfactorio, pulse el botón de descarga y la animación se grabará en forma de\nfichero .gif
."]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":["Banda 2 - Máximo de absorción de la clorofila - 442 nm"]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":["Banda 11 - Banda de absorción rama R del O2 - 761 nm"]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (corregido de atmósfera)"]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":["Los servicios de Sentinel-1 están disponibles tanto en EOCloud como en AWS. Las prestaciones\nde cada servicio son distintas. Más información en"]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":["Este marcador carece de descripción por ahora."]},"Measure":{"msgid":"Measure","msgstr":["Medición"]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":["Crear una animación de esta área"]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":["Aunque el formato TIFF (de \"tagged image file format\") puede incluir un gran número de bandas, muchos de los programas más habituales de visualización (como, por ejemplo, Windows Photo Viewer) no representan imágenes TIFF con más de 3 bandas.\nSi activa esta opción, solo se incluirán en la imagen de salida las tres primeras bandas.\nCon esta opción desactivada se incluirán todas las bandas en la imagen, pero entonces habrá que utilizar un programa que admita más de tres bandas (como, por ejemplo, QGIS) para representar el fichero TIFF resultante."]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":["EPSG:3857 no está disponible cuando se especifica un área de interés (ADI)."]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":["Añadir banda de máscara de datos a las capas crudas"]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":["Añadir bandas adicionales"]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":["Error: las visualizaciones con efectos solo se pueden descargar en los formatos JPEG o PNG."]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":["Advertencia: las capas siguientes usan dataProducts, por lo que no es posible adoptar el tipo de datos deseado:"]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":["Advertencia: el script no sigue un formato V3 habitual, por lo que no es posible adoptar el tipo de datos deseado: "]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":["Esto significa que probablemente está activo el método por defecto (AUTO) para alterar la resolución espacial. Para arreglarlo tendrá que modificar el script. La documentación incluye información adicional sobre los métodos para alterar la resolución espacial"]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":["Su programa navegador no ofrece las prestaciones 3D necesarias para representar este contenido."]},"More information":{"msgid":"More information","msgstr":["Información adicional"]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":["¡No se logró la conexión con el servicio 3D! ¿Lo vuelvo a intentar? "]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":["¡La imagen es demasiado grande para este dispositivo!\nTamaño de la imagen: {0}x{1}, máx: {2}"]},"Home":{"msgid":"Home","msgstr":["Inicio"]},"Shading":{"msgid":"Shading","msgstr":["Sombreado"]},"Sphere mode":{"msgid":"Sphere mode","msgstr":["Modo esférico"]},"Eye height":{"msgid":"Eye height","msgstr":["Altura del punto de vista"]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":["Imposible cargar la imagen"]},"Geometries":{"msgid":"Geometries","msgstr":["Formas geométricas"]},"Now":{"msgid":"Now","msgstr":["Ahora"]},"Terrain":{"msgid":"Terrain","msgstr":["Terreno"]},"Time":{"msgid":"Time","msgstr":["Hora"]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":["Índice de reflectancia de la antocianina modificado"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Composición en color natural\n\nLos sensores satelitales observan la Tierra en múltiples regiones del espectro electromagnético. Cada región de espectro recibe el nombre de banda. El satélite Landsat 8 dispone de 11 bandas. La composición en color natural recurre a las bandas de la luz visible: rojo, verde y azul, las cuales introduce en los tres canales (rojo, verde y azul) correspondientes, de donde se obtiene un producto que muestra los colores verdaderos, es decir, una buena representación de la superficie terrestre tal y como la vería de manera natural un ser humano.\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) y [aquí.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":["# Fracción radiométrica efectiva de nubes\n\nLa fracción de nubes es la porción de la superficie cubierta de nubes dividida entre la superficie total. Las nubes ejercen efectos de apantallamiento, albedo y absorción sobre las medidas de gases traza. La fracción radiométrica efectiva de nubes es un parámetro importante para corregir estos efectos."]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Banda térmica 10\n\nEsta visualización térmica se basa en la banda 10 (una banda es una región del espectro electromagnético; los sensores satelitales observan la Tierra en múltiples bandas). La longitud de onda central de 10895 nm está situada en el infrarrojo térmico, o TIR (Thermal InfraRed). Mientras que las estaciones meteorológicas miden la temperatura del aire, la banda 10 registra la temperatura real del suelo, que suele estar bastante más caliente que el aire. La banda térmica 10 sirve para obtener temperaturas superficiales y se registra con una resolución espacial de 100 metros.\n\n\n\nMás información [aquí](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) y [aquí.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":["# Composición de infrarrojo de onda corta (SWIR)\n\nLas medidas tomadas en el infrarrojo de onda corta (SWIR, Short Wave InfraRed) sirven para estimar la cantidad de agua presente en la vegetación y en el suelo, porque el agua absorbe estas longitudes de onda. Las bandas del infrarrojo de onda corta (una banda es una región del espectro electromagnético; los sensores satelitales observan la Tierra en múltiples bandas) sirven también para distinguir los tipos de nubes (nubes de agua frente a nubes de hielo), la nieve y el hielo, todos los cuales aparecen blancos en luz visible. La vegetación adopta tonalidades de verde en esta composición, mientras que los suelos desnudos y las zonas urbanas tienen tonos pardos, y el agua se ve negra. El terreno recién quemado refleja con intensidad las bandas del infrarrojo de onda corta, lo que hace útil esta composición para cartografiar los daños debidos a incendios. Cada tipo de roca refleja de un modo distinto la luz infrarroja de onda corta, lo que permite elaborar mapas geológicos comparando la luz SWIR reflejada.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)"]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":["Efectos de color RGB avanzados"]},"Left button":{"msgid":"Left button","msgstr":["Botón izquierdo"]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":["Pulse con el botón izquierdo del ratón y arrastre para desplazarse por el mapa a una altitud fija. Use MAYÚS + botón izquierdo para rotar."]},"Right button":{"msgid":"Right button","msgstr":["Botón derecho"]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":["Pulse el botón derecho del ratón y arrastre hacia arriba o hacia abajo para cambiar la altura de la cámara. \nPulse el botón derecho del ratón y arrastre hacia la izquierda o la derecha para rotar la vista de la cámara. "]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":["Botón central o rueda"]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":["Use la rueda del ratón para cambiar la altura de la cámara (se obtiene el mismo efecto pulsando el botón derecho\ny arrastrando hacia arriba o hacia abajo). Pulse con el botón de la rueda y arrastre para cambiar el ángulo de la cámara."]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":["Navegación mediante teclado"]},"Arrow keys":{"msgid":"Arrow keys","msgstr":["Teclas de flechas"]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":["Use las teclas de flechas para moverse por el mapa a una altitud determinada."]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":["MAYÚS + teclas de flechas"]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":["Para cambiar la vista de la cámara pulse las flechas mientras mantiene pulsada la tecla MAYÚS."]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":["RePág/AvPág"]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":["Use las teclas RePág y AvPág para cambiar la altura de la cámara. "]},"Map navigation":{"msgid":"Map navigation","msgstr":["Navegación mediante mapa"]},"Pan console":{"msgid":"Pan console","msgstr":["Consola de panorama"]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":["La consola de panorama permite moverse sobre el mapa a una altitud determinada. Al pulsar y arrastrar\nse produce un desplazamiento continuo que será más veloz cuanto más se aleje del centro al arrastrar."]},"Camera console":{"msgid":"Camera console","msgstr":["Consola de la cámara"]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":["La consola de la cámara solo modifica lo que la cámara ve. Pulse y arrastre para cambiar la vista de la cámara.\nLa vista cambia con más velocidad cuanto más se aleje del centro al arrastrar."]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":["Botones de acercamiento y alejamiento"]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":["Al pulsar cambia la altura de la cámara. El botón \"+\" acerca la cámara al suelo, mientras que \nel botón \"-\" la aleja."]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":[""]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":[""]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":[""]},"Histogram":{"msgid":"Histogram","msgstr":[""]},"Disabled":{"msgid":"Disabled","msgstr":[""]},"Yes":{"msgid":"Yes","msgstr":[""]},"Orthorectification":{"msgid":"Orthorectification","msgstr":[""]},"Cancel":{"msgid":"Cancel","msgstr":["Cancelar"]},"Error":{"msgid":"Error","msgstr":["Error"]},"Help":{"msgid":"Help","msgstr":["Ayuda"]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":[""]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":[""]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":[""]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":[""]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":[""]},"User Instances":{"msgid":"User Instances","msgstr":[""]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":[""]},"Recalculate":{"msgid":"Recalculate","msgstr":[""]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":[""]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":[""]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":[""]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":[""]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":[""]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":[""]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":[""]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":[""]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":[""]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":[""]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":[""]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":[""]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":[""]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":[""]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":[""]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":[""]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":[""]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":[""]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":[""]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":[""]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":[""]},"File upload":{"msgid":"File upload","msgstr":["Carga de archivo"]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Cargue un fichero KML/KMZ, GPX o GEOJSON/JSON para definir el área de interés. Esa área se utilizará para recortar al exportar la imagen."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":["Suelte un fichero KML/KMZ, GPX o GEOJSON/JSON, o busque uno en su computadora"]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":[""]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":[""]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":[""]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":[""]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":[""]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":[""]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":[""]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":[""]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":[""]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":[""]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":[""]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":[""]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":[""]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":[""]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":[""]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":[""]},"Zoom to location":{"msgid":"Zoom to location","msgstr":[""]},"Remove layer":{"msgid":"Remove layer","msgstr":[""]},"Please select a layer":{"msgid":"Please select a layer","msgstr":[""]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":[""]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":[""]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":[""]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":[""]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":[""]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":[""]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":[""]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":[""]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":[""]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":[""]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":[""]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":[""]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":[""]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":[""]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":[""]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":[""]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":[""]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":[""]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":[""]},"Level 1":{"msgid":"Level 1","msgstr":[""]},"Level 2":{"msgid":"Level 2","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":[""]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":[""]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":[""]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Máx. cobertura nubosa:"]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Cargar datos"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]}}}}
\ No newline at end of file
+{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","plural-forms":"nplurals=2; plural=(n != 1);","language":"es","mime-version":"1.0","content-transfer-encoding":"8bit","project-id-version":"","pot-creation-date":"","po-revision-date":"","last-translator":"","language-team":"","x-generator":"Poedit 2.4.2"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nPlural-Forms: nplurals=2; plural=(n != 1);\nLanguage: es\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\nProject-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLast-Translator: \nLanguage-Team: \nx-generator: Poedit 2.4.2\n"]},"Education":{"msgid":"Education","msgstr":["Educación"]},"Normal":{"msgid":"Normal","msgstr":["Normal"]},"Close":{"msgid":"Close","msgstr":["Cerrar"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Cerrar y no mostrar más"]},"Previous":{"msgid":"Previous","msgstr":["Anterior"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Terminar el tutorial"]},"Next":{"msgid":"Next","msgstr":["Siguiente"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Acceder al tutorial"]},"Don't show again":{"msgid":"Don't show again","msgstr":["No mostrar más"]},"Show info":{"msgid":"Show info","msgstr":["Mostrar información"]},"Discover":{"msgid":"Discover","msgstr":["Descubrir"]},"Visualize":{"msgid":"Visualize","msgstr":["Visualizar"]},"Compare":{"msgid":"Compare","msgstr":["Comparar"]},"Pins":{"msgid":"Pins","msgstr":["Marcadores"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Error al extraer las imágenes:"]},"No tile found":{"msgid":"No tile found","msgstr":["Tesela no encontrada"]},"Dataset":{"msgid":"Dataset","msgstr":["Conjunto de datos"]},"Show":{"msgid":"Show","msgstr":["Mostrar"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Mostrar efectos y opciones avanzadas"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Mostrar visualización"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Añadir a marcadores"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Añadir a comparar"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Ajustar a la tesela"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Ocultar capa"]},"Show layer":{"msgid":"Show layer","msgstr":["Mostrar capa"]},"Share":{"msgid":"Share","msgstr":["Compartir"]},"Custom":{"msgid":"Custom","msgstr":["Personalizar"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Crear visualización personalizada"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Ampliar para ver los datos"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Regístrese gratis"]},"for all features":{"msgid":"for all features","msgstr":["para acceder a todas las prestaciones"]},"Powered by":{"msgid":"Powered by","msgstr":["Desarrollado por"]},"with contributions by":{"msgid":"with contributions by","msgstr":["con aportaciones de"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["¡Seleccione la(s) fuente(s) de datos!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["¡Intervalo temporal no válido!"]},"No results found":{"msgid":"No results found","msgstr":["Sin resultados"]},"Theme":{"msgid":"Theme","msgstr":["Tema"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Definir la configuración"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Acceda para aplicar su configuración personalizada."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["¡Error al extraer datos adicionales!"]},"Search":{"msgid":"Search","msgstr":["Buscar"]},"Highlights":{"msgid":"Highlights","msgstr":["Destacados"]},"Data sources":{"msgid":"Data sources","msgstr":["Fuentes de datos"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Por favor, elija un tema"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Intervalo temporal [UTC]"]},"Date":{"msgid":"Date","msgstr":["Fecha"]},"Hide description":{"msgid":"Hide description","msgstr":["Ocultar descripción"]},"Show description":{"msgid":"Show description","msgstr":["Mostrar descripción"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Este tema carece de destacados"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Basado en: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["1 día (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["5 días (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["10 días (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["O3 (ozono)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (dióxido de nitrógeno)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (dióxido de azufre)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (monóxido de carbono)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (formaldehído)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (metano)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER AI (índice de aerosoles)"]},"Cloud":{"msgid":"Cloud","msgstr":["Nubosidad"]},"Other":{"msgid":"Other","msgstr":["Otros"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Máx. cobertura nubosa"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Búsqueda avanzada"]},"Data location":{"msgid":"Data location","msgstr":["Ubicación de los datos"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Por favor, ¡seleccione al menos una ubicación!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Modo de adquisición"]},"Polarization":{"msgid":"Polarization","msgstr":["Polarización"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Por favor, ¡seleccione al menos un modo de adquisición!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Por favor, ¡seleccione al menos un modo de polarización!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Dirección orbital"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Por favor, ¡seleccione al menos una dirección orbital!"]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["**MERIS** (espectrómetro de resolución intermedia) era un sensor a bordo del satélite [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) con la misión principal de observar el color del territorio y los océanos, así como la atmósfera. Ya no está activo y lo sustituye Sentinel-3.\n\n**Resolución espacial:** Resolución máxima en terreno y costas: 260 m x 290 m (solo pueden verse detalles mayores que 260 m x 290 m).\n\n**Tiempo de revisita:** pasa un máximo de 3 días entre dos visitas a la misma zona.\n\n**Disponibilidad de datos:** de junio de 2002 hasta abril de 2012.\n\n**Uso habitual:** Monitorización del océano (fitopancton, materia en suspensión), de la atmósfera (vapor de agua, CO2, nubosidad, aerosoles) y del territorio (índice de vegetación, cobertura global, humedad)."]},"Credits:":{"msgid":"Credits:","msgstr":["Créditos:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["**GIBS** (Global Imagery Browse Services, Servicios de Búsqueda Global de Imágenes) proporciona\nun acceso rápido a más de 600 productos de imágenes satelitales que cubren todo el mundo. La mayoría\nde las imágenes está disponible pocas horas tras el paso del satélite y hay productos que abarcan casi\n30 años."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["Los satélites **Landsat** de la NASA y el Servicio Geológico de EE. UU. son similares a Sentinel-2 (captan longitudes de onda tanto visibles como infrarrojas)\ny en algunos casos detectan el infrarrojo térmico (Landsat 8). La serie Landsat tiene una larga historia de captación de imágenes que abarca casi cinco décadas.\nEsta plataforma da acceso a imágenes de los Landsat 5, 7 y 8.\n\n**Resolución espacial:** 15 m, 30 m y 100 m remuestreado a 30 m, según la longitud de onda (es decir, solo pueden verse detalles mayores que 10 m o 30 m). Más información [aquí](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Tiempo de revisita:** pasa un máximo de 8 días entre dos visitas a la misma zona mediante alguno de los dos satélites operativos, Landsat 7 u 8.\n\n**Disponibilidad de datos:** \n Europa y norte de África 1984-2011 (Landsat 5), 1999-2003 (Landsat 7), 2013 hasta hoy (Landsat 8), en el archivo de ESA. El archivo global del Servicio Geológico de EE. UU. (USGS) abarca desde abril de 2013 hasta hoy (solo para Landsat 8).\n\n**Uso habitual:** Monitorización de la vegetación, mapas de cobertura y uso del suelo, monitorización de cambios, etc."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["**MODIS** (Espectrorradiómetro de Imágenes de Resolución Moderada), de la NASA, toma datos con el fin de\nmejorar el conocimiento de los procesos globales que afectan al territorio. EO Browser proporciona datos de\nobservación del territorio (bandas 1-7).\n\n**Resolución espacial:** 250 m (bandas 1-2), 500 m (bandas 3-7), 1000 m (bandas 8-36).\n\n**Tiempo de revisita:** Cobertura global en 1-2 días con ambos satélites, Aqua y Terra.\n\n**Disponibilidad de datos:** desde enero de 2013.\n\n**Uso habitual:** monitorización del territorio, nubosidad y color del océano a escala global."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["El pequeño satélite **Proba-V** está diseñado para cartografiar la cobertura del suelo y el crecimiento\nde la vegetación en todo el globo cada dos días. EO Browser proporciona sus productos derivados\nminimizando la nubosidad al combinar las medidas libres de nubes tomadas dentro de un periodo de\n1 día (S1), 5 días (S5) o 10 días (S10).\n\n**Resolución espacial:** 100 m para S1 y S5, 333 m para S1 y S10, 1000 m para S1 y S10.\n\n**Tiempo de revisita:** 1 día para latitudes 35-75°N y 35-56°S, 2 días para latitudes entre 35°N\ny 35°S.\n\n**Disponibilidad de datos:** desde octubre de 2013.\n\n**Uso habitual:** observación de la cobertura del suelo, crecimiento de la vegetación, diagnóstico del impacto\nclimático, gestión de recursos hídricos, monitorización agrícola y estimaciones de seguridad alimentaria, monitorización\nde los recursos hídricos continentales, seguimiento del avance de los desiertos y la deforestación."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":["**Sentinel-1** proporciona imágenes de radar obtenidas en cualesquiera condiciones meteorológicas, de día\no de noche, de tierra y mar. EO Browser da acceso a los datos tomados tanto en el modo IW (interferometric wide swath,\nbarrido interferométrico ancho) como EW (extra-wide swath, barrido extra-ancho), procesados al Nivel 1 GRD (ground\nrange detected, detección de la distancia al suelo).\n\n**Pixel spacing:** 10 m (IW), 40 m (EW).\n\n**Tiempo de revisita:** <= 5 días si se usan ambos satélites.\n\n**Tiempo de revisita:** (combinando pasos ascendentes y descendentes y superposiciones): <= 3 días, véase la\n[estrategia de observación](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n \n**Disponibilidad de datos:** desde octubre de 2014.\n\n**Uso habitual:** monitorización del territorio y de los mares, respuesta a emergencias, cambio climático."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["**Sentinel-2** obtiene imágenes de alta resolución en longitudes de onda visibles e infrarrojas con el fin de monitorizar la vegetación, el suelo y la cobertura del territorio, los cursos de agua continentales y las zonas costeras.\n\n**Resolución espacial:** 10 m, 20 m y 60 m, según la longitud de onda (es decir, solo llegan a distinguirse los detalles mayores que 10 m, 20 m o 60 m). Más información [aquí](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Tiempo de revisita:** hay un máximo de 5 días entre visitas consecutivas a la misma zona por medio de los dos satélites.\n\n**Disponibilidad de datos:** desde junio de 2015. Cobertura global completa desde marzo de 2017.\n\n**Uso habitual:** mapas de cobertura del suelo, mapas de detección de cambios en el territorio, monitorización de la vegetación, monitorización de áreas quemadas."]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Los datos de Nivel 2A son aquellos de gran calidad en los que se corrigen los efectos de la atmósfera sobre la luz reflejada por la superficie de la Tierra. Estos datos están disponibles para todo el globo desde marzo de 2017.\n\nMás información sobre la corrección de efectos atmosféricos [aquí](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction)."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Los datos de Nivel 1C poseen calidad suficiente para la mayoría de investigaciones y tienen aplicadas todas las correcciones excepto las de efectos debidos a la atmósfera. Estos datos están disponibles para todo el globo desde junio de 2015."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["El objetivo principal de la misión **Sentinel-3** consiste en medir la topografía de la superficie, la temperatura de los mares y del terreno, así como el color de los océanos y del territorio. Sentinel-3 porta cuatro instrumentos distintos. Esta plataforma ofrece los datos del Instrumento para el Color del Océano y del Suelo (OLCI, Ocean and Land Colour Instrument) y del Instrumento para la Temperatura del Mar y de la Superficie del Terreno (SLSTR, Sean and Land Surface Temperature Instrument).\n\n**Disponibilidad de datos:** desde mayo de 2016."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["El **Instrumento para la Temperatura del Mar y de la Superficie del Terreno (SLSTR, Sean and Land Surface Temperature Instrument)** a bordo de \nSentinel-3 mide la temperatura del suelo y de los mares a escala tanto regional como global. SLSTR abarca longitudes de onda visibles, infrarrojo cercano \ne infrarrojo térmico. \n\n**Resolución espacial:** 500 m para las longitudes de onda visibles y del infrarrojo cercano, 1 km para el infrarrojo térmico (es decir, solo se distinguen\ndetalles mayores que 500 m o 1 km, respectivamente).\n\n**Tiempo de revisita:** 1 día como máximo entre visitas consecutivas a la misma área, usando los dos satélites.\n\n**Disponibilidad de datos:** desde mayo de 2016.\n\n**Uso habitual:** monitorización del cambio climático, monitorización de la vegetación, detección activa de incendios, monitorización de la temperatura superficial de los océanos y del terreno."]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["El **Instrumento para el Color del Océano y del Suelo (OLCI, Ocean and Land Colour Instrument)** a bordo de Sentinel-3 es un espectrómetro que \nmide la radiación solar reflejada por la Tierra y monitoriza los océanos, el medio ambiente y el clima. Proporciona imágenes con una frecuencia mayor\nque el Sentinel-2, pero con menos resolución espacial, si bien cubre más longitudes de onda. El instrumento OLCI del Sentinel-3 prolonga las medidas\nque antes tomaba el instrumento MERIS de Envisat, cuya misión ya finalizó.\n\n**Resolución espacial:** 300 m (es decir, solo se distinguen detalles mayores que 300 m).\n\n**Tiempo de revisita:** pasan como máximo 2 días entre visitas consecutivas a la misma zona, usando ambos satélites.\n\n**Disponibilidad de datos:** desde mayo de 2016.\n\n**Uso habitual:** topografía de la superficie, observación y monitorización del color de los mares y de la superficie del terreno."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["**Sentinel-5P** es un satélite que obtiene medidas atmosféricas orientadas a evaluar la calidad del aire, monitorizar el ozono, evaluar\nla radiación UV y monitorizar y pronosticar el estado del tiempo.\n\n**Resolución espacial:** 7 x 3.5 km (es decir, solo se distinguen detalles mayores que 7 x 3.5 km).\n\n**Tiempo de revisita:** como máximo pasa 1 día entre visitas consecutivas a la misma zona.\n\n**Disponibilidad de datos:** desde abril de 2018.\n\n**Uso habitual:** monitorización de la concentración de monóxido de carbono (CO), dióxido de nitrógeno (NO2) y ozono (O3). Monitorización\nde índice de aerosoles UV (AER_AI) y de otros parámetros geofísicos diversos de las nubes (Cloud)."]},"Copied":{"msgid":"Copied","msgstr":["Copiado"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["Copiar al portapapeles"]},"Data source name":{"msgid":"Data source name","msgstr":["Nombre de la fuente de datos"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Hora de la toma"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Cobertura nubosa"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Altura del Sol"]},"MGRS location":{"msgid":"MGRS location","msgstr":["Posición MGRS"]},"AWS path":{"msgid":"AWS path","msgstr":["Banda en AWS"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["Banda en EO Cloud"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["Banda en CreoDIAS"]},"SciHub link":{"msgid":"SciHub link","msgstr":["Enlace a SciHub"]},"Back to search":{"msgid":"Back to search","msgstr":["Volver a buscar"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Mostrando ${this.state.results.length} resultado","Mostrando ${this.state.results.length} resultados"]},"Load more":{"msgid":"Load more","msgstr":["Cargar más"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Cargar más resultados..."]},"Results":{"msgid":"Results","msgstr":["Resultados"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Mostrando ${ this.state.selectedTiles.length } resultado.","Mostrando ${ this.state.selectedTiles.length } resultados."]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Modificar la descripción del marcador"]},"Reject changes":{"msgid":"Reject changes","msgstr":["Descartar cambios"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Confirmar cambios"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Renombrar marcador"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Eliminar marcador"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Ampliación sobre el marcador"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Lat/Lon"]},"Zoom":{"msgid":"Zoom","msgstr":["Ampliación"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["Vd. va a añadir ${ N_PINS } marcador(es) a su colección. ¿Quiere continuar?"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["ADVERTENCIA: Vd. va a eliminar un marcador. ¿Quiere continuar?"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":["ADVERTENCIA: Vd. va a eliminar todos los marcadores. ¿Quiere continuar?"]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["No hay marcadores. Vaya a la pestaña Visualizar para guardar alguno, o cargue un fichero JSON con marcadores guardados."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Tenga en cuenta que los marcadores se guardarán solo si Vd. ha accedido como usuario registrado. De otro modo los marcadores se perderán al cerrar el programa."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Desmarcar todo"]},"Select all":{"msgid":"Select all","msgstr":["Marcar todo"]},"No pins.":{"msgid":"No pins.","msgstr":["No hay marcadores."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Crear enlace (${ selectedPins.length } marcador seleccionado)","Crear enlace (${ selectedPins.length } marcadores seleccionados)"]},"File type not supported":{"msgid":"File type not supported","msgstr":["Tipo de archivo no admitido"]},"not supported":{"msgid":"not supported","msgstr":["no admitido"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["No se encontraron marcadores."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Error al procesar el archivo:"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":["Cargar un fichero JSON con marcadores guardados."]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["Suelte un fichero JSON o busque en su computadora"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Conservar los marcadores existentes"]},"Share pins":{"msgid":"Share pins","msgstr":["Compartir marcadores"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Crear una secuencia a partir de marcadores"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Exportar marcadores a la computadora"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Importar marcadores de un fichero guardado"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Eliminar todos los marcadores"]},"Story":{"msgid":"Story","msgstr":["Secuencia"]},"Export":{"msgid":"Export","msgstr":["Exportar"]},"Import":{"msgid":"Import","msgstr":["Importar"]},"Clear":{"msgid":"Clear","msgstr":["Vaciar"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Compartir enlaces de marcadores"]},"Creating link...":{"msgid":"Creating link...","msgstr":["Creando enlace..."]},"OK":{"msgid":"OK","msgstr":["De acuerdo"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Actualizando la colección de marcadores."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Sucedió un problema al actualizar de manera permanente la colección de marcadores: ${ updatingPinsError }."]},"Hello,":{"msgid":"Hello,","msgstr":["Hola,"]},"Opacity":{"msgid":"Opacity","msgstr":["Opacidad"]},"Split position":{"msgid":"Split position","msgstr":["Posición de la cortina"]},"split":{"msgid":"split","msgstr":["cortina"]},"opacity":{"msgid":"opacity","msgstr":["opacidad"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["No hay capas que comparar."]},"Remove all":{"msgid":"Remove all","msgstr":["Eliminar todo"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Añadir todos los marcadores"]},"Split":{"msgid":"Split","msgstr":["Cortina"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Sucedió un problema durante la descarga de sus parámetros"]},"Download":{"msgid":"Download","msgstr":["Descargar"]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Visualizar el terreno en 3D"]},"Go to Place":{"msgid":"Go to Place","msgstr":["Ir al lugar"]},"Labels":{"msgid":"Labels","msgstr":["Etiquetas"]},"Borders":{"msgid":"Borders","msgstr":["Fronteras"]},"Roads":{"msgid":"Roads","msgstr":["Carreteras"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Acercar"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Alejar"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["Acerca de EO Browser"]},"Contact us":{"msgid":"Contact us","msgstr":["Contacto"]},"Get data":{"msgid":"Get data","msgstr":["Obtener datos"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Función accesible solo a usuarios registrados."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Por favor, seleccione una capa."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["No es posible descargar imágenes en modo de comparación."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["No se admite esta fuente de datos."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":["Información estadística / Ficha de información"]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Información estadística / Ficha de información - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["por favor, seleccione una capa"]},"not available for ":{"msgid":"not available for ","msgstr":["no disponible para "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["no disponible para \"${ props.presetLayerName }\" (la capa carece de ese valor)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Primero busque datos."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Crear una animación"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Marcar un punto de interés"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Centrar el mapa en este detalle"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Eliminar la forma"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Área de interés"]},"Select mode":{"msgid":"Select mode","msgstr":["Elija el modo"]},"Mode:":{"msgid":"Mode:","msgstr":["Modo:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Eliminar medida"]},"km":{"msgid":"km","msgstr":["km"]},"m":{"msgid":"m","msgstr":["m"]},"Gain":{"msgid":"Gain","msgstr":["Ganancia"]},"Gamma":{"msgid":"Gamma","msgstr":["Gamma"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Calidad mínima"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Incremento de resolución"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Reducción de resolución"]},"Reset all":{"msgid":"Reset all","msgstr":["Reiniciar todo"]},"filter by months":{"msgid":"filter by months","msgstr":["filtro por meses"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Copiar la forma geométrica al portapapeles"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Cancelar la modificación."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Trazar el área de interés"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Cobertura nubosa mínima"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Usar conjuntos de datos adicionales (avanzado)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Orden del mosaico"]},"Most recent":{"msgid":"Most recent","msgstr":["Más reciente"]},"Least recent":{"msgid":"Least recent","msgstr":["Menos reciente"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Personalizar el intervalo temporal"]},"Back":{"msgid":"Back","msgstr":["Atrás"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Error al cargar el script. Compruebe la URL."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Desmarcar la carga del script desde la URL para modificar el código"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Cargar script desde URL"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["Introduzca la URL de su script"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Script cargado."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Solo se permiten dominios HTTPS."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Cargar el script en el editor de código"]},"Refresh":{"msgid":"Refresh","msgstr":["Refrescar"]},"orbit":{"msgid":"orbit","msgstr":["órbita"]},"day":{"msgid":"day","msgstr":["día"]},"week":{"msgid":"week","msgstr":["semana"]},"month":{"msgid":"month","msgstr":["mes"]},"year":{"msgid":"year","msgstr":["año"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Seleccione una imagen por:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Animación"]},"Select All":{"msgid":"Select All","msgstr":["Seleccionar todo"]},"Speed:":{"msgid":"Speed:","msgstr":["Velocidad:"]},"frames / s":{"msgid":"frames / s","msgstr":["fotogramas / s"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Preparando..."]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Archivos no descargados:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Imposible descargar desde el lienzo"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["No se comprimieron en ZIP los ficheros:"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Sucedió un problema en la descarga de la imagen"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Error al trasladar la imagen: ¡la URL está vacía!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Error al trasladar la imagen:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["No se pudo cargar la imagen de la zona"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Trasladar las bandas a los campos RGB."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Trasladar las bandas a la ecuación de índice"]},"Index ":{"msgid":"Index ","msgstr":["Índice "]},"Threshold":{"msgid":"Threshold","msgstr":["Umbral"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Retirar el selector de color"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Añadir selector de color"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Pulse para colocar marcador"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Pulse para colocar el primer vértice"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Pulse para seguir trazando"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Pulse en el primer marcador para terminar"]},"Show captions":{"msgid":"Show captions","msgstr":["Mostrar pies de figura"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Mostrar el título de la diapositiva"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Añadir capas superpuestas"]},"Show legend":{"msgid":"Show legend","msgstr":["Mostrar leyenda"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["El campo de visión actual no contiene marcadores."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Se ignoran algunos marcadores (${ N_PINS_OUTSIDE_BOUNDS }) porque caen fuera del área seleccionada."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["Para crear una secuencia de marcadores, vaya al lugar deseado del mapa.\n\nLa secuencia de marcadores incluirá todos los marcadores que entren en el campo de visión, y se ignorarán los demás."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["El archivo portará un logotipo."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["A las bandas crudas que se descarguen se añadirá, como segunda banda, una máscara de datos (dataMask-band)."]},"Show logo":{"msgid":"Show logo","msgstr":["Mostrar el logotipo"]},"Image format":{"msgid":"Image format","msgstr":["Formato de imagen"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Resolución"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Sistema de coordenadas"]},"Layers":{"msgid":"Layers","msgstr":["Capas"]},"Visualized":{"msgid":"Visualized","msgstr":["Visibles"]},"Raw":{"msgid":"Raw","msgstr":["Crudas"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["La imagen incluirá las capas superpuestas (etiquetas de lugares, calles y fronteras administrativas)."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Las imágenes exportadas incluirán la fuente de los datos así como la fecha, el nivel de ampliación y marca de origen"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Añada una descripción breve de la imagen exportada"]},"Description":{"msgid":"Description","msgstr":["Descripción"]},"Image format:":{"msgid":"Image format:","msgstr":["Formato de imagen:"]},"Basic":{"msgid":"Basic","msgstr":["Básico"]},"Analytical":{"msgid":"Analytical","msgstr":["Analítico"]},"High-res print":{"msgid":"High-res print","msgstr":["Impresión en alta resolución"]},"Download image":{"msgid":"Download image","msgstr":["Descargar imagen"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["Ha ocurrido un error en el traslado de alguna imagen:"]},"min/px":{"msgid":"min/px","msgstr":["min/px"]},"sec/px":{"msgid":"sec/px","msgstr":["seg/px"]},"Resolution":{"msgid":"Resolution","msgstr":["Resolución"]},"lat.":{"msgid":"lat.","msgstr":["lat."]},"deg/px":{"msgid":"deg/px","msgstr":["grados/px"]},"long.":{"msgid":"long.","msgstr":["long."]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Resolución proyectada: ${ formattedResolution } m/px"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Error: la fusión de datos no admite los formatos KMZ/JPG ni KMZ/PNG."]},"Image download":{"msgid":"Image download","msgstr":["Descarga de imagen"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Anchura de la imagen [pulgadas]:"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Altura de la imagen [pulgadas]:"]},"DPI:":{"msgid":"DPI:","msgstr":["Puntos por pulgada:"]},"5 years":{"msgid":"5 years","msgstr":["5 años"]},"2 years":{"msgid":"2 years","msgstr":["2 años"]},"1 year":{"msgid":"1 year","msgstr":["1 año"]},"6 months":{"msgid":"6 months","msgstr":["6 meses"]},"3 months":{"msgid":"3 months","msgstr":["3 meses"]},"1 month":{"msgid":"1 month","msgstr":["1 mes"]},"Retry":{"msgid":"Retry","msgstr":["Reintentar"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Cargando, espere"]},"mean":{"msgid":"mean","msgstr":["media"]},"median":{"msgid":"median","msgstr":["mediana"]},"st. dev.":{"msgid":"st. dev.","msgstr":["desv. est."]},"min / max":{"msgid":"min / max","msgstr":["mín / máx"]},"Export CSV":{"msgid":"Export CSV","msgstr":["Exportar como CSV"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Intervalo:"]},"Date:":{"msgid":"Date:","msgstr":["Fecha:"]},"Single date":{"msgid":"Single date","msgstr":["Fecha única"]},"Timespan":{"msgid":"Timespan","msgstr":["Intervalo"]},"hh":{"msgid":"hh","msgstr":["hh"]},"mm":{"msgid":"mm","msgstr":["mm"]},"From:":{"msgid":"From:","msgstr":["Desde:"]},"Until:":{"msgid":"Until:","msgstr":["Hasta:"]},"Apply":{"msgid":"Apply","msgstr":["Aplicar"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Compartir en Facebook"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Compartir en Twitter"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Comprobar esto "]},"Logout":{"msgid":"Logout","msgstr":["Salir"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["Acceda como usuario registrado para disponer de prestaciones avanzadas como animaciones, descargas analíticas, configuración personalizada, y más."]},"Login":{"msgid":"Login","msgstr":["Acceder"]},"Default":{"msgid":"Default","msgstr":["Por defecto"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Monitorización de la Tierra desde el espacio"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Agricultura"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Atmósfera y contaminación del aire"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Detección de cambios"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Inundaciones y sequías"]},"Geology":{"msgid":"Geology","msgstr":["Geología"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Océanos y masas de agua"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Nieve y glaciares"]},"Urban":{"msgid":"Urban","msgstr":["Urbano"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Vegetación y bosques"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Volcanes"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Incendios forestales"]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":["# ¡Le damos la bienvenida a EO Browser!\n\nUn archivo completo de Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, así como\nel archivo de ESA de Landsat 5, 7 y 8, cobertura global de Landsat 8, MERIS en Envisat, \nMODIS, Proba-V y los productos de GIBS, todo en un solo sitio.\n\n[Página de presentación de EO Browser](https://www.sentinel-hub.com/explore/eobrowser/) \n[Guía de uso de EO Browser](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":["#### Resumen de las prestaciones de EO Browser\n\nEO Browser reúne un archivo completo de Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, así como el archivo de ESA de Landsat 5, 7 y 8, cobertura global de Landsat 8, MERIS en Envisat, MODIS, Proba-V y los productos de GIBS, todo en un solo sitio. Permite revisar y comparar imágenes de alta resolución procedentes de todas estas fuentes. Solo hay que ir al área de interés, seleccionar las fuentes de datos, así como el intervalo temporal y la cobertura nubosa, y consultar el resultado. \n\nAcceda al tutorial pulsando el botón \"Siguiente\", o ciérrelo. Siempre podrá retomar el tutorial a través del icono de información que hay en la esquina superior derecha, en caso de que lo cierre por error o porque quiera probar otras opciones."]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":["La pestaña **Descubrir** le permite:\n\n- Elegir un **Tema**.\n- **Buscar** datos.\n- Ver los **Destacados** del tema.\n\nEl menú **Tema** ofrece varios temas predefinidos, así como sus propios apartados personalizados si ha accedido como usuario registrado. Para crear un apartado pulse en\nel icono de ajustes y acceda con su usuario y contraseña de EO Browser.\n\nEn **Buscar** puede ajustar los criterios de búsqueda:\n- Elegir los satélites de los que desea recibir datos, marcando las casillas de selección.\n- Seleccionar las opciones adicionales permitidas como, por ejemplo, la cobertura nubosa, que se fija mediante una barra deslizable.\n- Seleccionar el intervalo temporal, bien tecleando las fechas, o bien eligiéndolas en el calendario.\n\nAcceda a explicaciones acerca de cada satélite pulsando el icono con la interrogación\n que hay junto al nombre de cada fuente de datos.\n\nAl pulsar Buscar se obtiene una lista de resultados, cada uno de los cuales \nse muestra con una vista previa acompañada de algunos datos relevantes sobre su procedencia. Con algunas fuentes de datos aparece también el icono junto a los resultados.\nAl pulsarlo se obtienen enlaces a las imágenes crudas en EO Cloud o en SciHub. El botón Visualizar abre la pestaña **Visualizar** para el resultado en cuestión.\n\n**Destacados** da acceso a ubicaciones interesantes, preseleccionadas por su relación con el tema elegido."]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":["La pestaña Visualizar permite elegir entre varias combinaciones de bandas espectrales predefinidas o personalizadas para visualizar los datos del resultado seleccionado.\n\nAlgunas opciones frecuentes:\n- **Color natural** - Interpretación visual de la cobertura del suelo.\n- **Color falso** - Interpretación visual de la vegetación.\n- **NDVI** - Índice normalizado de vegetación.\n- **NDMI** - Índice normalizado de humedad.\n- **SWIR** - Índice infrarrojo de onda corta.\n- **NDWI** - Índice normalizado de agua.\n- **NDSI** - Índice normalizado de nieve.\n\nLa mayoría de visualizaciones incluye una descripción y una leyenda que se pueden ver pulsando en el icono de desplegar .\n \nLa mayoría de las fuentes de datos dispone de la opción **Script personalizado**. Pulse para elegir combinaciones personalizadas de bandas espectrales o de índices, o para elaborar su propio script de clasificación para visualizar los datos. También puede acceder a scripts personalizados guardados en otros lugares, como en Google Drive, en GitHub o en nuestro [repositorio de scripts](https://custom-scripts.sentinel-hub.com/). \nCopie y pegue la dirección (URL) del script en una caja de texto en el panel de edición avanzada de scripts y pulse Refrescar.\n \nPuede modificar directamente los datos que haya en la pestaña Visualizar sin tener que volver a la pestaña **Descubrir**. Teclee una nueva fecha o selecciónela en el calendario .\n\nSobre las visualizaciones aparece la barra de herramientas adicionales. Observe que las herramientas disponibles cambian según la fuente de los datos.\n- **Añadir a marcadores** permite guardarla en la aplicación para su uso posterior: pulse en el icono de la chincheta .\n- Elija **opciones avanzadas**, como los métodos para alterar la resolución espacial o distintos **efectos**, como cambios de contraste (ganancia) o luminancia (gamma), mediante el icono con forma de barras deslizantes .\n- Añada una capa a la pestaña **Comparar** para cotejarla con otras más tarde, mediante el icono de comparar .\n- **Amplíe** al centro de la tesela pulsando en la cruceta .\n- Active o desactive la **visibilidad de la capa** mediante el icono de visibilidad .\n- Puede **compartir** la visualización en las redes sociales a través del icono correspondiente: ."]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":["En la pestaña **Comparar** encontrará todas las visualizaciones que haya añadido mediante . \n\nDispone de dos modos:\n - **Opacidad** (Mueva el deslizador a derecha o izquierda para inducir la transición entre las imágenes comparadas)\n - **Cortina** (Mueva el deslizador a derecha o izquierda para ubicar la frontera entre las imágenes comparadas)\n\nUsted puede añadir todos los marcadores al panel de comparación mediante **Añadir todos los marcadores** o eliminar todas las visualizaciones de la pestaña **Comparar** mediante el botón **Eliminar todo**."]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":["La pestaña **Marcadores** contiene tus visualizaciones favoritas. Cada marcador incluye información\nsobre la ubicación, la fuente de los datos y la capa concreta, nivel de ampliación y marca temporal.\n \n\nEs posible actuar sobre cada marcador de distintas maneras:\n\n- Alterar el **orden** - mediante el icono de mover\n\n \n \n \nque hay en la esquina superior izquierda del marcador: pulsa en él y arrastra arriba o abajo en la lista.\n- **Renombrar** - con el icono del lápiz que aparece junto al nombre.\n- Añadir a la pestaña **Comparar** - mediante el icono correspondiente \n- Aportar una **descripción** - mediante el icono de desplegar .\n- **Eliminar** - mediante el icono de la papelera .\n- **Ampliar** sobre el marcador - pulsando sobre los valores de longitud y latitud.\n\nEn la línea situada sobre los marcadores se ofrecen varias acciones que se pueden aplicar globalmente a todos:\n- Crear tu propia secuencia a partir de los marcadores mediante **Secuencia**.\n- Compartir tus marcadores mediante un enlace, pulsando en **Compartir**.\n- Exportar los marcadores a un fichero JSON mediante **Exportar**.\n- Importar marcadores de un fichero JSON mediante **Importar**.\n- Eliminar todos los marcadores mediante **Vaciar**."]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":["Busque una ubicación bien desplazando el mapa con el ratón o bien introduciendo el nombre de la misma en \nel campo de búsqueda."]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":["Seleccione la capa base y las superpuestas (carreteras, fronteras, leyendas) que aparecerán en el mapa."]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":["Alterne entre los modos **normal** y **educación**. El modo **educación** ofrece una versión simplificada de la aplicación.\nTambién está accesible en su [dirección específica en internet](https://apps.sentinel-hub.com/eo-browser-education/)."]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":["Acceda al tutorial en cualquier momento mediante el icono de información\n\n\n."]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":["Esta herramienta permite trazar un polígono sobre el mapa y mostrar su tamaño.\n\nTodas las capas que contienen valores individuales (como NDVI, NDMI, NDWI,...) permiten obtener el\nvalor del índice para el área seleccionada a lo largo del tiempo. Pulse en el icono de ficha \npara obtener la gráfica. Puede borrar el polígono con el icono .\n\nTambién es posible cargar un fichero KML/KMZ, GPX o GEOJSON/JSON con los datos del polígono.\n\nEl icono de las dos hojas permite copiar las coordenadas del polígono en formato GEOJSON; la cruceta \ncentra el mapa en el polígono trazado.\n\nLas imágenes exportadas se recortan al área de interés en las descargas analíticas."]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":["Esta herramienta sirve para marcar un punto en el mapa.\n\nPuede acceder también a datos estadísticos acerca de algunas capas si pulsa en el icono de ficha\n. \nElimine el marcador con el icono correspondiente .\n"]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":["Esta herramienta sirve para medir distancias y áreas sobre el mapa.\n\nCada pulsación del ratón inserta un punto más en la trayectoria. Para dejar de añadir puntos pulse la tecla\nEsc
key\no haga doble clic sobre el mapa.\nElimine la medida con el icono correspondiente ."]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":["Esta herramienta permite descargar una imagen con los datos visualizados de la ubicación mostrada. Puede elegir\nsi se muestra o no un encabezado, así como añadir su propia descripción.\nSi activa el modo analítico podrá optar entre varios formatos de imagen, resoluciones espaciales\ny sistemas de coordenadas. También es posible seleccionar varias capas y grabarlas en un fichero .zip
.\n\nPulse el botón de descargar\nDescargar\ny comenzará la descarga de sus imágenes. El proceso podría tardar unos segundos, dependiendo de la resolución\nelegida y del número de capas seleccionadas.\n\nAntes de descargar se puede definir un área de interés (ADI) pulsando en el icono de la herramienta\nde selección de áreas. Los datos se recortarán para ajustarse a esa área."]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":["Ha llegado al final del tutorial. Si tiene más preguntas puede plantearlas en el [foro](https://forum.sentinel-hub.com/)\no puede escribirnos por [correo electrónico](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nSi quiere repasar de nuevo el tutorial más adelante, siempre puede acceder a él con el icono de información\n\n\n\nsituado en la esquina superior derecha."]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":["#### Resumen de las prestaciones de EO Browser\n\nSi utiliza una pantalla pequeña, entonces vaya [aquí](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nPuede volver a ver esta información en cualquier momento si pulsa el icono de información\n\n\n\nque hay en la esquina superior derecha.\n\n#### Otros recursos\n- [Página de presentación de EO Browser](https://www.sentinel-hub.com/explore/eobrowser/)\n- [Actualizaciones de EO Browser verano 2018 - vídeo](https://www.youtube.com/embed/m3pron0C0kE)"]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":["¿Qué es EO Browser?"]},"User Account":{"msgid":"User Account","msgstr":["Cuenta de usuario"]},"Discover Tab":{"msgid":"Discover Tab","msgstr":["Pestaña Descubrir"]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":["Pestaña Visualizar"]},"Compare Tab":{"msgid":"Compare Tab","msgstr":["Pestaña Comparar"]},"Pins Tab":{"msgid":"Pins Tab","msgstr":["Pestaña de marcadores"]},"Search Places":{"msgid":"Search Places","msgstr":["Búsqueda de lugares"]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":["Capas y capas superpuestas"]},"Education Mode":{"msgid":"Education Mode","msgstr":["Modo educación"]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":["Información y tutorial"]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":["Trazar área de interés"]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":["Marcar punto de interés"]},"Measure Distances":{"msgid":"Measure Distances","msgstr":["Medida de distancias"]},"Download Image":{"msgid":"Download Image","msgstr":["Descargar imagen"]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":["Crear una animación"]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":["¡Feliz exploración!"]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":["¡Le damos la bienvenida a EO Browser!"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":["Banda 1 - Materia orgánica disuelta y pigmentos detríticos - 412.5 nm"]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":["Banda 3 - Clorofila y otros pigmentos - 490 nm"]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":["Banda 4 - Sedimentos en suspensión, mareas rojas - 510 nm"]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":["Banda 5 - Mínimo de absorción de la clorofila - 560 nm"]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":["Banda 6 - Sedimentos en suspensión - 620 nm"]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":["Banda 7 - Absorción de la clorofila y referencia para fluorescencia - 665 nm"]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":["Banda 8 - Pico de fluorescencia de la clorofila - 681 nm"]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":["Banda 9 - Referencia para fluorescencia, correcciones atmosféricas - 709 nm"]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":["Banda 10 - Vegetación, nubes - 753 nm"]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":["Banda 12 - Correcciones atmosféricas - 779 nm"]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":["Banda 13 - Vegetación, referencia de vapor de agua - 865 nm"]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":["Banda 14 - Correcciones atmosféricas - 885 nm"]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":["Banda 15 - Vapor de agua, tierra - 900 nm"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Banda 1 - Azul - 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Banda 2 - Verde - 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Banda 3 - Rojo - 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Banda 4 - NIR - 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Banda 5 - SWIR-1 - 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Banda 7 - SWIR-2 - 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Banda 8 - Pancromática - 520-900 nm"]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":["Banda 1 - Aerosoles costeros - 433-453 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Banda 2 - Azul - 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Banda 3 - Verde - 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Banda 4 - Rojo - 630-680 nm"]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":["Banda 5 - Infrarrojo cercano (NIR) - 845-885 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Banda 6 - SWIR-1 - 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Banda 7 - SWIR-2 - 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Banda 8 - Pancromática - 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Banda 9 - Cirros - 1360-1390 nm"]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":["Landsat 5 (archivo ESA)"]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":["Landsat 7 (Archivo ESA)"]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":["Landsat 8 (archivo ESA)"]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":["Landsat 8 (archivo USGS)"]},"Red band":{"msgid":"Red band","msgstr":["Banda roja"]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":["841 - 876 nm (NIR)"]},"Blue band":{"msgid":"Blue band","msgstr":["Banda azul"]},"Green band":{"msgid":"Green band","msgstr":["Banda verde"]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":["1230 - 1250 nm"]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":["1628 - 1652 nm"]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":["2105 - 2155 nm"]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":["Banda 1 - Aerosoles costeros - 443 nm"]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":["Banda 2 - Azul - 490 nm"]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":["Banda 3 - Verde - 560 nm"]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":["Banda 4 - Rojo - 665 nm"]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":["Banda 5 - Borde rojo de la vegetación - 705 nm"]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":["Banda 6 - Borde rojo de la vegetación - 740 nm"]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":["Banda 7 - Borde rojo de la vegetación - 783 nm"]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":["Banda 8 - Infrarrojo cercano (NIR) - 842 nm"]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":["Banda 9 - Vapor de agua - 945 nm"]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":["Banda 10 - Infrarrojo de onda corta (SWIR) - Cirros - 1375 nm"]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":["Banda 11 - Infrarrojo de onda corta (SWIR) - 1610 nm"]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":["Banda 12 - Infrarrojo de onda corta (SWIR) - 2190 nm"]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":["Banda 8A - Borde rojo de la vegetación - 865 nm"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":["Banda 1 - Corrección de aerosoles, recuperación mejorada de constituyentes del agua - 400 nm"]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":["Banda 2 - Materia orgánica en suspensión y pigmentos detríticos (turbidez) - 412 nm"]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":["Banda 3 - Máximo de absorción de la clorofila, biogeoquímica, vegetación - 442.5 nm"]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":["Banda 4 - Clorofila alta, otros pigmentos - 490 nm"]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":["Banda 5 - Clorofila, sedimentos, turbidez, marea roja - 510 nm"]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":["Banda 6 - Referencia para la clorofila (mínimo de la clorofila) - 560 nm"]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":["Banda 7 - Carga de sedimentos - 620 nm"]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":["Banda 8 - Segundo máximo de absorción de la clorofila, sedimentos, materia orgánica en suspensión/vegetación - 665 nm"]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":["Banda 10 - Borde rojo del pico de fluorescencia de la clorofila - 681.25 nm"]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":["Banda 11 - Borde rojo de la línea de base de fluorescencia de la clorofila - 708.75 nm"]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":["Banda 12 - Absorción de O2/nubes, vegetación - 753.75 nm"]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":["Banda 13 - Banda de absorción de O2/corrección de aerosoles - 761.25 nm"]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":["Banda 14 - Corrección atmosférica - 764.375 nm"]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":["Banda 15 - O2A para la presión en la cima nubosa, fluorescencia sobre tierra - 767.5 nm"]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":["Banda 16 - Corrección atmosférica/de aerosoles - 778.75 nm"]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":["Banda 17 - Corr. atmosf./corr. aerosoles, nubes, co-registro de píxeles - 865 nm"]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":["Banda 18 - Referencia para la absorción por vapor de agua, común con el instrumento SLSTR. Monitorización de vegetación - 885 nm"]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":["Banda 19 - Absorción por vapor de agua/monitorización de vegetación (reflectancia máxima) - 900 nm"]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":["Banda 20 - Absorción por vapor de agua, corrección atmosférica/de aerosoles - 940 nm"]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":["Banda 21 - Corrección atmosférica/de aerosoles - 1020 nm"]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":["Banda F1 - Emisión del fuego en el infrarrojo térmico (TIR) - Incendio activo - 3742.00 nm"]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":["Banda F2 - Emisión del fuego en el infrarrojo térmico (TIR) - Incendio activo - 10854.00 nm"]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":["Banda S1 - VNIR - Filtrado de nubes, monitorización de vegetación, aerosoles - 554.27 nm"]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":["Banda S2 - VNIR - Índice normalizado de vegetación (NDVI), monitorización de vegetación, aerosoles - 659.47 nm"]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":["Banda S3 - VNIR - Índice normalizado de vegetación (NDVI), marcado de nubes, co-registro de píxeles - 868.00 nm"]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":["Banda S4 - SWIR - Detección de cirros sobre tierra - 1374.80 nm"]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":["Banda S5 - SWIR - Corrección de nubes, hielo, nieve, monitorización de vegetación - 1613.40 nm"]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":["Banda S6 - SWIR - Estado de la vegetación y corrección de nubes - 2255.70 nm"]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":["Banda S7 - Infrarrojo térmico (TIR) ambiental - Temperatura superficial de mar y tierra, incendio activo - 3742.00 nm"]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":["Banda S8 - Infrarrojo térmico (TIR) ambiental - Temperatura superficial de mar y tierra, incendio activo - 10854.00 nm"]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":["Banda S9 - Infrarrojo térmico (TIR) ambiental - Temperatura superficial de mar y tierra - 12022.50 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Reflectancia"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Temperatura de brillo"]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":["A partir de la combinación de las bandas 4, 3, 2"]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":["A partir de la combinación de bandas (B04-B03)/(B04+B03)"]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":["A partir de la combinación de las bandas 5, 4, 3"]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":["Basado en las bandas de color natural 4, 3, 2, y en la pancromática 8"]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":["A partir de la combinación de bandas (B05-B04)/(B05+B04)"]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":["VV - lineal gamma0 - ortocorregido"]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":["VV - gamma0 lineal - no ortocorregido"]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":["VH - gamma0 lineal - ortocorregido"]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":["A partir de la combinación de las bandas 3, 2, 1"]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":["VH - gamma0 lineal - no ortocorregido"]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":["Imagen en color tras combinar las bandas de entrada. Valor [RGB] = [VV, 2 VH, VV / VH / 100.0] - gamma0 lineal - ortocorregida"]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":["VV - gamma0 decibélico [-20,0] - ortocorregido"]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":["VH - gamma0 decibélico [-20,0] - ortocorregido"]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":["Devuelve una composición de (VH, VV, VH-VV)"]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":["HH - gamma0 lineal - ortocorregido"]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":["HV - gamma0 lineal - ortocorregido"]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":["Imagen en color tras combinar las bandas de entrada. Valor [RGB] = [HH, 2 HV, HH / HV / 100.0] - gamma0 lineal - ortocorregida"]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":["HH - gamma0 decibélico [-20,0] - ortocorregido"]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":["HV - gamma0 decibélico [-20,0] - ortocorregido"]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":["HH - gamma0 lineal - no ortocorregido"]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":["A partir de las bandas 4, 3, 2"]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":["A partir de las bandas 8, 4, 3"]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":["A partir de las bandas 12, 11, 4"]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":["A partir de la combinación de bandas (B8 - B4)/(B8 + B4)"]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":["A partir de la combinación de bandas (B8A - B11)/(B8A + B11)"]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":["A partir de las bandas 12, 8A, 4"]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":["A partir de la combinación de bandas (B3 - B8)/(B3 + B8)"]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":["A partir de la combinación de bandas (B3 - B11)/(B3 + B11); los valores superiores a 0.42 indican nieve"]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":["Clasificación de los datos de Sentinel2 por el algoritmo de clasificación de escenas de la ESA."]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":["Índice UV de aerosoles (380 y 340 nm)"]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":["A partir de la combinación de bandas (B3 - B11)/(B3 + B11)"]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":["Índice OLCI de clorofila terrestre, basado en la combinación de bandas (B12 - B11)/(B11 - B10)"]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":["Índice UV de aerosoles (388 y 354 nm)"]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":["Razón de mezcla del metano promediada en la columna de aire seco"]},"Cloud base height":{"msgid":"Cloud base height","msgstr":["Altitud de la base de las nubes"]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":["Presión en la base de las nubes"]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":["Fracción de nubes radiométrica efectiva"]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":["Espesor óptico de las nubes"]},"Cloud top height":{"msgid":"Cloud top height","msgstr":["Altitud de la cima de las nubes"]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":["Presión en la cima de las nubes"]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":["Columna total de monóxido de carbono"]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":["Columna vertical troposférica de formaldehído"]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":["Columna troposférica de dióxido de nitrógeno"]},"Ozone total column":{"msgid":"Ozone total column","msgstr":["Columna total de ozono"]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":["Columna total de dióxido de azufre"]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":["A partir de las bandas 2, 1, 4"]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":["A partir de la combinación de bandas (B02 - B01)/(B02 + B01)"]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":["A partir de la combinación de bandas (B02 - B05)/(B02 + B05)"]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":["A partir de la combinación de bandas (B06 - B07)/(B06 + B07)"]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":["A partir de las bandas 1, 4, 3"]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":["A partir de la combinación de las bandas 7, 5, 3"]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":["(B09 - B08)/(B09 + B08)"]},"B09 / B08":{"msgid":"B09 / B08","msgstr":["B09 / B08"]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":["A partir de la combinación de las bandas 13, 5, 2"]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":["A partir de las bandas 13, 4, 1"]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":["A partir de las bandas 13, 5, 2"]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":["A partir de la combinación de bandas (B13-B07) / (B13+B07)"]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":["Índice de clorofila terrestre"]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V: síntesis de 10 días\nDosel vegetal (corregido de atmósfera)\nResolución temporal: 10 días\nResolución: 333 m (tamaño del píxel)"]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V: síntesis diaria\nCima de la atmósfera\nResolución temporal: diaria\nResolución: 333 m (tamaño del píxel)"]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V: síntesis de 5 días\nCima de la atmósfera\nResolución temporal: 5 días\nResolución: 100 m (tamaño del píxel)"]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V: síntesis diaria\nDosel vegetal (corregido de atmósfera)\nResolución temporal: diaria\nResolución: 333 m (tamaño del píxel)"]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":["OrbitTracks_Aqua_Descending"]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V: síntesis de 5 días\nDosel vegetal (corregido de atmósfera)\nResolución temporal: 5 días\nResolución: 100 m (tamaño del píxel)"]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":["OrbitTracks_Aqua_Ascending"]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":["OrbitTracks_Aura_Ascending"]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":["OrbitTracks_Aura_Descending"]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":["OrbitTracks_CloudSat_Ascending"]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":["OrbitTracks_Calipso_Ascending"]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":["OrbitTracks_Calipso_Descending"]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":["OrbitTracks_CloudSat_Descending"]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":["OrbitTracks_CYGNSS_Ascending"]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":["OrbitTracks_CYGNSS_Descending"]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":["OrbitTracks_GCOM-C_Ascending"]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":["OrbitTracks_GCOM-C_Descending"]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":["OrbitTracks_GCOM-W1_Ascending"]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":["OrbitTracks_GCOM-W1_Descending"]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":["OrbitTracks_GOSAT-2_Ascending"]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":["OrbitTracks_GOSAT-2_Descending"]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":["OrbitTracks_GOSAT_Ascending"]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":["OrbitTracks_GOSAT_Descending"]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":["OrbitTracks_GPM_Ascending"]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":["OrbitTracks_GPM_Descending"]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":["OrbitTracks_ICESAT-2_Ascending"]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":["OrbitTracks_ICESAT-2_Descending"]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":["OrbitTracks_ISS_Ascending"]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":["OrbitTracks_ISS_Descending"]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":["OrbitTracks_Landsat-7_Ascending"]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":["OrbitTracks_Landsat-7_Descending"]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":["OrbitTracks_Landsat-8_Ascending"]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":["OrbitTracks_METOP-A_Ascending"]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":["OrbitTracks_METOP-B_Descending"]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":["OrbitTracks_Landsat-8_Descending"]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":["OrbitTracks_METOP-C_Ascending"]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":["OrbitTracks_METOP-C_Descending"]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":["OrbitTracks_NOAA-20_Ascending"]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":["OrbitTracks_NOAA-20_Descending"]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":["OrbitTracks_METOP-B_Ascending"]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":["OrbitTracks_OCO-2_Ascending"]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":["OrbitTracks_OCO-2_Descending"]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":["OrbitTracks_SAOCOM1-A_Ascending"]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":["OrbitTracks_SAOCOM1-A_Descending"]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":["OrbitTracks_Sentinel-1A_Ascending"]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":["OrbitTracks_Sentinel-1B_Ascending"]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":["OrbitTracks_Sentinel-1A_Descending"]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":["OrbitTracks_METOP-A_Descending"]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":["OrbitTracks_Sentinel-1B_Descending"]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":["OrbitTracks_Sentinel-2A_Ascending"]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":["OrbitTracks_Sentinel-2A_Descending"]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":["OrbitTracks_Sentinel-2B_Ascending"]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":["OrbitTracks_Sentinel-2B_Descending"]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":["OrbitTracks_Sentinel-5P_Ascending"]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":["OrbitTracks_Sentinel-5P_Descending"]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":["OrbitTracks_SMAP_Ascending"]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":["OrbitTracks_SMAP_Descending"]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":["OrbitTracks_Suomi_NPP_Descending"]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":["OrbitTracks_Suomi_NPP_Ascending"]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":["OrbitTracks_Terra_Descending"]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":["OrbitTracks_Terra_Ascending"]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":["A partir de las bandas 4, 3, 2 y con realce mediante las bandas 12 y 11."]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":["A partir de las bandas B07, B06, B04"]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":["A partir de la combinación de bandas (B8 - B4)/(B8 + B4)"]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":["A partir de la banda térmica 10"]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":["A partir de las bandas B12, B11, B8A"]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":["A partir de la combinación de bandas (B08 - B12)/(B08 + B12)"]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":["Visualización en color natural realzado"]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":["A partir de la combinación de las bandas 8, 6, 4"]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":["Índice de vegetación realzado"]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":["A partir de la combinación de las bandas BSI, B08, B11"]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":["Índice normalizado de humedad (NDMI) clasificado para riego"]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":["A partir de las bandas B11, B08, B02"]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":["Color falso 13, 5, 2"]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":["A partir de la combinación de bandas (B13 - B07) / (B13 + B07)"]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":["A partir de las bandas 12, 8, 2"]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":["A partir de las bandas 4, 3, 2"]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":["A partir de las bandas 8, 4, 3"]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":["A partir de las bandas 12, 8, 2"]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":["A partir de las bandas 8, 11, 12"]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":["Contenido de sedimentos y clorofila del agua"]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":["A partir de las bandas 12, 8A, 4"]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":["Basado en el índice normalizado de nieve (NDSI)"]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":["A partir de la combinación de las bandas 11, 8, 2"]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":["A partir de las bandas B12, B11, B04"]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":["A partir de las bandas 4, 3, 2"]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":["Índice de vegetación atmosféricamente resistente"]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":["Índice de vegetación ajustado al suelo (SAVI)"]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":["# Bandas de emisión térmica infrarroja del fuego\n\nEl Instrumento de Temperatura Superficial del Mar y del Terreno (SLSTR, \"Sea and Land Surface Temperature Instrument\") de Sentinel-3 cuenta con dos canales (F1 y F2) dedicados a medir la temperatura de la superficie del terreno (LST, «Land Surface Temperature\"). El canal F2 tiene la longitud de onda central en 10854 nm, en el infrarrojo térmico, o TIR, y resulta muy útil para monitorizar incendios o sucesos de altas temperaturas con una resolución espacial de 1 km. \n\n\n\nMás información [aquí.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)"]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":["# Metano (CH4)\n\n\n\nTras el dióxido de carbono, el metano constituye la principal aportación al incremento antropogénico (causado por el ser humano) del efecto invernadero. Las medidas se dan en partes por mil millones (ppb) con una resolución espacial de 7 km x 3.5 km.\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/methane)"]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":["# Formaldehído (HCHO)\n\n\n\nLas observaciones satelitales a largo plazo del formaldehído (HCHO) son esenciales para los estudios de la calidad del aire y de la interdependencia entre la química y el clima, tanto a escala regional como global. Las variaciones estacionales e interanuales de la distribución de formaldehído se relacionan, sobre todo, con cambios de temperatura y con incendios, pero también con cambios antropogénicos (debidos a la actividad humana). La vida del formaldehído en la atmósfera asciende a apenas unas cuantas horas, por lo que cabe relacionar directamente su concentración en la capa límite con la emisión de hidrocarburos de vida corta, los cuales no suelen ser detectables desde el espacio. Las medidas se dan en moles por metro cuadrado (mol/ m^2).\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/formaldehyde)"]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":["# Dióxido de azufre (SO2)\n\n\n\nEl dióxido de azufre accede a la atmósfera terrestre mediante procesos naturales y antropogénicos (causados por el ser humano). Tiene relevancia química a nivel local y global y su incidencia abarca desde contaminación de corta duración hasta efectos en el clima. Solo alrededor del 30% de las emisiones de SO2 procede de fuentes naturales; la mayoría es de origen antropogénico. El instrumento TROPOMI de Sentinel-5P muestrea la superficie terrestre a intervalos de un día con una resolución espacial de 3.5 x 7 km, lo que permite distinguir detalles finos que incluyen los penachos más reducidos de SO2. Las medidas se dan en moles por metro cuadrado (mol/ m^2).\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/sulphur-dioxide)"]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":["# Ozono (O3)\n\n\n\nEl ozono es trascendental para el equilibrio de la atmósfera terrestre. La capa de ozono de la estratosfera protege la biosfera de la peligrosa radiación ultravioleta. En la troposfera, el ozono actúa como un agente limpiador muy eficaz, pero en concentraciones elevadas resulta perjudicial para la salud humana, de los animales y de las plantas. El ozono es, además, uno de los gases con una contribución significativa al efecto invernadero responsable del cambio climático en curso. Desde el descubrimiento del agujero de ozono antártico en la década de 1980 y el posterior protocolo de Montreal que regula la producción de sustancias cloradas destructoras de ozono, este gas se ha venido monitorizando de manera rutinaria tanto desde el suelo como desde el espacio. Las medidas se dan en moles por metro cuadrado (mol/ m^2)\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/total-ozone-column)"]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":["# Dióxido de nitrógeno (NO2)\n\n\n\nEl dióxido de nitrógeno (NO2) y el monóxido de nitrógeno (NO) suelen recibir la denominación conjunta de óxidos de nitrógeno. Son gases traza importantes en la atmósfera terrestre y se encuentran tanto en la troposfera como en la estratosfera. Aparecen en la atmósfera debido a actividades antropogénicas (en especial por la quema de combustibles fósiles y de biomasa), pero también por procesos naturales como ciertas actividades microbianas en los suelos, incendios forestales y caída de rayos. Las medidas se dan en moles por metro cuadrado (mol/ m^2).\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/nitrogen-dioxide)"]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":["# Monóxido de carbono (CO)\n\n\n\nEl monóxido de carbono (CO) es un gas traza muy importante. Actúa como uno de los contaminantes principales en las zonas urbanas. Sus fuentes principales son la quema de combustibles fósiles, la combustión de biomasa y la oxidación atmosférica de metano y otros hidrocarburos. La columna total de monóxido de carbono se mide en moles por metro cuadrado (mol/ m^2).\n\n\n\nMás información [aquí.](http://www.tropomi.eu/data-products/carbon-monoxide)"]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":["# Índice de aerosoles\n\nEl índice de aerosoles (AI, Aerosol Index) es un indicador cualitativo que evalúa la presencia de capas de aerosoles suspendidos en la atmósfera. Puede usarse para detectar aerosoles absorbentes de la radiación UV, como el polvo del desierto o los penachos de cenizas volcánicas. Adopta valores positivos (del azul claro al rojo) cuando hay aerosoles que absorben UV. El índice se calcula a partir de dos parejas de longitudes de onda: 340/380 nm y 354/388 nm.\n\nMás información [aquí.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)"]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":["# Altitud de la base de las nubes\n\nAltitud de la base de las nubes medida en metros (m)."]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":["# Presión en la base de las nubes\n\nPresión en la base de las nubes medida en pascales (Pa)."]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":["# Espesor óptico de las nubes\n\nEl espesor óptico es un parámetro crucial para caracterizar las propiedades ópticas de las nubes. Constituye una medida de la cantidad de luz solar que logra atravesarlas hasta alcanzar la superficie terrestre. Cuanto mayor sea el espesor óptico de una nube, mayor es la cantidad de luz solar esparcida y reflejada por ella. El azul oscuro indica valores bajos de espesor óptico, mientras que el rojo corresponde a espesores mayores."]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":["# Altitud de la cima de las nubes\n\nAltitud de la cima de las nubes medida en metros (m)."]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":["# Presión en la cima de las nubes\n\nPresión en la cima de las nubes medida en pascales (Pa)."]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":["# Índice normalizado de vegetación (NDVI)\n\nEl índice normalizado de vegetación (NDVI, Normalized Difference Vegetation Index) es un índice simple, pero efectivo, para cuantificar la vegetación verde. Ofrece una medida del estado de salud de la vegetación, basado en el modo en que las plantas reflejan la luz de ciertas longitudes de onda. NDVI adopta valores entre -1 y +1. Los valores negativos (los más cercanos a -1) corresponden a agua. Valores en torno a cero (entre -0.1 y +0.1) suelen indicar zonas sin vegetación como roca, arena o nieve. Los valores positivos pero pequeños se corresponden con arbustos y campos cubiertos de hierba (aproximadamente entre +0.2 y +0.4), en tanto que cifras más elevadas describen los bosques templados y las selvas tropicales (valores cercanos a +1).\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) y [aquí.](https://eos.com/ndvi/)"]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":["# Índice realzado de vegetación (EVI)\n\nEl índice realzado de vegetación (EVI, Enhanced Vegetation Index\") es un índice de vegetación optimizado que corrige las señales de fondo debidas al terreno y a efectos atmosféricos. Resulta muy útil en áreas de cobertura boscosa densa. El EVI adopta valores entre -1 y +1, y la vegetación sana suele caer entre +0.20 y +0.80.\n\n\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) y [aquí.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)"]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Índice de vegetación resistente a la atmósfera (ARVI)\n\nEl índice de vegetación resistente a la atmósfera (ARVI, Atmospherically Resistant Vegetation Index) minimiza el efecto del esparcimiento atmosférico. Resulta especialmente útil en regiones donde la atmósfera presenta un contenido elevado de aerosoles (niebla, polvo, humo, contaminación del aire). El ARVI adopta valores entre -1 y +1, donde la vegetación verde suele aparecer entre 0.10 y 0.80.\n\n\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) y [aquí.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Índice de vegetación ajustado al suelo (SAVI)\n\nEl índice de vegetación ajustado al suelo (SAVI, Soil Adjusted Vegetation Index) es similar al índice normalizado de vegetación (NDVI), pero resulta más adecuado en zonas con cobertura vegetal pobre (< 40 %). Este índice incorpora una técnica de transformación para minimizar el brillo del suelo en el cálculo de los índices de vegetación que emplean longitudes de onda rojas y en el infrarrojo cercano (NIR). Este índice es útil para analizar cultivos tempranos, regiones áridas con vegetación dispersa y superficies con suelo desnudo.\n\n\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) y [aquí.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":["# Índice de reflectancia de la antocianina modificado (mARI/ARI2)\n\nLas antocianinas son pigmentos frecuentes en las plantas superiores, responsables de sus colores rojos, azules y morados. Proporcionan información valiosa sobre el estado fisiológico de las plantas, puesto que se consideran indicadores de varios tipos de estrés vegetal. La reflectancia de las antocianinas es máxima en torno a 550 nm. Sin embargo, también la clorofila refleja esas longitudes de onda. Por eso, para aislar las antocianinas se sustrae la banda espectral de 700 nm, reflejada por la clorofila pero no por las antocianinas.\n\nEn este índice modificado (mARI, o bien ARI2) se añade al índice básico ARI una banda espectral del infrarrojo cercano (en las longitudes de onda recomendadas de 760-800 nm) para aplicar una corrección que tiene en cuenta la densidad y el grosor de las hojas, dado que esta banda está relacionada con la luz esparcida por las hojas. \n\nLos valores de mARI van desde 0 hasta 8 para la muestra de árboles examinada en este [artículo original.](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/)\n\n\n\n\n\nMás información [aquí.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)"]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":["# Script de ciudad verde\n\nEl script de ciudad verde pretende promover la concienciación sobre las zonas vedes en las ciudades de todo el mundo. El script tiene en cuenta el índice normalizado de vegetación (NDVI) y las longitudes de onda del color natural para distinguir entre áreas edificadas y zonas verdes, lo que lo hace útil para identificar zonas urbanas. Las áreas edificadas se muestran en gris, y la vegetación, en verde.\n\n\n\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":["# Script de clasificación urbana\n\nEl script de clasificación urbana detecta áreas edificadas y las distingue del suelo desnudo, de la vegetación y del agua. Las zonas con niveles elevados de humedad se muestran en azul; las zonas clasificadas como edificadas aparecen en blanco; las superficies cubiertas de vegetación se plasman en verde, y todo lo demás corresponde a suelo desnudo y se codifica con tonos pardos.\n\n\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)"]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":["# Script de suelo urbano en colores infrarrojos\n\nEste script, obra de Leo Tolari, combina la visualización en color natural con longitudes de onda en el infrarrojo cercano (NIR) y en el infrarrojo de onda corta (SWIR). Logra una apariencia realista, pero realza las zonas urbanas mejor que el color natural.\n\n\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)"]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":["# NDMI para el estrés hídrico\n\nEl índice normalizado de humedad (NDMI, Normalized Difference Moisture Index) aplicado al estrés hídrico detecta el riego. Para cualquier valor de este índice por encima de 0, y conocido el uso y la cobertura del suelo, es posible detectar si se ha producido riego. Si además se conoce el tipo de cultivo (por ejemplo, cítricos), cabe identificar si el riego está siendo o no eficaz durante la fase crucial de crecimiento en el estío, así como esclarecer si hay porciones de las explotaciones agrarias con riego insuficiente o excesivo.\n\n\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)"]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":["# Índice normalizado de humedad (NDMI)\n\nEl índice normalizado de humedad (NDMI, Normalized Difference Moisture Index) se emplea para determinar el contenido de agua de la vegetación y para monitorizar sequías. NDMI adopta valores entre -1 y +1. Los valores negativos (cercanos a -1) corresponden a suelo desnudo. Valores alrededor de cero (de -0.2 a +0.4) suelen indicar estrés hídrico. Los valores positivos más altos representan cubiertas vegetales elevadas y sin estrés hídrico (aproximadamente desde +0.4 hasta +1).\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":["# Índice normalizado de agua (NDWI)\n\nEl índice normalizado de agua (NDWI, Normalized Difference Water Index\") resulta adecuado para cartografiar masas de agua, que presentan valores de este índice superiores a +0.5. La vegetación ostenta valores menores. Las edificaciones tienen valores positivos entre cero y +0.2.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":["# Índice normalizado de agua (NDWI)\n\nEl índice normalizado de agua (NDWI, Normalized Difference Water Index) resulta adecuado para cartografiar masas de agua, que presentan valores de este índice superiores a +0.5. La vegetación ostenta valores menores. Las edificaciones tienen valores positivos entre cero y +0.2."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Composición en color falso\n\nUna composición en color falso recurre al menos a una imagen obtenida en longitudes de onda no visibles. Es frecuente la composición en color falso que utiliza las bandas del infrarrojo cercano, el rojo y el verde (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas). Estas composiciones en color falso se suelen emplear para diagnosticar la densidad y la salud de la vegetación, porque las plantas reflejan las bandas infrarroja y verde, mientras que absorben el color rojo. Las ciudades y el suelo desnudo aparecen grises o en tonos pardos, mientras que el agua se muestra azul o negra.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) y [aquí.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Composición en color falso\n\nUna composición en color falso recurre al menos a una imagen obtenida en longitudes de onda no visibles. Es frecuente la composición en color falso que utiliza las bandas del infrarrojo cercano, el rojo y el verde (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas). Estas composiciones en color falso se suelen emplear para diagnosticar la densidad y la salud de la vegetación, porque las plantas reflejan las bandas infrarroja y verde, mientras que absorben el color rojo. Las ciudades y el suelo desnudo aparecen grises o en tonos pardos, mientras que el agua se muestra azul o negra.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) y [aquí.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":["# Composición en color falso\n\nUna composición en color falso recurre al menos a una imagen obtenida en longitudes de onda no visibles. Es frecuente la composición en color falso que utiliza las bandas del infrarrojo cercano, el rojo y el verde (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas). Estas composiciones en color falso se suelen emplear para diagnosticar la densidad y la salud de la vegetación, porque las plantas reflejan las bandas infrarroja y verde, mientras que absorben el color rojo. Las ciudades y el suelo desnudo aparecen grises o en tonos pardos, mientras que el agua se muestra azul o negra.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) y [aquí.](https://earthobservatory.nasa.gov/features/FalseColor)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Composición en color falso\n\nUna composición en color falso recurre al menos a una imagen obtenida en longitudes de onda no visibles. Es frecuente la composición en color falso que utiliza las bandas del infrarrojo cercano, el rojo y el verde (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas). Estas composiciones en color falso se suelen emplear para diagnosticar la densidad y la salud de la vegetación, porque las plantas reflejan las bandas infrarroja y verde, mientras que absorben el color rojo. Las ciudades y el suelo desnudo aparecen grises o en tonos pardos, mientras que el agua se muestra azul o negra.\n\n\n\nMás información [aquí.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":["# Composición en color natural\n\nLos sensores satelitales captan imágenes de la Tierra en diferentes regiones del espectro electromagnético, cada una de las cuales se conoce como una banda. La composición en color natural introduce las bandas de luz visible roja, verde y azul en los tres canales correspondientes, rojo, verde y azul, para ofrecer un producto en color que constituye una buena representación de la Tierra tal y como la vería el ser humano de manera natural.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) y [aquí.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy)."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Composición en color natural\n\nLos sensores satelitales captan imágenes de la Tierra en diferentes regiones del espectro electromagnético, cada una de las cuales se conoce como una banda. La composición en color natural introduce las bandas de luz visible roja, verde y azul en los tres canales correspondientes, rojo, verde y azul, para ofrecer un producto en color que constituye una buena representación de la Tierra tal y como la vería el ser humano de manera natural.\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) y [aquí.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Composición en color natural\n\nLos sensores satelitales captan imágenes de la Tierra en diferentes regiones del espectro electromagnético, cada una de las cuales se conoce como una banda. La composición en color natural introduce las bandas de luz visible roja, verde y azul en los tres canales correspondientes, rojo, verde y azul, para ofrecer un producto en color que constituye una buena representación de la Tierra tal y como la vería el ser humano de manera natural.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) y [aquí.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Composición en color natural\n\nLos sensores satelitales captan imágenes de la Tierra en diferentes regiones del espectro electromagnético, cada una de las cuales se conoce como una banda. La composición en color natural introduce las bandas de luz visible roja, verde y azul en los tres canales correspondientes, rojo, verde y azul, para ofrecer un producto en color que constituye una buena representación de la Tierra tal y como la vería el ser humano de manera natural.\n\n\n\nMás información [aquí.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":["# Composición en color natural\n\nLos sensores satelitales captan imágenes de la Tierra en diferentes regiones del espectro electromagnético, cada una de las cuales se conoce como una banda. La composición en color natural introduce las bandas de luz visible roja, verde y azul en los tres canales correspondientes, rojo, verde y azul, para ofrecer un producto en color que constituye una buena representación de la Tierra tal y como la vería el ser humano de manera natural.\n\n\n\nMás información [aquí.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)"]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":["# Visualización en color natural realzado\n\nEste script aplica técnicas de optimización para evitar la aparición de píxeles saturados y para equilibrar la exposición. Confiere una apariencia natural a las nubes y retiene toda la información visual posible. Las teselas de OLCI en Sentinel-3 cubren áreas muy amplias, lo que permite observar grandes formaciones nubosas como, por ejemplo, huracanes.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)"]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Refinado pancromático en color natural\n\nLa composición de refinado pancromático en color natural se obtiene a partir de los datos habituales de color natural (rojo, verde y azul, o RGB), los cuales se realzan mediante la banda pancromática 8 o con la banda pan (una banda es una región del espectro electromagnético; los sensores satelitales observan la Tierra en múltiples bandas). La imagen de la banda pan se parece a las que ofrecían las películas en blanco y negro: combina la luz de las regiones roja, verde y azul del espectro en una sola medida de la reflectancia general en luz visible. Las imágenes que resultan del refinado pancromático tienen el 4 veces la resolución que las composiciones normales en color natural, lo que incrementa mucho la utilidad de las imágenes Landsat.\n\n\n\nMás información [aquí](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) y [aquí.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":["# Composición urbana en color falso\n\nEsta composición se utiliza para visualizar mejor las zonas urbanizadas. La vegetación queda representada en tonos de verde, mientras que el terreno urbanizado aparece en blancos, grises y morados. El suelo, la arena y los minerales se revelan en toda una variedad de colores. La nieve y el hielo se muestran en azul oscuro, mientras que el agua se ve negra o azul. Las zonas inundadas se ven en azul muy oscuro, casi negro. Esta composición resulta útil para detectar incendios forestales y calderas volcánicas, que se reflejan en tonos rojos y amarillos.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) y [aquí.](https://eos.com/false-color/)"]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Composición urbana en color falso\n\nEsta composición recurre a una combinación de bandas en el rango visible y en el infrarrojo de onda corta (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas). La vegetación aparece en tonos verdes, más oscuros cuando es más densa, mientras que la vegetación dispersa adopta los tonos más claros. Las áreas urbanizadas se ven azules, mientras que el suelo se muestra en varios tonos pardos.\n\n\n\nMás información [aquí.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":["# Composición agrícola\n\nEsta composición emplea bandas de infrarrojo de onda corta, infrarrojo cercano y azul, para monitorizar la salud de los cultivos (una banda es un intervalo del espectro electromagnético; los sensores satelitales pueden fotografiar la Tierra en múltiples bandas). Las bandas de infrarrojo de onda corta y de infrarrojo cercano son especialmente adecuadas para realzar la vegetación densa, que se representa en verde oscuro en la composición. Los cultivos aparecen de un verde fosforescente, y el suelo desnudo se muestra en magenta. \n\n\n\nMás información [aquí](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) y [aquí.](https://gisgeography.com/sentinel-2-bands-combinations/)"]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":["# Clasificador de nieve\n\nEl algoritmo clasificador de nieve clasifica los píxeles a partir de umbrales tanto en diferencias de brillo como en el índice normalizado de nieve (NDSI, Normalized Difference Snow Index). Los valores considerados como nieve se representan en un azul claro llamativo. El script sobreestima la nieve cuando hay nubes.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":["# Visualizador Ulyssys de la calidad del agua (UWQV)\n\nEl script UWQV (Ulyssys Water Quality Viewer) permite visualizar de manera dinámica los contenidos de clorofila y sedimentos en las masas de agua, cantidades que suponen indicadores primarios de la calidad del agua. El contenido de clorofila se representa en colores desde el azul oscuro (bajo contenido de clorofila), pasando por el verde, hasta el rojo (valores elevados). La concentración de sedimentos se representa en tonos pardos; el marrón opaco indica un contenido de sedimentos elevado. \n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)"]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":["# Color natural optimizado\n\nEste script representa la Tierra en bellas imágenes en color natural. Aplica técnicas de optimización para evitar que aparezcan píxeles saturados, así como para equilibrar la exposición.\n\n\n\nMás información [aquí.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)"]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":["# Composición geológica 12, 8, 2\n\nEsta composición recurre a la banda 12 en el infrarrojo de onda corta (SWIR, Short-Wave InfraRed) para distinguir varios tipos de roca (una banda es una región del espectro electromagnético; los sensores satelitales observan la Tierra en múltiples bandas). Cada roca y mineral refleja de un modo distinto el infrarrojo de onda corta, lo que permite elaborar una cartografía geológica a través de la comparación de la luz infrarroja de onda corta reflejada. La banda 8 del infrarrojo cercano (NIR) realza la vegetación, mientras que la banda 2 es sensible a la humedad, lo que permite distinguir distintos materiales en el terreno. Esta composición resulta útil para localizar formaciones y estructuras geológicas como fallas y fracturas, estudiar la litología (distinguir granitos de basaltos, por ejemplo) o en aplicaciones mineras.\n\n\n\nMás información [aquí.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)"]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":["# Composición geológica 8, 11, 12\n\nEsta composición recurre a las bandas 11 y 12 del infrarrojo de onda corta (SWIR, Short-Wave InfraRed) para distinguir tipos de rocas (una banda es una región del espectro electromagnético; los sensores satelitales observan la Tierra en múltiples bandas). Cada roca y mineral refleja de un modo distinto la luz infrarroja de onda corta, lo que hace posible elaborar mapas geológicos comparando la luz infrarroja de onda corta reflejada. La banda 8 del infrarrojo cercano (NIR) realza la vegetación, lo que contribuye a distinguir los materiales superficiales. La vegetación aparece en color rojo en esta composición, que es útil para distinguir la vegetación del suelo, así como para identificar rasgos geológicos de interés en minería o para la explotación de minerales.\n\n\n\nMás información [aquí](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) y [aquí.](http://murphygeological.com/new---sentinel-2.html#)"]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":["# Incendios forestales\n\nEste script, desarrollado por Pierre Markuse, visualiza incendios forestales a partir de datos de Sentinel-2. Combina un fondo en color natural con algunos datos NIR/SWIR que permiten penetrar en el humo y añadir más detalles, a la vez que incorpora rasgos de B11 y B12 para representar los incendios en tonos rojos y naranjas.\n\n\n\nMás información [aquí.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)"]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":["# Color natural realzado\n\nEste script, desarrollado por Pierre Markuse, recurre a múltiples bandas (una banda es una región del espectro electromagnético; los sensores satelitales obtienen imágenes de la Tierra en múltiples bandas) y a controles de saturación y brillo para realzar la visualización en color natural.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)"]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":["# Índice de áreas quemadas\n\nEl indice de áreas quemadas aprovecha la gran anchura de las bandas visible, borde rojo, NIR y SWIR.\n\nDescripción de los valores:()=> El índice adopta valores desde `-1` hasta `1` para zonas quemadas, y de `1` hasta `6` para incendios activos. Distintos niveles corresponden a diferentes intensidades de fuego: el autor original calibró los valores actuales a partir, sobre todo, de las regiones mediterráneas.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)"]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":["# Fracción quemada normalizada (NBR)\n\nLa fracción quemada normalizada (NBR, Normalized Burn Ratio) se suele utilizar para estimar la gravedad de los incendios. Recurre a longitudes de onda en el infrarrojo cercano (NIR) y en el infrarrojo de onda corta (SWIR, Short-Wave InfraRed). La vegetación sana refleja mucho en la parte del espectro del infrarrojo cercano, pero poco en el infrarrojo de onda corta. En contraste, las zonas quemadas reflejan mucho en el infrarrojo de onda corta y poco en el infrarrojo cercano. Los píxeles más oscuros corresponden a áreas quemadas.\n\n\n\nMás información [aquí](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) y [aquí.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":["# Penetración atmosférica\n\nEsta composición utiliza varias bandas (una banda es un intervalo del espectro electromagnético; los sensores satelitales pueden fotografiar la Tierra en múltiples bandas) de la parte no visible del espectro electromagnético para reducir la influencia de la atmósfera sobre la imagen. Las áreas calientes reflejan mucho las bandas 11 y 12 del infrarrojo de onda corta, lo que las hace útiles para cartografiar incendios y áreas quemadas. Por el contrario, la banda 8 del infrarrojo de onda corta se refleja mucho en la vegetación, lo que indica ausencia de fuegos. La vegetación se representa en azul y muestra detalles relacionados con su vigor. La vegetación más sana aparece en azul claro, mientras que la vegetación sometida a estrés, dispersa o de zonas áridas figura en un azul más apagado. Los rasgos urbanos se ven en gris, cian o morado. \n\n\n\nMás información [aquí.](https://eos.com/atmospheric-penetration/)"]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":["# Visualización de suelo desnudo\n\nLa visualización de suelo desnudo puede ser útil en la cartografía de suelos, para investigar la ubicación de deslizamientos de tierra o el alcance de la erosión en áreas desprovistas de vegetación. Esta visualización muestra toda la vegetación en color verde y el suelo desnudo en rojo. El agua aparece en negro.\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) y [aquí.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)"]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":["# Composición en color natural con realce IR\n\nEsta composición realza la visualización en color natural mediante el añadido de información obtenida en el infrarrojo de onda corta para resaltar ciertos detalles. Las regiones calientes se muestran en rojo/anaranjado.\n\n\n\nMás información [aquí.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)"]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":["# Detección de zonas quemadas\n\nEste script detecta grandes zonas quemadas recientemente. Los píxeles que aparecen en rojo indican zonas quemadas, mientras que el resto de los píxeles se representa en color natural. El script tiende a sobrestimar las áreas quemadas sobre superficies de agua o sobre nubes.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":["# Índice de clorofila terrestre (OTCI)\n\n\n\nEl índice de clorofila terrestre (OTCI, Terrestrial Chlorophyill Index\") evalúa el contenido de clorofila de la vegetación terrestre y se aplica para monitorizar el estado y la salud de la vegetación. Valores bajos suelen significar agua, arena o nieve. Valores extremadamente elevados, que se muestran en color blanco, normalmente indican que tampoco hay clorofila y corresponden a suelo desnudo, rocas o nubes. Los valores codificados entre el rojo (valores pequeños) y el verde oscuro (valores elevados) pueden utilizarse para determinar la salud de la vegetación.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":["# Índice normalizado de salinidad\n\nEste índice valora la cantidad de sal que hay en los suelos. La salinización de los suelos supone uno de los procesos de degradación del territorio más frecuentes, sobre todo en regiones áridas y semiáridas, donde la precipitación es mayor que la evaporación. \n\nLos valores elevados indican mayor salinidad, mientras que los valores bajos apuntan a una salinidad menor. \n\nLea más al respecto [aquí,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf), [aquí](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) y [aquí.](https://www.indexdatabase.de/db/i-single.php?id=57)"]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":["Al entrar como **usuario registrado** podrá acceder a temas personalizados, guardar y cargar marcadores, crear \nsecuencias de marcadores, medir distancias, elaborar animaciones y utilizar la descarga avanzada de imágenes.\n\nCree una cuenta gratuita pulsando [aquí](https://services.sentinel-hub.com/oauth/subscription)\no, desde la aplicación, pulsando **Acceder** y, luego, \"Regístrese gratis\"."]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":["Esta herramienta permite crear una animación a partir de la capa visualizada y el emplazamiento mostrado.\n\nElija en primer lugar un intervalo temporal. Es posible refinar más los resultados si se filtran por meses\n(marque la casilla correspondiente) y/o seleccionando imágenes de un periodo definido (órbita, día, semana,\nmes, año).\n\nPulse luego Buscar y seleccione ahí las imágenes.\nPuede elegirlas todas con la casilla correspondiente, o filtrar las imágenes según la cobertura nubosa a través del mando deslizable. También es posible elegir las imágenes marcando todas de una en una en la lista. La casilla de **Bordes** activa o desactiva los bordes de la imagen. \n\nAcceda a la vista previa de la animación con el botón de reproducción situado debajo. También es posible\najustar la velocidad (fotogramas por segundo).\n\nCuando obtenga un resultado satisfactorio, pulse el botón de descarga y la animación se grabará en forma de\nfichero .gif
."]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":["Banda 2 - Máximo de absorción de la clorofila - 442 nm"]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":["Banda 11 - Banda de absorción rama R del O2 - 761 nm"]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (corregido de atmósfera)"]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":["Los servicios de Sentinel-1 están disponibles tanto en EOCloud como en AWS. Las prestaciones\nde cada servicio son distintas. Más información en"]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":["Este marcador carece de descripción por ahora."]},"Measure":{"msgid":"Measure","msgstr":["Medición"]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":["Crear una animación de esta área"]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":["Aunque el formato TIFF (de \"tagged image file format\") puede incluir un gran número de bandas, muchos de los programas más habituales de visualización (como, por ejemplo, Windows Photo Viewer) no representan imágenes TIFF con más de 3 bandas.\nSi activa esta opción, solo se incluirán en la imagen de salida las tres primeras bandas.\nCon esta opción desactivada se incluirán todas las bandas en la imagen, pero entonces habrá que utilizar un programa que admita más de tres bandas (como, por ejemplo, QGIS) para representar el fichero TIFF resultante."]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":["EPSG:3857 no está disponible cuando se especifica un área de interés (ADI)."]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":["Añadir banda de máscara de datos a las capas crudas"]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":["Añadir bandas adicionales"]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":["Error: las visualizaciones con efectos solo se pueden descargar en los formatos JPEG o PNG."]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":["Advertencia: las capas siguientes usan dataProducts, por lo que no es posible adoptar el tipo de datos deseado:"]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":["Advertencia: el script no sigue un formato V3 habitual, por lo que no es posible adoptar el tipo de datos deseado: "]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":["Esto significa que probablemente está activo el método por defecto (AUTO) para alterar la resolución espacial. Para arreglarlo tendrá que modificar el script. La documentación incluye información adicional sobre los métodos para alterar la resolución espacial"]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":["Su programa navegador no ofrece las prestaciones 3D necesarias para representar este contenido."]},"More information":{"msgid":"More information","msgstr":["Información adicional"]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":["¡No se logró la conexión con el servicio 3D! ¿Lo vuelvo a intentar? "]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":["¡La imagen es demasiado grande para este dispositivo!\nTamaño de la imagen: {0}x{1}, máx: {2}"]},"Home":{"msgid":"Home","msgstr":["Inicio"]},"Shading":{"msgid":"Shading","msgstr":["Sombreado"]},"Sphere mode":{"msgid":"Sphere mode","msgstr":["Modo esférico"]},"Eye height":{"msgid":"Eye height","msgstr":["Altura del punto de vista"]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":["Imposible cargar la imagen"]},"Geometries":{"msgid":"Geometries","msgstr":["Formas geométricas"]},"Now":{"msgid":"Now","msgstr":["Ahora"]},"Terrain":{"msgid":"Terrain","msgstr":["Terreno"]},"Time":{"msgid":"Time","msgstr":["Hora"]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":["Índice de reflectancia de la antocianina modificado"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Composición en color natural\n\nLos sensores satelitales observan la Tierra en múltiples regiones del espectro electromagnético. Cada región de espectro recibe el nombre de banda. El satélite Landsat 8 dispone de 11 bandas. La composición en color natural recurre a las bandas de la luz visible: rojo, verde y azul, las cuales introduce en los tres canales (rojo, verde y azul) correspondientes, de donde se obtiene un producto que muestra los colores verdaderos, es decir, una buena representación de la superficie terrestre tal y como la vería de manera natural un ser humano.\n\n\n\nMás información [aquí](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) y [aquí.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":["# Fracción radiométrica efectiva de nubes\n\nLa fracción de nubes es la porción de la superficie cubierta de nubes dividida entre la superficie total. Las nubes ejercen efectos de apantallamiento, albedo y absorción sobre las medidas de gases traza. La fracción radiométrica efectiva de nubes es un parámetro importante para corregir estos efectos."]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Banda térmica 10\n\nEsta visualización térmica se basa en la banda 10 (una banda es una región del espectro electromagnético; los sensores satelitales observan la Tierra en múltiples bandas). La longitud de onda central de 10895 nm está situada en el infrarrojo térmico, o TIR (Thermal InfraRed). Mientras que las estaciones meteorológicas miden la temperatura del aire, la banda 10 registra la temperatura real del suelo, que suele estar bastante más caliente que el aire. La banda térmica 10 sirve para obtener temperaturas superficiales y se registra con una resolución espacial de 100 metros.\n\n\n\nMás información [aquí](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) y [aquí.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":["# Composición de infrarrojo de onda corta (SWIR)\n\nLas medidas tomadas en el infrarrojo de onda corta (SWIR, Short Wave InfraRed) sirven para estimar la cantidad de agua presente en la vegetación y en el suelo, porque el agua absorbe estas longitudes de onda. Las bandas del infrarrojo de onda corta (una banda es una región del espectro electromagnético; los sensores satelitales observan la Tierra en múltiples bandas) sirven también para distinguir los tipos de nubes (nubes de agua frente a nubes de hielo), la nieve y el hielo, todos los cuales aparecen blancos en luz visible. La vegetación adopta tonalidades de verde en esta composición, mientras que los suelos desnudos y las zonas urbanas tienen tonos pardos, y el agua se ve negra. El terreno recién quemado refleja con intensidad las bandas del infrarrojo de onda corta, lo que hace útil esta composición para cartografiar los daños debidos a incendios. Cada tipo de roca refleja de un modo distinto la luz infrarroja de onda corta, lo que permite elaborar mapas geológicos comparando la luz SWIR reflejada.\n\n\n\nMás información [aquí.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)"]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":["Efectos de color RGB avanzados"]},"Left button":{"msgid":"Left button","msgstr":["Botón izquierdo"]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":["Pulse con el botón izquierdo del ratón y arrastre para desplazarse por el mapa a una altitud fija. Use MAYÚS + botón izquierdo para rotar."]},"Right button":{"msgid":"Right button","msgstr":["Botón derecho"]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":["Pulse el botón derecho del ratón y arrastre hacia arriba o hacia abajo para cambiar la altura de la cámara. \nPulse el botón derecho del ratón y arrastre hacia la izquierda o la derecha para rotar la vista de la cámara. "]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":["Botón central o rueda"]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":["Use la rueda del ratón para cambiar la altura de la cámara (se obtiene el mismo efecto pulsando el botón derecho\ny arrastrando hacia arriba o hacia abajo). Pulse con el botón de la rueda y arrastre para cambiar el ángulo de la cámara."]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":["Navegación mediante teclado"]},"Arrow keys":{"msgid":"Arrow keys","msgstr":["Teclas de flechas"]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":["Use las teclas de flechas para moverse por el mapa a una altitud determinada."]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":["MAYÚS + teclas de flechas"]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":["Para cambiar la vista de la cámara pulse las flechas mientras mantiene pulsada la tecla MAYÚS."]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":["RePág/AvPág"]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":["Use las teclas RePág y AvPág para cambiar la altura de la cámara. "]},"Map navigation":{"msgid":"Map navigation","msgstr":["Navegación mediante mapa"]},"Pan console":{"msgid":"Pan console","msgstr":["Consola de panorama"]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":["La consola de panorama permite moverse sobre el mapa a una altitud determinada. Al pulsar y arrastrar\nse produce un desplazamiento continuo que será más veloz cuanto más se aleje del centro al arrastrar."]},"Camera console":{"msgid":"Camera console","msgstr":["Consola de la cámara"]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":["La consola de la cámara solo modifica lo que la cámara ve. Pulse y arrastre para cambiar la vista de la cámara.\nLa vista cambia con más velocidad cuanto más se aleje del centro al arrastrar."]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":["Botones de acercamiento y alejamiento"]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":["Al pulsar cambia la altura de la cámara. El botón \"+\" acerca la cámara al suelo, mientras que \nel botón \"-\" la aleja."]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":[""]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":[""]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":[""]},"Histogram":{"msgid":"Histogram","msgstr":[""]},"Disabled":{"msgid":"Disabled","msgstr":[""]},"Yes":{"msgid":"Yes","msgstr":[""]},"Orthorectification":{"msgid":"Orthorectification","msgstr":[""]},"Cancel":{"msgid":"Cancel","msgstr":["Cancelar"]},"Error":{"msgid":"Error","msgstr":["Error"]},"Help":{"msgid":"Help","msgstr":["Ayuda"]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":[""]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":[""]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":[""]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":[""]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":[""]},"User Instances":{"msgid":"User Instances","msgstr":[""]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":[""]},"Recalculate":{"msgid":"Recalculate","msgstr":[""]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":[""]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":[""]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":[""]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":[""]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":[""]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":[""]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":[""]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":[""]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":[""]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":[""]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":[""]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":[""]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":[""]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":[""]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":[""]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":[""]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":[""]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":[""]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":[""]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":[""]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":[""]},"File upload":{"msgid":"File upload","msgstr":["Carga de archivo"]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Cargue un fichero KML/KMZ, GPX o GEOJSON/JSON para definir el área de interés. Esa área se utilizará para recortar al exportar la imagen."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":["Suelte un fichero KML/KMZ, GPX o GEOJSON/JSON, o busque uno en su computadora"]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":[""]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":[""]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":[""]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":[""]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":[""]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":[""]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":[""]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":[""]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":[""]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":[""]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":[""]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":[""]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":[""]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":[""]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":[""]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":[""]},"Zoom to location":{"msgid":"Zoom to location","msgstr":[""]},"Remove layer":{"msgid":"Remove layer","msgstr":[""]},"Please select a layer":{"msgid":"Please select a layer","msgstr":[""]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":[""]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":[""]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":[""]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":[""]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":[""]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":[""]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":[""]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":[""]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":[""]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":[""]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":[""]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":[""]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":[""]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":[""]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":[""]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":[""]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":[""]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":[""]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":[""]},"Level 1":{"msgid":"Level 1","msgstr":[""]},"Level 2":{"msgid":"Level 2","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":[""]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":[""]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":[""]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Máx. cobertura nubosa:"]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Cargar datos"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]},"Built-up probability [0 - 100 %] at 10 m spatial resolution":{"msgid":"Built-up probability [0 - 100 %] at 10 m spatial resolution","msgstr":[""]},"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).":{"msgid":"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).","msgstr":[""]},"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.":{"msgid":"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.","msgstr":[""]},"Resampling kernel":{"msgid":"Resampling kernel","msgstr":[""]},"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)":{"msgid":"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)","msgstr":[""]},"4x4 cubic convolution":{"msgid":"4x4 cubic convolution","msgstr":[""]},"nearest neighbour":{"msgid":"nearest neighbour","msgstr":[""]},"proprietary MTF kernel":{"msgid":"proprietary MTF kernel","msgstr":[""]},"Width":{"msgid":"Width","msgstr":[""]},"Height":{"msgid":"Height","msgstr":[""]},"Format":{"msgid":"Format","msgstr":[""]},"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.":{"msgid":"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.","msgstr":[""]},"Could not generate timelapse animation file. Try using lower resolution or fewer frames.":{"msgid":"Could not generate timelapse animation file. Try using lower resolution or fewer frames.","msgstr":[""]}}}}
\ No newline at end of file
diff --git a/src/translations/et.po b/src/translations/et.po
new file mode 100644
index 00000000..7d5d549e
--- /dev/null
+++ b/src/translations/et.po
@@ -0,0 +1,6889 @@
+msgid ""
+msgstr ""
+"Project-Id-Version: \n"
+"POT-Creation-Date: \n"
+"PO-Revision-Date: \n"
+"Last-Translator: \n"
+"Language-Team: \n"
+"Language: et\n"
+"mime-version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+"x-generator: Poedit 3.0.1\n"
+
+msgid "Education"
+msgstr "Haridus"
+
+msgid "Normal"
+msgstr "Tavaline"
+
+msgid "Close"
+msgstr "Sulge"
+
+msgid "Close and don't show again"
+msgstr "Sulge ja ära enam näita"
+
+msgid "Previous"
+msgstr "Eelmine"
+
+msgid "End tutorial"
+msgstr "Lõpeta õpetus"
+
+msgid "Next"
+msgstr "Järgmine"
+
+msgid "Continue with tutorial"
+msgstr "Jätka õpetusega"
+
+msgid "Don't show again"
+msgstr "Ära enam näita"
+
+msgid "Show info"
+msgstr "Näita teavet"
+
+msgid "Discover"
+msgstr "Avasta"
+
+msgid "Visualize"
+msgstr "Visualiseeri"
+
+msgid "Compare"
+msgstr "Võrdle"
+
+msgid "Pins"
+msgstr "Märgistused"
+
+msgid "An error has occurred while fetching images:"
+msgstr "Piltide laadimisel tekkis viga:"
+
+msgid "No tile found"
+msgstr "Ühtegi paani ei leitud"
+
+msgid "Dataset"
+msgstr "Andmekogum"
+
+msgid "Show"
+msgstr "Näita"
+
+msgid "Show effects and advanced options"
+msgstr "Näita efekte ja lisavõimalusi"
+
+msgid "Show visualization"
+msgstr "Näita vaadet"
+
+msgid "Add to Pins"
+msgstr "Lisa märgistusse"
+
+msgid "Add to compare"
+msgstr "Lisa võrdluseks"
+
+msgid "Zoom to tile"
+msgstr "Paani suurendamine"
+
+msgid "Hide layer"
+msgstr "Peida kiht"
+
+msgid "Show layer"
+msgstr "Näita kihti"
+
+msgid "Share"
+msgstr "Jaga"
+
+msgid "Custom"
+msgstr "Kohandatud"
+
+msgid "Create custom visualization"
+msgstr "Loo kohandatud vaade"
+
+msgid "Zoom in to view data"
+msgstr "Suumi sisse andmete vaatamiseks"
+
+msgid "Free sign up"
+msgstr "Tasuta registreerumine"
+
+msgid "for all features"
+msgstr "kõikide võimaluste jaoks"
+
+msgid "Powered by"
+msgstr "Platvorm"
+
+msgid "with contributions by"
+msgstr "panustanud"
+
+msgid "Please select data source(s)!"
+msgstr "Palun vali andmete allikas(d)!"
+
+msgid "Invalid time range!"
+msgstr "Sobimatu ajavahemik!"
+
+msgid "No results found"
+msgstr "Ei leitud tulemusi"
+
+msgid "Theme"
+msgstr "Teema"
+
+msgid "Manage configuration instances"
+msgstr "Halda seadistusi"
+
+msgid "Login to use custom configuration instances."
+msgstr "Logi sisse, et kasutada kohandatud seadistusi."
+
+msgid "Error retrieving additional data!"
+msgstr "Tekkis viga täiendavate andmete saamisel!"
+
+msgid "Search"
+msgstr "Otsi"
+
+msgid "Highlights"
+msgstr "Esiletõsted"
+
+msgid "Data sources"
+msgstr "Andmeallikad"
+
+msgid "Please select a theme"
+msgstr "Palun vali teema"
+
+msgid "Time range [UTC]"
+msgstr "Ajavahemik [UTC]"
+
+msgid "Date"
+msgstr "Kuupäev"
+
+msgid "Hide description"
+msgstr "Peida kirjeldus"
+
+msgid "Show description"
+msgstr "Näita kirjeldust"
+
+msgid "This theme has no highlights"
+msgstr "Sellel teemal ei ole esiletõsteid"
+
+msgid "Based on: "
+msgstr "Põhinedes: "
+
+msgid "1 day (S1)"
+msgstr "1 päev (S1)"
+
+msgid "5 day (S5)"
+msgstr "5 päeva (S5)"
+
+msgid "10 day (S10)"
+msgstr "10 päeva (S10)"
+
+msgid "O3 (Ozone)"
+msgstr "O3 (osoon)"
+
+msgid "NO2 (Nitrogen dioxide)"
+msgstr "NO2 (lämmastikdioksiid)"
+
+msgid "SO2 (Sulfur dioxide)"
+msgstr "SO2 (vääveldioksiid)"
+
+msgid "CO (Carbon monoxide)"
+msgstr "CO (süsinikoksiid)"
+
+msgid "HCHO (Formaldehyde)"
+msgstr "HCHO (formaldehüüd)"
+
+msgid "CH4 (Methane)"
+msgstr "CH4 (metaan)"
+
+msgid "AER AI (Aerosol Index)"
+msgstr "AER AI (aerosooli indeks)"
+
+msgid "Cloud"
+msgstr "Pilvisus"
+
+msgid "Other"
+msgstr "Muu"
+
+msgid "Max. cloud coverage"
+msgstr "Maksimaalne pilvisus"
+
+msgid "Advanced search"
+msgstr "Täpsem otsing"
+
+msgid "Data location"
+msgstr "Andmete asukoht"
+
+msgid ""
+"Sentinel-1 services are available both on EOCloud and AWS. The capabilities "
+"of each\n"
+"service differ. More infos at"
+msgstr ""
+"Sentinel-1 teenused on kättesaadavad nii EOCloudis ja AWS-is. Teenuste "
+"võimekus erineb. Rohkem infot"
+
+msgid "Please select at least one location!"
+msgstr "Palun vali vähemalt üks asukoht!"
+
+msgid "Acquisition mode"
+msgstr "Andmehõivemoodus"
+
+msgid "Polarization"
+msgstr "Polarisatsioon"
+
+msgid "Please select at least one data acquisition mode!"
+msgstr "Palun vali vähemalt üks andmehõivemoodus!"
+
+msgid "Please select at least one polarization!"
+msgstr "Palun vali vähemalt üks polarisatsioon!"
+
+msgid "Orbit direction"
+msgstr "Orbiidi suund"
+
+msgid "Please select at least one orbit direction!"
+msgstr "Palun vali vähemalt üks orbiidi suund!"
+
+msgid ""
+"**MERIS** (Medium-resolution spectrometer) was a sensor on board the "
+"[ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-"
+"missions/envisat) satellite with the primary mission to observe land and "
+"ocean colour and the atmosphere. It is no longer active and has been "
+"succeeded by Sentinel-3.\n"
+"\n"
+"**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is "
+"only details bigger than 260m x 290m can be seen).\n"
+"\n"
+"**Revisit time:** maximum 3 days to revisit the same area.\n"
+"\n"
+"**Data availability:** From June 2002 to April 2012.\n"
+"\n"
+"**Common usage:** Ocean monitoring (phytoplankton, suspended matter), "
+"atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation "
+"index, global coverage, moisture)."
+msgstr ""
+"**MERIS** (Keskmise resolutsiooniga spektromeeter) oli sensor satelliidi "
+"[ENVISAT] "
+"(https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/"
+"envisat) pardal, mille esmane missioon oli seirata maapinda, ookeanide "
+"värvust ja atmosfääri. Envisat ei tööta enam ja ta on asendatud "
+"satelliidiga Sentinel-3.\n"
+"\n"
+"**Ruumiline lahutus:** Maa ja ranniku täislahutus: 260 m x 290 m (näha on "
+"ainult suuremaid detaile kui 260 m x 290 m ).\n"
+"\n"
+"**Ülelendude sagedus:** sama ala seire vähemalt üks kord kolme päeva "
+"jooksul.\n"
+"\n"
+"**Andmete saadavus:** alates juunist 2002 kuni aprillini 2012.\n"
+"\n"
+"**Kasutusala:** ookeanide seiramine (sinivetikad, heljum), atmosfäär "
+"(veeaur, CO2, pilved, aerosoolid) ja maapind (vegetatsiooni indeks, "
+"globaalne katvus, niiskus)."
+
+msgid "Credits:"
+msgstr "Tunnustus:"
+
+msgid ""
+"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 "
+"satellite imagery\n"
+"products, covering every part of the world. Most imagery is available "
+"within a few hours after\n"
+"satellite overpass, some products span almost 30 years."
+msgstr ""
+"**GIBS** (Global Imagery Browse Services) pakub kiiret juurdepääsu rohkem "
+"kui 600 satellidipildi\n"
+"tootele, mis hõlmavad kogu Maa. Enamik pilte on kättesaadavad mõne tunni "
+"jooksul\n"
+"pärast satelliidi ülelendu, mõned tooted on peaaegu 30 aasta vanused."
+
+msgid ""
+"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are "
+"similar to Sentinel-2 (they capture visible and infrared wavelengths)\n"
+"and additionally can capture thermal infrared (Landsat 8). The Landsat "
+"series has a long history of imagery spanning nearly five decades.\n"
+" This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n"
+"\n"
+"**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on "
+"the wavelength (that is, only details bigger than 10m and 30m, can be "
+"seen). More info "
+"[here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-"
+"satellites?qt-news_science_products=0#qt-news_science_products).\n"
+"\n"
+"**Revisit time:** Maximum 8 days to revisit the same area using the two "
+"operational satellites Landsat 7 and Landsat 8.\n"
+"\n"
+"**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat "
+"5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA "
+"archive. The global U.S. Geological Survey (USGS) archive since April 2013 "
+"until today (Landsat 8 only) .\n"
+"\n"
+"**Common usage:** Vegetation monitoring, land use, land cover maps, change "
+"monitoring, etc."
+msgstr ""
+"NASA/USA geoloogiateenistuse (USGS) **Landsati** seeria satelliidid on "
+"sarnased satelliidile Sentinel-2 (nad püüavad kinni nähtava ja infrapunase "
+"lainepikkuse)\n"
+"ja lisaks suudavad kinni püüda soojusliku infrapunase kiirguse (Landsat 8). "
+"Landsat seeria satelliitide pildiajalugu on peaaegu 50 aastat vana.\n"
+"See platvorm annab sulle ligipääsu piltidele, mis on saadud satelliitidelt "
+"Landsat 5, 7 ja 8.\n"
+"\n"
+"**Ruumiline lahutusvõime:** 15 m, 30 m, and 100 m (teisendatud 30 "
+"meetrile), sõltub lainepikkusest (näha on ainult esemed, mis on suuremad "
+"kui 10 m ja 30 m). Rohkem infot "
+"[siin](https://www.usgs.gov/faqs/what-are-band-designations-landsat-"
+"satellites?qt-news_science_products=0#qt-news_science_products).\n"
+"\n"
+"**Ülelendude sagedus:** Ülelend maksimaalselt 8 päeva tagant üle sama ala, "
+"kasutades selleks kahte töötavat satelliiti Landsat 7 ja Landsat 8.\n"
+"\n"
+"**Andmete kättesaadavus:** Euroopa ja Põhja-Aafrika 1984-2011 (Landsat 5), "
+"1999-2003 (Landsat 7), 2013 kuni praeguseni (Landsat 8) ESA arhiivist. USA "
+"geoloogiateenistuse (USGS) arhiiv alates aprillist 2013 kuni praeguseni "
+"(Landsat 8 ainult).\n"
+"\n"
+"**Kasutusalad:** Taimkatte seiramine, maa kasutus, maa-kaardid, muutuste "
+"jälgimine jm."
+
+msgid ""
+"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires "
+"data with the objective\n"
+"to improve our understanding of global processes occurring on land. EO "
+"browser provides data for\n"
+"observation of land (bands 1-7).\n"
+"\n"
+"**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands "
+"8-36).\n"
+"\n"
+"**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra "
+"satellites.\n"
+"\n"
+"**Data availability:** Since January 2013.\n"
+"\n"
+"**Common usage:** Monitoring of land, clouds, ocean colour at a global "
+"scale."
+msgstr ""
+"NASA **MODIS** – (mõõduka ruumilise lahutusega spektraalradiomeeter) hangib "
+"andmeid eesmärgiga\n"
+"parandada meie arusaamist maapinnal toimuvatest globaalsetest "
+"protsessidest. EO brauser pakub maapinna vaatluse andmeid (kanalid 1-7).\n"
+"\n"
+"**Ruumiline lahutusvõime:** 250 m (kanalid 1-2), 500 m (kanalid 3-7), 1000 "
+"m (kanalid 8-36).\n"
+"\n"
+"**Ülelendude sagedus:** Globaalne katvus 1 kuni 2 päeva tagant nii Aqua kui "
+"ka Terra satelliitidega.\n"
+"\n"
+"**Andmete kättesaadavus:** Alates jaanuarist 2013.\n"
+"\n"
+"**Kasutusala:** Maa, pilvede, ookeani värvuse seire kogu maailmas."
+
+msgid ""
+"The **Proba-V** satellite is a small satellite designed to map land cover "
+"and vegetation growth\n"
+"across the entire globe every two days. EO Browser provides derived "
+"products which minimize cloud\n"
+"cover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) "
+"and 10 day (S10) period.\n"
+"\n"
+"**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for "
+"S1 and S10.\n"
+"\n"
+"**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for "
+"latitudes between 35°N\n"
+"and 35°S.\n"
+"\n"
+"**Data availability:** Since October 2013.\n"
+"\n"
+"**Common usage:** MThe observation of land cover, vegetation growth, "
+"climate impact assessment,\n"
+"water resource management, agricultural monitoring and food security "
+"estimates, inland water\n"
+"resource monitoring and tracking the steady spread of deserts and "
+"deforestation."
+msgstr ""
+"**Proba-V** on väike satelliit, mis on mõeldud maapinna kaardistamiseks ja "
+"taimkatte jälgimiseks.\n"
+"üle kogu maakera iga kahe päeva tagant. EO Browser pakub tuletatud tooteid, "
+"mis minimeerivad pilvisust,\n"
+"\n"
+"kombineerides selleks pilvevabad mõõtmised ühe päeva (S1), 5 päeva (S5) ja "
+"10 päeva (S10) jooksul. \n"
+"\n"
+"**Ruumiline lahutus:** 100 m (S1 ja S5), 333 m (S1 ja S10), 1000 m (S1 ja "
+"S10).\n"
+"\n"
+"**Ülelendude sagedus:** 1 kord päevas laiuskraadidel 35-75° N ja 35-56° S, "
+"kord 2 päeva jooksul laiuskraadidel 35° N\n"
+"ja 35° S.\n"
+"\n"
+"**Andmete kättesaadavus:** alates oktoobrist 2013.\n"
+"\n"
+"**Kasutusala:** maapinna jälgimine, taimestiku kasv, kliimamõjude "
+"hindamine,\n"
+"veevarude majandamine, põllumajanduse seire ja toiduga kindlustatuse "
+"hinnangud, siseveekogude seire.\n"
+"ressursside jälgimine ning kõrbete leviku ja metsade raadamise pidev "
+"jälgimine."
+
+msgid ""
+"**Sentinel-1** provides all-weather, day and night radar imagery for land "
+"and ocean services. EO\n"
+"Browser provides data acquired in Interferometric Wide Swath (IW) and Extra "
+"Wide Swath (EW) modes\n"
+"processed to Level-1 Ground Range Detected (GRD).\n"
+"\n"
+"**Pixel spacing:** 10m (IW), 40m (EW).\n"
+"\n"
+"**Revisit time:** <= 5 days using both satellites.\n"
+"\n"
+"**Revisit time** (for asc/desc and overlap using both satellites): <= 3 "
+"days, see [observation "
+"scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/"
+"observation-scenario)\n"
+"\n"
+"**Data availability:** Since October 2014.\n"
+"\n"
+"**Common usage:** Maritime and land monitoring, emergency response, climate "
+"change."
+msgstr ""
+"** Sentinel-1 ** pakub radaripilte maa- ja ookeaniteenuste jaoks iga "
+"ilmaga, päeval ja öösel . EO\n"
+"Brauser pakub andmeid Interferometric Wide Swath (IW) ja Extra Wide Swath "
+"(EW) formaatides,\n"
+"töötlus 1. tase Ground Range Detected (GRD).\n"
+"\n"
+"**Piksli vahe:** 10 m (IW), 40 m (EW).\n"
+"\n"
+"**Ülelendude sagedus:** <= kord 5 päeva jooksul kasutades mõlemat "
+"satelliiti.\n"
+"\n"
+"**Ülelendude sagedus** (arvestatakse mõlema satelliidi ning laskumiste ja "
+"tõusmistega): <= 3 päeva, vaata [vaatluse "
+"stsenaarium](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/"
+"observation-scenario)\n"
+"\n"
+"**Andmete kättesaadavus:** alates oktoobrist 2014.\n"
+"\n"
+"**Kasutusala:** Mere- ja maismaa seire, hädaolukordadele reageerimine, "
+"kliimamuutused."
+
+msgid ""
+"**Sentinel-2** provides high-resolution images in the visible and infrared "
+"wavelengths, to monitor vegetation, soil and water cover, inland waterways "
+"and coastal areas. .\n"
+"\n"
+"**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength "
+"(that is, only details bigger than 10m, 20m, and 60m can be seen). More "
+"info "
+"[here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/"
+"resolutions/spatial). \n"
+"\n"
+"**Revisit time:** maximum 5 days to revisit the same area, using both "
+"satellites.\n"
+"\n"
+"**Data availability:** Since June 2015. Full global coverage since March "
+"2017.\n"
+"\n"
+"**Common usage:** Land-cover maps, land-change detection maps, vegetation "
+"monitoring, monitoring of burnt areas."
+msgstr ""
+"**Sentinel-2** pakub kõrge resolutsiooniga pilte nähtavas ja infrapuna "
+"lainepikkustel, et jälgida taimestikku, mullastiku, veepinda, siseveeteid "
+"ja rannikualasid. .\n"
+"\n"
+"**Ruumiline lahutus:** 10 m, 20 m ja 60 m, sõltuvalt lainepikkusest (st "
+"näha on ainult detaile, mis on suuremad kui 10 m, 20 m ja 60 m). Lisateave "
+"[siin](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/"
+"resolutions/spatial). \n"
+"\n"
+"**Ülelendude sagedus:** maksimaalselt viis päeva samast piirkonnast "
+"ülelennuks, kasutades mõlemat satelliiti.\n"
+"\n"
+"**Andmete kättesaadavus:** alates juunist 2015. Täielik ülemaailmne katvus "
+"alates märtsist 2017.\n"
+"\n"
+"**Kasutusala:** maapiina kaardid, maapinna muutuste tuvastamise kaardid, "
+"taimestiku seire, põlenud alade seire."
+
+msgid ""
+"Level 2A data are high quality data where the effects of the atmosphere on "
+"the light being reflected off of the surface of the Earth and reaching the "
+"sensor are excluded. Data are available globally since March 2017.\n"
+"\n"
+"More info about atmospheric correction "
+"[here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/"
+"preprocessing/atmospheric-correction)."
+msgstr ""
+"Taseme 2A andmed on kvaliteetsed andmed, kus välistatakse atmosfääri mõju "
+"valgusele, mis Maa pinnalt peegeldub ja sensorini jõuab. Andmed on "
+"ülemaailmselt saadaval alates 2017. aasta märtsist.\n"
+"\n"
+"Lisateave atmosfääri korrigeerimise kohta [siin] "
+"(http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/"
+"preprocessing/atmospheric-correction)."
+
+msgid ""
+"Level 1C data are data of sufficient quality for most investigations, where "
+"all image corrections were done except for the atmospheric correction. Data "
+"are available globally since June 2015 onwards."
+msgstr ""
+"Taseme 1C andmed on piisava kvaliteediga andmed enamike uuringute jaoks. "
+"Seal on tehtud kõik pildiparandused, välja arvatud atmosfääri "
+"korrigeerimine. Andmed on ülemaailmselt saadaval alates 2015. aasta juunist."
+
+msgid ""
+"**Sentinel-3** mission main objective is to measure sea surface topography, "
+"sea and land surface temperature, ocean and land surface colour. Sentinel-3 "
+"has four different instruments on board. Data acquired by the Ocean and "
+"Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature "
+"Instrument (SLSTR) are available in this platform.\n"
+"\n"
+"**Data availability:** Since May 2016 onwards."
+msgstr ""
+"**Sentinel-3** missiooni peamine eesmärk on mõõta merepinna topograafiat, "
+"mere- ja maapinna temperatuuri, ookeani- ja maapinna värvust. Sentinel-3 "
+"pardal on neli erinevat instrumenti. Sellel platvormil on kättesaadavad "
+"ookeani- ja maapinna värviinstrumendi (OLCI) ja mere- ning maapinna "
+"instrumendi (SLSTR) andmed.\n"
+"\n"
+"**Andmete kättesaadavus:** alates mai 2016."
+
+msgid ""
+"The **Sea and Land Surface Temperature (SLSTR)** instrument on board "
+"Sentinel-3 measures the global and regional sea and land surface \n"
+"temperature. The SLSTR covers the visible, shortwave infrared, and thermal "
+"infrared wavelengths of the electromagnetic spectrum. \n"
+"\n"
+"**Spatial resolution:** 500m for visible, near- and shortwave infrared "
+"wavelengths and 1km for thermal infrared (that is, only details \n"
+"bigger than 500m and 1km can be seen, respectively).\n"
+"\n"
+"**Revisit time:** Maximum 1 day to revisit the same area, using both "
+"satellites.\n"
+"\n"
+"**Data availability:** Since May 2016 onwards.\n"
+"\n"
+"**Common usage:** Climate change monitoring, vegetation monitoring, active "
+"fire detection, land and sea surface temperature monitoring."
+msgstr ""
+"Sentinel-3 pardal olev **mere ja maapinna temperatuuri (SLSTR) ** "
+"mõõteriist mõõdab ülemaailmset ja piirkondlikku mere- ja maapinna\n"
+"temperatuuri. SLSTR hõlmab elektromagnetspektri nähtava, lühilaine- ja "
+"soojusliku infrapunase lainepikkused.\n"
+"\n"
+"**Ruumiline lahutus:** 500 m nähtav, lähedane ja lühilaineline infrapunane "
+"lainepikkus ja 1 km soojuslik infrapunane lainepikkus (st näha on ainult "
+"objektid, mis on suuremad kui 500 m ja 1 km). \n"
+"\n"
+"**Ülelendude sagedus:** kasutades mõlemat satelliiti samast piirkonnast "
+"ülelend maksimaalselt kord päevas.\n"
+"\n"
+"**Andmete kättesaadavus:** alates maist 2016.\n"
+"\n"
+"**Kasutusala:** kliimamuutuste seire, taimkatte seire, aktiivne tulekahjude "
+"avastamine, maa ja merepinna temperatuuri jälgimine."
+
+msgid ""
+"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a "
+"spectrometer that \n"
+"measures the solar radiation reflected by Earth, and it monitors the ocean, "
+"the environment, \n"
+"and climate. It provides more frequent visible imagery than Sentinel-2 but "
+"at a lower resolution\n"
+"and with more wavelengths covered. The Sentinel-3 OLCI instrument continues "
+"the measurements previously performed by the MERIS instrument on board "
+"Envisat, whose mission concluded.\n"
+"\n"
+"**Spatial resolution:** 300m (that is, only details bigger than 300m can be "
+"seen).\n"
+"\n"
+"**Revisit time:** Maximum 2 days to revisit the same area, using both "
+"satellites.\n"
+"\n"
+"**Data availability:** Since May 2016 onwards.\n"
+"\n"
+"**Common usage:** Surface topography, ocean and land surface colour "
+"observations and monitoring."
+msgstr ""
+"Sentinel-3 pardal on spektromeeter ** Ookeani- ja maapinna värviinstrument "
+"(OLCI) **, mis\n"
+"mõõdab Maalt peegelduvat päikesekiirgust ja jälgib ookeani, keskkonda\n"
+"ja kliimat. See pakub nähtavaid pilte sagedamini kui Sentinel-2, pildid on "
+"madalama lahutusega\n"
+"ja suurema lainepikkusega kaetud. Sentinel-3 OLCI-sensor jätkab mõõtmisi, "
+"mida varem tegi missiooni lõpetanud MERIS-seade Envisati pardal.\n"
+"\n"
+"\n"
+"**Ruumiline lahutus:** 300 m (ainult objektid suuremad kui 300 m on "
+"nähtavad).\n"
+"\n"
+"**Ülelendude sagedus:** maksimaalselt 2 päeva sama piirkonna ülelennuks "
+"mõlema satelliidi abil.\n"
+"\n"
+"**Andmete kättesaadavus:** alates maist 2016.\n"
+"\n"
+"**Kasutusala:** pinna topograafia, ookeani ja maapinna värvide vaatlused ja "
+"seire."
+
+msgid ""
+"**Sentinel-5P** is a satellite that provides atmospheric measurements to be "
+"used for air quality, ozone monitoring, UV radiation,\n"
+"and climate monitoring and forecasting.\n"
+"\n"
+"**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x "
+"3.5km can be seen).\n"
+"\n"
+"**Revisit time:** Maximum 1 day to revisit the same area.\n"
+"\n"
+"**Data availability:** Since April 2018 onwards.\n"
+"\n"
+"**Common usage:** Monitoring the concentration of carbon monoxide (CO), "
+"nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol "
+"index (AER_AI) and various geophysical parameters of clouds (Cloud)."
+msgstr ""
+"**Sentinel-5P** satelliit pakub atmosfääri mõõtmisi, mida saab kasutada õhu "
+"kvaliteedi, osooni, UV-kiirguse\n"
+" ja kliima jälgimiseks ning ilmaennustuseks.\n"
+"\n"
+"**Ruumiline lahutus:** 7x3,5 km (ainult objektid suuremad kui 7x3,5 km on "
+"nähtavad).\n"
+"\n"
+"**Ülelendude sagedus:** maksimaalselt üks päev sama piirkonna ülelennuks.\n"
+"\n"
+"**Andmete kättesaadavus:** alates aprillist 2018.\n"
+"\n"
+"**Kasutusala:** süsinikmonooksiidi (CO), lämmastikdioksiidi (NO2) ja osooni "
+"(O3) kontsentratsiooni jälgimine õhus. UV-aerosooli indeksi (AER_AI) ja "
+"erinevate pilvede geofüüsikaliste parameetrite (Cloud) jälgimine."
+
+msgid "Copied"
+msgstr "Kopeeritud"
+
+msgid "Copy to clipboard"
+msgstr "Kopeeri lõikelauale"
+
+msgid "Data source name"
+msgstr "Andmete allika nimi"
+
+msgid "Sensing time"
+msgstr "Ülelennu aeg"
+
+msgid "Cloud coverage"
+msgstr "Pilvkate"
+
+msgid "Sun elevation"
+msgstr "Päikese kõrgus silmapiirist"
+
+msgid "MGRS location"
+msgstr "MGRS asukoht"
+
+msgid "AWS path"
+msgstr "AWS ühenduse link (path)"
+
+msgid "EO Cloud path"
+msgstr "EO Cloud ühenduse link (path)"
+
+msgid "CreoDIAS path"
+msgstr "CreoDIAS ühenduse link (path)"
+
+msgid "SciHub link"
+msgstr "SciHub link"
+
+msgid "Back to search"
+msgstr "Tagasi otsingusse"
+
+#, javascript-format
+msgid "Showing ${ this.state.results.length } result"
+msgid_plural "Showing ${ this.state.results.length } results"
+msgstr[0] "Näitab ${ this.state.results.length } tulemust"
+msgstr[1] "Näitab ${ this.state.results.length } tulemusi"
+
+msgid "Load more"
+msgstr "Lae rohkem"
+
+msgid "Loading more results ..."
+msgstr "Laeb rohkem tulemusi ..."
+
+msgid "Results"
+msgstr "Tulemused"
+
+#, javascript-format
+msgid "Showing ${ this.state.selectedTiles.length } result."
+msgid_plural "Showing ${ this.state.selectedTiles.length } results."
+msgstr[0] "Tulemuse ${ this.state.selectedTiles.length } näitamine."
+msgstr[1] "Tulemuste ${ this.state.selectedTiles.length } näitamine."
+
+msgid "Edit pin description"
+msgstr "Korrigeeri märgistuse kirjeldust"
+
+msgid "Reject changes"
+msgstr "Ignoreeri muudatusi"
+
+msgid "Confirm changes"
+msgstr "Kinnita muudatused"
+
+msgid "Rename pin"
+msgstr "Muuda märgistuse nimi"
+
+msgid "Remove pin"
+msgstr "Eemalda märgistus"
+
+msgid "Zoom to pinned location"
+msgstr "Suumi märgistatud asukoht"
+
+msgid "Lat/Lon"
+msgstr "Laiuskraad/pikkuskraad"
+
+msgid "Zoom"
+msgstr "Suumi"
+
+#, javascript-format
+msgid ""
+"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want "
+"to proceed?"
+msgstr ""
+"${ N_PINS } märgistus(t) lisatakse sinu märgistuste kogusse. Kas soovid "
+"jätkata?"
+
+msgid "WARNING: You're about to delete a pin. Do you wish to continue?"
+msgstr "HOIATUS: Hakkad kustutama märgistust. Kas soovid jätkata?"
+
+msgid "WARNING: You're about to delete all pins. Do you wish to continue?"
+msgstr "HOIATUS: Hakkad kustutama kõiki märgistusi. Kas soovid jätkata?"
+
+msgid ""
+"No pins. Go to the Visualize tab to save a pin or upload a JSON file with "
+"saved pins."
+msgstr ""
+"Märgistusi ei ole. Mine jaotusesse Visualiseeri ja salvesta märgistused või "
+"laadi alla salvestatud märgistustega JSON fail."
+
+msgid ""
+"Note that the pins will be saved only if you log in. Otherwise, the pins "
+"will be lost once the application is closed."
+msgstr ""
+"Pane tähele, et märgistused salvestatakse ainult siis, kui sa oled sisse "
+"logitud. Muudel juhtudel märgistused kaovad pärast rakenduse sulgemist."
+
+msgid "Deselect all"
+msgstr "Tühista valikud"
+
+msgid "Select all"
+msgstr "Vali kõik"
+
+#. the space before the second string is on purpose to have a space between the texts
+msgid "No pins."
+msgstr "Märgistusi ei ole."
+
+#, javascript-format
+msgid "Create link (${ selectedPins.length } pin selected)"
+msgid_plural "Create link (${ selectedPins.length } pins selected)"
+msgstr[0] "Loo link (${ selectedPins.length } märgistus valitud)"
+msgstr[1] "Loo link (${ selectedPins.length } märgistused valitud)"
+
+msgid "File type not supported"
+msgstr "Faili tüüp ei ole toetatud"
+
+msgid "not supported"
+msgstr "ei ole toetatud"
+
+msgid "No pins were found."
+msgstr "Märgistusi ei leitud."
+
+msgid "Error parsing file:"
+msgstr "Viga faili parsimisel:"
+
+msgid "File upload"
+msgstr "Faili üleslaadimine"
+
+msgid "Upload a JSON file with saved pins."
+msgstr "Lae JSON fail üles koos salvestatud märgistustega."
+
+msgid "Drop JSON file or search your computer"
+msgstr "Pukseeri JSON fail või otsi oma arvutist"
+
+msgid "Keep existing pins"
+msgstr "Hoia olemasolevad märgistused"
+
+msgid "Share pins"
+msgstr "Jaga märgistusi"
+
+msgid "Create a story from pins"
+msgstr "Loo märgistustest lugu"
+
+msgid "Export pins to the computer"
+msgstr "Ekspordi märgistused arvutisse"
+
+msgid "Import pins from a saved file"
+msgstr "Impordi märgistused salvestatud failist"
+
+msgid "Delete all pins"
+msgstr "Kustuta kõik märgistused"
+
+msgid "Story"
+msgstr "Lugu"
+
+msgid "Export"
+msgstr "Ekspordi"
+
+msgid "Import"
+msgstr "Impordi"
+
+msgid "Clear"
+msgstr "Puhasta"
+
+msgid "Share pins link"
+msgstr "Jaga märgistuste linki"
+
+msgid "Creating link..."
+msgstr "Loo link..."
+
+msgid "OK"
+msgstr "OK"
+
+msgid "Updating pin collection."
+msgstr "Märgistuste kogu värskendamine."
+
+#, javascript-format
+msgid ""
+"There was a problem permanently updating the pin collection: ${ "
+"updatingPinsError }."
+msgstr "Tekkis probleem märgistuste kogu uuendamisel: ${ updatingPinsError }."
+
+msgid "Hello,"
+msgstr "Tere,"
+
+msgid "Opacity"
+msgstr "Läbipaistmatus"
+
+msgid "Split position"
+msgstr "Jagatud positsioon"
+
+msgid "split"
+msgstr "jaga"
+
+msgid "opacity"
+msgstr "läbipaistmatus"
+
+msgid "No layers to compare."
+msgstr "Võrdlemiseks pole kihte."
+
+msgid "Remove all"
+msgstr "Eemalda kõik"
+
+msgid "Add all pins"
+msgstr "Lisa kõik märgistused"
+
+msgid "Split"
+msgstr "Jaga"
+
+msgid "There was a problem downloading your instances"
+msgstr "Probleem allalaadimisel"
+
+msgid "Download"
+msgstr "Allalaadimine"
+
+msgid "Visualize terrain in 3D"
+msgstr "Visualiseeri maastik 3D-na"
+
+msgid "Go to Place"
+msgstr "Mine asukohta"
+
+msgid "Labels"
+msgstr "Sildid"
+
+msgid "Borders"
+msgstr "Piirid"
+
+msgid "Roads"
+msgstr "Teed"
+
+msgid "Zoom in"
+msgstr "Suumi sisse"
+
+msgid "Zoom out"
+msgstr "Suumi välja"
+
+msgid "About EO Browser"
+msgstr "EO Brauserist"
+
+msgid "Contact us"
+msgstr "Kontakteeru meiega"
+
+msgid "Get data"
+msgstr "Andmete saamine"
+
+msgid "You need to log in to use this function."
+msgstr "Selle funktsiooni kasutamiseks peab sisse logima."
+
+msgid "Please select a layer."
+msgstr "Palun vali kiht."
+
+msgid "Downloading image in compare mode is not possible."
+msgstr "Andmete allalaadimine võrdlevas režiimis ei ole võimalik."
+
+msgid "This datasource is not supported."
+msgstr "Seda andmeallikat ei toetata."
+
+#. jsx-a11y/anchor-is-valid
+#. eslint-disable-next-line
+msgid "Statistical Info / Feature Info Service chart"
+msgstr "Statistilise teabe / funktsioonide teabe teenuse diagramm"
+
+msgid "Statistical Info / Feature Info Service chart - "
+msgstr "Statistilise teabe / funktsioonide teabe teenuse diagramm - "
+
+msgid "please select a layer"
+msgstr "palun vali kiht"
+
+msgid "not available for "
+msgstr "ei ole saadaval "
+
+#, javascript-format
+msgid ""
+"not available for \"${ props.presetLayerName }\" (layer with value is not "
+"set up)"
+msgstr ""
+"ei ole saadaval \"${ props.presetLayerName }\" (väärtusega kihti ei ole "
+"seadistatud)"
+
+msgid "Search for data first."
+msgstr "Otsi esmalt andmeid."
+
+msgid "Create timelapse animation"
+msgstr "Loo aegvõttega animatsioon"
+
+msgid "Mark point of interest"
+msgstr "Märgi huvipunkt"
+
+#. jsx-a11y/anchor-is-valid
+#. eslint-disable-next-line
+msgid "Center map on feature"
+msgstr "Too kaardi kese valitud tunnusele"
+
+#. jsx-a11y/anchor-is-valid
+#. eslint-disable-next-line
+msgid "Remove geometry"
+msgstr "Eemalda geomeetria"
+
+msgid "Area of interest"
+msgstr "Huvipakkuv piirkond"
+
+msgid "Select mode"
+msgstr "Vali režiim"
+
+msgid "Mode:"
+msgstr "Režiim:"
+
+#. jsx-a11y/anchor-is-valid
+#. eslint-disable-next-line
+msgid "Remove measurement"
+msgstr "Eemalda mõõtmine"
+
+msgid "Measure"
+msgstr "Mõõda"
+
+msgid "km"
+msgstr "km"
+
+msgid "m"
+msgstr "m"
+
+msgid "Gain"
+msgstr "Koefitsient"
+
+msgid "Gamma"
+msgstr "Gamma"
+
+msgid "R"
+msgstr "R"
+
+msgid "G"
+msgstr "G"
+
+msgid "B"
+msgstr "B"
+
+msgid "Min. data quality"
+msgstr "Min. andmete kvaliteet"
+
+msgid "Upsampling"
+msgstr "Suurendamine"
+
+msgid "Downsampling"
+msgstr "Vähendamine"
+
+#. jsx-a11y/anchor-is-valid
+#. eslint-disable-next-line
+msgid "Reset all"
+msgstr "Lähtesta kõik"
+
+msgid "filter by months"
+msgstr "filtreeri kuude kaupa"
+
+msgid "Copy geometry to clipboard"
+msgstr "Kopeeri geomeetria lõikelauale"
+
+#. jsx-a11y/anchor-is-valid
+#. eslint-disable-next-line
+msgid "Cancel edit."
+msgstr "Tühista muudatus."
+
+msgid "Draw area of interest"
+msgstr "Määratle huvipakkuv ala"
+
+#. jsx-a11y/anchor-is-valid
+#. eslint-disable-next-line
+msgid "Upload data"
+msgstr "Lae andmed ülesse"
+
+msgid "Least cloud coverage"
+msgstr "Vähim pilvisus"
+
+msgid "Use additional datasets (advanced)"
+msgstr "Kasuta täiendavaid andmekogumeid (täpsem)"
+
+msgid "Mosaicking order"
+msgstr "Mosaiikimine"
+
+msgid "Most recent"
+msgstr "Uusimad"
+
+msgid "Least recent"
+msgstr "Vanimad"
+
+msgid "Customize timespan"
+msgstr "Kohanda ajavahemikku"
+
+#. eslint-disable-next-line
+msgid "Back"
+msgstr "Tagasi"
+
+msgid "Error loading script. Check your URL."
+msgstr "Tõrge skripti laadimisel. Kontrolli URLi."
+
+msgid "Uncheck Load script from URL to edit the code"
+msgstr "Tühjenda URLis laadimise skript, et muuta koodi"
+
+msgid "Load script from URL"
+msgstr "Lae skript URList"
+
+msgid "Enter URL to your script"
+msgstr "Sisesta URL skripti"
+
+msgid "Script loaded."
+msgstr "Skript laetud."
+
+msgid "Only HTTPS domains are allowed."
+msgstr "Ainult HTTPS domeenid on lubatud."
+
+#. eslint-disable-next-line
+msgid "Load script into code editor"
+msgstr "Lae skript koodi korrektorisse"
+
+msgid "Refresh"
+msgstr "Värskenda"
+
+msgid "orbit"
+msgstr "orbiit"
+
+msgid "day"
+msgstr "päev"
+
+msgid "week"
+msgstr "nädal"
+
+msgid "month"
+msgstr "kuu"
+
+msgid "year"
+msgstr "aasta"
+
+msgid "Select 1 image per:"
+msgstr "Vali üks pilt:"
+
+msgid "Timelapse"
+msgstr "Aegvõte"
+
+msgid "Select All"
+msgstr "Vali kõik"
+
+msgid "Speed:"
+msgstr "Kiirus:"
+
+msgid "frames / s"
+msgstr "kaadrid /s"
+
+msgid "Preparing..."
+msgstr "Valmistamine..."
+
+msgid "Could not download files:"
+msgstr "Ei saa faili alla laadida:"
+
+msgid "Can't download via canvas"
+msgstr "Lõuendi kaudu ei saa alla laadida"
+
+msgid "Could not ZIP files:"
+msgstr "Ei saa faile kokku pakkida (ZIP)"
+
+msgid "There was a problem downloading image"
+msgstr "Pildi allalaadmisel tekkis probleem"
+
+msgid "Error fetching image: url is empty!"
+msgstr "Pildi kättesaamisel ilmnes tõrge: url on tühi!"
+
+msgid "Error fetching image:"
+msgstr "Viga pildi kättesaamisel:"
+
+msgid "Could not load image from blob"
+msgstr "Bloobist ei saanud pilti laadida"
+
+msgid "Drag bands onto RGB fields."
+msgstr "Lohista kanalid RGB väljale."
+
+msgid "Drag bands into the index equation"
+msgstr "Lohista kanalid indeksvõrrandisse"
+
+msgid "Index "
+msgstr "Indeks "
+
+msgid "Threshold"
+msgstr "Lävi"
+
+msgid "Remove color picker"
+msgstr "Eemalda värvivalija"
+
+msgid "Add color picker"
+msgstr "Lisa värvivalija"
+
+msgid "Click to place marker"
+msgstr "Klõpsa tähise asetamiseks"
+
+msgid "Click to place first vertex"
+msgstr "Klõpsa esimese tipu asetamiseks"
+
+msgid "Click to continue drawing"
+msgstr "Klõpsa joonistamise jätkamiseks"
+
+msgid "Click first marker to finish"
+msgstr "Lõpetamiseks klõpsa esimesel tähisel"
+
+msgid "Create a timelapse of this area"
+msgstr "Loo aegvõte sellest piirkonnast"
+
+msgid "Show captions"
+msgstr "Näita pealdisi"
+
+msgid "Show slide title"
+msgstr "Näita slaidi pealkirja"
+
+msgid "Add map overlays"
+msgstr "Lisa kaardi ülekatted"
+
+msgid "Show legend"
+msgstr "Näita legendi"
+
+msgid "No pins were found within the current field of view."
+msgstr "Praegusel vaateväljal ei leitud ühtegi märgistust."
+
+#, javascript-format
+msgid ""
+"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not "
+"within the selected area."
+msgstr ""
+"Mõnda märgistust (${ N_PINS_OUTSIDE_BOUNDS }) eiratakse, kuna nad ei asu "
+"valitud alal."
+
+msgid ""
+"To create a pin story, navigate to the desired position on the map.\n"
+"\n"
+"All pins within the current field of view will be used to create the story, "
+"the rest will be ignored."
+msgstr ""
+"Märgistustega loo tegemiseks minge kaardil soovitud asukohta.\n"
+"\n"
+"Kõiki praeguse vaatevälja märgistusi kasutatakse loo tegemiseks, ülejäänud "
+"osasid ignoreeritakse."
+
+msgid "File will have logo attached."
+msgstr "Failile lisatakse logo."
+
+msgid "A dataMask-band will be included in the downloaded raw bands as second band."
+msgstr "DataMask-kanal lisatakse allalaaditud toorandmete kanalisse kui teine kanal."
+
+msgid ""
+"The Tagged Image File Format (TIFF) can hold a large number of bands, "
+"however many common image viewers (e.g. Windows Photo Viewer) can't display "
+"TIFF images with more than 3 bands.\n"
+"If this option is enabled, only the first 3 bands will be included in the "
+"image.\n"
+"If this option is disabled, all bands will be included in the image, but "
+"you will have to use an application which supports more than 3 bands (e.g. "
+"QGIS) to display the TIFF image."
+msgstr ""
+"Märgistatud pildifailivorming (TIFF) mahutab suure hulga kanaleid, kuid "
+"paljud tavalised pildivaaturid (nt Windows Photo Viewer) ei saa kuvada "
+"rohkem kui 3 kanaliga TIFF-pilte.\n"
+"Kui see valik on lubatud, lisatakse pildile ainult kolm esimest kanalit.\n"
+"Kui see valik on keelatud, lisatakse pildile kõik kanalid, kuid TIFF-pildi "
+"kuvamiseks peate kasutama rakendust, mis toetab rohkem kui kolme kanalit "
+"(nt QGIS)."
+
+msgid "Show logo"
+msgstr "Kuva logo"
+
+msgid "Image format"
+msgstr "Pildi formaat"
+
+msgid "Image resolution"
+msgstr "Pildi lahutusvõime"
+
+msgid "Coordinate system"
+msgstr "Koordinaatide süsteem"
+
+msgid "Add dataMask band to raw layers"
+msgstr "Lisa dataMask kanal toorandmete kihtidele"
+
+msgid "Clip extra bands"
+msgstr "Lõika lisakanalid"
+
+msgid "Layers"
+msgstr "Kihid"
+
+msgid "Visualized"
+msgstr "Visualiseeritud"
+
+msgid "Raw"
+msgstr "Algne"
+
+msgid ""
+"The map's overlay layers (place labels, streets and political boundaries) "
+"will be added to the image."
+msgstr ""
+"Pildile lisatakse kaardi ülekattekihid (kohasildid, tänavad ja poliitilised "
+"piirid)."
+
+msgid "Exported image(s) will include datasource and date, zoom scale and branding"
+msgstr ""
+"Eksporditud pilt (pildid) sisaldab andmeallikat ja kuupäeva, suumiskaalat "
+"ja kaubamärki"
+
+msgid "Add a short description to the exported image"
+msgstr "Lisa lühikirjeldus eksporditud pildile"
+
+msgid "Exported image will include legend"
+msgstr "Eskporditud pilt sisaldab legendi"
+
+msgid "Description"
+msgstr "Kirjeldus"
+
+msgid "Image format:"
+msgstr "Pildi formaat:"
+
+msgid "Basic"
+msgstr "Elementaarne"
+
+msgid "Analytical"
+msgstr "Analüütiline"
+
+msgid "High-res print"
+msgstr "Kõrge resolutsiooniga pilt"
+
+msgid "Download image"
+msgstr "Lae pilt alla"
+
+msgid "An error has occurred while fetching some of the images:"
+msgstr "Piltide laadimisel tekkis viga:"
+
+msgid "min/px"
+msgstr "min/px"
+
+msgid "sec/px"
+msgstr "sek/px"
+
+msgid "Resolution"
+msgstr "Lahutus"
+
+msgid "lat."
+msgstr "laiuskraad."
+
+msgid "deg/px"
+msgstr "kraad/px"
+
+msgid "long."
+msgstr "pikkuskraad."
+
+#, javascript-format
+msgid "Projected resolution: ${ formattedResolution } m/px"
+msgstr "Prognoositav lahutusvõime: ${ formattedResolution } m/px"
+
+msgid "Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats."
+msgstr "Viga: andmete ühildamine ei toeta KMZ/JPG ja KMZ/PNG formaate."
+
+msgid ""
+"Error: You can only download visualization with effects in JPEG or PNG "
+"formats."
+msgstr "Viga: efektidega vaadet saab alla laadida ainult JPEG ja PNG formaadis."
+
+msgid "Image download"
+msgstr "Pildi allalaadimine"
+
+msgid ""
+"Warning: Following layers use dataProducts, so the desired data type might "
+"not be set:"
+msgstr ""
+"Hoiatus: järgmised kihid kasutavad dataProduct'e, seega on võimalik, et "
+"soovitud andmetüüpi ei seadistata:"
+
+msgid ""
+"Warning: Evalscript is not in a typical V3 format and the desired data type "
+"could not be set for:"
+msgstr ""
+"Hoiatus: Evalscript pole tavalises V3-vormingus ja soovitud andmetüüpi ei "
+"saanud seadistada:"
+
+msgid ""
+"This means \"sampleType\" parameter is likely set to default (AUTO). You "
+"can fix this by editing your evalscript. Learn more about \"sampleType\" in "
+"the documentation"
+msgstr ""
+"See tähendab, et parameeter \"sampleType\" on tõenäoliselt seatud "
+"vaikeväärtusele (AUTO). Selle saad parandada, muutes oma evalscripti. "
+"Lisateave \"sampleType\" kohta dokumentatsioonis"
+
+msgid "Image width [inches]:"
+msgstr "Pildi laius [tollid]:"
+
+msgid "Image height [inches]:"
+msgstr "Pildi kõrgus [tollid]:"
+
+msgid "DPI:"
+msgstr "DPI:"
+
+msgid "5 years"
+msgstr "5 aastat"
+
+msgid "2 years"
+msgstr "2 aastat"
+
+msgid "1 year"
+msgstr "1 aasta"
+
+msgid "6 months"
+msgstr "6 kuud"
+
+msgid "3 months"
+msgstr "3 kuud"
+
+msgid "1 month"
+msgstr "1 kuu"
+
+msgid "Retry"
+msgstr "Proovi uuesti"
+
+msgid "Loading, please wait"
+msgstr "Laeb, palun oota"
+
+msgid "mean"
+msgstr "keskmine"
+
+msgid "median"
+msgstr "mediaan"
+
+msgid "st. dev."
+msgstr "standardhälve."
+
+msgid "min / max"
+msgstr "min / maks"
+
+msgid "Export CSV"
+msgstr "Ekspordi CSV"
+
+msgid "Timespan:"
+msgstr "Ajavahemik:"
+
+msgid "Date:"
+msgstr "Kuupäev:"
+
+msgid "Single date"
+msgstr "Kuupäev"
+
+msgid "Timespan"
+msgstr "Ajavahemik"
+
+msgid "hh"
+msgstr "tt"
+
+msgid "mm"
+msgstr "mm"
+
+msgid "From:"
+msgstr "Alates:"
+
+msgid "Until:"
+msgstr "Kuni:"
+
+msgid "Apply"
+msgstr "Rakenda"
+
+msgid "Share on Facebook"
+msgstr "Jaga Facebookis"
+
+msgid "Share on Twitter"
+msgstr "Jaga Twitteris"
+
+msgid "Check this out "
+msgstr "Proovi "
+
+msgid "Logout"
+msgstr "Logi välja"
+
+msgid ""
+"Login to unlock advanced features such as timelapse, analytical download, "
+"own configurations and more."
+msgstr ""
+"Logi sisse, et saaks kasutada lisavõimalusi, nt aegvõtet, analüütilist "
+"allalaadimist, enda seadistusi ja palju muud."
+
+msgid "Login"
+msgstr "Logi sisse"
+
+msgid "Monitoring Earth from Space"
+msgstr "Maa seiramine kosmosest"
+
+msgid "Agriculture"
+msgstr "Põllumajandus"
+
+msgid "Atmosphere and Air Pollution"
+msgstr "Atmosfäär ja õhusaaste"
+
+msgid "Change Detection through Time"
+msgstr "Muutuste tuvastamine ajas"
+
+msgid "Floods and Droughts"
+msgstr "Üleujutused ja põuad"
+
+msgid "Geology"
+msgstr "Geoloogia"
+
+msgid "Ocean and Water Bodies"
+msgstr "Ookeanid ja veekogud"
+
+msgid "Snow and Glaciers"
+msgstr "Lumi ja liustikud"
+
+msgid "Urban"
+msgstr "Linnapiirkond"
+
+msgid "Vegetation and Forestry"
+msgstr "Taimestik ja mets"
+
+msgid "Volcanoes"
+msgstr "Vulkaanid"
+
+msgid "Wildfires"
+msgstr "Metsatulekahjud"
+
+msgid "Default"
+msgstr "Vaikimisi"
+
+msgid ""
+"# Welcome To EO Browser!\n"
+"\n"
+"A complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, "
+"ESA’s \n"
+"archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, "
+" \n"
+"MODIS, Proba-V and GIBS products in one place.\n"
+"\n"
+"[EO Browser presentation "
+"page](https://www.sentinel-hub.com/explore/eobrowser/) \n"
+"[EO Browser user "
+"guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"
+msgstr ""
+"# Tere tulemast EO Brauserisse!\n"
+"\n"
+"Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA \n"
+"Landsati 5, 7 ja 8 täielik arhiiv, globaalne katvus Landsat 8, Envisat "
+"Meris, \n"
+"MODIS, Proba-V ja GIBS toodete kohta ühes kohas.\n"
+"\n"
+"[EO brauseri esitlusleht](https://www.sentinel-hub.com/explore/eobrowser/) "
+"\n"
+"[EO brauseri "
+"kasutusjuhend](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"
+
+msgid ""
+"#### Quick overview of EO Browser features\n"
+"\n"
+"EO Browser combines a complete archive of Sentinel-1, Sentinel-2, "
+"Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global "
+"coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in "
+"one place and makes it possible to browse and compare full resolution "
+"images from those sources. You simply go to your area of interest, select "
+"data sources, time range and cloud coverage, and inspect the resulting "
+"data.\n"
+"\n"
+"You can continue the tutorial by clicking on the \"Next\" button or you can "
+"close it. By clicking the info icon in the top right corner you can always resume the "
+"tutorial in case you closed it by mistake or because you wanted to try "
+"things."
+msgstr ""
+"#### Kiire ülevaade EO brauseri võimalustest\n"
+"\n"
+"EO brauser ühendab Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA "
+"Landsat 5, 7 ja 8 arhiivid, Landsat 8 globaalse kattuvuse, Envisat Merise, "
+"MODISe, Proba-V ja GIBSi tooted ühes kohas ja võimaldab sirvida ja võrrelda "
+"täisresolutsiooniga pilte nendest allikatest. Sa lähed soovitud piirkonda, "
+"valid andmeallikad, aja ja pilvisuse ning seejärel saad kontrollida saadud "
+"andmeid.\n"
+"\n"
+"Sa saad jätkata juhendi vaatamist, kui vajutad nuppu \"Järgmine\" või võid "
+"selle sulgeda. Klõpsates paremas ülanurgas olevat teabeikooni saad alati jätkata "
+"õpetuse vaatamisega, juhul kui sa sulgesid selle kogemata või tahtsid "
+"vahepeal midagi katsetada."
+
+msgid ""
+"In the Visualize tab you can select different pre-installed or "
+"custom spectral band combinations to visualise data for the selected "
+"result.\n"
+"\n"
+"Some of the common options:\n"
+"- **True Color** - Visual interpretation of land cover.\n"
+"- **False Color** - Visual interpretation of vegetation.\n"
+"- **NDVI** - Vegetation index.\n"
+"- **Moisture index** - Moisture index\n"
+"- **SWIR** - Shortwave-infrared index.\n"
+"- **NDWI** - Normalized Difference Water Index.\n"
+"- **NDSI** - Normalized Difference Snow Index.\n"
+"\n"
+"Most visualizations are given a description and a legend, which you can "
+"view by clicking on the expand\n"
+"icon .\n"
+" \n"
+"For most data sources the **Custom Script** option is available. Click on "
+"it to select custom\n"
+"band combinations, index combinations or write your own classification "
+"script for the visualisation of data. You can also\n"
+"use custom scripts, which are stored elsewhere, either on Google drive, "
+"GitHub or in our [Custom script "
+"repository](https://custom-scripts.sentinel-hub.com/). \n"
+"Paste the URL of the script into a text box in the advanced script editing "
+"panel and click Refresh.\n"
+" \n"
+"You can change the date directly in the Visualize tab, without going "
+"back to the **Discover** tab. Type in or select it from the calendar .\n"
+"\n"
+"Above the visualizations you have on line of additional tools. Note that "
+"their avalibilty depends on the data source.\n"
+"- **Pin layer** to save it in the application for future use - by clicking "
+"on the pin icon .\n"
+"- Select **advanced options** like the sampling method or apply different "
+"**effects** such as contrast (gain) and luminance (gamma) - by clicking on "
+"the effect sliders icon .\n"
+"- Add a layer to the **Compare** tab for later comparison - by clicking on "
+"the compare icon .\n"
+"- **Zoom** to the centre of the tile - by clicking on the crosshair .\n"
+"- Toggle **layer visibility** - by clicking on the visibility icon .\n"
+"- **Share** your visualization on social media - by clicking on the share "
+"icon ."
+msgstr ""
+"Vahekaardil Visualiseeri saab valida erinevaid eelnevalt "
+"installeeritud või kohandatud spektrikanalite kombinatsioone, et "
+"visualiseerida andmed valitud tulemuseks.\n"
+"\n"
+"Mõned levinud valikud:\n"
+"- **True Color** - maapinna visualiseeritud tõlgendus.\n"
+"- **Valevärv** - taimkatte visualiseeritud tõlgendus.\n"
+"- **NDVI** - taimkatte normaliseeritud vaheindeks.\n"
+"- **Niiskuse indeks** - niiskuse indeks\n"
+"- **SWIR** - lühilaine-infrapuna indeks.\n"
+"- **NDWI** - vee normaliseeritud vaheindeks.\n"
+"- **NDSI** - lume normaliseeritud vaheindeks.\n"
+"\n"
+"Enamikule visualiseeringutest lisatakse kirjeldus ja legend, mida saab "
+"vaadata klõpsates laienduse\n"
+"ikoonil .\n"
+" \n"
+"Enamiku andmeallikate jaoks on saadaval valik ** Kohandatud skript**. "
+"Klõpsa sellel, et valida kohandatud\n"
+"kanalite kombinatsioone, indeksite kombinatsioone või kirjuta andmete "
+"visualiseerimiseks oma klassifikatsiooniskript. Sa saad ka\n"
+"kasutada kohandatud skripte, mis on salvestatud mujale, kas Google Drive'i, "
+"GitHubi või meie [Kohandatud skriptihoidla] "
+"(https://custom-scripts.sentinel-hub.com/).\n"
+"Kleebi skripti URL skripti täpsema redigeerimise paneeli tekstikasti ja "
+"klõpsa käsku Värskenda. \n"
+" \n"
+"Kuupäeva saab muuta vahekaardil Visualiseeri, ei pea minema tagasi "
+"vahekaardile **Avasta**. Sisesta see või vali kalendrist .\n"
+"\n"
+"Visualiseeringute kohal on täiendavaid tööriistu. Pane tähele, et nende "
+"kättesaadavus sõltub andmeallikast.\n"
+"- **Märgistuste kiht** klõpsa märgistuste ikoonil, et salvestada see "
+"rakenduses edasiseks kasutamiseks .\n"
+"- Vali **täpsemad valikud** nagu valimi meetod või rakenda erinevaid "
+"**efekte** nagu kontrastsus (võimendus) ja heledus (gamma) - klõpsa "
+"efektide ikoonil .\n"
+"- Lisa kiht vahekaardile ** Võrdle ** hilisemaks võrdlemiseks - klõpsa "
+"võrdlusikoonil .\n"
+"- **Suumi** paani keskele - klõpsa sihikujoonestikul .\n"
+"- Lülita sisse **kihi nähtavus** - klõpsa ikoonil tee nähtavaks.\n"
+"- **Jaga** oma visualiseeringuid sotsiaalmeedias - klõpsa jagamise ikoonil "
+"."
+
+msgid ""
+"In the **Compare** tab you will find all visualizations that you added via "
+" to **Compare**. \n"
+"\n"
+"There are two modes:\n"
+" - **Opacity** (Draw opacity slider left or right to fade between compared "
+"images)\n"
+" - **Split** (Draw split slider left or right to set the boundary between "
+"compared images)\n"
+"\n"
+"You can add all pins to the compare panel using **Add all pins** or remove all visualizations\n"
+"from the **Compare** tab with the **Remove "
+"all** button."
+msgstr ""
+"Vahekaardil ** Võrdle ** leiad kõik visualiseeringud, mille oled lisanud kaudu lehele ** Võrdle **.\n"
+"\n"
+"Kaks režiimi:\n"
+" - ** läbipaistmatus ** (Võrreldud piltide vahelise piiri hägustamiseks "
+"tõmba liugurit vasakule või paremale)\n"
+" - ** poolitamine ** (Võrreldud piltide vahelise piiri määramiseks tõmba "
+"liugurit vasakule või paremale)\n"
+"\n"
+"Kõiki märgistusi saad lisada võrdlusesse, kasutades valikut ** Lisa kõik märgistused ** või eemalda kõik "
+"visualiseeringud\n"
+"vahekaardilt ** Võrdle ** nupuga ** "
+"Eemalda kõik **."
+
+msgid ""
+"Here you can select which base layer and overlays (roads, borders, labels) "
+"are shown on the map."
+msgstr ""
+"Siin saad valida, milline aluskiht ja ülekatted (teed, piirid, sildid) "
+"kaardil kuvatakse."
+
+msgid ""
+"Here you can switch between the **normal** and the **education** mode. The "
+"**education** mode offers you a slightly simplified version of the app.\n"
+"It can also be accessed directly via its [dedicated "
+"URL](https://apps.sentinel-hub.com/eo-browser-education/)."
+msgstr ""
+"Siin saad vahetada režiimi ** tavaline ** ja ** haridus ** vahel. ** "
+"Haridus ** režiim pakub teile rakenduse veidi lihtsustatud versiooni.\n"
+"Sellele pääseb juurde ka otse oma [määratud URL-i] "
+"(https://apps.sentinel-hub.com/eo-browser-education/) kaudu."
+
+msgid ""
+"You can view the tutorial anytime by clicking on this info icon\n"
+"\n"
+"\n"
+"."
+msgstr ""
+"Sa võid vaadata õpetust igal ajal, vajutades info ikoonile\n"
+"\n"
+"\n"
+"."
+
+msgid ""
+"#### Quick overview of EO Browser features\n"
+"\n"
+"If you have a small screen, please go "
+"[here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view "
+"our user guide.\n"
+"\n"
+"You can always view this info again by clicking the info icon\n"
+"\n"
+"\n"
+"\n"
+"in the top right corner.\n"
+"\n"
+"#### Other resources\n"
+"- [EO Browser presentation "
+"page](https://www.sentinel-hub.com/explore/eobrowser/)\n"
+"- [EO Browser Summer 2018 updates - "
+"video](https://www.youtube.com/embed/m3pron0C0kE)"
+msgstr ""
+"#### Kiire ülevaade EO brauseri funktsioonidest\n"
+"\n"
+"Kui sul on väike ekraan, mine meie kasutusjuhendi vaatamiseks [siia] "
+"(https://www.sentinel-hub.com/explore/eobrowser/user-guide/).\n"
+"\n"
+"Saad seda infot alati uuesti vaadata, kui klõpsad info ikoonil\n"
+"\n"
+"\n"
+"\n"
+"paremal ülanurgas.\n"
+"\n"
+"#### Muud vahendid\n"
+"- [EO brauseri esitlusleht] "
+"(https://www.sentinel-hub.com/explore/eobrowser/)\n"
+"- [EO brauseri suvi 2018 värskendused - video] "
+"(https://www.youtube.com/embed/m3pron0C0kE)"
+
+msgid "What Is EO Browser?"
+msgstr "Mis on EO brauser?"
+
+msgid "User Account"
+msgstr "Kasutajakonto"
+
+msgid "Discover Tab"
+msgstr "Avasta vaheleht"
+
+msgid "Visualize Tab"
+msgstr "Visualiseeri vaheleht"
+
+msgid "Compare Tab"
+msgstr "Võrdle vaheleht"
+
+msgid "Pins Tab"
+msgstr "Märgistuste vaheleht"
+
+msgid "Search Places"
+msgstr "Otsi kohti"
+
+msgid "Layers And Overlays"
+msgstr "Kihid ja ülekatted"
+
+msgid "Education Mode"
+msgstr "Hariduslik režiim"
+
+msgid "Information And Tutorial"
+msgstr "Info ja õpetus"
+
+msgid "Draw Area Of Interest"
+msgstr "Märgi huvipakkuv piirkond"
+
+msgid "Mark Point Of Interest"
+msgstr "Märgi huvipakkuv koht"
+
+msgid "Measure Distances"
+msgstr "Mõõda vahemaad"
+
+msgid "Download Image"
+msgstr "Lae pilt alla"
+
+msgid "Create Timelapse Animation"
+msgstr "Loo aegvõttega animatsioon"
+
+msgid "Happy Browsing!"
+msgstr "Mõnusat sirvimist!"
+
+msgid "Welcome To EO Browser!"
+msgstr "Tere tulemast EO brauserisse!"
+
+msgid "Band 1 - Yellow substance and detrital pigments - 412.5 nm"
+msgstr "Kanal 1 - kollane aine ja detritaalsed pigmendid - 412.5 nm"
+
+msgid "Band 3 - Chlorophyll and other pigments - 490 nm"
+msgstr "Kanal 3 - klorofüll ja teised pigmendid - 490 nm"
+
+msgid "Band 4 - Suspended sediment, red tides - 510 nm"
+msgstr "Kanal 4 - hõljunud sete, punased looded - 510 nm"
+
+msgid "Band 5 - Chlorophyll absorption minimum - 560 nm"
+msgstr "Kanal 5 - klorofülli neeldumise miinimum - 560 nm"
+
+msgid "Band 6 - Suspended sediment - 620 nm"
+msgstr "Kanal 6 - hõljunud sete - 620 nm"
+
+msgid "Band 7 - Chlorophyll absorption & fluo. reference - 665 nm"
+msgstr "Kanal 7 - klorofülli neeldumine ja fluorestsentsi võrdlus - 665 nm"
+
+msgid "Band 8 - Chlorophyll fluorescence peak - 681 nm"
+msgstr "Kanal 8 - klorofülli fluorestsentsi maksimum - 681nm"
+
+msgid "Band 9 - Fluo. reference, atmosphere corrections - 709 nm"
+msgstr "Kanal 9 - fluorestsentsi võrdlusväärtus, atmosfääri korrektsioon - 709 nm"
+
+msgid "Band 10 - Vegetation, cloud - 753 nm"
+msgstr "Kanal 10 - taimkate, pilved - 753 nm"
+
+msgid "Band 12 - Atmosphere corrections - 779 nm"
+msgstr "Kanal 12 - atmosfääri korrektsioonid - 779 nm"
+
+msgid "Band 13 - Vegetation, water vapour reference - 865 nm"
+msgstr "Kanal 13 - taimkatte, veeauru võrdlusväärtus - 865 nm"
+
+msgid "Band 14 - Atmosphere corrections - 885 nm"
+msgstr "Kanal 14 - Atmosfääri korrektsioon - 885 nm"
+
+msgid "Band 15 - Water vapour, land - 900 nm"
+msgstr "Kanal 15 - veeaur, maapind - 900 nm"
+
+msgid "Band 1 - Blue - 450-515 nm"
+msgstr "Kanal 1 - sinine - 450-515 nm"
+
+msgid "Band 2 - Green - 525-605 nm"
+msgstr "Kanal 2 - roheline - 525-605 nm"
+
+msgid "Band 3 - Red - 630-690 nm"
+msgstr "Kanal 3 - punane - 630-690 nm"
+
+msgid "Band 4 - NIR - 750-900 nm"
+msgstr "Kanal 4 - NIR - 750-900 nm"
+
+msgid "Band 5 - SWIR-1 - 1550-1750 nm"
+msgstr "Kanal 5 - SWIR-1 - 1550-1750 nm"
+
+msgid "Band 7 - SWIR-2 - 2090-2350 nm"
+msgstr "Kanal 7 - SWIR-2 - 2090-2350 nm"
+
+msgid "Band 8 - Panchromatic - 520-900 nm"
+msgstr "Kanal 8 - pankromaatiline - 520-900 nm"
+
+msgid "Band 1 - Coastal/Aerosol - 433-453 nm"
+msgstr "Kanal 1 - rannikuala/aerosool - 433-453 nm"
+
+msgid "Band 2 - Blue - 450-515 nm"
+msgstr "Kanal 2 - sinine - 450-515 nm"
+
+msgid "Band 3 - Green - 525-600 nm"
+msgstr "Kanal 3 - roheline - 525-600 nm"
+
+msgid "Band 4 - Red - 630-680 nm"
+msgstr "Kanal 4 - punane - 630-680 nm"
+
+msgid "Band 5 - NIR - 845-885 nm"
+msgstr "Kanal 5 - NIR - 845-885 nm"
+
+msgid "Band 6 - SWIR-1 - 1560-1660 nm"
+msgstr "Kanal 6 - SWIR-1 - 1560-1660 nm"
+
+msgid "Band 7 - SWIR-2 - 2100-2300 nm"
+msgstr "Kanal 7 - SWIR-2 - 2100-2300 nm"
+
+msgid "Band 8 - Panchromatic - 500-680 nm"
+msgstr "Kanal 8 - pankromaatiline - 500-680 nm"
+
+msgid "Band 9 - Cirrus - 1360-1390 nm"
+msgstr "Kanal 9 - kiudpilved - 1360-1390 nm"
+
+msgid "Landsat 5 (ESA archive)"
+msgstr "Landsat 5 (ESA arhiiv)"
+
+msgid "Landsat 7 (ESA archive)"
+msgstr "Landsat 7 (ESA arhiiv)"
+
+msgid "Landsat 8 (ESA archive)"
+msgstr "Landsat 8 (ESA arhiiv)"
+
+msgid "Landsat 8 (USGS archive)"
+msgstr "Landsat 8 (USGS arhiiv)"
+
+msgid "Red band"
+msgstr "Punane kanal"
+
+msgid "841 - 876 nm (NIR)"
+msgstr "841 - 876 nm (NIR)"
+
+msgid "Blue band"
+msgstr "Sinine kanal"
+
+msgid "Green band"
+msgstr "Roheline kanal"
+
+msgid "1230 - 1250 nm"
+msgstr "1230 - 1250 nm"
+
+msgid "1628 - 1652 nm"
+msgstr "1628 - 1652 nm"
+
+msgid "2105 - 2155 nm"
+msgstr "2105 - 2155 nm"
+
+msgid "Band 1 - Coastal aerosol - 443 nm"
+msgstr "Kanal 1 - rannikualade aerosoolid - 443 nm"
+
+msgid "Band 2 - Blue - 490 nm"
+msgstr "Kanal 2 - sinine - 490 nm"
+
+msgid "Band 3 - Green - 560 nm"
+msgstr "Kanal 3 - roheline - 560 nm"
+
+msgid "Band 4 - Red - 665 nm"
+msgstr "Kanal 4 - punane - 665 nm"
+
+msgid "Band 5 - Vegetation Red Edge - 705 nm"
+msgstr "Kanal 5 - taimkate, lühilaineline infrapuna - 705 nm"
+
+msgid "Band 6 - Vegetation Red Edge - 740 nm"
+msgstr "Kanal 6 - taimkate, lühilaineline infrapuna - 740 nm"
+
+msgid "Band 7 - Vegetation Red Edge - 783 nm"
+msgstr "Kanal 7 - taimkate, lühilaineline infrapuna - 783 nm"
+
+msgid "Band 8 - NIR - 842 nm"
+msgstr "Kanal 8 - NIR - 842 nm"
+
+msgid "Band 9 - Water vapour - 945 nm"
+msgstr "Kanal 9 - veeaur - 945 nm"
+
+msgid "Band 10 - SWIR - Cirrus - 1375 nm"
+msgstr "Kanal 10 - SWIR - kiudpilved - 1375 nm"
+
+msgid "Band 11 - SWIR - 1610 nm"
+msgstr "Kanal 11 - SWIR - 1610 nm"
+
+msgid "Band 12 - SWIR - 2190 nm"
+msgstr "Kanal 12 - SWIR - 2190 nm"
+
+msgid "Band 8A - Vegetation Red Edge - 865 nm"
+msgstr "Kanal 8A - taimkate, lühilaineline infrapuna - 865 nm"
+
+msgid "Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm"
+msgstr ""
+"Kanal 1 - aerosooli korrektsioon, vee optiliselt aktiivsete ainete "
+"väärtuste täpsem leidmine - 400 nm"
+
+msgid "Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm"
+msgstr "Kanal 2 - värvunud lahustunud orgaaniline aine ja mineraalne hõljum - 412 nm"
+
+msgid "Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm"
+msgstr "Kanal 3 - klorofülli neeldumise maksimum, biogeokeemia, taimkate - 442.5 nm"
+
+msgid "Band 4 - High Chl, other pigments - 490 nm"
+msgstr "Kanal 4 - kõrge klorofüll, teised pigmendid - 490 nm"
+
+msgid "Band 5 - Chl, sediment, turbidity, red tide - 510 nm"
+msgstr ""
+"Kanal 5 - klorofüll, sete, hägusus, fütoplankton, mis annab veele punase "
+"värvuse - 510 nm"
+
+msgid "Band 6 - Chlorophyll reference (Chl minimum) - 560 nm"
+msgstr "Kanal 6 - klorofülli võrdlusväärtus (klorofülli minimum) - 560 nm"
+
+msgid "Band 7 - Sediment loading - 620 nm"
+msgstr "Kanal 7 - settehulk - 620 nm"
+
+msgid ""
+"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - "
+"665 nm"
+msgstr ""
+"Kanal 8 - klorofüll (teine klorofülli neeldumise maksimum), sete, värvunud "
+"lahustunud orgaaniline aine/ taimkate"
+
+msgid "Band 10 - Chl fluorescence peak, red edge - 681.25 nm"
+msgstr ""
+"Kanal 10 - klorofülli fluorestsentsi maksimum, peegeldusteguri järsk muutus "
+"(red edge) - 681.25 nm"
+
+msgid "Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm"
+msgstr ""
+"Kanal 11 - klorofülli fluorestsentsi baasväärtus, peegeldusteguri järsu "
+"muutuse siire (red edge transition) - 708.75 nm"
+
+msgid "Band 12 - O2 absorption/clouds, vegetation - 753.75 nm"
+msgstr "Kanal 12 - hapniku neeldumine/pilved, taimkate - 753.75 nm"
+
+msgid "Band 13 - O2 absorption band/aerosol corr. - 761.25 nm"
+msgstr "Kanal 13 - hapniku neeldumine/ aerosooli korrektsioon - 761.25 nm"
+
+msgid "Band 14 - Atmospheric correction - 764.375 nm"
+msgstr "Kanal 14 - atmosfääri korrektsioon - 764.375 nm"
+
+msgid "Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm"
+msgstr ""
+"Kanal 15 - O2A kasutatakse rõhu määramiseks pilve tipus, maapealne "
+"fluorestsents - 767.5 nm"
+
+msgid "Band 16 - Atmos. corr./aerosol corr. - 778.75 nm"
+msgstr "Kanal 16 - atmosfääri korrektsioon/aerosoolide korrektsioon - 778.75 nm"
+
+msgid "Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm"
+msgstr ""
+"Kanal 17 - atmosfääri korrektsioon/ aerosooli korrektsioon, pilved, "
+"pikslite koordinaatide põhine sidumine - 865 nm"
+
+msgid ""
+"Band 18 - Water vapour absorption reference band. Common reference band "
+"with SLSTR instrument. Vegetation monitoring - 885 nm"
+msgstr ""
+"Kanal 18 - veeauru neeldumise võrdlusväärtus. Üldine võrdlusväärtuse kanal "
+"instrumendiga SLSTR. Taimkatte seire - 885 nm"
+
+msgid ""
+"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) "
+"- 900 nm"
+msgstr ""
+"Kanal 19 - Veeauru neeldumise/taimkatte seire (maks. peegeldusvõime) - 900 "
+"nm"
+
+msgid "Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm"
+msgstr "Kanal 20 - Veeauru neeldumise, atmosfääri/aerosoolide korrektsioon - 940 nm"
+
+msgid "Band 21 - Atmos./aerosol corr. - 1020 nm"
+msgstr "Kanal 21 - Atmosfääri/aerosoolide korrektsioon - 1020 nm"
+
+msgid "Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm"
+msgstr ""
+"Kanal F1 - tule soojuskiirguse infrapunane osa - aktiivse tule korral - "
+"3742.00 nm"
+
+msgid "Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm"
+msgstr ""
+"Kanal F2 - tule soojuskiirguse infrapunane osa - aktiivse tule korral - "
+"10854.00 nm"
+
+msgid "Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm"
+msgstr ""
+"Kanal S1 - VNIR - pilvede skriining (valikuline kontroll), taimkatte seire, "
+"aerosool - 554.27 nm"
+
+msgid "Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm"
+msgstr "Kanal S2 - VNIR - NDVI, taimestiku seire, aerosoolid - 659.47 nm"
+
+msgid "Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm"
+msgstr ""
+"Kanal S3 - VNIR - NDVI, pilvede märgistamine, pikslite koordinaatide põhine "
+"sidumine - 868.00 nm"
+
+msgid "Band S4 - SWIR - Cirrus detection over land - 1374.80 nm"
+msgstr "Kanal S4 - SWIR - Kiudpilvede tuvastamine üle maa - 1374.80 nm"
+
+msgid ""
+"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 "
+"nm"
+msgstr "Kanal S5 - SWIR - Pilvisuse, jää. lume, taimkatte seire - 1613.40 nm"
+
+msgid "Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm"
+msgstr "Kanal S6 - SWIR - Taimkatte seisund ja pilvisus 2255.70 nm"
+
+msgid "Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm"
+msgstr ""
+"Kanal S7 - Ümbritseva keskkonna soojuskiirgus - SST, LST, aktiivse tule "
+"faas - 3742.00 nm"
+
+msgid "Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm"
+msgstr ""
+"Kanal S8 - Ümbritseva keskkonna soojuskiirgus - SST, LST, aktiivse tule "
+"faas - 10854.00 nm"
+
+msgid "Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm"
+msgstr "Kanal S9 - Ümbritseva keskkonna soojuskiirgus - SST, LST - 12022.50 nm"
+
+msgid "Reflectance"
+msgstr "Peegeldus"
+
+msgid "Brightness temperature"
+msgstr "Heleduse temperatuur"
+
+msgid "Based on the combination of bands 4, 3, 2"
+msgstr "Põhineb kanalite kominatsioonil 4, 3, 2"
+
+msgid "Based on the combination of bands (B04-B03)/(B04+B03)"
+msgstr "Põhineb kanalite (B04-B03)/(B04+B03) kombinatsioonil"
+
+msgid "Based on the combination of bands 5, 4, 3"
+msgstr "Põhineb kanalite 5, 4, 3 kombinatsioonil"
+
+msgid "Based on true color bands 4, 3, 2 and a pan band 8"
+msgstr ""
+"Põhineb loomilike värvidega (true color) kanalitel 4, 3, 2 ja must-valgel "
+"kanalil 8"
+
+msgid "Based on the combination of bands (B05-B04)/(B05+B04)"
+msgstr "Põhineb kanalite (B05-B04)/(B05+B04) kombinatsioonil"
+
+msgid "VV - linear gamma0 - orthorectified"
+msgstr "VV polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseeritud"
+
+msgid "VV - linear gamma0 - non-orthorectified"
+msgstr "VV polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseerimata"
+
+msgid "VH - linear gamma0 - orthorectified"
+msgstr "VH polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseeritud"
+
+msgid "Based on the combination of bands 3, 2, 1"
+msgstr "Põhineb kanalite 3, 2, 1 kombinatsioonil"
+
+msgid "VH - linear gamma 0 - non-orthorectified"
+msgstr "VH polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseerimata"
+
+msgid ""
+"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / "
+"100.0] - linear gamma0 - orthorectified"
+msgstr ""
+"Pildi värviliseks muutmine sisemiste kanalite kaardistamise abil. [RGB] "
+"väärtus = [VV, 2 VH, VV / VH / 100.0] - lineaarse skaala gamma0 - "
+"ortorektifitseeritud"
+
+msgid "VV - decibel gamma0 [-20,0] - orthorectified"
+msgstr ""
+"VV polarimeetrilise kanali detsibell skaalal gamma0 [-20,0] - "
+"ortorektifitseeritud"
+
+msgid "VH - decibel gamma0 [-20,0] - orthorectified"
+msgstr ""
+"VH polarimeetrilise kanali detsibell skaalal gamma0 [-20,0] - "
+"ortorektifitseeritud"
+
+msgid "Returns a composite of (VH, VV, VH-VV)"
+msgstr "Tagastab polarisatsioonide komposiidi (VH, VV, VH-VV)"
+
+msgid "HH - linear gamma0 - orthorectified"
+msgstr "HH polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseeritud"
+
+msgid "HV - linear gamma0 - orthorectified"
+msgstr "HV polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseeritud"
+
+msgid ""
+"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / "
+"100.0] - linear gamma0 - orthorectified"
+msgstr ""
+"Pildi värviliseks muutmine sisemiste kanalite kaardistamise abil. [RGB] "
+"väärtus = [HH, 2 HV, HH / HV / 100.0] - lineaarse skaala gamma0 - "
+"ortorektifitseeritud"
+
+msgid "HH - decibel gamma0 [-20,0] - orthorectified"
+msgstr ""
+"HH polarimeetrilise kanali detsibell skaalal gamma0 [-20,0] - "
+"ortorektifitseeritud"
+
+msgid "HV - decibel gamma0 [-20,0] - orthorectified"
+msgstr ""
+"HV polarimeetrilise kanali detsibell skaalal gamma0 [-20,0] - "
+"ortorektifitseeritud"
+
+msgid "HH - linear gamma0 - non-orthorectified"
+msgstr "HH polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseerimata"
+
+msgid "Based on bands 4,3,2"
+msgstr "Põhineb kanalitele 4, 3, 2"
+
+msgid "Based on bands 8,4,3"
+msgstr "Põhineb kanalitele 8, 4, 3"
+
+msgid "Based on bands 12,11,4"
+msgstr "Põhineb kanalitele 12, 11, 4"
+
+msgid "Based on combination of bands (B8 - B4)/(B8 + B4)"
+msgstr "Põhineb kanalite (B8 - B4)/(B8 + B4) kombinatsioonil"
+
+msgid "Based on combination of bands (B8A - B11)/(B8A + B11)"
+msgstr "Põhineb kanalite (B8A - B11)/(B8A + B11) kombinatsioonil"
+
+msgid "Based on bands 12,8A,4"
+msgstr "Põhineb kanalitel 12,8A,4"
+
+msgid "Based on combination of bands (B3 - B8)/(B3 + B8)"
+msgstr "Põhineb kanalite (B3 - B8)/(B3 + B8) kombinatsioonil"
+
+msgid ""
+"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are "
+"regarded as snowy"
+msgstr ""
+"Põhineb kanalite (B3 - B11) / (B3 + B11) kombinatsioonil; väärtusi üle 0,42 "
+"peetakse lumisteks"
+
+msgid ""
+"Classification of Sentinel2 data as result of ESA's Scene classificaiton "
+"algorithm."
+msgstr ""
+"Sentinel2 andmete klassifikatsioon vastavalt ESA Scene klassifikatsiooni "
+"algoritmile."
+
+msgid "UV Aerosol Index from 380 and 340 nm"
+msgstr "UV aerosooli indeks 380 nm ja 340 nm juures"
+
+msgid "Based on combination of bands (B3 - B11)/(B3 + B11)"
+msgstr "Põhineb kanalite (B3 - B11)/(B3 + B11) kombinatsioonil"
+
+msgid ""
+"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - "
+"B11)/(B11 - B10)"
+msgstr ""
+"OLCI maismaa klorofülli indeks, põhineb kanalite kombinatsioonil (B12 – "
+"B11)/(B11 – B10)"
+
+msgid "UV Aerosol Index from 388 and 354 nm"
+msgstr "UV aerosooli indeks 388 nm ja 354 nm juures"
+
+msgid "Column averaged dry air mixing ratio of methane"
+msgstr "Metaani vertikaalselt keskmistatud kuiva õhu segusuhe"
+
+msgid "Cloud base height"
+msgstr "Pilvede alumise piiri kõrgus"
+
+msgid "Cloud base pressure"
+msgstr "Rõhk pilvede alumisel piiril"
+
+msgid "Effective radiometric cloud fraction"
+msgstr "Radiomeetriliselt efektiivne pilvede osakaal"
+
+msgid "Cloud optical thickness"
+msgstr "Pilvede optiline paksus"
+
+msgid "Cloud top height"
+msgstr "Pilvede ülemise piiri kõrgus"
+
+msgid "Cloud top pressure"
+msgstr "Rõhk pilvede ülemisel piiril"
+
+msgid "Carbon Monoxide total column"
+msgstr "Süsinikmonooksiidi koguhulk atmosfääri sambas"
+
+msgid "Formaldehyde troposheric vertical column"
+msgstr "Formaldehüüdi koguhulk troposfääri sambas"
+
+msgid "Nitrogen Dioxide tropospheric column"
+msgstr "Lämmastikdioksiidi koguhulk troposfääri sambas"
+
+msgid "Ozone total column"
+msgstr "Osooni koguhulk atmosfääri sambas"
+
+msgid "Sulfur Dioxide total column"
+msgstr "Vääveldioksiidi koguhulk atmosfääri sambas"
+
+msgid "Based on bands 2, 1, 4"
+msgstr "Põhineb kanalitel 2, 1, 4"
+
+msgid "Based on combination of bands (B02 - B01)/(B02 + B01)"
+msgstr "Põhineb kanalite (B02 - B01)/(B02 + B01) kombinatsioonil"
+
+msgid "Based on combination of bands (B02 - B05)/(B02 + B05)"
+msgstr "Põhineb kanalite (B02 - B05)/(B02 + B05) kombinatsioonil"
+
+msgid "Based on combination of bands (B06 - B07)/(B06 + B07)"
+msgstr "Põhineb kanalite (B06 - B07)/(B06 + B07) kombinatsioonil"
+
+msgid "Based on bands 1, 4, 3"
+msgstr "Põhineb kanalitel 1, 4, 3"
+
+msgid "Based on the combination of bands 7, 5, 3"
+msgstr "Põhineb kanalite 7, 5, 3 kombinatsioonil"
+
+msgid "(B09 - B08)/(B09 + B08)"
+msgstr "(B09 - B08)/(B09 + B08)"
+
+msgid "B09 / B08"
+msgstr "B09 / B08"
+
+msgid "Based on the combination of bands 13, 5, 2"
+msgstr "Põhineb kanalite kombinatsioonil 13, 5, 2"
+
+msgid "Based on bands 13, 4, 1"
+msgstr "Põhineb kanalitele 13, 4, 1"
+
+msgid "Based on bands 13, 5, 2"
+msgstr "Põhineb kanalitel 13, 5, 2"
+
+msgid "Based on the combination of bands (B13-B07) / (B13+B07)"
+msgstr "Põhineb kanalite (B13-B07) / (B13+B07) kombinatsioonil"
+
+msgid "Terrestrial Chlorophyl Index"
+msgstr "Maismaa klorofülli indeks"
+
+msgid ""
+"PROBA-V 10-daily Synthesis\n"
+"Top of Canopy (Atmospherically corrected)\n"
+"temporal resolution: 10-daily\n"
+"Resolution: 333M (pixel size)"
+msgstr ""
+"PROBA-V 10-päevane süntees\n"
+"Puuvõra kõrgus (atmosfääri korrektsioon tehtud)\n"
+"Ajaline resolutsioon: 10-päeva\n"
+"Ruumiline resolutsioon: 333M (piksli suurus)"
+
+msgid ""
+"PROBA-V daily Synthesis\n"
+"Top of Atmosphere\n"
+"temporal resolution: daily\n"
+"Resolution: 333M (pixel size)"
+msgstr ""
+"PROBA-V igapäevane süntees\n"
+"Atmosfääri ülapiir\n"
+"Ajaline resolutsioon: 1 päev\n"
+"Ruumiline resolutsioon: 333M (piksli suurus)"
+
+msgid ""
+"PROBA-V 5-daily Synthesis\n"
+"Top of Atmosphere\n"
+"temporal resolution: 5-daily\n"
+"Resolution: 100M (pixel size)"
+msgstr ""
+"PROBA-V 5-päevane süntees\n"
+"Atmosfääri ülapiir\n"
+"Ajaline resolutsioon: 5 päeva \n"
+"Ruumiline resolutsioon: 333M (piksli suurus)"
+
+msgid ""
+"PROBA-V daily Synthesis\n"
+"Top of Canopy (Atmospherically corrected)\n"
+"temporal resolution: daily\n"
+"Resolution: 333M (pixel size)"
+msgstr ""
+"PROBA-V igapäevane süntees\n"
+"Puuvõra (canopy) kõrgus (atmosfääri korrektsioon tehtud)\n"
+"Ajaline resolutsioon: 1 päev\n"
+"Ruumiline resolutsioon: 333M (piksli suurus)"
+
+msgid "OrbitTracks_Aqua_Descending"
+msgstr "OrbitTracks_Aqua_Descending"
+
+msgid ""
+"PROBA-V 5-daily Synthesis\n"
+"Top of Canopy (Atmospherically corrected)\n"
+"temporal resolution: 5-daily\n"
+"Resolution: 100M (pixel size)"
+msgstr ""
+"PROBA-V 5-päevane süntees\n"
+"Puuvõra kõrgus (atmosfääri korrektsioon tehtud)\n"
+"Ajaline resolutsioon: 5 päeva \n"
+"Ruumiline resolutsioon: 100 M (piksli suurus)"
+
+msgid "OrbitTracks_Aqua_Ascending"
+msgstr "OrbitTracks_Aqua_Ascending"
+
+msgid "OrbitTracks_Aura_Ascending"
+msgstr "OrbitTracks_Aura_Ascending"
+
+msgid "OrbitTracks_Aura_Descending"
+msgstr "OrbitTracks_Aura_Descending"
+
+msgid "OrbitTracks_CloudSat_Ascending"
+msgstr "OrbitTracks_CloudSat_Ascending"
+
+msgid "OrbitTracks_Calipso_Ascending"
+msgstr "OrbitTracks_Calipso_Ascending"
+
+msgid "OrbitTracks_Calipso_Descending"
+msgstr "OrbitTracks_Calipso_Descending"
+
+msgid "OrbitTracks_CloudSat_Descending"
+msgstr "OrbitTracks_CloudSat_Descending"
+
+msgid "OrbitTracks_CYGNSS_Ascending"
+msgstr "OrbitTracks_CYGNSS_Ascending"
+
+msgid "OrbitTracks_CYGNSS_Descending"
+msgstr "OrbitTracks_CYGNSS_Descending"
+
+msgid "OrbitTracks_GCOM-C_Ascending"
+msgstr "OrbitTracks_GCOM-C_Ascending"
+
+msgid "OrbitTracks_GCOM-C_Descending"
+msgstr "OrbitTracks_GCOM-C_Descending"
+
+msgid "OrbitTracks_GCOM-W1_Ascending"
+msgstr "OrbitTracks_GCOM-W1_Ascending"
+
+msgid "OrbitTracks_GCOM-W1_Descending"
+msgstr "OrbitTracks_GCOM-W1_Descending"
+
+msgid "OrbitTracks_GOSAT-2_Ascending"
+msgstr "OrbitTracks_GOSAT-2_Ascending"
+
+msgid "OrbitTracks_GOSAT-2_Descending"
+msgstr "OrbitTracks_GOSAT-2_Descending"
+
+msgid "OrbitTracks_GOSAT_Ascending"
+msgstr "OrbitTracks_GOSAT_Ascending"
+
+msgid "OrbitTracks_GOSAT_Descending"
+msgstr "OrbitTracks_GOSAT_Descending"
+
+msgid "OrbitTracks_GPM_Ascending"
+msgstr "OrbitTracks_GPM_Ascending"
+
+msgid "OrbitTracks_GPM_Descending"
+msgstr "OrbitTracks_GPM_Descending"
+
+msgid "OrbitTracks_ICESAT-2_Ascending"
+msgstr "OrbitTracks_ICESAT-2_Ascending"
+
+msgid "OrbitTracks_ICESAT-2_Descending"
+msgstr "OrbitTracks_ICESAT-2_Descending"
+
+msgid "OrbitTracks_ISS_Ascending"
+msgstr "OrbitTracks_ISS_Ascending"
+
+msgid "OrbitTracks_ISS_Descending"
+msgstr "OrbitTracks_ISS_Descending"
+
+msgid "OrbitTracks_Landsat-7_Ascending"
+msgstr "OrbitTracks_Landsat-7_Ascending"
+
+msgid "OrbitTracks_Landsat-7_Descending"
+msgstr "OrbitTracks_Landsat-7_Descending"
+
+msgid "OrbitTracks_Landsat-8_Ascending"
+msgstr "OrbitTracks_Landsat-8_Ascending"
+
+msgid "OrbitTracks_METOP-A_Ascending"
+msgstr "OrbitTracks_METOP-A_Ascending"
+
+msgid "OrbitTracks_METOP-B_Descending"
+msgstr "OrbitTracks_METOP-B_Descending"
+
+msgid "OrbitTracks_Landsat-8_Descending"
+msgstr "OrbitTracks_Landsat-8_Descending"
+
+msgid "OrbitTracks_METOP-C_Ascending"
+msgstr "OrbitTracks_METOP-C_Ascending"
+
+msgid "OrbitTracks_METOP-C_Descending"
+msgstr "OrbitTracks_METOP-C_Descending"
+
+msgid "OrbitTracks_NOAA-20_Ascending"
+msgstr "OrbitTracks_NOAA-20_Ascending"
+
+msgid "OrbitTracks_NOAA-20_Descending"
+msgstr "OrbitTracks_NOAA-20_Descending"
+
+msgid "OrbitTracks_METOP-B_Ascending"
+msgstr "OrbitTracks_METOP-B_Ascending"
+
+msgid "OrbitTracks_OCO-2_Ascending"
+msgstr "OrbitTracks_OCO-2_Ascending"
+
+msgid "OrbitTracks_OCO-2_Descending"
+msgstr "OrbitTracks_OCO-2_Descending"
+
+msgid "OrbitTracks_SAOCOM1-A_Ascending"
+msgstr "OrbitTracks_SAOCOM1-A_Ascending"
+
+msgid "OrbitTracks_SAOCOM1-A_Descending"
+msgstr "OrbitTracks_SAOCOM1-A_Descending"
+
+msgid "OrbitTracks_Sentinel-1A_Ascending"
+msgstr "OrbitTracks_Sentinel-1A_Ascending"
+
+msgid "OrbitTracks_Sentinel-1B_Ascending"
+msgstr "OrbitTracks_Sentinel-1B_Ascending"
+
+msgid "OrbitTracks_Sentinel-1A_Descending"
+msgstr "OrbitTracks_Sentinel-1A_Descending"
+
+msgid "OrbitTracks_METOP-A_Descending"
+msgstr "OrbitTracks_METOP-A_Descending"
+
+msgid "OrbitTracks_Sentinel-1B_Descending"
+msgstr "OrbitTracks_Sentinel-1B_Descending"
+
+msgid "OrbitTracks_Sentinel-2A_Ascending"
+msgstr "OrbitTracks_Sentinel-2A_Ascending"
+
+msgid "OrbitTracks_Sentinel-2A_Descending"
+msgstr "OrbitTracks_Sentinel-2A_Descending"
+
+msgid "OrbitTracks_Sentinel-2B_Ascending"
+msgstr "OrbitTracks_Sentinel-2B_Ascending"
+
+msgid "OrbitTracks_Sentinel-2B_Descending"
+msgstr "OrbitTracks_Sentinel-2B_Descending"
+
+msgid "OrbitTracks_Sentinel-5P_Ascending"
+msgstr "OrbitTracks_Sentinel-5P_Ascending"
+
+msgid "OrbitTracks_Sentinel-5P_Descending"
+msgstr "OrbitTracks_Sentinel-5P_Descending"
+
+msgid "OrbitTracks_SMAP_Ascending"
+msgstr "OrbitTracks_SMAP_Ascending"
+
+msgid "OrbitTracks_SMAP_Descending"
+msgstr "OrbitTracks_SMAP_Descending"
+
+msgid "OrbitTracks_Suomi_NPP_Descending"
+msgstr "OrbitTracks_Suomi_NPP_Descending"
+
+msgid "OrbitTracks_Suomi_NPP_Ascending"
+msgstr "OrbitTracks_Suomi_NPP_Ascending"
+
+msgid "OrbitTracks_Terra_Descending"
+msgstr "OrbitTracks_Terra_Descending"
+
+msgid "OrbitTracks_Terra_Ascending"
+msgstr "OrbitTracks_Terra_Ascending"
+
+msgid "Based on bands 4, 3, 2 enhanced by bands 12 and 11."
+msgstr "Põhineb kanalitel 4, 3, 2, parandatud kanalitega 12 ja 11."
+
+msgid "Based on bands B07, B06, B04"
+msgstr "Põhineb kanalitel B07, B06, B04"
+
+msgid "Based on the combination of bands (B8 - B4)/(B8 + B4)"
+msgstr "Põhineb kanalite (B8 - B4)/(B8 + B4) kombinatsioonil"
+
+msgid "Based on thermal band 10"
+msgstr "Põhineb kanalil 10"
+
+msgid "Based on bands B12, B11, B8A"
+msgstr "Põhineb kanalitel B12, B11, B8A"
+
+msgid "Based on the combination of bands (B08 - B12)/(B08 + B12)"
+msgstr "Põhineb kanalite (B08 - B12)/(B08 + B12) kombinatsioonil"
+
+msgid "Enhanced natural color visualization"
+msgstr "Loomulike värvidega visualiseerimine (täiustatud)"
+
+msgid "Based on the combination of bands 8, 6, 4"
+msgstr "Põhineb kanalite 8, 6, 4 kombinatsioonil"
+
+msgid "Enhanced Vegetation Index"
+msgstr "Täiustatud vegetatsiooni indeks"
+
+msgid "Based on the combination: BSI, B08, B11"
+msgstr "Põhineb kanalite kombinatsioonil: BSI, B08, B11"
+
+msgid "Classified NDMI for irrigation"
+msgstr "Niisutamise jaoks klassifitseeritud NDMI"
+
+msgid "Based on bands B11, B08, B02"
+msgstr "Põhineb kanalitele B11, B08, B02"
+
+msgid "False Color 13, 5, 2"
+msgstr "Valevärv 13, 5, 2"
+
+msgid "Based on the combination of bands (B13 - B07) / (B13 + B07)"
+msgstr "Põhineb kanalite kombinatsioonile (B13 - B07) / (B13 + B07)"
+
+msgid "Based on bands 12,8,2"
+msgstr "Põhineb kanalitel 12, 8, 2"
+
+msgid "Based on bands 4, 3, 2"
+msgstr "Põhineb kanalitel 4, 3, 2"
+
+msgid "Based on bands 8, 4, 3"
+msgstr "Põhineb kanalitel 8, 4, 3"
+
+msgid "Based on bands 12, 8, 2"
+msgstr "Põhineb kanalitel 12, 8, 2"
+
+msgid "Based on bands 8, 11, 12"
+msgstr "Põhineb kanalitel 8, 11, 12"
+
+msgid "Water sedimentation and chlorophyll content"
+msgstr "Vee sete ja klorofülli sisaldus"
+
+msgid "Based on bands 12, 8A, 4"
+msgstr "Põhineb kanalitel 12, 8A, 4"
+
+msgid "Based on NDSI"
+msgstr "Põhineb NDSI-l"
+
+msgid "Based on the combination of bands 11, 8, 2"
+msgstr "Põhineb kanalite 11, 8, 2 kombinatsioonil"
+
+msgid "Based on bands B12, B11, B04"
+msgstr "Põhineb kanalitel B12, B11, B04"
+
+msgid "Based on bands 4, 3 ,2"
+msgstr "Põhineb kanalitel 4, 3, 2"
+
+msgid "Atmospherically Resistant Vegetation Index"
+msgstr "Atmosfäärile vastupidav vegetatsiooni indeks"
+
+msgid "Soil Adjusted Vegetation Index"
+msgstr "Vegetatsiooni indeks, mis võtab arvesse mulla näitajaid"
+
+msgid ""
+"# Thermal IR fire emission bands\n"
+"\n"
+"Sentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two "
+"dedicated channels (F1 and F2) that aim to detect Land Surface Temperature "
+"(LST). F2 Channel, with a central wavelength of 10854 nm measures in the "
+"thermal infrared, or TIR. It is very useful for fire and high temperature "
+"event monitoring at 1 km resolution.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/"
+"overview/geophysical-measurements/land-surface-temperature)"
+msgstr ""
+"# Termilised IR tulekahju ribad\n"
+"\n"
+"Sentinel-3 mere- ja maapinna temperatuuri instrumendil (SLSTR) on kaks "
+"spetsiaalset kanalit (F1 ja F2), mille eesmärk on tuvastada maapinna "
+"temperatuur (LST). F2-kanal, kesklainepikkusega 10854 nm, mõõdetakse "
+"termilise infrapuna ehk TIR-ga. See on väga kasulik tulekahjude ja kõrge "
+"temperatuuri sündmuste jälgimiseks 1 km eraldusvõimega.\n"
+"\n"
+"\n"
+"\n"
+"Lisateave [siin.] "
+"(Https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/"
+"overview/geophysical-measurements/land-surface-temperature)"
+
+msgid ""
+"# Methane (CH4)\n"
+"\n"
+"\n"
+"\n"
+"Methane is, after carbon dioxide, the most important contributor to the "
+"anthropogenically (caused by human activity) enhanced greenhouse effect. "
+"Measurements are provided in parts per billion (ppb) with a spatial "
+"resolution of 7 km x 3.5 km.\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](http://www.tropomi.eu/data-products/methane)"
+msgstr ""
+"# Metaan (CH4)\n"
+"\n"
+"\n"
+"\n"
+"Metaani panus antropogeense kasvuhooneefekti tugevnemisse on "
+"süsinikdiooksidi järel teisel kohal. Mõõdetakse osakest miljardi kohta (ppb "
+"- parts per billion) ruumilise resolutsiooniga 7 km x 3.5 km.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot [siin.](http://www.tropomi.eu/data-products/methane)"
+
+msgid ""
+"# Formaldehyde (HCHO)\n"
+"\n"
+"\n"
+"\n"
+"Long term satellite observations of tropospheric formaldehyde (HCHO) are "
+"essential to support air quality and chemistry-climate related studies from "
+"the regional to the global scale. The seasonal and inter-annual variations "
+"of the formaldehyde distribution are principally related to temperature "
+"changes and fire events, but also to changes in anthropogenic (human-made) "
+"activities. Its lifetime being of the order of a few hours, HCHO "
+"concentrations in the boundary layer can be directly related to the release "
+"of short-lived hydrocarbons, which mostly cannot be observed directly from "
+"space. Measurements are in mol per square meter (mol/ m^2).\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](http://www.tropomi.eu/data-products/formaldehyde)"
+msgstr ""
+"# Formaldehüüd (HCHO)\n"
+"\n"
+"\n"
+"\n"
+"Troposfääri formaldehüüdi (HCHO) pikaajaline satelliitseire on oluline, et "
+"toetada õhukvaliteedi ning atmosfäärikeemiaga seotud uuringuid nii "
+"piirkondlikul kui ülemaailmsel tasandil. Formaldehüüdi jaotumise "
+"hooajalised ja aastatevahelised kõikumised on peamiselt seotud "
+"temperatuurimuutuste ja tulekahjudega, aga ka muutustega inimtegevuses. "
+"Kuna HCHO eluiga on suurusjärgus mõni tund, siis selle kontsentratsioon "
+"atmosfääri piirkihis võib olla otseselt seotud lühiealiste süsivesinike "
+"vabanemisega, mida enamasti ei ole võimalik vahetult kosmosest jälgida. "
+"Mõõtmised on moolides ruutmeetri kohta (mol/m²).\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot [siin.](http://www.tropomi.eu/data-products/formaldehyde)"
+
+msgid ""
+"# Sulfur Dioxide (SO2)\n"
+"\n"
+"\n"
+"\n"
+"Sulphur dioxide enters the Earth’s atmosphere through both natural and "
+"anthropogenic (human made) processes. It plays a role in chemistry on a "
+"local and global scale and its impact ranges from short term pollution to "
+"effects on climate. Only about 30% of the emitted SO2 comes from natural "
+"sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI "
+"instrument samples the Earth’s surface with a revisit time of one day with "
+"a spatial resolution of 3.5 x 7 km which allows the resolution of fine "
+"details including the detection of smaller SO2 plumes. Measurements are in "
+"mol per square meter (mol/ m^2).\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)"
+msgstr ""
+"# Vääveldioksiid (SO2)\n"
+"\n"
+"\n"
+"\n"
+"Vääveldioksiid satub Maa atmosfääri nii looduslike kui ka inimtekkeliste "
+"(inimese loodud) protsesside kaudu. See mängib rolli nii kohalikul kui ka "
+"ülemaailmsel tasandil ning selle mõju ulatub lühiajalisest reostusest kuni "
+"kliimani . Ainult umbes 30% eralduvast SO2-st pärineb looduslikest "
+"allikatest; enamus on inimtekkelist päritolu. Sentinel-5P / "
+"TROPOMI-instrument mõõdab Maa pinda kord päevas ruumilise eraldusvõimega "
+"3,5 x 7 km, mis võimaldab eraldada peeneid detaile, sealhulgas tuvastada "
+"väiksemaid SO2 osakesi. Mõõtmised on moolides ruutmeetri kohta (mol/m²).\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot [siin.](http://www.tropomi.eu/data-products/sulphur-dioxide)"
+
+msgid ""
+"# Ozone (O3)\n"
+"\n"
+"\n"
+"\n"
+"Ozone is of crucial importance for the equilibrium of the Earth atmosphere. "
+"In the stratosphere, the ozone layer shields the biosphere from dangerous "
+"solar ultraviolet radiation. In the troposphere, it acts as an efficient "
+"cleansing agent, but at high concentration it also becomes harmful to the "
+"health of humans, animals, and vegetation. Ozone is also an important "
+"greenhouse-gas contributor to ongoing climate change. Since the discovery "
+"of the Antarctic ozone hole in the 1980s and the subsequent Montreal "
+"Protocol regulating the production of chlorine-containing ozone-depleting "
+"substances, ozone has been routinely monitored from the ground and from "
+"space. Measurements are in mol per square meter (mol/ m^2)\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](http://www.tropomi.eu/data-products/total-ozone-column)"
+msgstr ""
+"# Osoon (O3)\n"
+"\n"
+"\n"
+"\n"
+"Osoon on Maa atmosfääri tasakaalu jaoks otsustava tähtsusega. Stratosfääris "
+"olev osoonikiht kaitseb biosfääri ohtliku ultraviolettkiirguse eest. "
+"Troposfääris toimib see tõhusa puhastusainena, kuid suure kontsentratsiooni "
+"korral muutub kahjulikuks inimestele, loomadele ja taimedele. Osoon on ka "
+"oluline kasvuhoonegaas. Alates Antarktika osooniaugu avastamisest 1980. "
+"aastatel ja sellele järgnenud Montreali protokollist (mis reguleerib kloori "
+"sisaldavate osoonikihti kahandavate ainete tootmist), on osooni "
+"regulaarselt maapinnalt ja kosmosest jälgitud. Mõõtmised on moolides "
+"ruutmeetri kohta (mol/m²).\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot [siin.](http://www.tropomi.eu/data-products/total-ozone-column)"
+
+msgid ""
+"# Nitrogen Dioxide (NO2)\n"
+"\n"
+"\n"
+"\n"
+"Nitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually "
+"referred to as nitrogen oxides. They are important trace gases in the "
+"Earth’s atmosphere, present in both the troposphere and the stratosphere. "
+"They enter the atmosphere as a result of anthropogenic activities "
+"(particularly fossil fuel combustion and biomass burning) and natural "
+"processes (such as microbiological processes in soils, wildfires and "
+"lightning). Measurements are in mol per square meter (mol/ m^2).\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)"
+msgstr ""
+"# Naatriumdioksiid (NO2)\n"
+"\n"
+"\n"
+"\n"
+"Lämmastikdioksiidi (NO2) ja lämmastikoksiidi (NO) koos nimetatakse "
+"tavaliselt lämmastikoksiidideks. Need on Maa atmosfääris olulised "
+"jälggaasid, mis esinevad nii troposfääris kui ka stratosfääris. Nad "
+"sisenevad atmosfääri inimtekkeliste tegevuste (eelkõige fossiilkütuste ja "
+"biomassi põletamise) ja looduslike protsesside (nt mulla mikrobioloogiliste "
+"protsesside, metsatulekahjude ja välgu) tulemusena. Mõõtmised on moolides "
+"ruutmeetri kohta (mol/m²).\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot [siin.](http://www.tropomi.eu/data-products/nitrogen-dioxide)"
+
+msgid ""
+"# Carbon Monoxide (CO)\n"
+"\n"
+"\n"
+"\n"
+"Carbon monoxide (CO) is an important atmospheric trace gas. In certain "
+"urban areas, it is a major atmospheric pollutant. Main sources of CO are "
+"combustion of fossil fuels, biomass burning, and atmospheric oxidation of "
+"methane and other hydrocarbons. The carbon monoxide total column is "
+"measured in mol per square meter (mol/ m^2).\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)"
+msgstr ""
+"# Süsinikmonoksiid (CO)\n"
+"\n"
+"\n"
+"\n"
+"Süsinikmonooksiid (CO) on oluline atmosfääri jälggaas. Osades "
+"linnapiirkondades on see peamine õhku saastav aine. Peamised CO-allikad on "
+"fossiilkütuste põletamine, biomassi põletamine ning metaani ja muude "
+"süsivesinike oksüdeerumine. Süsinikmonooksiidi koguhulka atmosfääri sambas "
+"mõõdetakse moolides ruutmeetri kohta (mol/m²).\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot [siin.](http://www.tropomi.eu/data-products/carbon-monoxide)"
+
+msgid ""
+"# Aerosol Index\n"
+"\n"
+"The Aerosol Index (AI) is a qualitative index indicating the presence of "
+"elevated layers of aerosols in the atmosphere. It can be used to detect the "
+"presence of UV absorbing aerosols such as desert dust and volcanic ash "
+"plumes. Positive values (from light blue to red) indicate the presence of "
+"UV-absorbing aerosol. This index is calculated for two pairs of "
+"wavelengths: 340/380 nm and 354/388 nm.\n"
+"\n"
+"More info "
+"[here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/"
+"level-2/aerosol-index)"
+msgstr ""
+"# Aerosooli indeks\n"
+"\n"
+"Aerosoolide indeks (AI) on kvalitatiivne indeks, mis näitab suurema "
+"aerosoolide kontsentratsiooniga kihtide olemasolu atmosfääris. Seda saab "
+"kasutada UV-kiirgust absorbeerivate aerosoolide, näiteks kõrbetolmu ja "
+"vulkaanituha, olemasolu tuvastamiseks. Positiivsed väärtused (helesinisest "
+"punaseni) näitavad UV-kiirgust absorbeerivate aerosoolide olemasolu. See "
+"indeks arvutatakse kahe lainepikkuste paari kohta: 340/380 nm ja 354/388 "
+"nm.\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/"
+"level-2/aerosol-index)"
+
+msgid ""
+"# Cloud base height\n"
+"\n"
+"Height of cloud base measured in meters (m)."
+msgstr ""
+"# Pilvede alumise piiri kõrgus\n"
+"\n"
+"Pilvede alumise piiri kõrgust mõõdetakse meetrites (m)."
+
+msgid ""
+"# Cloud base pressure\n"
+"\n"
+"Pressure measured at cloud base in Pascal (Pa)."
+msgstr ""
+"# rõhk pilvede alumisel piiril\n"
+"\n"
+"Rõhku pilvede alumisel piiril mõõdetakse paskalites (Pa)."
+
+msgid ""
+"# Cloud optical thickness\n"
+"\n"
+"The cloud thickness is a key parameter to characterise optical properties "
+"of clouds. It is a measure of how much sunlight passes through the cloud to "
+"reach Earth's surface. The higher a cloud's optical thickness, the more "
+"sunlight the cloud is scattering and reflecting. Dark blue shows where "
+"there are low cloud optical thickness values and red shows larger cloud "
+"optical thickness."
+msgstr ""
+"# Pilvede optiline paksus\n"
+"\n"
+"Pilvede paksus on pilvede optiliste omaduste iseloomustamisel "
+"põhiparameeter. See näitab kui palju päikesevalgust jõuab läbi pilve Maa "
+"pinnale. Mida suurem on pilve optiline paksus, seda rohkem päikesevalgust "
+"pilv hajutab ja peegeldab. Tumesinine värv näitab väikseid pilve optilise "
+"paksuse väärtusi ja punane suuremaid pilve optilise paksuse väärtusi."
+
+msgid ""
+"# Cloud top height\n"
+"\n"
+"Height of cloud top measured in meters (m)."
+msgstr ""
+"# Pilvede ülemise piiri kõrgus\n"
+"\n"
+"Pilvede ülemise piiri kõrgust mõõdetakse meetrites (m)."
+
+msgid ""
+"# Cloud top pressure\n"
+"\n"
+"Pressure measured at cloud top in Pascal (Pa)."
+msgstr ""
+"# Rõhk pilvede ülemisel piiril\n"
+"\n"
+"Rõhku pilvede ülemisel piiril mõõdetakse paskalites (Pa)."
+
+msgid ""
+"# Normalized Difference Vegetation Index (NDVI)\n"
+"\n"
+"The normalized difference vegetation index is a simple, but effective index "
+"for quantifying green vegetation. It is a measure of the state of "
+"vegetation health based on how plants reflect light at certain wavelengths. "
+"The value range of the NDVI is -1 to 1. Negative values of NDVI (values "
+"approaching -1) correspond to water. Values close to zero (-0.1to 0.1) "
+"generally correspond to barren areas of rock, sand, or snow. Low, positive "
+"values represent shrub and grassland (approximately 0.2 to 0.4), while high "
+"values indicate temperate and tropical rainforests (values approaching 1).\n"
+"\n"
+"More info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) "
+"and [here.](https://eos.com/ndvi/)"
+msgstr ""
+"# Normaliseeritud vegetatsiooni indeks (NDVI)\n"
+"\n"
+"Normaliseeritud vegetatsiooni indeks on lihtne, kuid tõhus indeks rohelise "
+"taimestiku kvantifitseerimiseks. See on taimestiku seisundi näitaja, mis "
+"põhineb sellel, kuidas taimed peegeldavad valgust teatud lainepikkustel. "
+"NDVI väärtusvahemik on -1 kuni 1. NDVI negatiivsed väärtused (lähenevad "
+"-1-le) vastavad veele. Nullilähedased väärtused (–0,1 kuni 0,1) vastavad "
+"tavaliselt kivide, liiva või lume viljatutele aladele. Madalad positiivsed "
+"väärtused tähistavad põõsastikku ja rohumaad (ligikaudu 0,2–0,4), kõrged "
+"väärtused viitavad parasvöötme ja troopilistele vihmametsadele (väärtused "
+"lähenevad 1-le).\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and "
+"[here.](https://eos.com/ndvi/)"
+
+msgid ""
+"# Enhanced Vegetation Index (EVI)\n"
+"\n"
+"The enhanced vegetation index (EVI) is an 'optimized' vegetation index as "
+"it corrects for soil background signals and atmospheric influences. It is "
+"very useful in areas of dense canopy cover. The range of values for EVI is "
+"-1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"More infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) "
+"and "
+"[here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/"
+"measuring_vegetation_4.php)"
+msgstr ""
+"# Tõhustatud vegetatsiooni indeks (EVI)\n"
+"\n"
+"Tõhustatud vegetatsiooni indeks (EVI) on optimeeritud vegetatsiooni indeks, "
+"korrigeeritud on ka mulla taustasignaalid ning atmosfääri mõjud. See on "
+"väga oluline tihedate puude all olevatel aladel. EVI väärtusvahemik on -1 "
+"kuni 1, hea tervise juures olevate toimede korral on vahemik 0.2 kuni 0.8. \n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and "
+"[here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/"
+"measuring_vegetation_4.php)"
+
+msgid ""
+"# Atmospherically Resistant Vegetation Index (ARVI)\n"
+"\n"
+"The Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index "
+"that minimizes the effects of atmospheric scattering. It is most useful for "
+"regions with high content of atmospheric aerosol (fog, dust, smoke, air "
+"pollution). The range for an ARVI is -1 to 1 where green vegetation "
+"generally falls between values of 0.20 to 0.80.\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"More info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) "
+"and "
+"[here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-"
+"vegetation-analysis-complete/)"
+msgstr ""
+
+msgid ""
+"# Soil Adjusted Vegetation Index (SAVI)\n"
+"\n"
+"The Soil Adjusted Vegetation Index is similar to Normalized Difference "
+"Vegetation Index (NDVI) but is used in areas where vegetative cover is low "
+"(< 40%). The index is a transformation technique that minimizes soil "
+"brightness influences from spectral vegetation indices involving red and "
+"near-infrared (NIR) wavelengths. The index is helpful when analysing young "
+"crops, arid regions with sparse vegetation and exposed soil surfaces.\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"More info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) "
+"and "
+"[here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-"
+"vegetation-analysis-complete/)"
+msgstr ""
+
+msgid ""
+"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n"
+"\n"
+"Anthocyanins are pigments common in higher plants, causing their red, blue "
+"and purple coloration. They provide valuable information about the "
+"physiological status of plants, as they are considered indicators of "
+"various types of plant stresses. The reflectance of anthocyanin is highest "
+"around 550nm. However, the same wavelengths are reflected by chlorophyll as "
+"well. To isolate the anthocyanins, the 700nm spectral band, that reflects "
+"only chlorophyll and not anthocyanins, is subtracted.\n"
+"\n"
+"To correct for leaf density and thickness, the near infrared spectral band "
+"(in the recommended wavelengths of 760-800nm), which is related to leaf "
+"scattering, is added to the basic ARI index. The new index is called "
+"modified ARI or mARI (also ARI2).\n"
+"\n"
+"mARI values for the examined trees in [this original "
+"article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged "
+"in values from 0 to 8.\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&"
+"context=natrespapers)"
+msgstr ""
+"# Modifitseeritud antotsüaniini peegeldusindeks (mARI/ARI2)\n"
+"\n"
+"Antotsüaniinidid on pigmendid, mis esinevad tavaliselt kõrgemates taimedes, "
+"põhjustades nende punast, sinist ja lillat värvust. Nad annavad "
+"väärtuslikku teavet taimede füsioloogilise seisundi kohta, kuna neid "
+"peetakse erinevat tüüpi taimestresside näitajateks. Antotsüaniini peegeldus "
+"on kõrgeim 550 nm juures. Seejuures peegelduvad samad lainepikkused ka "
+"klorofüllis. Antotsüaniinide isoleerimiseks lahutatakse 700nm "
+"spektraalkanal, mis kajastab ainult klorofülli ja mitte antotsüaniini.\n"
+"\n"
+"Lehtede tiheduse ja paksuse korrigeerimiseks lisatakse ARI põhiindeksile "
+"ligilähedane infrapunaspektrer (soovitataval lainepikkusel 760–800nm), mis "
+"on seotud hajumisega lehtede pinnalt. Uut indeksit nimetatakse "
+"modifitseeritud ARI või mARI (ka ARI2).\n"
+"\n"
+"mARI väärtused vaadeldavate puude juures on vahemikus 0 kuni 8 "
+"[originaalartikkel](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/"
+").\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&"
+"context=natrespapers)"
+
+msgid ""
+"# Green City Script\n"
+"\n"
+"The Green city script aims to raise awareness of green areas in cities "
+"around the world. The script takes into account the Normalized Difference "
+"Vegetation Index (NDVI) and true color wavelengths; it separates built up "
+"areas from vegetated ones, making it useful for detecting urban areas. "
+"Built up areas are displayed in grey and vegetation is displayed in green.\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"
+msgstr ""
+"# Rohelise linna skript\n"
+"\n"
+"Rohelise linna skripti eesmärk on suurendada teadlikkust rohealadest "
+"linnades üle kogu maailma. Skriptis võetakse arvesse normaliseeritud "
+"vegetatsiooniindeksit (NDVI) ja RGB lainepikkusi; see eraldab hoonestatud "
+"alad taimkattega aladest, mis muudab selle kasulikuks linnapiirkondade "
+"tuvastamisel. Hoonestatud alad on esitatud halliga ja taimestik on esitatud "
+"rohelisega.\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"
+
+msgid ""
+"# Urban Classified Script\n"
+"\n"
+"The Urban Classified script aims to detect built up areas by separating "
+"them from barren ground, vegetation and water. Areas with a high moisture "
+"content are returned in blue; areas indicating built up areas are returned "
+"in white; vegetated areas are returned in green; everything else indicates "
+"barren ground and is displayed in brown colors.\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/"
+")"
+msgstr ""
+"# Linnaalade klassifitseerimise skript\n"
+"\n"
+"Linnaalade klassifitseerimise skripti eesmärk on tuvastada hoonestatud "
+"alad, eraldades need paljast maapinnast, taimestikust ja veest. Suure "
+"niiskusesisaldusega alad kuvatakse sinisena; hoonestatud alad kuvatakse "
+"valgena; taimkattega alad rohelisena; kõik muu on paljas maapind ning "
+"kuvatakse pruunina.\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/"
+")"
+
+msgid ""
+"# Urban Land Infrared Color Script\n"
+"\n"
+"This script, made by Leo Tolari, combines true color visualization with "
+"near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script "
+"highlights urban areas better than true color, while still looking natural.\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_"
+"infrared/)"
+msgstr ""
+"# Linnaalade infrapuna skript\n"
+"\n"
+"See Leo Tolari koostatud skript ühendab loomulike värvide (true color) "
+"visualiseerimise lähis-infrapuna (NIR) ja lühilaine infrapuna (SWIR) "
+"lainepikkustega. Skript toob linnapiirkonnad paremini esile kui loomulikud "
+"värvid üksi, näides siiski loomulik.\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_"
+"infrared/)"
+
+msgid ""
+"# NDMI for Moisture Stress\n"
+"\n"
+"The Normalized Difference Moisture Index (NDMI) for moisture stress can be "
+"used to detect irrigation. For all the index values above 0, knowing the "
+"land use and land cover, it is possible to determine whether irrigation has "
+"taken place. Knowing the type of crop grown (e.g. citrus crops), it is "
+"possible to identify whether irrigation is being effective or not during "
+"the crucial growing summer season, as well as find out if some parts of the "
+"farm are being under or over-irrigated.\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)"
+msgstr ""
+
+msgid ""
+"# Normalized Difference Moisture Index (NDMI)\n"
+"\n"
+"The normalized difference moisture Index (NDMI) is used to determine "
+"vegetation water content and monitor droughts. The value range of the NDMI "
+"is -1 to 1. Negative values of NDMI (values approaching -1) correspond to "
+"barren soil. Values around zero (-0.2 to 0.4) generally correspond to water "
+"stress. High, positive values represent high canopy without water stress "
+"(approximately 0.4 to 1).\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)"
+msgstr ""
+
+msgid ""
+"# Normalized Difference Water Index (NDWI)\n"
+"\n"
+"The normalized difference water index is most appropriate for water body "
+"mapping. Values of water bodies are larger than 0.5. Vegetation has smaller "
+"values. Built-up features have positive values between zero and 0.2.\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)"
+msgstr ""
+
+msgid ""
+"# Normalized Difference Water Index (NDWI)\n"
+"\n"
+"The normalized difference water index is most appropriate for water body "
+"mapping. Values of water bodies are larger than 0.5. Vegetation has smaller "
+"values. Built-up features have positive values between zero and 0.2."
+msgstr ""
+
+msgid ""
+"# False color composite\n"
+"\n"
+"A false color composite uses at least one non-visible wavelength to image "
+"Earth. The false color composite using near infrared, red and green bands "
+"is very popular (a band is a region of the electromagnetic spectrum; a "
+"satellite sensor can image Earth in different bands). The false colour "
+"composite is most commonly used to assess plant density and health, since "
+"plants reflect near infrared and green light, while they absorb red. Cities "
+"and exposed ground are grey or tan, and water appears blue or black.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_"
+"infrared/) and "
+"[here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"
+msgstr ""
+"# Pseudovärvide komposiit\n"
+"\n"
+"Pseudovärvide komposiit kasutab vähemalt ühte mittenähtavat lainepikkust. "
+"Väga levinud on kasutada infrapunast, punast ja rohelist kanalit (kanal on "
+"elektromagnetilise spektri piirkond; satelliitsensor suudab Maad kujutada "
+"erinevates kanalites). Pseudovärvide komposiiti kasutatakse kõige "
+"sagedamini taimede tiheduse ja tervise hindamiseks, kuna taimed peegeldavad "
+"lähis infrapuna- ja rohelist valgust ning neelavad punast valgust. "
+"Linnaalad ja paljad maapinnad kuvatakse halli või pruuniga, vesi sinise või "
+"mustaga.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_"
+"infrared/) and "
+"[here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"
+
+msgid ""
+"# False color composite\n"
+"\n"
+"A false color composite uses at least one non-visible wavelength to image "
+"Earth. The false color composite using near infrared, red and green bands "
+"is very popular (a band is a region of the electromagnetic spectrum; a "
+"satellite sensor can image Earth in different bands). The false colour "
+"composite is most commonly used to assess plant density and health, since "
+"plants reflect near infrared and green light, while they absorb red. Cities "
+"and exposed ground are grey or tan, and water appears blue or black.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and "
+"[here.](https://earthobservatory.nasa.gov/features/FalseColor)"
+msgstr ""
+"# Pseudovärvide komposiit\n"
+"\n"
+"Pseudovärvide komposiit kasutab vähemalt ühte mittenähtavat lainepikkust. "
+"Väga levinud on kasutada infrapunast, punast ja rohelist kanalit (kanal on "
+"elektromagnetilise spektri piirkond; satelliitsensor suudab Maad kujutada "
+"erinevates kanalites). Pseudovärvide komposiiti kasutatakse kõige "
+"sagedamini taimede tiheduse ja tervise hindamiseks, kuna taimed peegeldavad "
+"lähis infrapuna- ja rohelist valgust ning neelavad punast valgust. "
+"Linnaalad ja paljad maapinnad kuvatakse halli või pruuniga, vesi sinise või "
+"mustaga.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and "
+"[here.](https://earthobservatory.nasa.gov/features/FalseColor)"
+
+msgid ""
+"# False color composite\n"
+"\n"
+"A false color composite uses at least one non-visible wavelength to image "
+"Earth. The false color composite using near infrared, red and green bands "
+"is very popular (a band is a region of the electromagnetic spectrum; a "
+"satellite sensor can image Earth in different bands). The false colour "
+"composite is most commonly used to assess plant density and health, since "
+"plants reflect near infrared and green light, while they absorb red. Cities "
+"and exposed ground are grey or tan, and water appears blue or black.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/"
+"meris/)"
+msgstr ""
+"# Pseudovärvide komposiit\n"
+"\n"
+"Pseudovärvide komposiit kasutab vähemalt ühte mittenähtavat lainepikkust. "
+"Väga levinud on kasutada infrapunast, punast ja rohelist kanalit (kanal on "
+"elektromagnetilise spektri piirkond; satelliitsensor suudab Maad kujutada "
+"erinevates kanalites). Pseudovärvide komposiiti kasutatakse kõige "
+"sagedamini taimede tiheduse ja tervise hindamiseks, kuna taimed peegeldavad "
+"lähis infrapuna- ja rohelist valgust ning neelavad punast valgust. "
+"Linnaalad ja paljad maapinnad kuvatakse halli või pruuniga, vesi sinise või "
+"mustaga.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/"
+"meris/)"
+
+msgid ""
+"# True color composite\n"
+"\n"
+"Sensors carried by satellites can image Earth in different regions of the "
+"electromagnetic spectrum. Each region in the spectrum is referred to as a "
+"band. Landsat 5 has 7 bands. True color composite uses visible light bands "
+"red, green and blue in the corresponding red, green and blue color "
+"channels, resulting in a natural colored product, that is a good "
+"representation of the Earth as humans would see it naturally.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and "
+"[here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-"
+"science_support_page_related_con=0#qt-science_support_page_related_con)"
+msgstr ""
+"# Loomulike värvide komposiit\n"
+"\n"
+"Satelliidil olevad andurid võivad Maad kujutada elektromagnetilise spektri "
+"eri piirkondades. Iga piirkonda nimetatakse kanaliks. Satelliidil Landsat 5 "
+"on 7 kanalit. Loomulike värvide komposiit kasutab nähtava spektri punast, "
+"rohelist ja sinist vastavates punase, rohelise ja sinise värvi kanalites. "
+"Tulemuseks on Maa kujutis loomulikes värvides, mida on inimestel lihtne "
+"mõista.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and "
+"[here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-"
+"science_support_page_related_con=0#qt-science_support_page_related_con)"
+
+msgid ""
+"# True color composite\n"
+"\n"
+"Sensors carried by satellites can image Earth in different regions of the "
+"electromagnetic spectrum. Each region in the spectrum is referred to as a "
+"band. Landsat 7 has 8 bands. True color composite uses visible light bands "
+"red, green and blue in the corresponding red, green and blue color "
+"channels, resulting in a natural colored product, that is a good "
+"representation of the Earth as humans would see it naturally.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and "
+"[here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-"
+"science_support_page_related_con=0#qt-science_support_page_related_con)"
+msgstr ""
+"# Loomulike värvide komposiit\n"
+"\n"
+"Satelliidil olevad andurid võivad Maad kujutada elektromagnetilise spektri "
+"eri piirkondades. Iga piirkonda nimetatakse kanaliks. Satelliidil Landsat 7 "
+"on 8 kanalit. Loomulike värvide komposiit kasutab nähtava spektri punast, "
+"rohelist ja sinist vastavates punase, rohelise ja sinise värvi kanalites. "
+"Tulemuseks on Maa kujutis loomulikes värvides, mida on inimestel lihtne "
+"mõista.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and "
+"[here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-"
+"science_support_page_related_con=0#qt-science_support_page_related_con)"
+
+msgid ""
+"# True color composite\n"
+"\n"
+"Sensors carried by satellites can image Earth in different regions of the "
+"electromagnetic spectrum. Each region in the spectrum is referred to as a "
+"band. Landsat 8 has 11 bands. True color composite uses visible light bands "
+"red, green and blue in the corresponding red, green and blue color "
+"channels, resulting in a natural colored product, that is a good "
+"representation of the Earth as humans would see it naturally.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and "
+"[here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"
+msgstr ""
+"# Loomulike värvide komposiit\n"
+"\n"
+"Satelliidil olevad andurid võivad tada Maad elektromagnetilise spektri eri "
+"piirkondades. Iga piirkonda nimetatakse kanaliks. Satelliidil Landsat 8 on "
+"11 kanalit. Loomulike värvide komposiit kasutab nähtava spektri punast, "
+"rohelist ja sinist vastavates punase, rohelise ja sinise värvi kanalites. "
+"Tulemuseks on Maa kujutis loomulikes värvides, mida on inimestel lihtne "
+"mõista.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and "
+"[here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"
+
+msgid ""
+"# True color composite\n"
+"\n"
+"Sensors carried by satellites can image Earth in different regions of the "
+"electromagnetic spectrum Each region in the spectrum is referred to as a "
+"band. True color composite uses visible light bands red, green and blue in "
+"the corresponding red, green and blue color channels, resulting in a "
+"natural colored product, that is a good representation of the Earth as "
+"humans would see it naturally.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/"
+"meris/)"
+msgstr ""
+"# Loomulike värvide komposiit\n"
+"\n"
+"Satelliidil olevad andurid võivad tada Maad elektromagnetilise spektri eri "
+"piirkondades. Iga piirkonda nimetatakse kanaliks. Loomulike värvide "
+"komposiit kasutab nähtava spektri punast, rohelist ja sinist vastavates "
+"punase, rohelise ja sinise värvi kanalites. Tulemuseks on Maa kujutis "
+"loomulikes värvides, mida on inimestel lihtne mõista.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/"
+"meris/)"
+
+msgid ""
+"# True color composite\n"
+"\n"
+"Sensors carried by satellites can image Earth in different regions of the "
+"electromagnetic spectrum . Each region in the spectrum is referred to as a "
+"band. True color composite uses visible light bands red, green and blue in "
+"the corresponding red, green and blue color channels, resulting in a "
+"natural colored product, that is a good representation of the Earth as "
+"humans would see it naturally.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/"
+"overview/heritage)"
+msgstr ""
+"# Loomulike värvide komposiit\n"
+"\n"
+"Satelliidil olevad andurid võivad tada Maad elektromagnetilise spektri eri "
+"piirkondades. Iga piirkonda nimetatakse kanaliks. Loomulike värvide "
+"komposiit kasutab nähtava spektri punast, rohelist ja sinist vastavates "
+"punase, rohelise ja sinise värvi kanalites. Tulemuseks on Maa kujutis "
+"loomulikes värvides, mida on inimestel lihtne mõista.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/"
+"overview/heritage)"
+
+msgid ""
+"# Enhanced True Color Visualization\n"
+"\n"
+"This script uses highlight optimization to avoid burnt out pixels and to "
+"even out the exposure. It makes clouds look natural and keep as much visual "
+"information as possible. Sentinel-3 OLCI tiles cover large areas, making it "
+"possible to observe large cloud formations, such as hurricanes.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_"
+"highlight_optimized/)"
+msgstr ""
+
+msgid ""
+"# False Color Urban composite\n"
+"\n"
+"This composite uses a combination of bands in visible and in short wave "
+"infrared (a band is a region of the electromagnetic spectrum; a satellite "
+"sensor can image Earth in different bands). It displays vegetation in "
+"shades of green. While darker shades of green indicate denser vegetation, "
+"sparse vegetation have lighter shades. Urban areas are blue and soils have "
+"various shades of brown.\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](https://gisgeography.com/landsat-8-bands-combinations/)"
+msgstr ""
+"# Linnaalade pseudovärvidega komposiit\n"
+"\n"
+"See komposiit kasutab nähtava valguse ning lühilainelise infrapunakiirguse "
+"kanalite kombinatsiooni (kanal on elektromagnetilise spektri piirkond; "
+"satelliitsensor suudab Maad kujutada erinevates kanalites). Taimestik "
+"kuvatakse rohelistes toonides, mida tihedam on taimestik, seda tumedama "
+"rohelise tooniga see kuvatakse. Linnaalad kuvatakse sinisega ning muld "
+"erinevates pruunides toonides.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot [siin.](https://gisgeography.com/landsat-8-bands-combinations/)"
+
+msgid ""
+"# Agriculture composite\n"
+"\n"
+"This composite uses short-wave infrared, near-infrared and blue bands to "
+"monitor crop health (a band is a region of the electromagnetic spectrum; a "
+"satellite sensor can image Earth in different bands). Both short-wave and "
+"near infrared bands are particularly good at highlighting dense vegetation, "
+"which appears dark green in the composite. Crops appear in a vibrant green "
+"and bare earth appears magenta.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and "
+"[here.](https://gisgeography.com/sentinel-2-bands-combinations/)"
+msgstr ""
+"# Põllumajanduse komposiit\n"
+"\n"
+"See komposiit kasutab põllukultuuride tervise jälgimiseks lühilaine "
+"infrapuna-, lähi-infrapuna- ja sinist kanalit (kanal on elektromagnetilise "
+"spektri piirkond; satelliitsensor suudab Maad kujutada erinevates "
+"kanalites). Nii lühilaine kui ka lähi-infrapunari kanalid on eriti head "
+"tiheda taimestiku esiletõstmiseks, mis kuvatakse komposiidis "
+"tumerohelisega. Põllukultuurid kuvatakse ererohelisega ja paljas maapind "
+"magentapunasega.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) ja "
+"[siin.](https://gisgeography.com/sentinel-2-bands-combinations/)"
+
+msgid ""
+"# Snow Classifier\n"
+"\n"
+"The Snow Classifier algorithm aims to detect snow by classifying pixels "
+"based on different brightness and Normalized Difference Snow Index (NDSI) "
+"thresholds. Values classified as snow are returned in bright vivid blue. "
+"The script can overestimate snow areas over clouds.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"
+msgstr ""
+"# Lume klassifitseerija\n"
+"\n"
+"Lume klassifitseerija algoritmi eesmärk on lume tuvastamine, liigitades "
+"pikslid erinevate heleduse ja normaaliseeritud lumeindeksi (NDSI) künniste "
+"põhjal. Lumeks klassifitseeritud väärtused kuvatakse erksa sinisega. Skript "
+"võib pilvedega kaetud aladel lumeala ülehinnata.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"
+
+msgid ""
+"# Ulyssys Water Quality Viewer (UWQV)\n"
+"\n"
+"The script aims to dynamically visualize the chlorophyll and sediment "
+"conditions of water bodies, which are primary indicators of water quality. "
+"The chlorophyll content ranges in colors from dark blue (low chlorophyll "
+"content) through green to red (high chlorophyll content). Sediment "
+"concentrations are colored brown; opaque brown indicates high sediment "
+"content.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_"
+"quality_viewer/)"
+msgstr ""
+
+msgid ""
+"# Highlight Optimized Natural Color\n"
+"\n"
+"This script aims to display the Earth in beautiful natural color images. It "
+"uses highlight optimization to avoid burnt out pixels and to even out the "
+"exposure.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_"
+"optimized_natural_color/#)"
+msgstr ""
+
+msgid ""
+"# Geology 12, 8, 2 composite\n"
+"\n"
+"This composite uses short-wave infrared (SWIR) band 12 to differentiate "
+"among different rock types (a band is a region of the electromagnetic "
+"spectrum; a satellite sensor can image Earth in different bands). Each rock "
+"and mineral type reflects short-wave infrared light differently, making it "
+"possible to map out geology by comparing reflected SWIR light. Near "
+"infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, "
+"both contributing to differentiation of ground materials. The composite is "
+"useful for finding geological formations and features (e.g. faults, "
+"fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR."
+"pdf)"
+msgstr ""
+"# Geoloogia 12, 8, 2 komposiit\n"
+"\n"
+"See komposiit kasutab erinevate kivimitüüpide eristamiseks lühilaine "
+"infrapuna (SWIR) kanalit 12 (kanal on elektromagnetilise spektri piirkond; "
+"satelliitsensor suudab Maad kujutada erinevates kanalites). Iga kivimi ja "
+"mineraali tüüp peegeldab lühilaine infrapunavalgust erinevalt, võimaldades "
+"geoloogiat kaardistada peegeldunud SWIR-valguse võrdlemisel. Lähi-Infrapuna "
+"(NIR) kanal 8 tõstab esile taimkatte ja kanal 2 tuvastab niiskust, mõlemad "
+"on olulised pinnamaterjalide eristamisel. Komposiit on kasulik "
+"geoloogiliste moodustiste ja omaduste (nt murrang, lõhe) leidmisel, "
+"litoloogia (eri tüüpi kivimite nt graniit, basalt) ja kaevanduse "
+"valdkonnas.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR."
+"pdf)"
+
+msgid ""
+"# Geology 8, 11, 12 composite\n"
+"\n"
+"This composite uses both short-wave infrared (SWIR) bands 11 and 12 to "
+"differentiate among different rock types (a band is a region of the "
+"electromagnetic spectrum; a satellite sensor can image Earth in different "
+"bands). Each rock and mineral type reflects shortwave infrared light "
+"differently, making it possible to map out geology by comparing reflected "
+"SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing "
+"to differentiation of ground materials. Vegetation in the composite appears "
+"red. The composite is useful for differentiating vegetation, and land "
+"especially geologic features that can be useful for mining and mineral "
+"exploration.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and "
+"[here.](http://murphygeological.com/new---sentinel-2.html#)"
+msgstr ""
+"# Geoloogia 8, 11, 12 komposiit\n"
+"\n"
+"See komposiit kasutab erinevate kivimitüüpide eristamiseks lühilaine "
+"infrapuna (SWIR) kanaleid 11 ja 12 (kanal on elektromagnetilise spektri "
+"piirkond; satelliitsensor suudab Maad kujutada erinevates kanalites). Iga "
+"kivimi ja mineraali tüüp peegeldab lühilaine infrapunavalgust erinevalt, "
+"võimaldades geoloogiat kaardistada peegeldunud SWIR-valguse võrdlemisel. "
+"Lähi-Infrapuna (NIR) kanal 8 tõstab esile taimkatte, aidates kaasa maapinna "
+"materjalide eristamisele. Kompositsioonis olev taimestik kuvatakse "
+"punasega. Komposiit on kasulik taimestiku ja maapinna tüüpide eristamiseks, "
+"tuues välja geoloogilisi erisusi, mis võivad osutuda kasulikuks "
+"kaevandamise ja maavarade uurimise juures.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) ja "
+"[siin.] (Http://murphygeological.com/new---sentinel-2.html#)"
+
+msgid ""
+"# Wildfires\n"
+"\n"
+"This script, created by Pierre Markuse, visualizes wildfires using "
+"Sentinel-2 data. It combines natural color background with some NIR/SWIR "
+"data for smoke penetration and more detail, while adding highlights from "
+"B11 and B12 to show fires in red and orange colors.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-"
+"sentinel-2-imagery-eo-browser/)"
+msgstr ""
+"# Maastikutulekahjud\n"
+"\n"
+"See Pierre Markuse loodud skript visualiseerib maastikutulekahjusid "
+"Sentinel-2 andmete abil. See ühendab loomulike värvidega tausta mõnede "
+"NIR/SWIR andmetega suitsu edasitungimise kohta ning tule kujutamisega "
+"punase ja oranži värviga kanalitelt B11 ja B12.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-"
+"sentinel-2-imagery-eo-browser/)"
+
+msgid ""
+"# Enhanced True Color\n"
+"\n"
+"This script, created by Pierre Markuse, uses multiple bands (a band is a "
+"region of the electromagnetic spectrum; a satellite sensor can image Earth "
+"in different bands) and saturation and brightness control to enhance the "
+"true color visualization.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_"
+"color-2/#)"
+msgstr ""
+
+msgid ""
+"# Burned Area Index\n"
+"\n"
+"Burned Area Index takes advantage of the wider spectrum of Visible, "
+"Red-Edge, NIR and SWIR bands.\n"
+"\n"
+"Values description:()=> The range of values for the index is `-1` to `1` "
+"for burn scars, and `1` - `6` for active fires. Different fire intensities "
+"may result in different thresholds; the current values were calibrated, as "
+"per original author, on mostly Mediterranen regions.\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)"
+msgstr ""
+
+msgid ""
+"# Normalized Burn Ratio (NBR)\n"
+"\n"
+"Normalized Burn Ratio is frequently used to estimate burn severity. It uses "
+"near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy "
+"vegetation has a high reflectance in the near-infrared portion of the "
+"spectrum, and a low short-wave infrared reflectance. On the other hand, "
+"burned areas have a high shortwave infrared reflectance but low reflectance "
+"in the near infrared Darker pixels indicate burned areas.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR."
+"html) and "
+"[here.](https://mybinder.org/v2/gh/sentinel-hub/education/"
+"master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"
+msgstr ""
+"# Normaliseeritud põlemise suhtarv (NBR)\n"
+"\n"
+"Normaliseeritud põlemise suhtarvu kasutatakse sageli põlengu raskusastme "
+"hindamiseks. See kasutab lähi-infrapuna (NIR) ja lühilaine-infrapuna (SWIR) "
+"lainepikkusi. Terve taimestik peegeldab tugevasti spektri lähi-infrapunases "
+"spektris ja nõrgalt lühilaine infrapuna spektris. Samas põlenud aladel on "
+"vastupidi ning.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR."
+"html) and "
+"[here.](https://mybinder.org/v2/gh/sentinel-hub/education/"
+"master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"
+
+msgid ""
+"# Atmospheric penetration\n"
+"\n"
+"This composite uses different bands (a band is a region of the "
+"electromagnetic spectrum; a satellite sensor can image Earth in different "
+"bands) in the non-visible part of the electromagnetic spectrum to reduce "
+"the influence of the atmosphere in the image. Short wave infrared bands 11 "
+"and 12 are highly reflected by the heated areas, making them useful for "
+"fire and burned area mapping. Short wave infrared band 8, is on contrary, "
+"highly reflected by vegetation, which signifies absence of fire. Vegetation "
+"appears blue, displaying details related to the vegetation vigor. Healthy "
+"vegetation is shown in light blue while the stressed, sparse or/and arid "
+"vegetation appears in dull blue. Urban features are white, grey, cyan or "
+"purple.\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](https://eos.com/atmospheric-penetration/)"
+msgstr ""
+"# Atmosfääri läbipaistvuse suurendamine\n"
+"\n"
+"See komposiit kasutab erinevaid kanaleid (kanal on elektromagnetilise "
+"spektri piirkond; satelliitsensor suudab Maad kujutada erinevates "
+"kanalites) elektromagnetilise spektri mittenähtavas osas, et vähendada "
+"atmosfääri mõju kujutisele. Lühilaine-infrapuna kanaleid 11 ja 12 "
+"kasutatakse tulekahjude ja põlenud alade kaardistamiseks kuna kõrgema "
+"temperatuuriga piirkondades peegeldavad need kanalid tugevalt. Samas "
+"taimestikuga alad peegeldavad tugevalt lühilaine-infrapuna kanalit 8, see "
+"märgib tulekahju puudumist. Tervet ja tugevat taimestikku kujutatakse "
+"helesinisega, stressis taimestikku tuhmi sinisega. Linnaalasid kujutatakse "
+"valge, halli, tsüaani või lillaga.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot [siin.](https://eos.com/atmospheric-penetration/)"
+
+msgid ""
+"# Barren Soil Visualization\n"
+"\n"
+"The Barren Soil Visualization can be useful for soil mapping, to "
+"investigate the location of landslides or the extent of erosion in "
+"non-vegetated areas. This visualization shows all vegetation in green and "
+"the barren ground in red. Water appears black.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and "
+"[here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-"
+"satellite-images-with-custom-scripts-8ef0e6a474c6)"
+msgstr ""
+"# Taimkatteta mullapinna visualiseerimine\n"
+"\n"
+"Taimkatteta mullapinna visualiseerimine võib olla kasulik mulla "
+"kaardistamiseks, maalihete asukoha või erosiooni ulatuse uurimiseks "
+"taimkatteta aladel. Visualiseerimisel kujutatakse taimestik rohelisena ja "
+"taimkatteta mullapind punasena. Vesi kujutatakse mustana.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) ja "
+"[siin.](https://medium.com/sentinel-hub/create-useful-and-beautiful-"
+"satellite-images-with-custom-scripts-8ef0e6a474c6)"
+
+msgid ""
+"# True Color with IR Highlights composite\n"
+"\n"
+"This composite enhances the true color visualization by adding the "
+"shortwave infrared wavelengths to amplify details. It displays heated areas "
+"in red/orange.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-"
+"9d1de0133733)"
+msgstr ""
+"# Loomulike värvide ja infrapuna komposiit\n"
+"\n"
+"See komposiit muudab loomulike värvide kujutise detailid "
+"lühilaine-infrapuna lainepikkuste abiga paremini nähtavaks. Kõrgenenud "
+"temperatuuriga alad kujutatakse punase või oranžiga.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-"
+"9d1de0133733)"
+
+msgid ""
+"# Detection of Burned Areas\n"
+"\n"
+"This script is used to detect large scale recently burned areas. Pixels "
+"colored red highlight burned areas, and all other pixels are returned in "
+"true color. The script sometimes overestimates burned areas over water and "
+"clouds.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"
+msgstr ""
+"# Põlenud alade tuvastamine\n"
+"\n"
+"Seda skripti kasutatakse suurte hiljuti põlenud alade tuvastamiseks. "
+"Põlenud alade pikslid kujutatakse punasega, kõik teised alad on loomulikes "
+"värvides. Mõnikord skript ülehindab põlenud alade hulka suure "
+"veesisaldusega aladel või pilvedega kaetud piirkondades.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"
+
+msgid ""
+"# Terrestrial Chlorophyll Index (OTCI)\n"
+"\n"
+"\n"
+"\n"
+"The Terrestrial Chlorophyll Index (OTCI) is estimated based on the "
+"chlorophyll content in terrestrial vegetation and can be used to monitor "
+"vegetation condition and health. Low OTCI values usually signify water, "
+"sand or snow. Extremely high values, displayed with white, usually suggest "
+"the absence of chlorophyll as well. They generally represent either bare "
+"ground, rock or clouds. The chlorophyll values in between range from red "
+"(low chlorophyll values) to dark green (high chlorophyll values) can be "
+"used to determine vegetation health.\n"
+"\n"
+"\n"
+"\n"
+"More info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"
+msgstr ""
+"# Maismaa klorofülli indeks (OTCI)\n"
+"\n"
+"\n"
+"\n"
+"Maismaa klorofülli indeks (OTCI) põhineb klorofülli sisaldusel "
+"maismaataimestikus ning seda saab kasutada taimestiku seisundi ja tervise "
+"jälgimiseks. Madalad OTCI väärtused on seotud tavaliselt vee, liiva või "
+"lume pindadega. Väga kõrged väärtused (kujutatakse valgega) viitavad "
+"tavaliselt klorofülli puudumisele. Väga kõrged väärtused on seotud kas "
+"taimestikuvaba maapinna, kivi või pilvedega. Klorofülli väärtusi vahemikus "
+"punasest (madal klorofüll) kuni tumeroheliseni (kõrge klorofülli tase) võib "
+"kasutada taimestiku tervise kindlakstegemisel.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"
+
+msgid ""
+"# Normalized Difference Salinity Index\n"
+"\n"
+"The index visualizes the amount of salt present in soils. Soil salinization "
+"is one of the most common land degradation processes, especially in arid "
+"and semi-arid regions, where precipitation exceeds evaporation. \n"
+"\n"
+"Higher values indicate higher salinity and low values indicate lower "
+"salinity.\n"
+"\n"
+"Read more "
+"[here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/"
+"khaier.pdf) "
+"[here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) "
+"and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)"
+msgstr ""
+"# Normaliseeritud soolsuse indeks\n"
+"\n"
+"Indeks näitab mullas esinevate soolade kogust. Mulla sooldumine on üks "
+"levinumaid mulla degradatsiooni protsesse, eriti kuivades ja poolkuivades "
+"piirkondades.\n"
+"\n"
+"Indeksi kõrgemad väärtused märgivad kõrgemat soolsust ning madalamad "
+"väärtused madalamat soolsust.\n"
+"\n"
+"Rohkem infot "
+"[siin,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/"
+"khaier.pdf) "
+"[siin](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) "
+"ja [siin.](https://www.indexdatabase.de/db/i-single.php?id=57)"
+
+msgid "Disabled"
+msgstr ""
+
+msgid "Yes"
+msgstr ""
+
+msgid ""
+"**Logged-in users** can use their custom themes, save and load pins, create "
+"a pin story, measure distances, create a\n"
+"timelapse and use the advanced image download.\n"
+"\n"
+"To create a free account simply click [here]\n"
+"or within the app on **Login** and then \"Sign Up\"."
+msgstr ""
+"**Sisselogitud kasutajad** saavad kasutada oma kohandatud kujundusi, "
+"salvestada ja laadida märgistusi, luua märgistustega lugu, mõõta "
+"vahemaasid, luua \n"
+"aegvõtteid ja kasutada lisavõimalustega piltide allalaadimist.\n"
+"\n"
+"Tasuta konto loomiseks klõpsa lihtsalt ikoonil [siin]\n"
+"või rakenduses ikoonil **Logi sisse** ja seejärel \"Registreeru\"."
+
+msgid ""
+"In the **Discover** tab you can:\n"
+"\n"
+"- Select a **Theme.**\n"
+"- **Search** for data.\n"
+"- View theme **Highlights.**\n"
+"\n"
+"The **Theme** dropdown offers you different preconfigured themes as well as "
+"your own custom configured instances if you are Logged-in. To create an "
+"instance, click on\n"
+"the settings icon and log in with the same "
+"credentials as you used for EO Browser.\n"
+"\n"
+"Under **Search** you can set search criteria:\n"
+" - Choose from which satellites you want to receive the data by selecting "
+"checkboxes.\n"
+" - Select additional options where applicable, for example, cloud coverage "
+"with the slider.\n"
+" - Select the time range by either typing the date or select the date from "
+"the calendar.\n"
+"\n"
+"You can read explanations of satellites by clicking on the question icon\n"
+" next to the data source name.\n"
+"\n"
+"Once you hit Search you get a list of results. Each result is presented "
+"\n"
+"with a preview image, and relevant data specific to the datasource. For "
+"some data sources, the link icon is also visible "
+"for each result.\n"
+"Clicking on it reveals direct links to the raw image of the result on EO "
+"Cloud or SciHub. Clicking on the Visualize button will open the "
+"**Visualize** tab for the selected result.\n"
+"\n"
+"Under **Highlight**, you find preselected interesting locations connected "
+"to the selected theme."
+msgstr ""
+"**Avasta** vahekaardil saab:\n"
+"\n"
+"- Valida **Teema.**\n"
+"- **Otsida** andmeid.\n"
+"- Vaadata teemat **Esile tõstetud.**\n"
+"\n"
+"Rippmenüü **Teemad** pakub erinevaid eelseadistatud teemasid kui ka "
+"kohandatud seadistusega valikuid (peab olema sisse logitud). Valiku "
+"loomiseks tuleb klõpsata \n"
+"seadete ikoonil ja sisse logida samade "
+"tunnustega nagu EO Brauseris.\n"
+"\n"
+"Jaotises **Otsing** saab määrata otsingukriteeriumid:\n"
+" - Valida, millistelt satelliitidelt soovitakse saada andmeid, valides "
+"selleks soovitud märkeruudud .\n"
+" - Valida lisavõimalusi, nt pilvkate.\n"
+" - Valida aeg, kas kirjutades ise kuupäev või valides kuupäeva kalendrist.\n"
+"\n"
+"Satelliitide kohta saab lisainfot klõpsates küsimuse ikoonil,\n"
+" mis on infoallika nime kõrval.\n"
+"\n"
+"Kui vajutada ikoonil Otsing, siis tekib tulemustega nimekiri. Iga "
+"tulemus esitletakse \n"
+"eelvaatepildiga ja andmeallikale omaste asjakohaste andmetega. Mõne "
+"andmeallika puhul on nähtav ka lingiikoon.\n"
+"Sellel klõpsates näidatakse otselinke toorpiltidele EO Cloudis või "
+"SciHubis. Klõpsides Visualiseeri nupul, avaneb **Visualiseeri** "
+"vaheleht valitud tulemustega.\n"
+"\n"
+"Jaotises **Esile tõstetud** on eelvalitud huvitavad asukohad, mis on seotud "
+"valitud teemaga."
+
+msgid ""
+"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items "
+"contain information\n"
+"about location, data source and its specific layer, zoom level and time.\n"
+"\n"
+"For each pin you have several options on how to interact with a single pin:\n"
+"\n"
+"- Change **order** - by clicking on the move icon\n"
+"\n"
+" \n"
+" \n"
+" \n"
+"in the top left corner of the pin and dragging the pin up or down the list.\n"
+"- **Rename** - by clicking on the pencil icon next to the pin's name.\n"
+"- Add to the **Compare** tab - by clicking on the compare icon \n"
+"- Enter a **description** - by clicking on the expand icon .\n"
+"- **Remove** - by clicking the remove icon .\n"
+"- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n"
+"\n"
+"In the line above all pins you have different options that apply for all "
+"pins:\n"
+"- Create your own story from pins - by clicking on "
+"**Story**.\n"
+"- Share your pins with others via a link - by clicking on **Share**.\n"
+"- Export pins as a JSON file - by clicking on **Export**.\n"
+"- Import pins from a JSON file - by clicking on **Import**.\n"
+"- Delete all pins - by clicking on **Clear**."
+msgstr ""
+"Vahekaart ** Märgistused** sisaldab sinu kinnitatud (lemmik / salvestatud) "
+"üksusi. Kinnitatud üksused sisaldavad teavet\n"
+"asukoha, andmeallika ja selle konkreetse kihi, suumi taseme ja aja kohta.\n"
+"\n"
+"Iga märgistuse puhul on sul ühe märgistusega suhtlemiseks mitu võimalust.\n"
+"\n"
+"- Muuda ** järjekorda ** - klõpsa teisaldamise ikoonil\n"
+"\n"
+" \n"
+" \n"
+"\n"
+"ja lohista märgistus loendis üles või alla.\n"
+"- ** Nimeta ümber ** - klõpsa pliiatsiikoonil, mis on märgistuse nime "
+"kõrval .\n"
+"- Lisa vahekaardile ** Võrdle ** - klõpsa võrdlusikoonil \n"
+"- Sisesta ** kirjeldus ** - klõpsa laienduse ikoonil .\n"
+"- ** Eemalda ** - klõpsa eemaldamisikoonil .\n"
+"- ** Suumi märgistuse asukoht - klõpsa laiuskraad / pikkuskraad\n"
+"\n"
+"Kõigi märgistuste kohal oleval real on erinevad valikud, mis kehtivad kõigi "
+"märgistuste jaoks:\n"
+"- Loo märgistustest oma lugu - klõpsa nuppu ** "
+"Lugu **.\n"
+"- Jaga oma märgistusi lingi kaudu teistega - klõpsa nuppu ** Jaga **.\n"
+"- Ekspordi märgistused JSON-failina - klõpsa nuppu ** Ekspordi **.\n"
+"- Impordi JSON-failist märgistused - klõpsa nuppu ** Impordi **.\n"
+"- Kustuta kõik märgistused - klõpsa nuppu ** "
+"Kustuta **."
+
+msgid ""
+"Search for a location either by scrolling the map with a mouse or enter the "
+"location in the search\n"
+"field."
+msgstr ""
+"Otsi asukohta kas hiirega kaarti kerides või sisesta asukoht otsingu\n"
+"lahtris."
+
+msgid ""
+"This tool allows you to draw a polygon on the map and display the polygon's "
+"size.\n"
+"\n"
+"All layers that return a single value (such as NDVI, Moisture index, "
+"NDWI,…) support viewing the\n"
+"index for the selected area over time. Clicking the chart icon will\n"
+"display the charts. You can remove the polygon by clicking the remove icon "
+".\n"
+"\n"
+"You can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon "
+"geometry.\n"
+"\n"
+"The two sheets icon lets you copy the polygon "
+"coordinates as a GEOJSON, the crosshair \n"
+"centres the map to the drawn polygon.\n"
+"\n"
+"Exported images will be cropped to the area of interest in analytical "
+"downloads."
+msgstr ""
+"See tööriist võimaldab joonistada kaardile hulknurga ning kuvada selle "
+"suurust.\n"
+"\n"
+"Kõik kihid, mis tagastavad üksiku väärstuse (nt NDVI, niiskusindeks, "
+"NDWI,…) toetavad indeksi\n"
+"ajalise muutlikkuse näitamist. Diagrammi ikoonil klõpsates \n"
+"kuvatakse diagramm. Hulknurga saab eemaldada klõpsates ikooni 'remove' .\n"
+"\n"
+"Võite üles laadida ka hulknurga geomeetriaga KML/KMZ, GPX või GEOJSON/JSON "
+"faile.\n"
+"\n"
+"Kahe lehe ikoon võimaldab kopeerida hulknurga "
+"koordinaadid nagu GEOJSON-i, niitrist \n"
+"tsentreerib kaardi joonistatud hulknurgale.\n"
+"\n"
+"Analüütilisel allalaadimisel kärbitakse eksporditud pildid huvipakkuva "
+"piirkonnani."
+
+msgid ""
+"With this tool, you can mark a point on the map.\n"
+"\n"
+"You can also view statistical data for some layers by clicking on the chart "
+"icon\n"
+". \n"
+"You can remove the mark by clicking the remove icon .\n"
+""
+msgstr ""
+"Selle tööriista abil saad kaardil märkida punkti.\n"
+"\n"
+"Mõne kihi statistilisi andmeid saad vaadata ka diagrammiikoonil klõpsates\n"
+".\n"
+"Märgistuse saad eemaldada, klõpsates eemaldamisikoonil .\n"
+""
+
+msgid ""
+"With this tool, you can measure distances and areas on the map.\n"
+"\n"
+"Every mouse click creates a new point on the path. To stop adding points, "
+"press Esc
key\n"
+"or double click on the map. \n"
+"You can remove the measurement by clicking the remove icon ."
+msgstr ""
+"Selle tööriista abil saad kaardil mõõta kaugusi ja pindalasid.\n"
+"\n"
+"Iga hiireklõps teeb uue punkti. Punktide lisamise lõpetamiseks vajutage "
+"klahvi Esc
\n"
+"või tehke kaardil topeltklõps.\n"
+"Mõõtmise saad eemaldada, kui klõpsad eemaldamisikoonil ."
+
+msgid ""
+"With this tool, you can download an image of visualized data for the "
+"displayed location. You can choose\n"
+"to show captions and you can add your own description.\n"
+"By enabling Analytical mode, you can choose between various image formats, "
+"image resolutions and\n"
+"coordinate systems. You can also select multiple layers and download them "
+"as a .zip
file.\n"
+"\n"
+"Click the download button\n"
+"Download\n"
+"and your image(s) will begin to download. The process can take a few "
+"seconds, depending on the selected\n"
+"resolution and the number of selected layers.\n"
+"\n"
+"Before downloading, you can define an area of interest (AOI) by clicking on "
+"the Area selection tool\n"
+"icon. Your data will be clipped to match this area."
+msgstr ""
+"Selle tööriista abil saad alla laadida visualiseeritud andmetega pildi "
+"kuvatud asukoha kohta. Sa võid valida\n"
+"pealdiste kuvamise ja saad lisada oma kirjelduse.\n"
+"Analüüsirežiimi lubades saad valida erinevate pildivormingute, eraldusvõime "
+"ja\n"
+"koordinaatsüsteemide vahel. Võid valida ka mitu kihti ja alla laadida need "
+"failina .zip
.\n"
+"\n"
+"Klõpsa allalaadimisnupul\n"
+" Lae alla \n"
+"ja sinu pilti (pilte) hakatakse alla laadima. Protsess võib kesta mõni "
+"sekund, olenevalt valitud\n"
+"eraldusvõimest ja valitud kihtide arvust.\n"
+"\n"
+"Enne allalaadimist saad määratleda huvipakkuva piirkonna (AOI), klõpsa "
+"selleks ikoonil ala valimine.\n"
+"Sinu andmed lõigatakse selle alaga sobivaks."
+
+msgid ""
+"With this tool, you can create a timelapse animation of the visualised "
+"layer and displayed location.\n"
+"\n"
+"First, choose a time range. You can refine your search results further by "
+"filtering them by months\n"
+"(filter by months checkbox) and/or selecting one image per defined period "
+"(orbit, day, week, month,\n"
+"year).\n"
+"\n"
+"Then press Search and select your "
+"images.\n"
+"You can select all by checking the checkbox or filter the images by cloud "
+"coverage by moving the slider. Or you can pick images one by\n"
+"one by scrolling through the list and selecting them. Via the **Borders** "
+"checkbox, you can enable/disable the borders on your image.\n"
+"\n"
+"You can preview the timelapse by pressing the play button on the bottom. "
+"You can also set the speed\n"
+"(frames per second).\n"
+"\n"
+"When you are satisfied with the result, click the download button and the "
+"timelapse will be\n"
+"downloaded as a .gif
file."
+msgstr ""
+"Selle tööriista abil saad luua aegvõtte visualiseeritud kihi ja kuvatud "
+"asukoha animatsiooni.\n"
+"\n"
+"Kõigepealt vali ajavahemik. Otsingutulemusi saad täpsemalt määratleda, "
+"filtreerides need kuude kaupa\n"
+"(filtreeri kuude järgi märkeruutude abil) ja/või vali üks pilt, määrates "
+"perioodi (orbiit, päev, nädal, kuu,\n"
+"aasta).\n"
+"\n"
+"Seejärel vajuta Otsi ja vali "
+"oma pildid.\n"
+"Pilte saab valida märkeruudu märkimisel või filtreerida pilte pilvisuse "
+"järgi, liigutades selleks liugurit. Samuti saad pilte valida ükshaaval,\n"
+"sirvides loendit ja valides pildid. Märkekasti ** Piirid ** kaudu saad oma "
+"pildil olevad piirid lubada või keelata.\n"
+"\n"
+"Aegvõtet saad eelvaadata, kui vajutad allosas asuvat nuppu esita. Samuti "
+"saad määrata kiiruse\n"
+"(kaadrit sekundis).\n"
+"\n"
+"Kui oled tulemusega rahul, klõpsa allalaadimisnupul ja aegvõte\n"
+"laetakse alla .gif
failina."
+
+msgid ""
+"You have reached the end of the tutorial. If you have any other questions, "
+"feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\n"
+"or contact us [via "
+"email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n"
+"\n"
+"\n"
+"If you want to view the tutorial in the future you can always view it by "
+"clicking the info icon\n"
+"\n"
+"\n"
+"\n"
+"in the top right corner."
+msgstr ""
+"Oled jõudnud õpetuse lõppu. Kui sul on muid küsimusi, küsi meilt [foorumis] "
+"(https://forum.sentinel-hub.com/)\n"
+"või võta meiega ühendust [e-posti teel] (mailto: info@sentinel-hub.com? "
+"Subject = EO% 20Browser% 20Feedback).\n"
+"\n"
+"\n"
+"Kui soovid õpetust tulevikus vaadata, saad seda alati vaadata, klõpsates "
+"info ikoonil\n"
+"\n"
+"\n"
+"\n"
+"paremal ülanurgas."
+
+msgid "Show evalscript"
+msgstr ""
+
+msgid "Show details"
+msgstr ""
+
+msgid "No layers found for date"
+msgstr ""
+
+msgid ""
+"Your user instances could not be loaded as your Sentinel Hub account was "
+"not set up/expired. You can still use EO Browser but you will not be able "
+"to use personal user instances. To be able to set up personal user "
+"instances you can apply for a 30-days free trial or consider subscribing to "
+"one of the plans: "
+msgstr ""
+
+msgid "Commercial data"
+msgstr ""
+
+msgid "These are theme parts which contain unavailable data sources:"
+msgstr ""
+
+msgid "Band 2 - Chlorophyll absorption maximum - 442 nm"
+msgstr "Kanal 2 - klorofülli neeldumimse maksimum - 442 nm"
+
+msgid "Band 11 - O2 R- branch absorption band - 761 nm"
+msgstr ""
+
+msgid "Level 1"
+msgstr ""
+
+msgid "Level 2"
+msgstr ""
+
+msgid "Landsat 8 L1"
+msgstr ""
+
+msgid "Landsat 8 L2"
+msgstr ""
+
+msgid "Landsat 1-5 MSS L1"
+msgstr ""
+
+msgid "Landsat 7 ETM+ L1"
+msgstr ""
+
+msgid "Landsat 7 ETM+ L2"
+msgstr ""
+
+msgid "L2A (atmospherically corrected)"
+msgstr "L2A (atmosfääri korrektsioon tehtud)"
+
+msgid ""
+"Band 9 - For improved fluorescence retrieval and to better account for "
+"spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - "
+"673.75 nm"
+msgstr ""
+
+msgid ""
+"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that "
+"consists of 44 land cover and land use classes, derived from a series of "
+"satellite missions. In the majority of European countries, CLC is produced "
+"using visual interpretation of high resolution satellite imagery. In a few "
+"countries semi-automatic solutions are applied, using national in-situ "
+"data, satellite image processing, GIS integration and generalisation. More "
+"information "
+"[here](https://github.com/sentinel-hub/public-collections/tree/main/"
+"collections/corine-land-cover). \n"
+"\n"
+"**Coverage**: Most of Europe.\n"
+"\n"
+"**Data availability**:\n"
+"CLC data is updated every 6 years. In EO Browser, data is available on the "
+"following dates:\n"
+"01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n"
+"\n"
+"**Common Usage**:\n"
+"Land use and land cover monitoring, analysis and change prediction for "
+"various applications, including environment, agriculture, transport and "
+"spatial planning."
+msgstr ""
+
+msgid ""
+"**Global Land Cover** products provide a discrete land cover classification "
+"map according to UN-FAO Land Cover Classification System. Additional "
+"continuous fractional layers for all basic land cover classes are included "
+"as bands, to provide more detailed information on each land cover class. "
+"More information "
+"[here](https://github.com/sentinel-hub/public-collections/tree/main/"
+"collections/global-land-cover). \n"
+"\n"
+"**Coverage**: Global.\n"
+"\n"
+"**Data availability**:\n"
+"Updated on a yearly basis. In EO Browser, data is available on the "
+"following dates:\n"
+"01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n"
+"\n"
+"**Common Usage**: \n"
+"Land use and land cover monitoring, used to aid policy decisions on various "
+"issues, including agriculture and food security, biodiversity, climate "
+"change, forest and water resources, land degradation & desertification and "
+"rural development."
+msgstr ""
+
+msgid ""
+"The **Water Bodies** product shows the surface extent covered by inland "
+"water on a permanent, seasonal or occasional basis on a global scale. It "
+"contains one main Water Body detection layer (WB) and one Quality layer "
+"(QUAL), that provides information on the seasonal dynamics of the detected "
+"water bodies. More information "
+"[here](https://collections.sentinel-hub.com/water-bodies/). \n"
+"\n"
+"**Coverage**:\n"
+"Global coverage from longitude -180°E to +180°W and latitude +80°N to "
+"-60°S. Depending on the month, some high latitude areas are not covered by "
+"Sentinel-2 satellites.\n"
+"\n"
+"**Data Availability**:\n"
+"Since October 2020, updated monthly. \n"
+"\n"
+"**Common Usage**\n"
+"Monitoring of water bodies, droughts, floods and climate change."
+msgstr ""
+
+msgid ""
+"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 "
+"imagery and shows various aspects of the spatio-temporal distribution of "
+"surface water between 1984 and 2020 (with annual revisions) at a global "
+"scale in six different layers. Surface water is considered as any uncovered "
+"stretch of water (fresh and salt water areas) greater than 30m² visible "
+"from space, including natural and artificial water bodies. More information "
+"[here](https://collections.sentinel-hub.com/global-surface-water/).\n"
+"\n"
+"**Coverage**: Global coverage from longitude 170°E to 180°W and latitude "
+"80°N to 50°S.\n"
+"\n"
+"**Data Availability**: 1984 - 2019, 1984 - 2020.\n"
+"\n"
+"**Spatial resolution**: 30 meters.\n"
+"\n"
+"**Common Usage**: Monitoring of water bodies for water resource management, "
+"climate modelling, biodiversity conservation and food security."
+msgstr ""
+
+msgid ""
+"The **Seasonal Trajectories** product is a filtered time series of Plant "
+"Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of "
+"the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution "
+"Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal "
+"Trajectories PPI is derived through fitting a smoothing and gap filling "
+"function to the yearly time-series raw PPI values generated from Sentinel-2 "
+"satellite observations. More information "
+"[here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n"
+"\n"
+"**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and "
+"latitude 26°N to 72°N.\n"
+"\n"
+"**Data Availability**: Since January 2017, updated every 10 days.\n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Plant phenology monitoring, such as tracking green canopy "
+"foliage dynamics through time."
+msgstr ""
+
+msgid ""
+"The **Vegetation Indices** product is part of the Copernicus Land "
+"Monitoring Service (CLMS), pan-European High Resolution Vegetation "
+"Phenology and Productivity (HR-VPP) product suite. The product is comprised "
+"of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 "
+"satellite observations. More information "
+"[here](https://collections.sentinel-hub.com/vegetation-indices/).\n"
+"\n"
+"**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and "
+"latitude 26°N to 72°N.\n"
+"\n"
+"**Data Availability**: Since October 2016, updated daily. \n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Plant phenology assessment and monitoring, including "
+"vegetation cover, density, productivity and health."
+msgstr ""
+
+msgid ""
+"The **Vegetation Phenology and Productivity Parameters** product is part of "
+"the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution "
+"Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP "
+"product is comprised of 13 parameters that describe specific stages of the "
+"seasonal vegetation growth cycle. These parameters are extracted from "
+"Seasonal Trajectories of the Plant Phenology Index (PPI) derived from "
+"Sentinel-2 satellite observations.\n"
+"More information "
+"[here](https://collections.sentinel-hub.com/vegetation-phenology-and-"
+"productivity-parameters-season-1/).\n"
+"\n"
+"**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and "
+"latitude 26°N to 72°N.\n"
+"\n"
+"**Data Availability**: Since January 2017, updated annually.\n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Detailed assessment of the impacts of human or climate "
+"change on the ecosystem through monitoring of vegetation responses to "
+"disturbances, e.g. droughts, storms, insect infestations, and to human "
+"influence from global to local levels."
+msgstr ""
+
+msgid ""
+"The **Vegetation Phenology and Productivity Parameters** product is part of "
+"the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution "
+"Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP "
+"product is comprised of 13 parameters that describe specific stages of the "
+"seasonal vegetation growth cycle. These parameters are extracted from "
+"Seasonal Trajectories of the Plant Phenology Index (PPI) derived from "
+"Sentinel-2 satellite observations.\n"
+"More information "
+"[here](https://collections.sentinel-hub.com/vegetation-phenology-and-"
+"productivity-parameters-season-2/).\n"
+"\n"
+"**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and "
+"latitude 26°N to 72°N.\n"
+"\n"
+"**Data Availability**: Since January 2017, updated annually.\n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Detailed assessment of the impacts of human or climate "
+"change on the ecosystem through monitoring of vegetation responses to "
+"disturbances, e.g. droughts, storms, insect infestations, and to human "
+"influence from global to local levels."
+msgstr ""
+
+msgid ""
+"The **Corine Land Cover Accounting Layers** are status layers modified for "
+"the purpose of consistent statistical analysis in the land cover change "
+"accounting system. The modification combines CLC status and change layers "
+"in the 100 m raster in order to create homogeneous quality time series of "
+"CLC / CLC-change layers for accounting purposes. The CLC inventory consists "
+"of 44 land cover and land use classes derived from a series of satellite "
+"missions since it was first established. More information "
+"[here](https://collections.eurodatacube.com/corine-land-cover-accounting-"
+"layers/).\n"
+"\n"
+"**Coverage**: Europe (EEA39 region).\n"
+"\n"
+"**Data Availability**: Since 2000, updated every 6 years. Data available "
+"for 2000, 2006, 2012 and 2018.\n"
+"\n"
+"**Spatial resolution**: 100 meters.\n"
+"\n"
+"**Common Usage**: Land use and land cover monitoring, analysis and change "
+"prediction for various applications, including environment, agriculture, "
+"transport and spatial planning."
+msgstr ""
+
+msgid ""
+"A **DEM** (Digital Elevation Model) is a digital representation of a "
+"terrain (usually Earth's surface). It is obtained by dividing the whole "
+"globe into grid cells, each holding a corresponding altitude value in "
+"meters. Depending on the gride cell size, a DEM can be more detailed (high "
+"resolution) or less detailed (low resolution). Sentinel Hub DEM data "
+"collections (Mapzen and Copernicus) are static (independent of date) and "
+"globally available.\n"
+"\n"
+"**Common usage:** Modelling water flows, orthorectification of Sentinel-1 "
+"imagery and engineering."
+msgstr ""
+
+msgid ""
+"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography "
+"Mission) and [other sources]( "
+"https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The "
+"bathymetry data is taken from "
+"[ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static "
+"collection (independent of date) with global coverage.\n"
+"\n"
+"**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n"
+"\n"
+"Credits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)"
+msgstr ""
+
+msgid ""
+"The **Copernicus DEM** represents the surface of the Earth including "
+"buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is "
+"based on a combination of different DEMs (basis "
+"[WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-"
+"standard-of-global-elevation-models/)). It is a static collection "
+"(independent of date) with global coverage.\n"
+"\n"
+"**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not "
+"released).\n"
+"\n"
+"Credits: "
+"[ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=39"
+"4198)"
+msgstr ""
+
+msgid ""
+"The **Copernicus DEM** represents the surface of the Earth including "
+"buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is "
+"based on a combination of different DEMs (basis "
+"[WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-"
+"standard-of-global-elevation-models/)). It is a static collection "
+"(independent of date) with global coverage.\n"
+"\n"
+"**Spatial resolution:** 90 m\n"
+"\n"
+"Credits: "
+"[ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=39"
+"4198)"
+msgstr ""
+
+msgid ""
+"The **ESA WorldCover** product is the first global land cover map at 10 m "
+"resolution based on both Sentinel-1 and Sentinel-2 data. More information "
+"[here](https://esa-worldcover.org/).\n"
+"\n"
+"**Coverage**: Global coverage.\n"
+"\n"
+"**Data Availability**: 2020.\n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Development of novel services to help with preserving "
+"biodiversity, food security, carbon assessment and climate modelling."
+msgstr ""
+
+msgid ""
+"The **Global Human Settlement** (GHS) framework produces global maps of "
+"built-up areas, population density and settlements to monitor human "
+"presence on Earth over time.\n"
+"\n"
+"**Coverage**: Global coverage with longitude from 180°W to 180°E and "
+"latitude from 72°N to 56°S\n"
+"\n"
+"**Data Availability**: Reference year 2018\n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Knowledge of population distribution and density has a "
+"number of applications, including disaster risk management or the study and "
+"management of urbanisation processes, not only but also in relation to the "
+"challenges of climate change and environmental degradation."
+msgstr ""
+
+msgid ""
+"The **Landsat 4-5 TM** collection includes imagery produced with the "
+"Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 "
+"satellites. There are 6 optical and one thermal infrared band available, "
+"all in 30 meter resolution. Data is archived, with global coverage over "
+"land, available from 1982 to 2012. Top of the atmosphere level-1, and "
+"surface reflectance level-2 products are provided.\n"
+"\n"
+"**Spatial resolution**: 30 meter\n"
+"\n"
+"**Revisit time** 16 days\n"
+"\n"
+"**Data availability**: global, Level-1 from August 1982 to May 2012, "
+"Level-2 from July 1984 to May 2012. \n"
+"\n"
+"**Common Usage**: Monitoring of vegetation, ice and water resources, change "
+"detection and the creation of land use - land cover maps."
+msgstr ""
+
+msgid ""
+"**Landsat 1-5 MSS** collection includes imagery produced with the "
+"Multispectral Scanner System (MSS), which was carried onboard Landsat 1 "
+"through Landsat 5 satellites. There are 4 optical bands available in 60 m "
+"resolution. Data is archived and includes global imagery since 1972. \n"
+"\n"
+"**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n"
+"\n"
+"**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n"
+"\n"
+"**Data availability**: Global, since:\n"
+"- Landsat 1 from July 1972 to January 1978\n"
+"- Landsat 2 from January 1975 to February 1982\n"
+"- Landsat 3 from March 1978 to March 1983\n"
+"- Landsat 4 from July 1982 to December 1993\n"
+"- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n"
+"\n"
+"**Common Usage**: Monitoring of vegetation, ice and water resources, change "
+"detection and the creation of land use - land cover maps."
+msgstr ""
+
+msgid ""
+"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic "
+"Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There "
+"are 8 optical and 1 thermal infrared bands available. Global data is "
+"available since 1999, with a revisit time of 16 days. Top of the atmosphere "
+"level-1, and surface reflectance level-2 products are provided. Note that "
+"there are data gaps for all images acquired since 2003-05-30 due to sensor "
+"failure.\n"
+"\n"
+"**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n"
+"\n"
+"**Revisit time**: 16 days\n"
+"\n"
+"**Data availability**: global, since April 1999\n"
+"\n"
+"**Common Usage**: Monitoring of vegetation, ice and water resources, change "
+"detection and the creation of land use - land cover maps."
+msgstr ""
+
+msgid ""
+"**Landsat 8** is the most recently launched Landsat satellite (provided by "
+"NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal "
+"Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. "
+"These two sensors provide seasonal coverage of the global landmass.\n"
+"\n"
+"**Spatial resolution:** 15 m for the panchromatic band and 30 m for the "
+"rest (the thermal bands is re-sampled from 100 m).\n"
+"\n"
+"**Revisit time:** 16 days\n"
+"\n"
+"**Data availability:** Since February 2013\n"
+"\n"
+"**Common usage:** Vegetation monitoring, land use, land cover maps, change "
+"monitoring, etc."
+msgstr ""
+
+msgid ""
+"**Level-1** data (from **Landsat Collection 2**) provides global top of the "
+"atmosphere reflectance and top of the atmosphere brightness temperature "
+"products. \n"
+"\n"
+"The data underwent several processing steps including geometric and "
+"radiometric improvements. \n"
+"\n"
+"More info about Level-1 data "
+"[here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-"
+"collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_"
+"support_page_related_con) and "
+"[here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)"
+msgstr ""
+
+msgid ""
+"**Level-2** data (from **Landsat Collection 2**) provides global surface "
+"reflectance and surface temperature science products (CEOS Analysis Ready "
+"Data). \n"
+"\n"
+"The data products are generated from Collection 2 Level-1 inputs that meet "
+"the <76 degrees Solar Zenith Angle constraint and include the required "
+"auxiliary data inputs to generate a scientifically viable product. \n"
+"\n"
+"Learn more about Level-2 data "
+"[here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-"
+"collection-2-level-2-science-products) and "
+"[here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/)."
+msgstr ""
+
+msgid ""
+"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) "
+"reflectance imagery. Level-1 data is produced by processing Landsat TM data "
+"with standard processing parameters, such as cubic convolution and terrain "
+"correction. Learn more "
+"[here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and "
+"[here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-"
+"archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_"
+"objects=0#qt-science_center_objects)."
+msgstr ""
+
+msgid ""
+"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data "
+"to surface reflectance - an estimate of the surface spectral reflectance at "
+"ground level in the absence of atmospheric scattering and absorption. Learn "
+"more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and "
+"[here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-"
+"archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_"
+"objects=0#qt-science_center_objects)."
+msgstr ""
+
+msgid ""
+"**Landsat 7 ETM+ Level-1** \n"
+"\n"
+"Learn more "
+"[here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)"
+msgstr ""
+
+msgid ""
+"**Landsat 7 ETM+ Level-2** \n"
+"\n"
+"Learn more "
+"[here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/)."
+msgstr ""
+
+msgid ""
+"Through Norway’s International Climate & Forests Initiative, anyone can now "
+"access Planet’s high-resolution, \n"
+"analysis-ready mosaics of the world’s tropics in order to help reduce and "
+"reverse the loss of tropical forests, \n"
+"combat climate change, conserve biodiversity, and facilitate sustainable "
+"development. \n"
+"\n"
+"**Data availability:** world's tropics, September 2015 - August 2020 "
+"biannually, from September 2020 monthly."
+msgstr ""
+
+msgid "Corine Land Cover Accounting Layer"
+msgstr ""
+
+msgid "Continuous urban fabric"
+msgstr ""
+
+msgid "Discontinuous urban fabric"
+msgstr ""
+
+msgid "Industrial or commercial units"
+msgstr ""
+
+msgid "Road and rail networks and associated land"
+msgstr ""
+
+msgid "Port areas"
+msgstr ""
+
+msgid "Airports"
+msgstr ""
+
+msgid "Mineral extraction sites"
+msgstr ""
+
+msgid "Dump sites"
+msgstr ""
+
+msgid "Construction sites"
+msgstr ""
+
+msgid "Green urban areas"
+msgstr ""
+
+msgid "Sport and leisure facilities"
+msgstr ""
+
+msgid "Non-irrigated arable land"
+msgstr ""
+
+msgid "Permanently irrigated land"
+msgstr ""
+
+msgid "Rice fields"
+msgstr ""
+
+msgid "Vineyards"
+msgstr ""
+
+msgid "Fruit trees and berry plantations"
+msgstr ""
+
+msgid "Olive groves"
+msgstr ""
+
+msgid "Pastures"
+msgstr ""
+
+msgid "Annual crops associated with permanent crops"
+msgstr ""
+
+msgid "Complex cultivation patterns"
+msgstr ""
+
+msgid ""
+"Land principally occupied by agriculture with significant areas of natural "
+"vegetation"
+msgstr ""
+
+msgid "Agro-forestry areas"
+msgstr ""
+
+msgid "Broad-leaved fores"
+msgstr ""
+
+msgid "Coniferous fores"
+msgstr ""
+
+msgid "Mixed fores"
+msgstr ""
+
+msgid "Natural grasslands"
+msgstr ""
+
+msgid "Moors and heathland"
+msgstr ""
+
+msgid "Sclerophyllous vegetation"
+msgstr ""
+
+msgid "Transitional woodland-shrub"
+msgstr ""
+
+msgid "Beaches"
+msgstr ""
+
+msgid "Bare rocks"
+msgstr ""
+
+msgid "Sparsely vegetated areas"
+msgstr ""
+
+msgid "Burnt areas"
+msgstr ""
+
+msgid "Glaciers and perpetual snow"
+msgstr ""
+
+msgid "Inland marshes"
+msgstr ""
+
+msgid "Peat bogs"
+msgstr ""
+
+msgid "Salt marshes"
+msgstr ""
+
+msgid "Salines"
+msgstr ""
+
+msgid "Intertidal flats"
+msgstr ""
+
+msgid "Water courses"
+msgstr ""
+
+msgid "Water bodies"
+msgstr ""
+
+msgid "Coastal lagoons"
+msgstr ""
+
+msgid "Estuaries"
+msgstr ""
+
+msgid "Sea and ocean"
+msgstr ""
+
+msgid "NODATA"
+msgstr ""
+
+msgid "Main discrete land cover classification according to FAO LCCS scheme"
+msgstr ""
+
+msgid ""
+"Classification probability, a quality indicator for the discrete "
+"classification"
+msgstr ""
+
+msgid "Forest type for all pixels where tree cover fraction is bigger than 1 %"
+msgstr ""
+
+msgid "Fractional cover (%) for the bare and sparse vegetation class"
+msgstr ""
+
+msgid "Fractional cover (%) for the cropland class"
+msgstr ""
+
+msgid "Fractional cover (%) for the herbaceous vegetation class"
+msgstr ""
+
+msgid "Fractional cover (%) for the moss & lichen class"
+msgstr ""
+
+msgid "Fractional cover (%) for the shrubland class"
+msgstr ""
+
+msgid "Fractional cover (%) for the snow & ice class"
+msgstr ""
+
+msgid "Fractional cover (%) for the forest class"
+msgstr ""
+
+msgid "Fractional cover (%) for the built-up class"
+msgstr ""
+
+msgid "Fractional cover (%) for the permanent inland water bodies class"
+msgstr ""
+
+msgid "Fractional cover (%) for the seasonal inland water bodies class"
+msgstr ""
+
+msgid ""
+"Data density indicator showing quality of the EO input data (0 = bad, 100 = "
+"perfect data)"
+msgstr ""
+
+msgid ""
+"Quality layer regarding the change detection of the current mapped year to "
+"the previous mapped year. It is a 3 level confidence mask for all CONSO and "
+"NRT maps with value definitions as:\n"
+"0 = No change.\n"
+"1 - Potential confidence.\n"
+"2 - Medium confidence.\n"
+"3 = High confidence.\n"
+"NOTE: The values of Change_Confidence_layer band in 2015 data are not shown "
+"correctly, therefore this band in 2015 data should not be used."
+msgstr ""
+
+msgid ""
+"Changes in water occurrence between two epochs, the first ranging from 1984 "
+"to 1999 and the second covering 2000 to 2019."
+msgstr ""
+
+msgid "Maximum extent of surface water bodies in the 36-year time range."
+msgstr ""
+
+msgid ""
+"Intra- and inter-annual frequency of surface water presence in the time "
+"range between 1984 and 2019."
+msgstr ""
+
+msgid ""
+"Inter-annual variability of surface water presence in a defined water "
+"period within the entire time range from 1984 to 2019."
+msgstr ""
+
+msgid "Intra-annual distribution of surface water in 2019."
+msgstr ""
+
+msgid ""
+"Visualises changes in the three surface water classes (1) not water, (2) "
+"seasonal water, and (3) permanent water between the first and last year in "
+"the 36-year time period."
+msgstr ""
+
+msgid ""
+"Main Water Bodies detection layer showing water pixels and non-water pixels\n"
+"0 = Sea\n"
+"70 = Water\n"
+"251 = No data\n"
+"255 = No water"
+msgstr ""
+
+msgid ""
+"Quality layer which gives information on water bodies occurrence\n"
+"0 = Sea\n"
+"71 = Very low occurence\n"
+"72 = Low occurence\n"
+"73 = Medium occurence\n"
+"74 = High occurence\n"
+"75 = Very high occurence\n"
+"76 = Permanent occurence\n"
+"251 = No data\n"
+"252 = Cloud\n"
+"255 = Not water"
+msgstr ""
+
+msgid "Land cover classification"
+msgstr ""
+
+msgid "Built-up probability [0 - 100 %] at 10 m spatial resolution"
+msgstr ""
+
+msgid "Plant Phenology Index"
+msgstr ""
+
+msgid "Quality Flag"
+msgstr ""
+
+msgid "Normalized Difference Vegetation Index"
+msgstr ""
+
+msgid "Fraction of Absorbed Photosynthetically Active Radiation"
+msgstr ""
+
+msgid "Leaf Area Index"
+msgstr ""
+
+msgid "Day of start-of-season"
+msgstr ""
+
+msgid "Day of end-of-season"
+msgstr ""
+
+msgid "Day of maximum-of-season"
+msgstr ""
+
+msgid "Vegetation index value at SOSD"
+msgstr ""
+
+msgid "Vegetation index value at EOSD"
+msgstr ""
+
+msgid "Vegetation index value at MAXD"
+msgstr ""
+
+msgid ""
+"Average vegetation index value of minima on left and right sides of each "
+"season"
+msgstr ""
+
+msgid "Season amplitude (MAXV – MINV)"
+msgstr ""
+
+msgid "Length of Season (number of days between start and end)"
+msgstr ""
+
+msgid "Slope of the greening up period"
+msgstr ""
+
+msgid "Slope of the senescent period"
+msgstr ""
+
+msgid ""
+"Seasonal productivity. The growing season integral computed as the sum of "
+"all daily values between SOSD and EOSD"
+msgstr ""
+
+msgid ""
+"Total productivity. The growing season integral computed as sum of all "
+"daily values minus their base level value."
+msgstr ""
+
+msgid "Band 10 - Thermal Infrared (TIRS) - 10895 nm"
+msgstr ""
+
+msgid "Band 11 - Thermal Infrared (TIRS) - 12005 nm"
+msgstr ""
+
+msgid "Ultra Blue (443 nm)"
+msgstr ""
+
+msgid "Blue (482 nm)"
+msgstr ""
+
+msgid "Green (561.5 nm)"
+msgstr ""
+
+msgid "Red (654.5 nm)"
+msgstr ""
+
+msgid "Near Infrared (NIR) (865 nm)"
+msgstr ""
+
+msgid "Shortwave Infrared (SWIR) 1 (1608.5 nm)"
+msgstr ""
+
+msgid "Shortwave Infrared (SWIR) 2 (2200.5 nm)"
+msgstr ""
+
+msgid "Thermal Infrared (TIRS) 1(10895 nm)"
+msgstr ""
+
+msgid "Blue (450-520 nm)"
+msgstr ""
+
+msgid "Green (520-600 nm)"
+msgstr ""
+
+msgid "Red (630-690 nm)"
+msgstr ""
+
+msgid "Near Infrared (NIR) (760-900 nm)"
+msgstr ""
+
+msgid "Shortwave Infrared (SWIR) 1 (1550-1750 nm)"
+msgstr ""
+
+msgid "Thermal Infrared (10400-12500 nm)"
+msgstr ""
+
+msgid "Shortwave Infrared (SWIR) 2 (2080-2350 nm)"
+msgstr ""
+
+msgid "Green (500-600 nm)\t"
+msgstr ""
+
+msgid "Red (600-700 nm)"
+msgstr ""
+
+msgid "Ultra Red (700-800 nm)"
+msgstr ""
+
+msgid "Near Infrared (NIR) (800-1100 nm)"
+msgstr ""
+
+msgid "Panchromatic (520-900 nm)"
+msgstr ""
+
+msgid "This pin currently has no description."
+msgstr ""
+
+msgid "Order by:"
+msgstr ""
+
+msgid "Location"
+msgstr ""
+
+msgid "DatasetId"
+msgstr ""
+
+msgid "Title"
+msgstr ""
+
+msgid "Zoom to location"
+msgstr ""
+
+msgid "Remove layer"
+msgstr ""
+
+#, javascript-format
+msgid ""
+"Browse, visualise and analyze Very High Resolution (VHR) data directly in "
+"EO Browser, tapping into global archives of Planet "
+"[PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), "
+"Airbus "
+"[Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) "
+"and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as "
+"well as [Maxar "
+"WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/)."
+" \n"
+"\n"
+"Observe the planet at resolutions starting at 3 meters and all the way up "
+"to 0.5 meters for a cost down to 0.9 EUR per km².\n"
+"\n"
+"![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL "
+"}commercial-data-previews/high-res-image-example.png)\n"
+"\n"
+"© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed "
+"by Sentinel Hub\n"
+"\n"
+"What you need: \n"
+"- An active Sentinel Hub subscription to search the metadata. If you don't "
+"have an account yet: [Sign "
+"up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1&"
+"param_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback."
+"html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-06"
+"43d660a478&domainId=1).\n"
+"- Pre-purchased quota for any of the constellations. Go to "
+"[Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to "
+"establish a subscription and purchase commercial data plans."
+msgstr ""
+
+msgid ""
+"**General** \n"
+"The \"Commercial data\" tab allows you to search, purchase, and visualize "
+"Commercial Third-Party data.\n"
+"\n"
+"**Available constellations** \n"
+"We currently offer data from 4 different commercial data providers:\n"
+"- Planet [Planet "
+"scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 "
+"bands, 3m resolution)\n"
+"- Airbus "
+"[Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) "
+"(5 bands, 0.5m - 2m resolution)\n"
+"- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) "
+"(5 bands, 1.5m - 6m resolution)\n"
+"- Maxar "
+"[WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/)"
+" (5 bands, 0.5m - 2m resolution)\n"
+"\n"
+"As the term \"commercial\" implies, the data comes at a cost, which means "
+"**in addition to your existing Sentinel Hub subscription, you will need to "
+"purchase quota** for the data you are interested in.\n"
+"\n"
+"**Quota** \n"
+"Check *My quota* to see how much quota you have for each of the "
+"constellations. You can purchase quota through the [Sentinel Hub "
+"Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n"
+"\n"
+"**Purchase** \n"
+"To purchase commercial data, you must:\n"
+"- search for the data (*Search options*),\n"
+"- select a product from the results (*Results*),\n"
+"- add the product to your order,\n"
+"- specify where to save the order (*Order options*),\n"
+"- review the order and confirm it (*Created orders (not confirmed)* in *My "
+"orders*). Your order will now be listed under *Running orders* and move to "
+"*Finished orders* once the data has been purchased and ingested.\n"
+"\n"
+"**More information** \n"
+"For more information on ordering commercial data ( Third Party Data Import "
+"), please see the [Sentinel Hub Documentation "
+"Page](https://docs.sentinel-hub.com/api/latest/api/data-import/)."
+msgstr ""
+
+#, javascript-format
+msgid "Unable to get quotas: ${ err.message }"
+msgstr ""
+
+msgid "Search options"
+msgstr ""
+
+msgid "Order options"
+msgstr ""
+
+msgid "My orders"
+msgstr ""
+
+msgid "My quotas"
+msgstr ""
+
+msgid "Help"
+msgstr ""
+
+msgid "Select"
+msgstr ""
+
+msgid "Use current display area"
+msgstr ""
+
+msgid "Draw rectangular area of interest"
+msgstr ""
+
+msgid "Draw polygonal area of interest"
+msgstr ""
+
+msgid "Cancel"
+msgstr ""
+
+msgid "Product bundle"
+msgstr ""
+
+msgid "Max. Cloud Coverage"
+msgstr ""
+
+msgid "Advanced options"
+msgstr ""
+
+msgid "Min. Off Nadir"
+msgstr ""
+
+msgid "Max. Off Nadir"
+msgstr ""
+
+msgid "Min. Sun Elevation"
+msgstr ""
+
+msgid "Max. Sun Elevation"
+msgstr ""
+
+msgid "Sensor"
+msgstr ""
+
+msgid "Processing Level"
+msgstr ""
+
+msgid "Snow Coverage"
+msgstr ""
+
+msgid "Incidence Angle"
+msgstr ""
+
+msgid "From"
+msgstr ""
+
+msgid "To"
+msgstr ""
+
+msgid "Constellation"
+msgstr ""
+
+msgid "Show preview"
+msgstr ""
+
+msgid "Cloud cover"
+msgstr ""
+
+msgid "Processing level"
+msgstr ""
+
+msgid "Snow cover"
+msgstr ""
+
+msgid "Incidence angle"
+msgstr ""
+
+msgid "Coverage"
+msgstr ""
+
+msgid "Shadow percent"
+msgstr ""
+
+msgid "Pixel resolution"
+msgstr ""
+
+msgid "Hide details"
+msgstr ""
+
+msgid "add"
+msgstr ""
+
+msgid "remove"
+msgstr ""
+
+msgid "Product id"
+msgstr ""
+
+msgid "Accuisition date"
+msgstr ""
+
+msgid "Show results on map"
+msgstr ""
+
+msgid "Prepare order"
+msgstr ""
+
+msgid "Provider"
+msgstr ""
+
+msgid "Purchased km"
+msgstr ""
+
+msgid "Used km"
+msgstr ""
+
+msgid "No quotas available"
+msgstr ""
+
+msgid "Refresh quotas"
+msgstr ""
+
+msgid "Created orders (Not confirmed)"
+msgstr ""
+
+msgid "Running orders"
+msgstr ""
+
+msgid "Finished orders"
+msgstr ""
+
+msgid "Created at"
+msgstr ""
+
+msgid "Confirmed at"
+msgstr ""
+
+msgid "Size"
+msgstr ""
+
+msgid "Status"
+msgstr ""
+
+msgid "All input parameters"
+msgstr ""
+
+msgid "Order ID"
+msgstr ""
+
+msgid "Collection ID"
+msgstr ""
+
+#, javascript-format
+msgid "Hide ${ property } values"
+msgstr ""
+
+msgid "Show ${ property } values"
+msgstr ""
+
+msgid "Confirm"
+msgstr ""
+
+msgid "Delete"
+msgstr ""
+
+msgid "Show coverage"
+msgstr ""
+
+msgid "Show data"
+msgstr ""
+
+msgid ""
+"Note that it is technically possible to order more PlanetScope data than "
+"your purchased quota. Make sure your order is in line with the Hectares "
+"under Management (HUM) model to avoid overage fees."
+msgstr ""
+
+msgid "More information"
+msgstr ""
+
+msgid "No orders found"
+msgstr ""
+
+msgid "Error confirming order"
+msgstr ""
+
+msgid "Error deleting order"
+msgstr ""
+
+msgid "Confirm order"
+msgstr ""
+
+msgid "Are you sure you want to confirm this order?"
+msgstr ""
+
+msgid "Delete order"
+msgstr ""
+
+msgid "Are you sure you want to delete this order?"
+msgstr ""
+
+msgid "Refresh orders"
+msgstr ""
+
+msgid "Create a new collection"
+msgstr ""
+
+msgid "Manual Entry"
+msgstr ""
+
+msgid "Your collections"
+msgstr ""
+
+msgid "Order name"
+msgstr ""
+
+msgid ""
+"ORDER USING PRODUCTS IDS\n"
+"\n"
+"Search for data and add products to you order by clicking on \"Add to "
+"Order\" buttons. This will add product IDs to your Order Options under "
+"\"Added Products (by ID).\"\n"
+"\n"
+"ORDER USING QUERY\n"
+"\n"
+"Your order will be based on your AOI and time range, without searching for "
+"data and adding products to your order. Especially useful for ordering "
+"time-series data.\n"
+"It's possible for some products to be partially covered by clouds, despite "
+"the cloud coverage % information being 0."
+msgstr ""
+
+msgid "Ordered products will be clipped to the selected area."
+msgstr ""
+
+msgid "Set an approximate order limit to prevent undesired large area requests."
+msgstr ""
+
+msgid ""
+"Harmonization is not yet supported for surface reflectance products, thus "
+"this field must be explicitly set to NONE if productBundle is analytic_sr "
+"or analytic_sr_udm2."
+msgstr ""
+
+msgid ""
+"Enter a Planet API key, that you received via email after purchasing a "
+"Planet PlanetScope Sentinel Hub Package"
+msgstr ""
+
+msgid ""
+"When you click \"Create Order\", your order will be created. At this stage, "
+"the order will not go through and no quota will be substracted. This will "
+"happen when you confirm the order. Before you do, you will be able to "
+"review the requested quota and decide if you would like to proceed."
+msgstr ""
+
+msgid ""
+"Select the resampling kernel to use: \n"
+"- 4x4 cubic convolution (CC), \n"
+"- nearest neighbour (NN), \n"
+"- or the proprietary MTF kernel (MTF)"
+msgstr ""
+
+msgid "Creating order"
+msgstr ""
+
+msgid ""
+"Are you sure you want to create an order without a Collection ID? \n"
+"When you confirm your order a new collection will be created automatically."
+msgstr ""
+
+msgid "e.g.: My planet data"
+msgstr ""
+
+msgid "Order type"
+msgstr ""
+
+msgid "Order size (approx)"
+msgstr ""
+
+msgid "Order limit"
+msgstr ""
+
+msgid "Order limit (km2)"
+msgstr ""
+
+msgid "Create order"
+msgstr ""
+
+msgid "4x4 cubic convolution"
+msgstr ""
+
+msgid "nearest neighbour"
+msgstr ""
+
+msgid "proprietary MTF kernel"
+msgstr ""
+
+msgid "Resampling kernel"
+msgstr ""
+
+msgid "Harmonize data"
+msgstr ""
+
+msgid "Planet API Key"
+msgstr ""
+
+msgid "Your Planet API key"
+msgstr ""
+
+msgid "Please login to gain access to it"
+msgstr ""
+
+msgid "The theme you are trying to access is private"
+msgstr ""
+
+msgid "Continue without logging in"
+msgstr ""
+
+msgid "User Instances"
+msgstr ""
+
+msgid "Settings"
+msgstr ""
+
+msgid "Shading"
+msgstr ""
+
+msgid "Sun"
+msgstr ""
+
+msgid "Eye height"
+msgstr ""
+
+msgid "You can only view data in 3D while visualizing a collection."
+msgstr ""
+
+msgid "2D map view"
+msgstr ""
+
+msgid "Not supported in 3D mode."
+msgstr ""
+
+msgid ""
+"Image download in compare mode is currently available only for basic image "
+"download."
+msgstr ""
+
+msgid "Creating and editing a timelapse is not supported on mobile."
+msgstr ""
+
+msgid ""
+"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area "
+"will be used for clipping when exporting an image."
+msgstr ""
+"Lae KML/KMZ, GPX või GEOJSON/JSON fail, et luua huvipakkuv piirkond. "
+"Vastavalt huvipakkuvale piirkonnale lõigatakse eksporditud pilt parajaks."
+
+msgid "Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer"
+msgstr "Lohistage KML / KMZ, GPX, GEOJSON/JSON-fail või otsi arvutist"
+
+msgid "Layer default"
+msgstr ""
+
+msgid "Speckle Filter"
+msgstr ""
+
+msgid ""
+"Speckle filtering is only applied at zoom levels 12 and above for IW and "
+"zoom levels 8 and above for EW acquisition. Zoom levels outside this range "
+"will render without speckle filtering, even if it is set."
+msgstr ""
+
+msgid "Speckle filtering not applied. Zoom in to apply speckle filtering."
+msgstr ""
+
+msgid "Orthorectification"
+msgstr ""
+
+msgid "Processing parameters"
+msgstr ""
+
+msgid "Advanced RGB effects"
+msgstr ""
+
+#. jsx-a11y/anchor-is-valid
+#. eslint-disable-next-line
+msgid "Draw rectangular area of interest for image downloads"
+msgstr ""
+
+#. jsx-a11y/anchor-is-valid
+#. eslint-disable-next-line
+msgid "Draw polygonal area of interest for image downloads"
+msgstr ""
+
+msgid "DEM instance"
+msgstr ""
+
+msgid "Type"
+msgstr ""
+
+msgid "Currently only collections on services.sentinel-hub are supported."
+msgstr ""
+
+msgid "Primary dataset:"
+msgstr ""
+
+msgid "Datasource alias:"
+msgstr ""
+
+msgid "Additional datasets:"
+msgstr ""
+
+msgid "Drag classes onto RGB fields."
+msgstr ""
+
+msgid "Add layers from pins"
+msgstr ""
+
+msgid "Visualisations"
+msgstr ""
+
+msgid "Min. tile coverage"
+msgstr ""
+
+msgid "Edit timelapse"
+msgstr ""
+
+msgid "fps"
+msgstr ""
+
+msgid "Transition:"
+msgstr ""
+
+msgid "None"
+msgstr ""
+
+msgid "Fade"
+msgstr ""
+
+msgid "EPSG:3857 is not available when an AOI is specified."
+msgstr ""
+
+msgid ""
+"Map overlay is disabled when AOI is specified. Remove your AOI in order to "
+"use this option."
+msgstr ""
+
+msgid "You need to login to use this functionality."
+msgstr ""
+
+msgid "Exported image(s) will include datasource and date, zoom scale and branding."
+msgstr ""
+
+msgid "Enable captions in order to write a description."
+msgstr ""
+
+msgid "Add a short description to the exported image."
+msgstr ""
+
+msgid "Layer does not have any legend data."
+msgstr ""
+
+msgid "Exported image will include legend."
+msgstr ""
+
+msgid "you can only download an image while visualizing or comparing"
+msgstr ""
+
+msgid "you need to compare at least 2 layers"
+msgstr ""
+
+msgid "Image width [px]:"
+msgstr ""
+
+msgid "Image height [px]:"
+msgstr ""
+
+msgid ""
+"The setup function in the evalscript does not contain the correct output. "
+"The output needs to include:"
+msgstr ""
+
+msgid ""
+"You are visualising a layer that doesn't represent an index. The histogram "
+"feature currently only works for index layers (e.g. NDVI).\n"
+"\n"
+"Please select an index layer to use this feature."
+msgstr ""
+
+msgid "Please select a layer"
+msgstr ""
+
+msgid "Histogram can be displayed only while visualizing"
+msgstr ""
+
+msgid "Histogram not available for "
+msgstr ""
+
+msgid "Recalculate"
+msgstr ""
+
+msgid "Histogram"
+msgstr ""
+
+msgid "Based on the last band of the custom script."
+msgstr ""
+
+#. #__PURE__
+msgid "Max. cloud coverage:"
+msgstr ""
+
+msgid "Error"
+msgstr ""
+
+msgid ""
+"Your web browser doesn't support 3D capabilities, that are needed to "
+"display this content."
+msgstr ""
+
+msgid "Cannot connect to the 3D service! Retry?"
+msgstr ""
+
+msgid ""
+"The image is too big for this device!\n"
+"Image size: {0}x{1}, max: {2}"
+msgstr ""
+
+msgid "Home"
+msgstr ""
+
+msgid "Sphere mode"
+msgstr ""
+
+msgid "Cannot load the image"
+msgstr ""
+
+msgid "Geometries"
+msgstr ""
+
+msgid "Now"
+msgstr ""
+
+msgid "Terrain"
+msgstr ""
+
+msgid "Time"
+msgstr ""
+
+msgid "Position 3D camera based on 2D map"
+msgstr ""
+
+msgid "Sky / Atmosphere"
+msgstr ""
+
+msgid "Sun time (UTC)"
+msgstr ""
+
+msgid "Sun projected shadows"
+msgstr ""
+
+#. Applies to the luminance of the terrain, regarding the angle between the terrain and the light source. This does not include the shadows casted from the other objects.
+msgid "Shading parameters"
+msgstr ""
+
+#. These are the projected shadows, where other objects cast shadows to the terrain. To render the projected shadows, it requires some hardware support whose settings are set here.
+msgid "Shadow parameters"
+msgstr ""
+
+msgid "Ambient factor"
+msgstr ""
+
+msgid "Diffuse factor"
+msgstr ""
+
+msgid "Specular factor"
+msgstr ""
+
+msgid "Specular power"
+msgstr ""
+
+msgid "Shadow visibility"
+msgstr ""
+
+msgid "Shadow rendering distance"
+msgstr ""
+
+msgid "Shadow map size"
+msgstr ""
+
+msgid "Parameters"
+msgstr ""
+
+msgid "Local time on computer"
+msgstr ""
+
+msgid "Edit"
+msgstr ""
+
+msgid "Reset values"
+msgstr ""
+
+msgid "Current time"
+msgstr ""
+
+msgid "Mouse navigation"
+msgstr ""
+
+msgid "Left button"
+msgstr ""
+
+msgid ""
+"Click and drag using the left mouse button to move across the map at a "
+"fixed height. Use SHIFT + left button to rotate."
+msgstr ""
+
+msgid "Right button"
+msgstr ""
+
+msgid ""
+"Right click and drag up/down to change the elevation of the camera. Right "
+"click and\n"
+"drag left/right to rotate the camera's view."
+msgstr ""
+
+msgid "Middle button/wheel"
+msgstr ""
+
+msgid ""
+"Use the scroll wheel to change the elevation of the camera (same as right "
+"click + drag\n"
+"up/down). Click and drag the wheel button to change the angle of the camera."
+msgstr ""
+
+msgid "Keyboard navigation"
+msgstr ""
+
+msgid "Arrow keys"
+msgstr ""
+
+msgid "Use the arrow keys to move across the map at a fixed height."
+msgstr ""
+
+msgid "SHIFT + arrow keys"
+msgstr ""
+
+msgid ""
+"Hold the SHIFT key while pressing the arrow keys to change the camera's "
+"view."
+msgstr ""
+
+msgid "Page up/Page down"
+msgstr ""
+
+msgid "Use the PG UP or PG DN keys to change the elevation of the camera."
+msgstr ""
+
+msgid "Map navigation"
+msgstr ""
+
+msgid "Pan console"
+msgstr ""
+
+msgid ""
+"The pan console allows you to move across the map at a fixed height. Click "
+"and drag to move\n"
+"continuously. The farther you drag from the center, the faster you will "
+"move."
+msgstr ""
+
+msgid "Camera console"
+msgstr ""
+
+msgid ""
+"The camera console moves the camera's view only. Click and drag to change "
+"the camera's view.\n"
+"The farther you drag from the center, the faster you will change the view."
+msgstr ""
+
+msgid "Zoom buttons"
+msgstr ""
+
+msgid ""
+"Clicking them will change the elevation of the camera. The plus button will "
+"move the camera\n"
+"closer to the earth, the minus button will move the camera further away."
+msgstr ""
+
+msgid ""
+"Double clicking with the left mouse button on the camera console resets the "
+"camera view to North and down looking position."
+msgstr ""
+
+msgid "Vertical terrain scaling"
+msgstr ""
+
+msgid "Camera lens flare effect (with the sun)"
+msgstr ""
+
+#. Label for "pivot rotation" checkbox
+msgid "Rotate around the clicked point"
+msgstr ""
+
+#. Additional info for "pivot rotation" checkbox
+msgid ""
+"When checked, moving the mouse while the middle button is pressed, \n"
+"rotates the world around the clicked point, \n"
+"otherwise the camera rotates around itself. \n"
+"Alt key, being pressed when starting to rotate, toggles this behavior."
+msgstr ""
+
+msgid "Modified Anthocyanin Reflectance Index"
+msgstr "Modifitseeritud antotsüaniini peegeldusindeks"
+
+msgid ""
+"# Effective radiometric cloud fraction\n"
+"\n"
+"Effective radiometric cloud fraction represents the portion of the Earth's "
+"surface covered by clouds, divided by the total surface. Clouds have "
+"shielding, albedo, and in-cloud absorption effects on trace gas retrieval. "
+"The effective radiometric cloud fraction is an important parameter to "
+"correct these effects."
+msgstr ""
+
+msgid ""
+"# Thermal band 10\n"
+"\n"
+"This thermal visualization is based on band 10 (a band is a region of the "
+"electromagnetic spectrum; a satellite sensor can image Earth in different "
+"bands). At the central wavelength of 10895 nm it measures in the thermal "
+"infrared, or TIR. Instead of measuring the temperature of the air, like "
+"weather stations do, band 10 reports on the ground itself, which is often "
+"much hotter. Thermal band 10 is useful in providing surface temperatures "
+"and is collected with a 100-meter resolution.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-"
+"satellites?qt-news_science_products=0#qt-news_science_products) and "
+"[here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"
+msgstr ""
+
+msgid ""
+"# True color composite\n"
+"\n"
+"Sensors carried by satellites can image Earth in different regions of the "
+"electromagnetic spectrum. Each region in the spectrum is referred to as a "
+"band. Landsat 4-5 TM has 7 bands. The true color composite uses visible "
+"light bands red, green and blue in the corresponding red, green and blue "
+"color channels, resulting in a natural colored product, that is a good "
+"representation of the Earth as humans would see it naturally.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-"
+"satellites)."
+msgstr ""
+
+msgid ""
+"# Thermal band 6\n"
+"\n"
+"This thermal visualization is based on band 6 (a band is a region of the "
+"electromagnetic spectrum; a satellite sensor can image Earth in different "
+"bands). At the central wavelength of 11040 nm it measures in the thermal "
+"infrared, or TIR. Instead of measuring the temperature of the air, like "
+"weather stations do, band 6 reports on the ground itself, which is often "
+"much hotter. Thermal band 6 is useful in providing surface temperatures and "
+"is collected with a 120-meter resolution, resampled to 30-meter.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-"
+"satellites)."
+msgstr ""
+
+msgid ""
+"# Thermal Visualization\n"
+"\n"
+"This thermal visualization is based on band B06 (a band is a region of the "
+"electromagnetic spectrum; a satellite sensor can image Earth in different "
+"bands). At the central wavelength of 10400-12500 nm it measures in the "
+"thermal infrared, or TIR. Instead of measuring the temperature of the air, "
+"like weather stations do, band B06 reports on the ground itself, which is "
+"often much hotter. Thermal band B06 is useful in providing surface "
+"temperatures and is collected with a 60-meter resolution, resampled to "
+"30-meter.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-"
+"satellites) and "
+"[here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/)."
+msgstr ""
+
+msgid ""
+"# Thermal B06_VCID_1 Visualization\n"
+"\n"
+"This thermal visualization is based on band B06_VCID_1 (a band is a region "
+"of the electromagnetic spectrum; a satellite sensor can image Earth in "
+"different bands). At the central wavelength of 10400-12500 nm it measures "
+"in the thermal infrared, or TIR. Instead of measuring the temperature of "
+"the air, like weather stations do, B06_VCID_1 reports on the ground itself, "
+"which is often much hotter. It is useful in providing surface temperatures "
+"and is collected with a 60-meter resolution, resampled to 30-meters. As its "
+"dinamic range is wider than that of B06_VCID_2, it is less likely to "
+"oversaturate over hot areas. \n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-"
+"two-thermal-bands?qt-news_science_products=0#qt-news_science_products)."
+msgstr ""
+
+msgid ""
+"# Thermal B06_VCID_2 Visualization\n"
+"\n"
+"This thermal visualization is based on band B06_VCID_2 (a band is a region "
+"of the electromagnetic spectrum; a satellite sensor can image Earth in "
+"different bands). At the central wavelength of 10400-12500 nm it measures "
+"in the thermal infrared, or TIR. Instead of measuring the temperature of "
+"the air, like weather stations do, B06_VCID_2 reports on the ground itself, "
+"which is often much hotter. It is useful in providing surface temperatures "
+"and is collected with a 60-meter resolution, resampled to 30-meters. Its "
+"dinamic range is narrower than that of B06_VCID_1, which means it is more "
+"likely to oversaturate over hot areas, but in turn has slightly higher "
+"radiometric sensitivity.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-"
+"two-thermal-bands?qt-news_science_products=0#qt-news_science_products)."
+msgstr ""
+
+msgid ""
+"# False color composite\n"
+"\n"
+"A false color composite uses at least one non-visible wavelength to image "
+"Earth. The false color composite using near infrared, red and green bands "
+"is very popular (a band is a region of the electromagnetic spectrum; a "
+"satellite sensor can image Earth in different bands). The false colour "
+"composite is most commonly used to assess plant density and health, since "
+"plants reflect near infrared and green light, while they absorb red. Cities "
+"and exposed ground are grey or tan, and water appears blue or black. In "
+"this case, the NIR band used in the red channel is Ultra Red band 3 (700 - "
+"800 nm), which is particularly useful for distinguishing vegetation "
+"boundaries between land and water and various landforms. \n"
+"\n"
+"\n"
+"\n"
+"More info [here](https://eos.com/find-satellite/landsat-5-mss/) and "
+"[here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-"
+"ultrared/)."
+msgstr ""
+
+msgid ""
+"# False color composite\n"
+"\n"
+"A false color composite uses at least one non-visible wavelength to image "
+"Earth. The false color composite using near infrared, red and green bands "
+"is very popular (a band is a region of the electromagnetic spectrum; a "
+"satellite sensor can image Earth in different bands). The false colour "
+"composite is most commonly used to assess plant density and health, since "
+"plants reflect near infrared and green light, while they absorb red. Cities "
+"and exposed ground are grey or tan, and water appears blue or black. In "
+"this case, the NIR band used in the red channel is the NIR band 4 (800 - "
+"1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and "
+"distinguishes between land and water. \n"
+"\n"
+"\n"
+"\n"
+"More info [here](https://eos.com/find-satellite/landsat-5-mss/) and "
+"[here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-"
+"nir/)."
+msgstr ""
+
+msgid ""
+"# False color composite\n"
+"\n"
+"A false color composite uses at least one non-visible wavelength to image "
+"Earth. The false color composite using near infrared, red and green bands "
+"is very popular (a band is a region of the electromagnetic spectrum; a "
+"satellite sensor can image Earth in different bands). The false colour "
+"composite is most commonly used to assess plant density and health, since "
+"plants reflect near infrared and green light, while they absorb red. Cities "
+"and exposed ground are grey or tan, and water appears blue or black.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_"
+"infrared/) and "
+"[here.](https://gisgeography.com/landsat-8-bands-combinations/)"
+msgstr ""
+
+msgid ""
+"# False color composite\n"
+"\n"
+"A false color composite uses at least one non-visible wavelength to image "
+"Earth. The false color composite using near infrared, red and green bands "
+"is very popular (a band is a region of the electromagnetic spectrum; a "
+"satellite sensor can image Earth in different bands). The false colour "
+"composite is most commonly used to assess plant density and health, since "
+"plants reflect near infrared and green light, while they absorb red. Cities "
+"and exposed ground are grey or tan, and water appears blue or black.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"
+msgstr ""
+
+msgid ""
+"# True color composite\n"
+"\n"
+"Sensors carried by satellites can image Earth in different regions of the "
+"electromagnetic spectrum. Each region in the spectrum is referred to as a "
+"band. Sentinel-2 has 13 bands. True color composite uses visible light "
+"bands red, green and blue in the corresponding red, green and blue color "
+"channels, resulting in a natural colored product, that is a good "
+"representation of the Earth as humans would see it naturally.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and "
+"[here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/"
+"remote-sensing-systems/spectroscopy)."
+msgstr ""
+"# Loomulike värvide komposiit\n"
+"\n"
+"Satelliidil olevad andurid võivad Maad kujutada elektromagnetilise spektri "
+"eri piirkondades. Iga piirkonda nimetatakse kanaliks. Satelliidil "
+"Sentinel-2 on 13 kanalit. Loomulike värvide komposiit kasutab nähtava "
+"spektri punast, rohelist ja sinist vastavates punase, rohelise ja sinise "
+"värvi kanalites. Tulemuseks on Maa kujutis loomulikes värvides, mida on "
+"inimestel lihtne mõista.\n"
+"\n"
+"\n"
+"\n"
+"Rohkem infot "
+"[siin](https://custom-scripts.sentinel-hub.com/sentinel-2/composites/) ja "
+"[siin](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/"
+"remote-sensing-systems/spectroscopy)."
+
+msgid ""
+"# True color composite\n"
+"\n"
+"Sensors carried by satellites can image Earth in different regions of the "
+"electromagnetic spectrum. Each region in the spectrum is referred to as a "
+"band. True color composite uses visible light bands red, green and blue in "
+"the corresponding red, green and blue color channels, resulting in a "
+"natural colored product, that is a good representation of the Earth as "
+"humans would see it naturally.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)"
+msgstr ""
+
+msgid ""
+"# Pansharpened True Color\n"
+"\n"
+"The pansharpened true color composite is done by using the usual true color "
+"data (red, green and blue (RGB)) and enhancing them by using the "
+"panchromatic band 8, or pan band (a band is a region of the electromagnetic "
+"spectrum; a satellite sensor can image Earth in different bands). An image "
+"from the pan band is similar to black-and-white film: it combines light "
+"from the red, green, and blue parts of the spectrum into a single measure "
+"of overall visible reflectance. Pansharpened images have 4x the resolution "
+"of the usual true color composite, greatly enhancing the usefulness of "
+"Landsat imagery.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-"
+"landsat-live-e4717cd7c356) and "
+"[here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"
+msgstr ""
+
+msgid ""
+"# False Color Urban composite\n"
+"\n"
+"This composite is used to visualize urbanized areas more clearly. "
+"Vegetation is visible in shades of green, while urbanized areas are "
+"represented by white, grey, or purple. Soils, sand, and minerals are shown "
+"in a variety of colors. Snow and ice appear as dark blue, and water as "
+"black or blue. Flooded areas are very dark blue and almost black. The "
+"composite is useful for detecting wildfires and calderas of volcanoes, as "
+"they are displayed in shades of red and yellow.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-"
+"rgb/) and [here.](https://eos.com/false-color/)"
+msgstr ""
+"# Linnaalade pseudovärvidega komposiit\n"
+"\n"
+"Seda komposiiti kasutatakse linnastunud piirkondade selgemaks "
+"visualiseerimiseks. Taimestik antakse edasi rohelistes toonides, "
+"linnastunud alad kuvatakse valge, halli või lillana. Muld, liiv ja "
+"mineraalne materjal kuvatakse erinevate värvidega. Lumi ja jää kuvatakse "
+"tumesinisena ning vesi musta või sinisena. Üleujutatud alad kuvatakse "
+"mustjassinisena. Komposiit on kasulik metsatulekahjude ja aktiivsete "
+"vulkaanide avastamiseks, kuna need on esitatud punastes ja kollastes "
+"toonides.\n"
+"\\\n"
+"\n"
+"\n"
+"Rohkem infot [siin](https://eos.com/false-color/) ja "
+"[siin.](https://eos.com/false-color/)"
+
+msgid ""
+"# Short wave infrared composite (SWIR)\n"
+"\n"
+"Short wave infrared (SWIR) measurements can help scientists estimate how "
+"much water is present in plants and soil, as water absorbs SWIR "
+"wavelengths. Short wave infrared bands (a band is a region of the "
+"electromagnetic spectrum; a satellite sensor can image Earth in different "
+"bands) are also useful for distinguishing between cloud types (water clouds "
+"versus ice clouds), snow and ice, all of which appear white in visible "
+"light. In this composite vegetation appears in shades of green, soils and "
+"built-up areas are in various shades of brown, and water appears black. "
+"Newly burned land reflects strongly in SWIR bands, making them valuable for "
+"mapping fire damages. Each rock type reflects shortwave infrared light "
+"differently, making it possible to map out geology by comparing reflected "
+"SWIR light.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)"
+msgstr ""
+
+msgid ""
+"# Normalised Difference Snow Index (NDSI)\n"
+"\n"
+"The Sentinel-2 normalised difference snow index can be used to "
+"differentiate between cloud and snow cover as snow absorbs in the "
+"short-wave infrared light, but reflects the visible light, whereas cloud is "
+"generally reflective in both wavelengths. Snow cover is represented in "
+"bright vivid blue.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/)."
+msgstr ""
+
+msgid ""
+"# Scene classification\n"
+"\n"
+"\n"
+"\n"
+"Scene classification was developed to distinguish between cloudy pixels, "
+"clear pixels and water pixels of Sentinel-2 data and is a result of ESA's "
+"Scene classification algorithm. Twelve different classifications are "
+"provided including classes of clouds, vegetation, soils/desert, water and "
+"snow. It does not constitute a land cover classification map in a strict "
+"sense.\n"
+"\n"
+"\n"
+"\n"
+"More info "
+"[here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-"
+"classification/)."
+msgstr ""
+
+msgid ""
+"# Discrete Classification Map\n"
+"\n"
+"\n"
+"\n"
+"This layer visualises Global Land Cover discrete classification map with 23 "
+"classes defined using the UN-FAO Land Cover Classification System (LCCS) "
+"and with color scheme defined in the Product User Manual. Map "
+"[here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/"
+"CGLOPS1_PUM_LC100m-V3_I3.3.pdf)"
+msgstr ""
+
+msgid ""
+"# Forest Types\n"
+"\n"
+"\n"
+"\n"
+"Visualized forest types based on 6 classes, as defined in the UN-FAO Land "
+"Cover Classification System (LCCS). More "
+"[here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/"
+"CGLOPS1_PUM_LC100m-V3_I3.3.pdf)."
+msgstr ""
+
+msgid ""
+"# Corine Land Cover (CLC)\n"
+"\n"
+"\n"
+"\n"
+"In this Corine Land Cover layer, all 44 classes are shown. Learn about each "
+"class "
+"[here](https://land.copernicus.eu/user-corner/technical-library/corine-land-"
+"cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_"
+"guide_20190510.pdf) and see the evalscript "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_"
+"land_cover/)."
+msgstr ""
+
+msgid ""
+"# Corine Land Cover (CLC) - Artificial Surfaces\n"
+"\n"
+"\n"
+"\n"
+"In this Corine Land Cover layer, only the 11 artificial surface classes are "
+"shown, based on the classification "
+"[here](https://land.copernicus.eu/user-corner/technical-library/corine-land-"
+"cover-nomenclature-guidelines/html). Learn about each class "
+"[here](https://land.copernicus.eu/user-corner/technical-library/corine-land-"
+"cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_"
+"guide_20190510.pdf) and see the evalscript with all the classes "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_"
+"land_cover/)."
+msgstr ""
+
+msgid ""
+"# Corine Land Cover (CLC) - Agricultural Areas\n"
+"\n"
+"\n"
+"\n"
+"In this Corine Land Cover layer, only the 11 agricultural classes are "
+"shown, based on the classification "
+"[here](https://land.copernicus.eu/user-corner/technical-library/corine-land-"
+"cover-nomenclature-guidelines/html). Learn about each class "
+"[here](https://land.copernicus.eu/user-corner/technical-library/corine-land-"
+"cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_"
+"guide_20190510.pdf) and see the evalscript with all the classes "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_"
+"land_cover/)."
+msgstr ""
+
+msgid ""
+"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n"
+"\n"
+"\n"
+"\n"
+"In this Corine Land Cover layer, only the 12 Forest and Seminatural Area "
+"classes are shown, based on the classification "
+"[here](https://land.copernicus.eu/user-corner/technical-library/corine-land-"
+"cover-nomenclature-guidelines/html). Learn about each class "
+"[here](https://land.copernicus.eu/user-corner/technical-library/corine-land-"
+"cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_"
+"guide_20190510.pdf) and see the evalscript with all the classes "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_"
+"land_cover/)."
+msgstr ""
+
+msgid ""
+"# Corine Land Cover (CLC) - Wetlands\n"
+"\n"
+"\n"
+"\n"
+"In this Corine Land Cover layer, only the 5 Wetland classes are shown, "
+"based on the classification "
+"[here](https://land.copernicus.eu/user-corner/technical-library/corine-land-"
+"cover-nomenclature-guidelines/html). \n"
+"Learn about each class "
+"[here](https://land.copernicus.eu/user-corner/technical-library/corine-land-"
+"cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_"
+"guide_20190510.pdf) and see the evalscript with all the classes "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_"
+"land_cover/)."
+msgstr ""
+
+msgid ""
+"# Corine Land Cover (CLC) - Water Bodies\n"
+"\n"
+"\n"
+"\n"
+"In this Corine Land Cover layer, only the 6 Water body classes are shown, "
+"based on the classification "
+"[here](https://land.copernicus.eu/user-corner/technical-library/corine-land-"
+"cover-nomenclature-guidelines/html). Learn about each class "
+"[here](https://land.copernicus.eu/user-corner/technical-library/corine-land-"
+"cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_"
+"guide_20190510.pdf) and see the evalscript with all the classes "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_"
+"land_cover/)."
+msgstr ""
+
+msgid ""
+"# Corine Land Cover - Accounting\n"
+"\n"
+"\n"
+"\n"
+"This script visualises CORINE Land Cover (CLC) Accounting Layers according "
+"to the official CORINE Land Cover color scheme. CLC Accounting Layers are "
+"CLC status layers modified for the purpose of consistent statistical "
+"analysis in the land cover change accounting system at EEA. For more "
+"information about the layer, visit [this "
+"website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_"
+"land_cover_accounting_layer/)."
+msgstr ""
+
+msgid ""
+"# The Global Human Settlement Layer GHS-BUILT-S2 \n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"The Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up "
+"areas (expressed as probabilities from 0 to 100 %) at 10 m spatial "
+"resolution. It was derived from a Sentinel-2 global image composite for the "
+"reference year 2018 using Convolutional Neural Networks.\n"
+"\n"
+"This script visualises the built-up probabilities stretched to 0-255.\n"
+"\n"
+"For more information about the layer, visit [this "
+"website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-"
+"human-settlement-layer-ghs-built-s2/)."
+msgstr ""
+
+msgid ""
+"# Water Bodies - Occurrence\n"
+"\n"
+"\n"
+"\n"
+"This layer displays the 6 occurrence levels of the Quality layer (QUAL), "
+"providing information on the seasonal dynamics of the detected water "
+"bodies. QUAL is generated from water body occurrence statistics computed "
+"from previous monthly Water Bodies products. The occurrence statistics is "
+"ranked from low occurrence to permanent occurrence. More information "
+"[here](https://collections.sentinel-hub.com/water-bodies/readme.html), and "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-"
+"bodies-occurence/#)."
+msgstr ""
+
+msgid ""
+"# Global Surface Water - Occurrence\n"
+"\n"
+"\n"
+"\n"
+"The layer shows the (intra- and inter-annual) variations of surface water "
+"presence in the time range between March 1984 and December 2019. Permanent "
+"water areas with 100% occurrence over the 36 years are shown in blue, while "
+"lighter shades of pink and purple indicate lower degrees of water presence. "
+"Learn more "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_"
+"surface_water_occurrence/)."
+msgstr ""
+
+msgid ""
+"# Global Surface Water - Occurrence Change Intensity\n"
+"\n"
+"\n"
+"\n"
+"The layer visualises changes in water occurrence between two different "
+"epochs, the first ranging from March 1984 to December 1999, and the other "
+"covering the period from January 2000 to December 2019. Areas with increase "
+"in water occurrence are visualized in different shades of green, areas with "
+"no change are colored black and areas with decrease are shown in shades of "
+"red. Learn more "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_"
+"surface_water_change/)."
+msgstr ""
+
+msgid ""
+"# Global Surface Water - Seasonality\n"
+"\n"
+"\n"
+"\n"
+"The Seasonality layer provides information on the distribution of surface "
+"water in 2019. Permanent water bodies (water was present for 12 months) are "
+"colored in dark blue and seasonal water (water was present for less than 12 "
+"months) in gradually lighter shades of blue, with the lightest blue showing "
+"areas where water was present for only 1 month. Learn more "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_"
+"surface_water_seasonality/#)."
+msgstr ""
+
+msgid ""
+"# Global Surface Water - Recurrence\n"
+"\n"
+"\n"
+"\n"
+"The Recurrence layer shows how frequently water returned to a particular "
+"location in a defined water period between 1984 and 2019. Orange color "
+"indicates low recurrence (water returned to the area infrequently), and "
+"light blue color indicates high recurrence (water returned to the area "
+"frequently). Learn more "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_"
+"surface_water_recurrence/)."
+msgstr ""
+
+msgid ""
+"# Global Surface Water - Transitions\n"
+"\n"
+"\n"
+"\n"
+"The Transitions layer is derived from a comparison between the first and "
+"last year in the 36-year time period. It visualises conversions between "
+"seasonal and permanent water. For example, \"lost seasonal\" means, that "
+"previously seasonal water was converted to land, \"new seasonal\" means "
+"that land has been converted to seasonal waters and so on. Learn more "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_"
+"surface_water_transitions/) and learn what each class means "
+"[here](https://global-surface-water.appspot.com/faq)."
+msgstr ""
+
+msgid ""
+"# Global Surface Water - Extent\n"
+"\n"
+"\n"
+"\n"
+"This layer visualizes water in blue. It combines all the other layers and "
+"visualizes all the locations for which water presence has ever been "
+"detected over the 36-year period. Learn more "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_"
+"surface_water_extent/)."
+msgstr ""
+
+msgid ""
+"# Water Bodies\n"
+"\n"
+"\n"
+"\n"
+"This layer visualizes the Water Bodies detection layer (WB), which shows "
+"water bodies detected using the Modified Normalized Difference Water Index "
+"(MNDWI) derived from Sentinel-2 Level 1C data. More information "
+"[here](https://collections.sentinel-hub.com/water-bodies/readme.html), and "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-"
+"bodies/)."
+msgstr ""
+
+msgid ""
+"# ESA WorldCover Map\n"
+"\n"
+"\n"
+"\n"
+"The WorldCover product displays a global land cover map with 11 different "
+"land cover classes produced at 10m resolution based on combination of both "
+"Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are "
+"covered by clouds for an extended period of time, Sentinel-1 data provides "
+"complimentary information on the structural characteristics of the observed "
+"land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data "
+"makes it possible to update the land cover map almost in real time. Find "
+"more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/"
+"worldcover/)."
+msgstr ""
+
+msgid ""
+"# Yearly Time-Series of the Plant Phenology Index\n"
+"\n"
+"\n"
+"\n"
+"PPI (Plant Phenology Index) is a physically-based vegetation index derived "
+"from radiative transfer equation and is calculated from red and "
+"Near-Infrared (NIR) spectral bands. It is linearly related to the green "
+"leaf area index, and can be used to track canopy green foliage dynamics and "
+"therefore provides an efficient approach to retrieving plant phenology. "
+"Seasonal Trajectories PPI product is a filtered yearly time series of PPI, "
+"providing the vegetation status for each pixel on a regular 10-day time "
+"step. Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/)."
+msgstr ""
+
+msgid ""
+"# Amplitude Parameter\n"
+"\n"
+"\n"
+"\n"
+"This layer visualizes the seasonal amplitude parameter of the VPP "
+"(Vegetation Phenology and Productivity) parameter. It is calculated as a "
+"difference between the MAXV (season maximum value) and MINV (season minimum "
+"value) parameters. Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-"
+"amplitude-ampl/) and "
+"[here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-"
+"resolution-vegetation-phenology-and-productivity)."
+msgstr ""
+
+msgid ""
+"# Start of Season Values\n"
+"\n"
+"\n"
+"\n"
+"This layer visualizes the SOSV parameter of the VPP (Vegetation Phenology "
+"and Productivity) parameter. SOSV represents the PPI (plant phenology "
+"index) value of the start-of-season day. Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-"
+"start-of-season-value-sosv/) and "
+"[here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-"
+"resolution-vegetation-phenology-and-productivity)."
+msgstr ""
+
+msgid ""
+"# End of Season Values\n"
+"\n"
+"\n"
+"\n"
+"This layer visualizes the EOSV parameter of the VPP (Vegetation Phenology "
+"and Productivity) parameter. EOSV is a PPI (plant phenology index) value of "
+"the end-of-season day. Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-"
+"of-season-value-eosv/) and "
+"[here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-"
+"resolution-vegetation-phenology-and-productivity)."
+msgstr ""
+
+msgid ""
+"# Season Minimum Value\n"
+"\n"
+"\n"
+"\n"
+"This layer visualizes the MINV parameter of the VPP (Vegetation Phenology "
+"and Productivity) parameter. MINV represents the average PPI (plant "
+"phenology index) value of the minima on the left and right sides of each "
+"season. Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-"
+"season-minimum-value-minv/) and "
+"[here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-"
+"resolution-vegetation-phenology-and-productivity)."
+msgstr ""
+
+msgid ""
+"# Season Maximum Value\n"
+"\n"
+"\n"
+"\n"
+"This layer visualizes the MAXV parameter of the VPP (Vegetation Phenology "
+"and Productivity) parameter. MAXV represents the PPI (plant phenology "
+"index) value at the day of the season maximum. Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-"
+"season-maximum-value-maxv/) and "
+"[here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-"
+"resolution-vegetation-phenology-and-productivity)."
+msgstr ""
+
+msgid ""
+"# Slope of the Greening Period\n"
+"\n"
+"\n"
+"\n"
+"This layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology "
+"and Productivity) parameter. LSLOPE represents the slope of the PPI (plant "
+"phenology index) of the greening-up period. Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-"
+"slope-of-greening-up-period-lslope/) and "
+"[here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-"
+"resolution-vegetation-phenology-and-productivity)."
+msgstr ""
+
+msgid ""
+"# Slope of the Senescent Period\n"
+"\n"
+"\n"
+"\n"
+"This layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology "
+"and Productivity) parameter. RSLOPE represents the slope of the PPI (plant "
+"phenology index) of the senescent period. Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-"
+"slope-of-senescent-period-rslope/) and "
+"[here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-"
+"resolution-vegetation-phenology-and-productivity)."
+msgstr ""
+
+msgid ""
+"# Seasonal Productivity Parameter\n"
+"\n"
+"\n"
+"\n"
+"This layer visualizes the SPROD parameter of the VPP (Vegetation Phenology "
+"and Productivity) parameter. SPROD represents seasonal productivity. It is "
+"calculated as the sum of all daily PPI (plant phenology index) values "
+"between SOSD (start-of-season day) and EOSD (end-of-season day). Find more "
+"information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-"
+"seasonal-productivity-sprod/) and "
+"[here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-"
+"resolution-vegetation-phenology-and-productivity)."
+msgstr ""
+
+msgid ""
+"# Total Productivity Parameter\n"
+"\n"
+"\n"
+"\n"
+"This layer visualizes the TPROD parameter of the VPP (Vegetation Phenology "
+"and Productivity) parameter. TPROD represents total productivity. It is "
+"calculated as a sum of all daily PPI (plant phenology index) values, minus "
+"their base level value. Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-"
+"total-productivity-tprod/) and "
+"[here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-"
+"resolution-vegetation-phenology-and-productivity)."
+msgstr ""
+
+msgid ""
+"# Fraction of Absorbed Photosynthetically Active Radiation\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"FAPAR corresponds to the fraction of photosynthetically active radiation "
+"absorbed by the canopy. The index describes only the green parts of the "
+"canopy and is very useful for assessing the primary productivity of "
+"canopies. Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/"
+")."
+msgstr ""
+
+msgid ""
+"# Leaf Area Index\n"
+"\n"
+"\n"
+"LAI is defined as one half of the total area of photosynthetically active "
+"elements of the canopy per unit horizontal ground area. The LAI provided by "
+"HRVPP corresponds to actual LAI of all the canopy layers, including all "
+"green contributors. Practically, the LAI quantifies the thickness of the "
+"vegetation cover. Deeper green colors indicate thicker vegetation cover. "
+"Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#)"
+"."
+msgstr ""
+
+msgid ""
+"# Normalized Difference Vegetation Index\n"
+"\n"
+"\n"
+"\n"
+"NDVI quantifies vegetation photosynthetic capacity by measuring the "
+"difference between the Near-Infrared (NIR) (which vegetation strongly "
+"reflects) and red spectral bands (which vegetation absorbs). It is commonly "
+"used to monitor vegetation cover and density. Find more information "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/)"
+"."
+msgstr ""
+
+msgid ""
+"# Plant Phenology Index\n"
+"\n"
+"\n"
+"\n"
+"PPI (Plant Phenology Index) is a physically-based vegetation index derived "
+"from radiative transfer equation and is calculated from red and "
+"Near-Infrared (NIR) spectral bands. PPI is linearly related to the green "
+"leaf area index, and can be used to track canopy green foliage dynamics and "
+"therefore provides an efficient approach to retrieving plant phenology. "
+"Learn more "
+"[here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#)"
+" and "
+"[here](https://land.copernicus.eu/user-corner/technical-library/product-"
+"user-manual-of-vegetation-indices/)."
+msgstr ""
+
+msgid ""
+"Could not generate timelapse animation file. Try using lower resolution or "
+"fewer frames."
+msgstr ""
+
+msgid "Width"
+msgstr ""
+
+msgid "Height"
+msgstr ""
+
+msgid "Format"
+msgstr ""
+
+msgid ""
+"For optimisation reasons MPEG4 output format will always be used for "
+"generating a timelapse with transition \"fade\", even when GIF is selected."
+msgstr ""
\ No newline at end of file
diff --git a/src/translations/et.po.json b/src/translations/et.po.json
new file mode 100644
index 00000000..de685963
--- /dev/null
+++ b/src/translations/et.po.json
@@ -0,0 +1 @@
+{"charset":"utf-8","headers":{"project-id-version":"","pot-creation-date":"","po-revision-date":"","last-translator":"","language-team":"","language":"et","mime-version":"1.0","content-type":"text/plain; charset=utf-8","content-transfer-encoding":"8bit","plural-forms":"nplurals=2; plural=(n != 1);","x-generator":"Poedit 3.0.1"},"translations":{"":{"":{"msgid":"","msgstr":["Project-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLast-Translator: \nLanguage-Team: \nLanguage: et\nmime-version: 1.0\nContent-Type: text/plain; charset=utf-8\nContent-Transfer-Encoding: 8bit\nPlural-Forms: nplurals=2; plural=(n != 1);\nx-generator: Poedit 3.0.1\n"]},"Education":{"msgid":"Education","msgstr":["Haridus"]},"Normal":{"msgid":"Normal","msgstr":["Tavaline"]},"Close":{"msgid":"Close","msgstr":["Sulge"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Sulge ja ära enam näita"]},"Previous":{"msgid":"Previous","msgstr":["Eelmine"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Lõpeta õpetus"]},"Next":{"msgid":"Next","msgstr":["Järgmine"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Jätka õpetusega"]},"Don't show again":{"msgid":"Don't show again","msgstr":["Ära enam näita"]},"Show info":{"msgid":"Show info","msgstr":["Näita teavet"]},"Discover":{"msgid":"Discover","msgstr":["Avasta"]},"Visualize":{"msgid":"Visualize","msgstr":["Visualiseeri"]},"Compare":{"msgid":"Compare","msgstr":["Võrdle"]},"Pins":{"msgid":"Pins","msgstr":["Märgistused"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Piltide laadimisel tekkis viga:"]},"No tile found":{"msgid":"No tile found","msgstr":["Ühtegi paani ei leitud"]},"Dataset":{"msgid":"Dataset","msgstr":["Andmekogum"]},"Show":{"msgid":"Show","msgstr":["Näita"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Näita efekte ja lisavõimalusi"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Näita vaadet"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Lisa märgistusse"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Lisa võrdluseks"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Paani suurendamine"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Peida kiht"]},"Show layer":{"msgid":"Show layer","msgstr":["Näita kihti"]},"Share":{"msgid":"Share","msgstr":["Jaga"]},"Custom":{"msgid":"Custom","msgstr":["Kohandatud"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Loo kohandatud vaade"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Suumi sisse andmete vaatamiseks"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Tasuta registreerumine"]},"for all features":{"msgid":"for all features","msgstr":["kõikide võimaluste jaoks"]},"Powered by":{"msgid":"Powered by","msgstr":["Platvorm"]},"with contributions by":{"msgid":"with contributions by","msgstr":["panustanud"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["Palun vali andmete allikas(d)!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["Sobimatu ajavahemik!"]},"No results found":{"msgid":"No results found","msgstr":["Ei leitud tulemusi"]},"Theme":{"msgid":"Theme","msgstr":["Teema"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Halda seadistusi"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Logi sisse, et kasutada kohandatud seadistusi."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["Tekkis viga täiendavate andmete saamisel!"]},"Search":{"msgid":"Search","msgstr":["Otsi"]},"Highlights":{"msgid":"Highlights","msgstr":["Esiletõsted"]},"Data sources":{"msgid":"Data sources","msgstr":["Andmeallikad"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Palun vali teema"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Ajavahemik [UTC]"]},"Date":{"msgid":"Date","msgstr":["Kuupäev"]},"Hide description":{"msgid":"Hide description","msgstr":["Peida kirjeldus"]},"Show description":{"msgid":"Show description","msgstr":["Näita kirjeldust"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Sellel teemal ei ole esiletõsteid"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Põhinedes: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["1 päev (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["5 päeva (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["10 päeva (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["O3 (osoon)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (lämmastikdioksiid)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (vääveldioksiid)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (süsinikoksiid)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (formaldehüüd)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (metaan)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER AI (aerosooli indeks)"]},"Cloud":{"msgid":"Cloud","msgstr":["Pilvisus"]},"Other":{"msgid":"Other","msgstr":["Muu"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Maksimaalne pilvisus"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Täpsem otsing"]},"Data location":{"msgid":"Data location","msgstr":["Andmete asukoht"]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":["Sentinel-1 teenused on kättesaadavad nii EOCloudis ja AWS-is. Teenuste võimekus erineb. Rohkem infot"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Palun vali vähemalt üks asukoht!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Andmehõivemoodus"]},"Polarization":{"msgid":"Polarization","msgstr":["Polarisatsioon"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Palun vali vähemalt üks andmehõivemoodus!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Palun vali vähemalt üks polarisatsioon!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Orbiidi suund"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Palun vali vähemalt üks orbiidi suund!"]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["**MERIS** (Keskmise resolutsiooniga spektromeeter) oli sensor satelliidi [ENVISAT] (https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) pardal, mille esmane missioon oli seirata maapinda, ookeanide värvust ja atmosfääri. Envisat ei tööta enam ja ta on asendatud satelliidiga Sentinel-3.\n\n**Ruumiline lahutus:** Maa ja ranniku täislahutus: 260 m x 290 m (näha on ainult suuremaid detaile kui 260 m x 290 m ).\n\n**Ülelendude sagedus:** sama ala seire vähemalt üks kord kolme päeva jooksul.\n\n**Andmete saadavus:** alates juunist 2002 kuni aprillini 2012.\n\n**Kasutusala:** ookeanide seiramine (sinivetikad, heljum), atmosfäär (veeaur, CO2, pilved, aerosoolid) ja maapind (vegetatsiooni indeks, globaalne katvus, niiskus)."]},"Credits:":{"msgid":"Credits:","msgstr":["Tunnustus:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["**GIBS** (Global Imagery Browse Services) pakub kiiret juurdepääsu rohkem kui 600 satellidipildi\ntootele, mis hõlmavad kogu Maa. Enamik pilte on kättesaadavad mõne tunni jooksul\npärast satelliidi ülelendu, mõned tooted on peaaegu 30 aasta vanused."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["NASA/USA geoloogiateenistuse (USGS) **Landsati** seeria satelliidid on sarnased satelliidile Sentinel-2 (nad püüavad kinni nähtava ja infrapunase lainepikkuse)\nja lisaks suudavad kinni püüda soojusliku infrapunase kiirguse (Landsat 8). Landsat seeria satelliitide pildiajalugu on peaaegu 50 aastat vana.\nSee platvorm annab sulle ligipääsu piltidele, mis on saadud satelliitidelt Landsat 5, 7 ja 8.\n\n**Ruumiline lahutusvõime:** 15 m, 30 m, and 100 m (teisendatud 30 meetrile), sõltub lainepikkusest (näha on ainult esemed, mis on suuremad kui 10 m ja 30 m). Rohkem infot [siin](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Ülelendude sagedus:** Ülelend maksimaalselt 8 päeva tagant üle sama ala, kasutades selleks kahte töötavat satelliiti Landsat 7 ja Landsat 8.\n\n**Andmete kättesaadavus:** Euroopa ja Põhja-Aafrika 1984-2011 (Landsat 5), 1999-2003 (Landsat 7), 2013 kuni praeguseni (Landsat 8) ESA arhiivist. USA geoloogiateenistuse (USGS) arhiiv alates aprillist 2013 kuni praeguseni (Landsat 8 ainult).\n\n**Kasutusalad:** Taimkatte seiramine, maa kasutus, maa-kaardid, muutuste jälgimine jm."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["NASA **MODIS** – (mõõduka ruumilise lahutusega spektraalradiomeeter) hangib andmeid eesmärgiga\nparandada meie arusaamist maapinnal toimuvatest globaalsetest protsessidest. EO brauser pakub maapinna vaatluse andmeid (kanalid 1-7).\n\n**Ruumiline lahutusvõime:** 250 m (kanalid 1-2), 500 m (kanalid 3-7), 1000 m (kanalid 8-36).\n\n**Ülelendude sagedus:** Globaalne katvus 1 kuni 2 päeva tagant nii Aqua kui ka Terra satelliitidega.\n\n**Andmete kättesaadavus:** Alates jaanuarist 2013.\n\n**Kasutusala:** Maa, pilvede, ookeani värvuse seire kogu maailmas."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["**Proba-V** on väike satelliit, mis on mõeldud maapinna kaardistamiseks ja taimkatte jälgimiseks.\nüle kogu maakera iga kahe päeva tagant. EO Browser pakub tuletatud tooteid, mis minimeerivad pilvisust,\n\nkombineerides selleks pilvevabad mõõtmised ühe päeva (S1), 5 päeva (S5) ja 10 päeva (S10) jooksul. \n\n**Ruumiline lahutus:** 100 m (S1 ja S5), 333 m (S1 ja S10), 1000 m (S1 ja S10).\n\n**Ülelendude sagedus:** 1 kord päevas laiuskraadidel 35-75° N ja 35-56° S, kord 2 päeva jooksul laiuskraadidel 35° N\nja 35° S.\n\n**Andmete kättesaadavus:** alates oktoobrist 2013.\n\n**Kasutusala:** maapinna jälgimine, taimestiku kasv, kliimamõjude hindamine,\nveevarude majandamine, põllumajanduse seire ja toiduga kindlustatuse hinnangud, siseveekogude seire.\nressursside jälgimine ning kõrbete leviku ja metsade raadamise pidev jälgimine."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":["** Sentinel-1 ** pakub radaripilte maa- ja ookeaniteenuste jaoks iga ilmaga, päeval ja öösel . EO\nBrauser pakub andmeid Interferometric Wide Swath (IW) ja Extra Wide Swath (EW) formaatides,\ntöötlus 1. tase Ground Range Detected (GRD).\n\n**Piksli vahe:** 10 m (IW), 40 m (EW).\n\n**Ülelendude sagedus:** <= kord 5 päeva jooksul kasutades mõlemat satelliiti.\n\n**Ülelendude sagedus** (arvestatakse mõlema satelliidi ning laskumiste ja tõusmistega): <= 3 päeva, vaata [vaatluse stsenaarium](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Andmete kättesaadavus:** alates oktoobrist 2014.\n\n**Kasutusala:** Mere- ja maismaa seire, hädaolukordadele reageerimine, kliimamuutused."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["**Sentinel-2** pakub kõrge resolutsiooniga pilte nähtavas ja infrapuna lainepikkustel, et jälgida taimestikku, mullastiku, veepinda, siseveeteid ja rannikualasid. .\n\n**Ruumiline lahutus:** 10 m, 20 m ja 60 m, sõltuvalt lainepikkusest (st näha on ainult detaile, mis on suuremad kui 10 m, 20 m ja 60 m). Lisateave [siin](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Ülelendude sagedus:** maksimaalselt viis päeva samast piirkonnast ülelennuks, kasutades mõlemat satelliiti.\n\n**Andmete kättesaadavus:** alates juunist 2015. Täielik ülemaailmne katvus alates märtsist 2017.\n\n**Kasutusala:** maapiina kaardid, maapinna muutuste tuvastamise kaardid, taimestiku seire, põlenud alade seire."]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Taseme 2A andmed on kvaliteetsed andmed, kus välistatakse atmosfääri mõju valgusele, mis Maa pinnalt peegeldub ja sensorini jõuab. Andmed on ülemaailmselt saadaval alates 2017. aasta märtsist.\n\nLisateave atmosfääri korrigeerimise kohta [siin] (http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction)."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Taseme 1C andmed on piisava kvaliteediga andmed enamike uuringute jaoks. Seal on tehtud kõik pildiparandused, välja arvatud atmosfääri korrigeerimine. Andmed on ülemaailmselt saadaval alates 2015. aasta juunist."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["**Sentinel-3** missiooni peamine eesmärk on mõõta merepinna topograafiat, mere- ja maapinna temperatuuri, ookeani- ja maapinna värvust. Sentinel-3 pardal on neli erinevat instrumenti. Sellel platvormil on kättesaadavad ookeani- ja maapinna värviinstrumendi (OLCI) ja mere- ning maapinna instrumendi (SLSTR) andmed.\n\n**Andmete kättesaadavus:** alates mai 2016."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["Sentinel-3 pardal olev **mere ja maapinna temperatuuri (SLSTR) ** mõõteriist mõõdab ülemaailmset ja piirkondlikku mere- ja maapinna\ntemperatuuri. SLSTR hõlmab elektromagnetspektri nähtava, lühilaine- ja soojusliku infrapunase lainepikkused.\n\n**Ruumiline lahutus:** 500 m nähtav, lähedane ja lühilaineline infrapunane lainepikkus ja 1 km soojuslik infrapunane lainepikkus (st näha on ainult objektid, mis on suuremad kui 500 m ja 1 km). \n\n**Ülelendude sagedus:** kasutades mõlemat satelliiti samast piirkonnast ülelend maksimaalselt kord päevas.\n\n**Andmete kättesaadavus:** alates maist 2016.\n\n**Kasutusala:** kliimamuutuste seire, taimkatte seire, aktiivne tulekahjude avastamine, maa ja merepinna temperatuuri jälgimine."]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["Sentinel-3 pardal on spektromeeter ** Ookeani- ja maapinna värviinstrument (OLCI) **, mis\nmõõdab Maalt peegelduvat päikesekiirgust ja jälgib ookeani, keskkonda\nja kliimat. See pakub nähtavaid pilte sagedamini kui Sentinel-2, pildid on madalama lahutusega\nja suurema lainepikkusega kaetud. Sentinel-3 OLCI-sensor jätkab mõõtmisi, mida varem tegi missiooni lõpetanud MERIS-seade Envisati pardal.\n\n\n**Ruumiline lahutus:** 300 m (ainult objektid suuremad kui 300 m on nähtavad).\n\n**Ülelendude sagedus:** maksimaalselt 2 päeva sama piirkonna ülelennuks mõlema satelliidi abil.\n\n**Andmete kättesaadavus:** alates maist 2016.\n\n**Kasutusala:** pinna topograafia, ookeani ja maapinna värvide vaatlused ja seire."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["**Sentinel-5P** satelliit pakub atmosfääri mõõtmisi, mida saab kasutada õhu kvaliteedi, osooni, UV-kiirguse\n ja kliima jälgimiseks ning ilmaennustuseks.\n\n**Ruumiline lahutus:** 7x3,5 km (ainult objektid suuremad kui 7x3,5 km on nähtavad).\n\n**Ülelendude sagedus:** maksimaalselt üks päev sama piirkonna ülelennuks.\n\n**Andmete kättesaadavus:** alates aprillist 2018.\n\n**Kasutusala:** süsinikmonooksiidi (CO), lämmastikdioksiidi (NO2) ja osooni (O3) kontsentratsiooni jälgimine õhus. UV-aerosooli indeksi (AER_AI) ja erinevate pilvede geofüüsikaliste parameetrite (Cloud) jälgimine."]},"Copied":{"msgid":"Copied","msgstr":["Kopeeritud"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["Kopeeri lõikelauale"]},"Data source name":{"msgid":"Data source name","msgstr":["Andmete allika nimi"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Ülelennu aeg"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Pilvkate"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Päikese kõrgus silmapiirist"]},"MGRS location":{"msgid":"MGRS location","msgstr":["MGRS asukoht"]},"AWS path":{"msgid":"AWS path","msgstr":["AWS ühenduse link (path)"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["EO Cloud ühenduse link (path)"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["CreoDIAS ühenduse link (path)"]},"SciHub link":{"msgid":"SciHub link","msgstr":["SciHub link"]},"Back to search":{"msgid":"Back to search","msgstr":["Tagasi otsingusse"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Näitab ${ this.state.results.length } tulemust","Näitab ${ this.state.results.length } tulemusi"]},"Load more":{"msgid":"Load more","msgstr":["Lae rohkem"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Laeb rohkem tulemusi ..."]},"Results":{"msgid":"Results","msgstr":["Tulemused"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Tulemuse ${ this.state.selectedTiles.length } näitamine.","Tulemuste ${ this.state.selectedTiles.length } näitamine."]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Korrigeeri märgistuse kirjeldust"]},"Reject changes":{"msgid":"Reject changes","msgstr":["Ignoreeri muudatusi"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Kinnita muudatused"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Muuda märgistuse nimi"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Eemalda märgistus"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Suumi märgistatud asukoht"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Laiuskraad/pikkuskraad"]},"Zoom":{"msgid":"Zoom","msgstr":["Suumi"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["${ N_PINS } märgistus(t) lisatakse sinu märgistuste kogusse. Kas soovid jätkata?"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["HOIATUS: Hakkad kustutama märgistust. Kas soovid jätkata?"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":["HOIATUS: Hakkad kustutama kõiki märgistusi. Kas soovid jätkata?"]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["Märgistusi ei ole. Mine jaotusesse Visualiseeri ja salvesta märgistused või laadi alla salvestatud märgistustega JSON fail."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Pane tähele, et märgistused salvestatakse ainult siis, kui sa oled sisse logitud. Muudel juhtudel märgistused kaovad pärast rakenduse sulgemist."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Tühista valikud"]},"Select all":{"msgid":"Select all","msgstr":["Vali kõik"]},"No pins.":{"msgid":"No pins.","msgstr":["Märgistusi ei ole."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Loo link (${ selectedPins.length } märgistus valitud)","Loo link (${ selectedPins.length } märgistused valitud)"]},"File type not supported":{"msgid":"File type not supported","msgstr":["Faili tüüp ei ole toetatud"]},"not supported":{"msgid":"not supported","msgstr":["ei ole toetatud"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["Märgistusi ei leitud."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Viga faili parsimisel:"]},"File upload":{"msgid":"File upload","msgstr":["Faili üleslaadimine"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":["Lae JSON fail üles koos salvestatud märgistustega."]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["Pukseeri JSON fail või otsi oma arvutist"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Hoia olemasolevad märgistused"]},"Share pins":{"msgid":"Share pins","msgstr":["Jaga märgistusi"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Loo märgistustest lugu"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Ekspordi märgistused arvutisse"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Impordi märgistused salvestatud failist"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Kustuta kõik märgistused"]},"Story":{"msgid":"Story","msgstr":["Lugu"]},"Export":{"msgid":"Export","msgstr":["Ekspordi"]},"Import":{"msgid":"Import","msgstr":["Impordi"]},"Clear":{"msgid":"Clear","msgstr":["Puhasta"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Jaga märgistuste linki"]},"Creating link...":{"msgid":"Creating link...","msgstr":["Loo link..."]},"OK":{"msgid":"OK","msgstr":["OK"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Märgistuste kogu värskendamine."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Tekkis probleem märgistuste kogu uuendamisel: ${ updatingPinsError }."]},"Hello,":{"msgid":"Hello,","msgstr":["Tere,"]},"Opacity":{"msgid":"Opacity","msgstr":["Läbipaistmatus"]},"Split position":{"msgid":"Split position","msgstr":["Jagatud positsioon"]},"split":{"msgid":"split","msgstr":["jaga"]},"opacity":{"msgid":"opacity","msgstr":["läbipaistmatus"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["Võrdlemiseks pole kihte."]},"Remove all":{"msgid":"Remove all","msgstr":["Eemalda kõik"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Lisa kõik märgistused"]},"Split":{"msgid":"Split","msgstr":["Jaga"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Probleem allalaadimisel"]},"Download":{"msgid":"Download","msgstr":["Allalaadimine"]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Visualiseeri maastik 3D-na"]},"Go to Place":{"msgid":"Go to Place","msgstr":["Mine asukohta"]},"Labels":{"msgid":"Labels","msgstr":["Sildid"]},"Borders":{"msgid":"Borders","msgstr":["Piirid"]},"Roads":{"msgid":"Roads","msgstr":["Teed"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Suumi sisse"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Suumi välja"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["EO Brauserist"]},"Contact us":{"msgid":"Contact us","msgstr":["Kontakteeru meiega"]},"Get data":{"msgid":"Get data","msgstr":["Andmete saamine"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Selle funktsiooni kasutamiseks peab sisse logima."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Palun vali kiht."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["Andmete allalaadimine võrdlevas režiimis ei ole võimalik."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["Seda andmeallikat ei toetata."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":["Statistilise teabe / funktsioonide teabe teenuse diagramm"]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Statistilise teabe / funktsioonide teabe teenuse diagramm - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["palun vali kiht"]},"not available for ":{"msgid":"not available for ","msgstr":["ei ole saadaval "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["ei ole saadaval \"${ props.presetLayerName }\" (väärtusega kihti ei ole seadistatud)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Otsi esmalt andmeid."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Loo aegvõttega animatsioon"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Märgi huvipunkt"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Too kaardi kese valitud tunnusele"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Eemalda geomeetria"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Huvipakkuv piirkond"]},"Select mode":{"msgid":"Select mode","msgstr":["Vali režiim"]},"Mode:":{"msgid":"Mode:","msgstr":["Režiim:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Eemalda mõõtmine"]},"Measure":{"msgid":"Measure","msgstr":["Mõõda"]},"km":{"msgid":"km","msgstr":["km"]},"m":{"msgid":"m","msgstr":["m"]},"Gain":{"msgid":"Gain","msgstr":["Koefitsient"]},"Gamma":{"msgid":"Gamma","msgstr":["Gamma"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Min. andmete kvaliteet"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Suurendamine"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Vähendamine"]},"Reset all":{"msgid":"Reset all","msgstr":["Lähtesta kõik"]},"filter by months":{"msgid":"filter by months","msgstr":["filtreeri kuude kaupa"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Kopeeri geomeetria lõikelauale"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Tühista muudatus."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Määratle huvipakkuv ala"]},"Upload data":{"msgid":"Upload data","msgstr":["Lae andmed ülesse"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Vähim pilvisus"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Kasuta täiendavaid andmekogumeid (täpsem)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Mosaiikimine"]},"Most recent":{"msgid":"Most recent","msgstr":["Uusimad"]},"Least recent":{"msgid":"Least recent","msgstr":["Vanimad"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Kohanda ajavahemikku"]},"Back":{"msgid":"Back","msgstr":["Tagasi"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Tõrge skripti laadimisel. Kontrolli URLi."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Tühjenda URLis laadimise skript, et muuta koodi"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Lae skript URList"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["Sisesta URL skripti"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Skript laetud."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Ainult HTTPS domeenid on lubatud."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Lae skript koodi korrektorisse"]},"Refresh":{"msgid":"Refresh","msgstr":["Värskenda"]},"orbit":{"msgid":"orbit","msgstr":["orbiit"]},"day":{"msgid":"day","msgstr":["päev"]},"week":{"msgid":"week","msgstr":["nädal"]},"month":{"msgid":"month","msgstr":["kuu"]},"year":{"msgid":"year","msgstr":["aasta"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Vali üks pilt:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Aegvõte"]},"Select All":{"msgid":"Select All","msgstr":["Vali kõik"]},"Speed:":{"msgid":"Speed:","msgstr":["Kiirus:"]},"frames / s":{"msgid":"frames / s","msgstr":["kaadrid /s"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Valmistamine..."]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Ei saa faili alla laadida:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Lõuendi kaudu ei saa alla laadida"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["Ei saa faile kokku pakkida (ZIP)"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Pildi allalaadmisel tekkis probleem"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Pildi kättesaamisel ilmnes tõrge: url on tühi!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Viga pildi kättesaamisel:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["Bloobist ei saanud pilti laadida"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Lohista kanalid RGB väljale."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Lohista kanalid indeksvõrrandisse"]},"Index ":{"msgid":"Index ","msgstr":["Indeks "]},"Threshold":{"msgid":"Threshold","msgstr":["Lävi"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Eemalda värvivalija"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Lisa värvivalija"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Klõpsa tähise asetamiseks"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Klõpsa esimese tipu asetamiseks"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Klõpsa joonistamise jätkamiseks"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Lõpetamiseks klõpsa esimesel tähisel"]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":["Loo aegvõte sellest piirkonnast"]},"Show captions":{"msgid":"Show captions","msgstr":["Näita pealdisi"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Näita slaidi pealkirja"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Lisa kaardi ülekatted"]},"Show legend":{"msgid":"Show legend","msgstr":["Näita legendi"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["Praegusel vaateväljal ei leitud ühtegi märgistust."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Mõnda märgistust (${ N_PINS_OUTSIDE_BOUNDS }) eiratakse, kuna nad ei asu valitud alal."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["Märgistustega loo tegemiseks minge kaardil soovitud asukohta.\n\nKõiki praeguse vaatevälja märgistusi kasutatakse loo tegemiseks, ülejäänud osasid ignoreeritakse."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["Failile lisatakse logo."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["DataMask-kanal lisatakse allalaaditud toorandmete kanalisse kui teine kanal."]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":["Märgistatud pildifailivorming (TIFF) mahutab suure hulga kanaleid, kuid paljud tavalised pildivaaturid (nt Windows Photo Viewer) ei saa kuvada rohkem kui 3 kanaliga TIFF-pilte.\nKui see valik on lubatud, lisatakse pildile ainult kolm esimest kanalit.\nKui see valik on keelatud, lisatakse pildile kõik kanalid, kuid TIFF-pildi kuvamiseks peate kasutama rakendust, mis toetab rohkem kui kolme kanalit (nt QGIS)."]},"Show logo":{"msgid":"Show logo","msgstr":["Kuva logo"]},"Image format":{"msgid":"Image format","msgstr":["Pildi formaat"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Pildi lahutusvõime"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Koordinaatide süsteem"]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":["Lisa dataMask kanal toorandmete kihtidele"]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":["Lõika lisakanalid"]},"Layers":{"msgid":"Layers","msgstr":["Kihid"]},"Visualized":{"msgid":"Visualized","msgstr":["Visualiseeritud"]},"Raw":{"msgid":"Raw","msgstr":["Algne"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["Pildile lisatakse kaardi ülekattekihid (kohasildid, tänavad ja poliitilised piirid)."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Eksporditud pilt (pildid) sisaldab andmeallikat ja kuupäeva, suumiskaalat ja kaubamärki"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Lisa lühikirjeldus eksporditud pildile"]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":["Eskporditud pilt sisaldab legendi"]},"Description":{"msgid":"Description","msgstr":["Kirjeldus"]},"Image format:":{"msgid":"Image format:","msgstr":["Pildi formaat:"]},"Basic":{"msgid":"Basic","msgstr":["Elementaarne"]},"Analytical":{"msgid":"Analytical","msgstr":["Analüütiline"]},"High-res print":{"msgid":"High-res print","msgstr":["Kõrge resolutsiooniga pilt"]},"Download image":{"msgid":"Download image","msgstr":["Lae pilt alla"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["Piltide laadimisel tekkis viga:"]},"min/px":{"msgid":"min/px","msgstr":["min/px"]},"sec/px":{"msgid":"sec/px","msgstr":["sek/px"]},"Resolution":{"msgid":"Resolution","msgstr":["Lahutus"]},"lat.":{"msgid":"lat.","msgstr":["laiuskraad."]},"deg/px":{"msgid":"deg/px","msgstr":["kraad/px"]},"long.":{"msgid":"long.","msgstr":["pikkuskraad."]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Prognoositav lahutusvõime: ${ formattedResolution } m/px"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Viga: andmete ühildamine ei toeta KMZ/JPG ja KMZ/PNG formaate."]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":["Viga: efektidega vaadet saab alla laadida ainult JPEG ja PNG formaadis."]},"Image download":{"msgid":"Image download","msgstr":["Pildi allalaadimine"]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":["Hoiatus: järgmised kihid kasutavad dataProduct'e, seega on võimalik, et soovitud andmetüüpi ei seadistata:"]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":["Hoiatus: Evalscript pole tavalises V3-vormingus ja soovitud andmetüüpi ei saanud seadistada:"]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":["See tähendab, et parameeter \"sampleType\" on tõenäoliselt seatud vaikeväärtusele (AUTO). Selle saad parandada, muutes oma evalscripti. Lisateave \"sampleType\" kohta dokumentatsioonis"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Pildi laius [tollid]:"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Pildi kõrgus [tollid]:"]},"DPI:":{"msgid":"DPI:","msgstr":["DPI:"]},"5 years":{"msgid":"5 years","msgstr":["5 aastat"]},"2 years":{"msgid":"2 years","msgstr":["2 aastat"]},"1 year":{"msgid":"1 year","msgstr":["1 aasta"]},"6 months":{"msgid":"6 months","msgstr":["6 kuud"]},"3 months":{"msgid":"3 months","msgstr":["3 kuud"]},"1 month":{"msgid":"1 month","msgstr":["1 kuu"]},"Retry":{"msgid":"Retry","msgstr":["Proovi uuesti"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Laeb, palun oota"]},"mean":{"msgid":"mean","msgstr":["keskmine"]},"median":{"msgid":"median","msgstr":["mediaan"]},"st. dev.":{"msgid":"st. dev.","msgstr":["standardhälve."]},"min / max":{"msgid":"min / max","msgstr":["min / maks"]},"Export CSV":{"msgid":"Export CSV","msgstr":["Ekspordi CSV"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Ajavahemik:"]},"Date:":{"msgid":"Date:","msgstr":["Kuupäev:"]},"Single date":{"msgid":"Single date","msgstr":["Kuupäev"]},"Timespan":{"msgid":"Timespan","msgstr":["Ajavahemik"]},"hh":{"msgid":"hh","msgstr":["tt"]},"mm":{"msgid":"mm","msgstr":["mm"]},"From:":{"msgid":"From:","msgstr":["Alates:"]},"Until:":{"msgid":"Until:","msgstr":["Kuni:"]},"Apply":{"msgid":"Apply","msgstr":["Rakenda"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Jaga Facebookis"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Jaga Twitteris"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Proovi "]},"Logout":{"msgid":"Logout","msgstr":["Logi välja"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["Logi sisse, et saaks kasutada lisavõimalusi, nt aegvõtet, analüütilist allalaadimist, enda seadistusi ja palju muud."]},"Login":{"msgid":"Login","msgstr":["Logi sisse"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Maa seiramine kosmosest"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Põllumajandus"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Atmosfäär ja õhusaaste"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Muutuste tuvastamine ajas"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Üleujutused ja põuad"]},"Geology":{"msgid":"Geology","msgstr":["Geoloogia"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Ookeanid ja veekogud"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Lumi ja liustikud"]},"Urban":{"msgid":"Urban","msgstr":["Linnapiirkond"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Taimestik ja mets"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Vulkaanid"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Metsatulekahjud"]},"Default":{"msgid":"Default","msgstr":["Vaikimisi"]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":["# Tere tulemast EO Brauserisse!\n\nSentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA \nLandsati 5, 7 ja 8 täielik arhiiv, globaalne katvus Landsat 8, Envisat Meris, \nMODIS, Proba-V ja GIBS toodete kohta ühes kohas.\n\n[EO brauseri esitlusleht](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO brauseri kasutusjuhend](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":["#### Kiire ülevaade EO brauseri võimalustest\n\nEO brauser ühendab Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA Landsat 5, 7 ja 8 arhiivid, Landsat 8 globaalse kattuvuse, Envisat Merise, MODISe, Proba-V ja GIBSi tooted ühes kohas ja võimaldab sirvida ja võrrelda täisresolutsiooniga pilte nendest allikatest. Sa lähed soovitud piirkonda, valid andmeallikad, aja ja pilvisuse ning seejärel saad kontrollida saadud andmeid.\n\nSa saad jätkata juhendi vaatamist, kui vajutad nuppu \"Järgmine\" või võid selle sulgeda. Klõpsates paremas ülanurgas olevat teabeikooni saad alati jätkata õpetuse vaatamisega, juhul kui sa sulgesid selle kogemata või tahtsid vahepeal midagi katsetada."]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":["Vahekaardil Visualiseeri saab valida erinevaid eelnevalt installeeritud või kohandatud spektrikanalite kombinatsioone, et visualiseerida andmed valitud tulemuseks.\n\nMõned levinud valikud:\n- **True Color** - maapinna visualiseeritud tõlgendus.\n- **Valevärv** - taimkatte visualiseeritud tõlgendus.\n- **NDVI** - taimkatte normaliseeritud vaheindeks.\n- **Niiskuse indeks** - niiskuse indeks\n- **SWIR** - lühilaine-infrapuna indeks.\n- **NDWI** - vee normaliseeritud vaheindeks.\n- **NDSI** - lume normaliseeritud vaheindeks.\n\nEnamikule visualiseeringutest lisatakse kirjeldus ja legend, mida saab vaadata klõpsates laienduse\nikoonil .\n \nEnamiku andmeallikate jaoks on saadaval valik ** Kohandatud skript**. Klõpsa sellel, et valida kohandatud\nkanalite kombinatsioone, indeksite kombinatsioone või kirjuta andmete visualiseerimiseks oma klassifikatsiooniskript. Sa saad ka\nkasutada kohandatud skripte, mis on salvestatud mujale, kas Google Drive'i, GitHubi või meie [Kohandatud skriptihoidla] (https://custom-scripts.sentinel-hub.com/).\nKleebi skripti URL skripti täpsema redigeerimise paneeli tekstikasti ja klõpsa käsku Värskenda. \n \nKuupäeva saab muuta vahekaardil Visualiseeri, ei pea minema tagasi vahekaardile **Avasta**. Sisesta see või vali kalendrist .\n\nVisualiseeringute kohal on täiendavaid tööriistu. Pane tähele, et nende kättesaadavus sõltub andmeallikast.\n- **Märgistuste kiht** klõpsa märgistuste ikoonil, et salvestada see rakenduses edasiseks kasutamiseks .\n- Vali **täpsemad valikud** nagu valimi meetod või rakenda erinevaid **efekte** nagu kontrastsus (võimendus) ja heledus (gamma) - klõpsa efektide ikoonil .\n- Lisa kiht vahekaardile ** Võrdle ** hilisemaks võrdlemiseks - klõpsa võrdlusikoonil .\n- **Suumi** paani keskele - klõpsa sihikujoonestikul .\n- Lülita sisse **kihi nähtavus** - klõpsa ikoonil tee nähtavaks.\n- **Jaga** oma visualiseeringuid sotsiaalmeedias - klõpsa jagamise ikoonil ."]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":["Vahekaardil ** Võrdle ** leiad kõik visualiseeringud, mille oled lisanud kaudu lehele ** Võrdle **.\n\nKaks režiimi:\n - ** läbipaistmatus ** (Võrreldud piltide vahelise piiri hägustamiseks tõmba liugurit vasakule või paremale)\n - ** poolitamine ** (Võrreldud piltide vahelise piiri määramiseks tõmba liugurit vasakule või paremale)\n\nKõiki märgistusi saad lisada võrdlusesse, kasutades valikut ** Lisa kõik märgistused ** või eemalda kõik visualiseeringud\nvahekaardilt ** Võrdle ** nupuga ** Eemalda kõik **."]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":["Siin saad valida, milline aluskiht ja ülekatted (teed, piirid, sildid) kaardil kuvatakse."]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":["Siin saad vahetada režiimi ** tavaline ** ja ** haridus ** vahel. ** Haridus ** režiim pakub teile rakenduse veidi lihtsustatud versiooni.\nSellele pääseb juurde ka otse oma [määratud URL-i] (https://apps.sentinel-hub.com/eo-browser-education/) kaudu."]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":["Sa võid vaadata õpetust igal ajal, vajutades info ikoonile\n\n\n."]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":["#### Kiire ülevaade EO brauseri funktsioonidest\n\nKui sul on väike ekraan, mine meie kasutusjuhendi vaatamiseks [siia] (https://www.sentinel-hub.com/explore/eobrowser/user-guide/).\n\nSaad seda infot alati uuesti vaadata, kui klõpsad info ikoonil\n\n\n\nparemal ülanurgas.\n\n#### Muud vahendid\n- [EO brauseri esitlusleht] (https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO brauseri suvi 2018 värskendused - video] (https://www.youtube.com/embed/m3pron0C0kE)"]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":["Mis on EO brauser?"]},"User Account":{"msgid":"User Account","msgstr":["Kasutajakonto"]},"Discover Tab":{"msgid":"Discover Tab","msgstr":["Avasta vaheleht"]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":["Visualiseeri vaheleht"]},"Compare Tab":{"msgid":"Compare Tab","msgstr":["Võrdle vaheleht"]},"Pins Tab":{"msgid":"Pins Tab","msgstr":["Märgistuste vaheleht"]},"Search Places":{"msgid":"Search Places","msgstr":["Otsi kohti"]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":["Kihid ja ülekatted"]},"Education Mode":{"msgid":"Education Mode","msgstr":["Hariduslik režiim"]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":["Info ja õpetus"]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":["Märgi huvipakkuv piirkond"]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":["Märgi huvipakkuv koht"]},"Measure Distances":{"msgid":"Measure Distances","msgstr":["Mõõda vahemaad"]},"Download Image":{"msgid":"Download Image","msgstr":["Lae pilt alla"]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":["Loo aegvõttega animatsioon"]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":["Mõnusat sirvimist!"]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":["Tere tulemast EO brauserisse!"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":["Kanal 1 - kollane aine ja detritaalsed pigmendid - 412.5 nm"]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":["Kanal 3 - klorofüll ja teised pigmendid - 490 nm"]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":["Kanal 4 - hõljunud sete, punased looded - 510 nm"]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":["Kanal 5 - klorofülli neeldumise miinimum - 560 nm"]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":["Kanal 6 - hõljunud sete - 620 nm"]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":["Kanal 7 - klorofülli neeldumine ja fluorestsentsi võrdlus - 665 nm"]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":["Kanal 8 - klorofülli fluorestsentsi maksimum - 681nm"]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":["Kanal 9 - fluorestsentsi võrdlusväärtus, atmosfääri korrektsioon - 709 nm"]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":["Kanal 10 - taimkate, pilved - 753 nm"]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":["Kanal 12 - atmosfääri korrektsioonid - 779 nm"]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":["Kanal 13 - taimkatte, veeauru võrdlusväärtus - 865 nm"]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":["Kanal 14 - Atmosfääri korrektsioon - 885 nm"]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":["Kanal 15 - veeaur, maapind - 900 nm"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Kanal 1 - sinine - 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Kanal 2 - roheline - 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Kanal 3 - punane - 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Kanal 4 - NIR - 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Kanal 5 - SWIR-1 - 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Kanal 7 - SWIR-2 - 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Kanal 8 - pankromaatiline - 520-900 nm"]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":["Kanal 1 - rannikuala/aerosool - 433-453 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Kanal 2 - sinine - 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Kanal 3 - roheline - 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Kanal 4 - punane - 630-680 nm"]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":["Kanal 5 - NIR - 845-885 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Kanal 6 - SWIR-1 - 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Kanal 7 - SWIR-2 - 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Kanal 8 - pankromaatiline - 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Kanal 9 - kiudpilved - 1360-1390 nm"]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":["Landsat 5 (ESA arhiiv)"]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":["Landsat 7 (ESA arhiiv)"]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":["Landsat 8 (ESA arhiiv)"]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":["Landsat 8 (USGS arhiiv)"]},"Red band":{"msgid":"Red band","msgstr":["Punane kanal"]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":["841 - 876 nm (NIR)"]},"Blue band":{"msgid":"Blue band","msgstr":["Sinine kanal"]},"Green band":{"msgid":"Green band","msgstr":["Roheline kanal"]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":["1230 - 1250 nm"]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":["1628 - 1652 nm"]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":["2105 - 2155 nm"]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":["Kanal 1 - rannikualade aerosoolid - 443 nm"]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":["Kanal 2 - sinine - 490 nm"]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":["Kanal 3 - roheline - 560 nm"]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":["Kanal 4 - punane - 665 nm"]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":["Kanal 5 - taimkate, lühilaineline infrapuna - 705 nm"]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":["Kanal 6 - taimkate, lühilaineline infrapuna - 740 nm"]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":["Kanal 7 - taimkate, lühilaineline infrapuna - 783 nm"]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":["Kanal 8 - NIR - 842 nm"]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":["Kanal 9 - veeaur - 945 nm"]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":["Kanal 10 - SWIR - kiudpilved - 1375 nm"]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":["Kanal 11 - SWIR - 1610 nm"]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":["Kanal 12 - SWIR - 2190 nm"]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":["Kanal 8A - taimkate, lühilaineline infrapuna - 865 nm"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":["Kanal 1 - aerosooli korrektsioon, vee optiliselt aktiivsete ainete väärtuste täpsem leidmine - 400 nm"]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":["Kanal 2 - värvunud lahustunud orgaaniline aine ja mineraalne hõljum - 412 nm"]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":["Kanal 3 - klorofülli neeldumise maksimum, biogeokeemia, taimkate - 442.5 nm"]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":["Kanal 4 - kõrge klorofüll, teised pigmendid - 490 nm"]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":["Kanal 5 - klorofüll, sete, hägusus, fütoplankton, mis annab veele punase värvuse - 510 nm"]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":["Kanal 6 - klorofülli võrdlusväärtus (klorofülli minimum) - 560 nm"]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":["Kanal 7 - settehulk - 620 nm"]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":["Kanal 8 - klorofüll (teine klorofülli neeldumise maksimum), sete, värvunud lahustunud orgaaniline aine/ taimkate"]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":["Kanal 10 - klorofülli fluorestsentsi maksimum, peegeldusteguri järsk muutus (red edge) - 681.25 nm"]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":["Kanal 11 - klorofülli fluorestsentsi baasväärtus, peegeldusteguri järsu muutuse siire (red edge transition) - 708.75 nm"]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":["Kanal 12 - hapniku neeldumine/pilved, taimkate - 753.75 nm"]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":["Kanal 13 - hapniku neeldumine/ aerosooli korrektsioon - 761.25 nm"]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":["Kanal 14 - atmosfääri korrektsioon - 764.375 nm"]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":["Kanal 15 - O2A kasutatakse rõhu määramiseks pilve tipus, maapealne fluorestsents - 767.5 nm"]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":["Kanal 16 - atmosfääri korrektsioon/aerosoolide korrektsioon - 778.75 nm"]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":["Kanal 17 - atmosfääri korrektsioon/ aerosooli korrektsioon, pilved, pikslite koordinaatide põhine sidumine - 865 nm"]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":["Kanal 18 - veeauru neeldumise võrdlusväärtus. Üldine võrdlusväärtuse kanal instrumendiga SLSTR. Taimkatte seire - 885 nm"]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":["Kanal 19 - Veeauru neeldumise/taimkatte seire (maks. peegeldusvõime) - 900 nm"]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":["Kanal 20 - Veeauru neeldumise, atmosfääri/aerosoolide korrektsioon - 940 nm"]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":["Kanal 21 - Atmosfääri/aerosoolide korrektsioon - 1020 nm"]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":["Kanal F1 - tule soojuskiirguse infrapunane osa - aktiivse tule korral - 3742.00 nm"]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":["Kanal F2 - tule soojuskiirguse infrapunane osa - aktiivse tule korral - 10854.00 nm"]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":["Kanal S1 - VNIR - pilvede skriining (valikuline kontroll), taimkatte seire, aerosool - 554.27 nm"]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":["Kanal S2 - VNIR - NDVI, taimestiku seire, aerosoolid - 659.47 nm"]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":["Kanal S3 - VNIR - NDVI, pilvede märgistamine, pikslite koordinaatide põhine sidumine - 868.00 nm"]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":["Kanal S4 - SWIR - Kiudpilvede tuvastamine üle maa - 1374.80 nm"]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":["Kanal S5 - SWIR - Pilvisuse, jää. lume, taimkatte seire - 1613.40 nm"]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":["Kanal S6 - SWIR - Taimkatte seisund ja pilvisus 2255.70 nm"]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":["Kanal S7 - Ümbritseva keskkonna soojuskiirgus - SST, LST, aktiivse tule faas - 3742.00 nm"]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":["Kanal S8 - Ümbritseva keskkonna soojuskiirgus - SST, LST, aktiivse tule faas - 10854.00 nm"]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":["Kanal S9 - Ümbritseva keskkonna soojuskiirgus - SST, LST - 12022.50 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Peegeldus"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Heleduse temperatuur"]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":["Põhineb kanalite kominatsioonil 4, 3, 2"]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":["Põhineb kanalite (B04-B03)/(B04+B03) kombinatsioonil"]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":["Põhineb kanalite 5, 4, 3 kombinatsioonil"]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":["Põhineb loomilike värvidega (true color) kanalitel 4, 3, 2 ja must-valgel kanalil 8"]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":["Põhineb kanalite (B05-B04)/(B05+B04) kombinatsioonil"]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":["VV polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseeritud"]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":["VV polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseerimata"]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":["VH polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseeritud"]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":["Põhineb kanalite 3, 2, 1 kombinatsioonil"]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":["VH polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseerimata"]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":["Pildi värviliseks muutmine sisemiste kanalite kaardistamise abil. [RGB] väärtus = [VV, 2 VH, VV / VH / 100.0] - lineaarse skaala gamma0 - ortorektifitseeritud"]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":["VV polarimeetrilise kanali detsibell skaalal gamma0 [-20,0] - ortorektifitseeritud"]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":["VH polarimeetrilise kanali detsibell skaalal gamma0 [-20,0] - ortorektifitseeritud"]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":["Tagastab polarisatsioonide komposiidi (VH, VV, VH-VV)"]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":["HH polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseeritud"]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":["HV polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseeritud"]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":["Pildi värviliseks muutmine sisemiste kanalite kaardistamise abil. [RGB] väärtus = [HH, 2 HV, HH / HV / 100.0] - lineaarse skaala gamma0 - ortorektifitseeritud"]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":["HH polarimeetrilise kanali detsibell skaalal gamma0 [-20,0] - ortorektifitseeritud"]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":["HV polarimeetrilise kanali detsibell skaalal gamma0 [-20,0] - ortorektifitseeritud"]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":["HH polarimeetrilise kanali lineaarsel skaalal gamma0 - ortorektifitseerimata"]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":["Põhineb kanalitele 4, 3, 2"]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":["Põhineb kanalitele 8, 4, 3"]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":["Põhineb kanalitele 12, 11, 4"]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Põhineb kanalite (B8 - B4)/(B8 + B4) kombinatsioonil"]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":["Põhineb kanalite (B8A - B11)/(B8A + B11) kombinatsioonil"]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":["Põhineb kanalitel 12,8A,4"]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":["Põhineb kanalite (B3 - B8)/(B3 + B8) kombinatsioonil"]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":["Põhineb kanalite (B3 - B11) / (B3 + B11) kombinatsioonil; väärtusi üle 0,42 peetakse lumisteks"]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":["Sentinel2 andmete klassifikatsioon vastavalt ESA Scene klassifikatsiooni algoritmile."]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":["UV aerosooli indeks 380 nm ja 340 nm juures"]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":["Põhineb kanalite (B3 - B11)/(B3 + B11) kombinatsioonil"]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":["OLCI maismaa klorofülli indeks, põhineb kanalite kombinatsioonil (B12 – B11)/(B11 – B10)"]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":["UV aerosooli indeks 388 nm ja 354 nm juures"]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":["Metaani vertikaalselt keskmistatud kuiva õhu segusuhe"]},"Cloud base height":{"msgid":"Cloud base height","msgstr":["Pilvede alumise piiri kõrgus"]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":["Rõhk pilvede alumisel piiril"]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":["Radiomeetriliselt efektiivne pilvede osakaal"]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":["Pilvede optiline paksus"]},"Cloud top height":{"msgid":"Cloud top height","msgstr":["Pilvede ülemise piiri kõrgus"]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":["Rõhk pilvede ülemisel piiril"]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":["Süsinikmonooksiidi koguhulk atmosfääri sambas"]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":["Formaldehüüdi koguhulk troposfääri sambas"]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":["Lämmastikdioksiidi koguhulk troposfääri sambas"]},"Ozone total column":{"msgid":"Ozone total column","msgstr":["Osooni koguhulk atmosfääri sambas"]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":["Vääveldioksiidi koguhulk atmosfääri sambas"]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":["Põhineb kanalitel 2, 1, 4"]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":["Põhineb kanalite (B02 - B01)/(B02 + B01) kombinatsioonil"]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":["Põhineb kanalite (B02 - B05)/(B02 + B05) kombinatsioonil"]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":["Põhineb kanalite (B06 - B07)/(B06 + B07) kombinatsioonil"]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":["Põhineb kanalitel 1, 4, 3"]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":["Põhineb kanalite 7, 5, 3 kombinatsioonil"]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":["(B09 - B08)/(B09 + B08)"]},"B09 / B08":{"msgid":"B09 / B08","msgstr":["B09 / B08"]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":["Põhineb kanalite kombinatsioonil 13, 5, 2"]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":["Põhineb kanalitele 13, 4, 1"]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":["Põhineb kanalitel 13, 5, 2"]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":["Põhineb kanalite (B13-B07) / (B13+B07) kombinatsioonil"]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":["Maismaa klorofülli indeks"]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V 10-päevane süntees\nPuuvõra kõrgus (atmosfääri korrektsioon tehtud)\nAjaline resolutsioon: 10-päeva\nRuumiline resolutsioon: 333M (piksli suurus)"]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V igapäevane süntees\nAtmosfääri ülapiir\nAjaline resolutsioon: 1 päev\nRuumiline resolutsioon: 333M (piksli suurus)"]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-päevane süntees\nAtmosfääri ülapiir\nAjaline resolutsioon: 5 päeva \nRuumiline resolutsioon: 333M (piksli suurus)"]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V igapäevane süntees\nPuuvõra (canopy) kõrgus (atmosfääri korrektsioon tehtud)\nAjaline resolutsioon: 1 päev\nRuumiline resolutsioon: 333M (piksli suurus)"]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":["OrbitTracks_Aqua_Descending"]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-päevane süntees\nPuuvõra kõrgus (atmosfääri korrektsioon tehtud)\nAjaline resolutsioon: 5 päeva \nRuumiline resolutsioon: 100 M (piksli suurus)"]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":["OrbitTracks_Aqua_Ascending"]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":["OrbitTracks_Aura_Ascending"]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":["OrbitTracks_Aura_Descending"]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":["OrbitTracks_CloudSat_Ascending"]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":["OrbitTracks_Calipso_Ascending"]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":["OrbitTracks_Calipso_Descending"]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":["OrbitTracks_CloudSat_Descending"]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":["OrbitTracks_CYGNSS_Ascending"]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":["OrbitTracks_CYGNSS_Descending"]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":["OrbitTracks_GCOM-C_Ascending"]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":["OrbitTracks_GCOM-C_Descending"]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":["OrbitTracks_GCOM-W1_Ascending"]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":["OrbitTracks_GCOM-W1_Descending"]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":["OrbitTracks_GOSAT-2_Ascending"]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":["OrbitTracks_GOSAT-2_Descending"]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":["OrbitTracks_GOSAT_Ascending"]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":["OrbitTracks_GOSAT_Descending"]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":["OrbitTracks_GPM_Ascending"]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":["OrbitTracks_GPM_Descending"]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":["OrbitTracks_ICESAT-2_Ascending"]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":["OrbitTracks_ICESAT-2_Descending"]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":["OrbitTracks_ISS_Ascending"]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":["OrbitTracks_ISS_Descending"]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":["OrbitTracks_Landsat-7_Ascending"]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":["OrbitTracks_Landsat-7_Descending"]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":["OrbitTracks_Landsat-8_Ascending"]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":["OrbitTracks_METOP-A_Ascending"]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":["OrbitTracks_METOP-B_Descending"]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":["OrbitTracks_Landsat-8_Descending"]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":["OrbitTracks_METOP-C_Ascending"]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":["OrbitTracks_METOP-C_Descending"]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":["OrbitTracks_NOAA-20_Ascending"]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":["OrbitTracks_NOAA-20_Descending"]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":["OrbitTracks_METOP-B_Ascending"]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":["OrbitTracks_OCO-2_Ascending"]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":["OrbitTracks_OCO-2_Descending"]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":["OrbitTracks_SAOCOM1-A_Ascending"]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":["OrbitTracks_SAOCOM1-A_Descending"]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":["OrbitTracks_Sentinel-1A_Ascending"]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":["OrbitTracks_Sentinel-1B_Ascending"]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":["OrbitTracks_Sentinel-1A_Descending"]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":["OrbitTracks_METOP-A_Descending"]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":["OrbitTracks_Sentinel-1B_Descending"]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":["OrbitTracks_Sentinel-2A_Ascending"]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":["OrbitTracks_Sentinel-2A_Descending"]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":["OrbitTracks_Sentinel-2B_Ascending"]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":["OrbitTracks_Sentinel-2B_Descending"]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":["OrbitTracks_Sentinel-5P_Ascending"]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":["OrbitTracks_Sentinel-5P_Descending"]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":["OrbitTracks_SMAP_Ascending"]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":["OrbitTracks_SMAP_Descending"]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":["OrbitTracks_Suomi_NPP_Descending"]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":["OrbitTracks_Suomi_NPP_Ascending"]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":["OrbitTracks_Terra_Descending"]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":["OrbitTracks_Terra_Ascending"]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":["Põhineb kanalitel 4, 3, 2, parandatud kanalitega 12 ja 11."]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":["Põhineb kanalitel B07, B06, B04"]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Põhineb kanalite (B8 - B4)/(B8 + B4) kombinatsioonil"]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":["Põhineb kanalil 10"]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":["Põhineb kanalitel B12, B11, B8A"]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":["Põhineb kanalite (B08 - B12)/(B08 + B12) kombinatsioonil"]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":["Loomulike värvidega visualiseerimine (täiustatud)"]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":["Põhineb kanalite 8, 6, 4 kombinatsioonil"]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":["Täiustatud vegetatsiooni indeks"]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":["Põhineb kanalite kombinatsioonil: BSI, B08, B11"]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":["Niisutamise jaoks klassifitseeritud NDMI"]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":["Põhineb kanalitele B11, B08, B02"]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":["Valevärv 13, 5, 2"]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":["Põhineb kanalite kombinatsioonile (B13 - B07) / (B13 + B07)"]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":["Põhineb kanalitel 12, 8, 2"]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":["Põhineb kanalitel 4, 3, 2"]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":["Põhineb kanalitel 8, 4, 3"]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":["Põhineb kanalitel 12, 8, 2"]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":["Põhineb kanalitel 8, 11, 12"]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":["Vee sete ja klorofülli sisaldus"]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":["Põhineb kanalitel 12, 8A, 4"]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":["Põhineb NDSI-l"]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":["Põhineb kanalite 11, 8, 2 kombinatsioonil"]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":["Põhineb kanalitel B12, B11, B04"]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":["Põhineb kanalitel 4, 3, 2"]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":["Atmosfäärile vastupidav vegetatsiooni indeks"]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":["Vegetatsiooni indeks, mis võtab arvesse mulla näitajaid"]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":["# Termilised IR tulekahju ribad\n\nSentinel-3 mere- ja maapinna temperatuuri instrumendil (SLSTR) on kaks spetsiaalset kanalit (F1 ja F2), mille eesmärk on tuvastada maapinna temperatuur (LST). F2-kanal, kesklainepikkusega 10854 nm, mõõdetakse termilise infrapuna ehk TIR-ga. See on väga kasulik tulekahjude ja kõrge temperatuuri sündmuste jälgimiseks 1 km eraldusvõimega.\n\n\n\nLisateave [siin.] (Https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)"]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":["# Metaan (CH4)\n\n\n\nMetaani panus antropogeense kasvuhooneefekti tugevnemisse on süsinikdiooksidi järel teisel kohal. Mõõdetakse osakest miljardi kohta (ppb - parts per billion) ruumilise resolutsiooniga 7 km x 3.5 km.\n\n\n\nRohkem infot [siin.](http://www.tropomi.eu/data-products/methane)"]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":["# Formaldehüüd (HCHO)\n\n\n\nTroposfääri formaldehüüdi (HCHO) pikaajaline satelliitseire on oluline, et toetada õhukvaliteedi ning atmosfäärikeemiaga seotud uuringuid nii piirkondlikul kui ülemaailmsel tasandil. Formaldehüüdi jaotumise hooajalised ja aastatevahelised kõikumised on peamiselt seotud temperatuurimuutuste ja tulekahjudega, aga ka muutustega inimtegevuses. Kuna HCHO eluiga on suurusjärgus mõni tund, siis selle kontsentratsioon atmosfääri piirkihis võib olla otseselt seotud lühiealiste süsivesinike vabanemisega, mida enamasti ei ole võimalik vahetult kosmosest jälgida. Mõõtmised on moolides ruutmeetri kohta (mol/m²).\n\n\n\nRohkem infot [siin.](http://www.tropomi.eu/data-products/formaldehyde)"]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":["# Vääveldioksiid (SO2)\n\n\n\nVääveldioksiid satub Maa atmosfääri nii looduslike kui ka inimtekkeliste (inimese loodud) protsesside kaudu. See mängib rolli nii kohalikul kui ka ülemaailmsel tasandil ning selle mõju ulatub lühiajalisest reostusest kuni kliimani . Ainult umbes 30% eralduvast SO2-st pärineb looduslikest allikatest; enamus on inimtekkelist päritolu. Sentinel-5P / TROPOMI-instrument mõõdab Maa pinda kord päevas ruumilise eraldusvõimega 3,5 x 7 km, mis võimaldab eraldada peeneid detaile, sealhulgas tuvastada väiksemaid SO2 osakesi. Mõõtmised on moolides ruutmeetri kohta (mol/m²).\n\n\n\n\nRohkem infot [siin.](http://www.tropomi.eu/data-products/sulphur-dioxide)"]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":["# Osoon (O3)\n\n\n\nOsoon on Maa atmosfääri tasakaalu jaoks otsustava tähtsusega. Stratosfääris olev osoonikiht kaitseb biosfääri ohtliku ultraviolettkiirguse eest. Troposfääris toimib see tõhusa puhastusainena, kuid suure kontsentratsiooni korral muutub kahjulikuks inimestele, loomadele ja taimedele. Osoon on ka oluline kasvuhoonegaas. Alates Antarktika osooniaugu avastamisest 1980. aastatel ja sellele järgnenud Montreali protokollist (mis reguleerib kloori sisaldavate osoonikihti kahandavate ainete tootmist), on osooni regulaarselt maapinnalt ja kosmosest jälgitud. Mõõtmised on moolides ruutmeetri kohta (mol/m²).\n\n\n\nRohkem infot [siin.](http://www.tropomi.eu/data-products/total-ozone-column)"]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":["# Naatriumdioksiid (NO2)\n\n\n\nLämmastikdioksiidi (NO2) ja lämmastikoksiidi (NO) koos nimetatakse tavaliselt lämmastikoksiidideks. Need on Maa atmosfääris olulised jälggaasid, mis esinevad nii troposfääris kui ka stratosfääris. Nad sisenevad atmosfääri inimtekkeliste tegevuste (eelkõige fossiilkütuste ja biomassi põletamise) ja looduslike protsesside (nt mulla mikrobioloogiliste protsesside, metsatulekahjude ja välgu) tulemusena. Mõõtmised on moolides ruutmeetri kohta (mol/m²).\n\n\n\nRohkem infot [siin.](http://www.tropomi.eu/data-products/nitrogen-dioxide)"]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":["# Süsinikmonoksiid (CO)\n\n\n\nSüsinikmonooksiid (CO) on oluline atmosfääri jälggaas. Osades linnapiirkondades on see peamine õhku saastav aine. Peamised CO-allikad on fossiilkütuste põletamine, biomassi põletamine ning metaani ja muude süsivesinike oksüdeerumine. Süsinikmonooksiidi koguhulka atmosfääri sambas mõõdetakse moolides ruutmeetri kohta (mol/m²).\n\n\n\nRohkem infot [siin.](http://www.tropomi.eu/data-products/carbon-monoxide)"]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":["# Aerosooli indeks\n\nAerosoolide indeks (AI) on kvalitatiivne indeks, mis näitab suurema aerosoolide kontsentratsiooniga kihtide olemasolu atmosfääris. Seda saab kasutada UV-kiirgust absorbeerivate aerosoolide, näiteks kõrbetolmu ja vulkaanituha, olemasolu tuvastamiseks. Positiivsed väärtused (helesinisest punaseni) näitavad UV-kiirgust absorbeerivate aerosoolide olemasolu. See indeks arvutatakse kahe lainepikkuste paari kohta: 340/380 nm ja 354/388 nm.\n\nRohkem infot [siin.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)"]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":["# Pilvede alumise piiri kõrgus\n\nPilvede alumise piiri kõrgust mõõdetakse meetrites (m)."]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":["# rõhk pilvede alumisel piiril\n\nRõhku pilvede alumisel piiril mõõdetakse paskalites (Pa)."]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":["# Pilvede optiline paksus\n\nPilvede paksus on pilvede optiliste omaduste iseloomustamisel põhiparameeter. See näitab kui palju päikesevalgust jõuab läbi pilve Maa pinnale. Mida suurem on pilve optiline paksus, seda rohkem päikesevalgust pilv hajutab ja peegeldab. Tumesinine värv näitab väikseid pilve optilise paksuse väärtusi ja punane suuremaid pilve optilise paksuse väärtusi."]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":["# Pilvede ülemise piiri kõrgus\n\nPilvede ülemise piiri kõrgust mõõdetakse meetrites (m)."]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":["# Rõhk pilvede ülemisel piiril\n\nRõhku pilvede ülemisel piiril mõõdetakse paskalites (Pa)."]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":["# Normaliseeritud vegetatsiooni indeks (NDVI)\n\nNormaliseeritud vegetatsiooni indeks on lihtne, kuid tõhus indeks rohelise taimestiku kvantifitseerimiseks. See on taimestiku seisundi näitaja, mis põhineb sellel, kuidas taimed peegeldavad valgust teatud lainepikkustel. NDVI väärtusvahemik on -1 kuni 1. NDVI negatiivsed väärtused (lähenevad -1-le) vastavad veele. Nullilähedased väärtused (–0,1 kuni 0,1) vastavad tavaliselt kivide, liiva või lume viljatutele aladele. Madalad positiivsed väärtused tähistavad põõsastikku ja rohumaad (ligikaudu 0,2–0,4), kõrged väärtused viitavad parasvöötme ja troopilistele vihmametsadele (väärtused lähenevad 1-le).\n\n\nRohkem infot [siin](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)"]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":["# Tõhustatud vegetatsiooni indeks (EVI)\n\nTõhustatud vegetatsiooni indeks (EVI) on optimeeritud vegetatsiooni indeks, korrigeeritud on ka mulla taustasignaalid ning atmosfääri mõjud. See on väga oluline tihedate puude all olevatel aladel. EVI väärtusvahemik on -1 kuni 1, hea tervise juures olevate toimede korral on vahemik 0.2 kuni 0.8. \n\n\n\n\n\nRohkem infot [siin](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)"]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":[""]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":[""]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":["# Modifitseeritud antotsüaniini peegeldusindeks (mARI/ARI2)\n\nAntotsüaniinidid on pigmendid, mis esinevad tavaliselt kõrgemates taimedes, põhjustades nende punast, sinist ja lillat värvust. Nad annavad väärtuslikku teavet taimede füsioloogilise seisundi kohta, kuna neid peetakse erinevat tüüpi taimestresside näitajateks. Antotsüaniini peegeldus on kõrgeim 550 nm juures. Seejuures peegelduvad samad lainepikkused ka klorofüllis. Antotsüaniinide isoleerimiseks lahutatakse 700nm spektraalkanal, mis kajastab ainult klorofülli ja mitte antotsüaniini.\n\nLehtede tiheduse ja paksuse korrigeerimiseks lisatakse ARI põhiindeksile ligilähedane infrapunaspektrer (soovitataval lainepikkusel 760–800nm), mis on seotud hajumisega lehtede pinnalt. Uut indeksit nimetatakse modifitseeritud ARI või mARI (ka ARI2).\n\nmARI väärtused vaadeldavate puude juures on vahemikus 0 kuni 8 [originaalartikkel](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/).\n\n\n\n\n\nRohkem infot [siin.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)"]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":["# Rohelise linna skript\n\nRohelise linna skripti eesmärk on suurendada teadlikkust rohealadest linnades üle kogu maailma. Skriptis võetakse arvesse normaliseeritud vegetatsiooniindeksit (NDVI) ja RGB lainepikkusi; see eraldab hoonestatud alad taimkattega aladest, mis muudab selle kasulikuks linnapiirkondade tuvastamisel. Hoonestatud alad on esitatud halliga ja taimestik on esitatud rohelisega.\n\n\n\n\n\nRohkem infot [siin.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":["# Linnaalade klassifitseerimise skript\n\nLinnaalade klassifitseerimise skripti eesmärk on tuvastada hoonestatud alad, eraldades need paljast maapinnast, taimestikust ja veest. Suure niiskusesisaldusega alad kuvatakse sinisena; hoonestatud alad kuvatakse valgena; taimkattega alad rohelisena; kõik muu on paljas maapind ning kuvatakse pruunina.\n\n\n\n\n\nRohkem infot [siin.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)"]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":["# Linnaalade infrapuna skript\n\nSee Leo Tolari koostatud skript ühendab loomulike värvide (true color) visualiseerimise lähis-infrapuna (NIR) ja lühilaine infrapuna (SWIR) lainepikkustega. Skript toob linnapiirkonnad paremini esile kui loomulikud värvid üksi, näides siiski loomulik.\n\n\n\n\n\nRohkem infot [siin.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)"]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":[""]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":[""]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":[""]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Pseudovärvide komposiit\n\nPseudovärvide komposiit kasutab vähemalt ühte mittenähtavat lainepikkust. Väga levinud on kasutada infrapunast, punast ja rohelist kanalit (kanal on elektromagnetilise spektri piirkond; satelliitsensor suudab Maad kujutada erinevates kanalites). Pseudovärvide komposiiti kasutatakse kõige sagedamini taimede tiheduse ja tervise hindamiseks, kuna taimed peegeldavad lähis infrapuna- ja rohelist valgust ning neelavad punast valgust. Linnaalad ja paljad maapinnad kuvatakse halli või pruuniga, vesi sinise või mustaga.\n\n\n\nRohkem infot [siin](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":["# Pseudovärvide komposiit\n\nPseudovärvide komposiit kasutab vähemalt ühte mittenähtavat lainepikkust. Väga levinud on kasutada infrapunast, punast ja rohelist kanalit (kanal on elektromagnetilise spektri piirkond; satelliitsensor suudab Maad kujutada erinevates kanalites). Pseudovärvide komposiiti kasutatakse kõige sagedamini taimede tiheduse ja tervise hindamiseks, kuna taimed peegeldavad lähis infrapuna- ja rohelist valgust ning neelavad punast valgust. Linnaalad ja paljad maapinnad kuvatakse halli või pruuniga, vesi sinise või mustaga.\n\n\n\nRohkem infot [siin](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Pseudovärvide komposiit\n\nPseudovärvide komposiit kasutab vähemalt ühte mittenähtavat lainepikkust. Väga levinud on kasutada infrapunast, punast ja rohelist kanalit (kanal on elektromagnetilise spektri piirkond; satelliitsensor suudab Maad kujutada erinevates kanalites). Pseudovärvide komposiiti kasutatakse kõige sagedamini taimede tiheduse ja tervise hindamiseks, kuna taimed peegeldavad lähis infrapuna- ja rohelist valgust ning neelavad punast valgust. Linnaalad ja paljad maapinnad kuvatakse halli või pruuniga, vesi sinise või mustaga.\n\n\n\nRohkem infot [siin.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Loomulike värvide komposiit\n\nSatelliidil olevad andurid võivad Maad kujutada elektromagnetilise spektri eri piirkondades. Iga piirkonda nimetatakse kanaliks. Satelliidil Landsat 5 on 7 kanalit. Loomulike värvide komposiit kasutab nähtava spektri punast, rohelist ja sinist vastavates punase, rohelise ja sinise värvi kanalites. Tulemuseks on Maa kujutis loomulikes värvides, mida on inimestel lihtne mõista.\n\n\n\nRohkem infot [siin](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Loomulike värvide komposiit\n\nSatelliidil olevad andurid võivad Maad kujutada elektromagnetilise spektri eri piirkondades. Iga piirkonda nimetatakse kanaliks. Satelliidil Landsat 7 on 8 kanalit. Loomulike värvide komposiit kasutab nähtava spektri punast, rohelist ja sinist vastavates punase, rohelise ja sinise värvi kanalites. Tulemuseks on Maa kujutis loomulikes värvides, mida on inimestel lihtne mõista.\n\n\n\nRohkem infot [siin](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Loomulike värvide komposiit\n\nSatelliidil olevad andurid võivad tada Maad elektromagnetilise spektri eri piirkondades. Iga piirkonda nimetatakse kanaliks. Satelliidil Landsat 8 on 11 kanalit. Loomulike värvide komposiit kasutab nähtava spektri punast, rohelist ja sinist vastavates punase, rohelise ja sinise värvi kanalites. Tulemuseks on Maa kujutis loomulikes värvides, mida on inimestel lihtne mõista.\n\n\n\nRohkem infot [siin](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Loomulike värvide komposiit\n\nSatelliidil olevad andurid võivad tada Maad elektromagnetilise spektri eri piirkondades. Iga piirkonda nimetatakse kanaliks. Loomulike värvide komposiit kasutab nähtava spektri punast, rohelist ja sinist vastavates punase, rohelise ja sinise värvi kanalites. Tulemuseks on Maa kujutis loomulikes värvides, mida on inimestel lihtne mõista.\n\n\n\nRohkem infot [siin.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":["# Loomulike värvide komposiit\n\nSatelliidil olevad andurid võivad tada Maad elektromagnetilise spektri eri piirkondades. Iga piirkonda nimetatakse kanaliks. Loomulike värvide komposiit kasutab nähtava spektri punast, rohelist ja sinist vastavates punase, rohelise ja sinise värvi kanalites. Tulemuseks on Maa kujutis loomulikes värvides, mida on inimestel lihtne mõista.\n\n\n\nRohkem infot [siin.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)"]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":[""]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Linnaalade pseudovärvidega komposiit\n\nSee komposiit kasutab nähtava valguse ning lühilainelise infrapunakiirguse kanalite kombinatsiooni (kanal on elektromagnetilise spektri piirkond; satelliitsensor suudab Maad kujutada erinevates kanalites). Taimestik kuvatakse rohelistes toonides, mida tihedam on taimestik, seda tumedama rohelise tooniga see kuvatakse. Linnaalad kuvatakse sinisega ning muld erinevates pruunides toonides.\n\n\n\nRohkem infot [siin.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":["# Põllumajanduse komposiit\n\nSee komposiit kasutab põllukultuuride tervise jälgimiseks lühilaine infrapuna-, lähi-infrapuna- ja sinist kanalit (kanal on elektromagnetilise spektri piirkond; satelliitsensor suudab Maad kujutada erinevates kanalites). Nii lühilaine kui ka lähi-infrapunari kanalid on eriti head tiheda taimestiku esiletõstmiseks, mis kuvatakse komposiidis tumerohelisega. Põllukultuurid kuvatakse ererohelisega ja paljas maapind magentapunasega.\n\n\n\nRohkem infot [siin](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) ja [siin.](https://gisgeography.com/sentinel-2-bands-combinations/)"]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":["# Lume klassifitseerija\n\nLume klassifitseerija algoritmi eesmärk on lume tuvastamine, liigitades pikslid erinevate heleduse ja normaaliseeritud lumeindeksi (NDSI) künniste põhjal. Lumeks klassifitseeritud väärtused kuvatakse erksa sinisega. Skript võib pilvedega kaetud aladel lumeala ülehinnata.\n\n\n\nRohkem infot [siin.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":[""]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":[""]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":["# Geoloogia 12, 8, 2 komposiit\n\nSee komposiit kasutab erinevate kivimitüüpide eristamiseks lühilaine infrapuna (SWIR) kanalit 12 (kanal on elektromagnetilise spektri piirkond; satelliitsensor suudab Maad kujutada erinevates kanalites). Iga kivimi ja mineraali tüüp peegeldab lühilaine infrapunavalgust erinevalt, võimaldades geoloogiat kaardistada peegeldunud SWIR-valguse võrdlemisel. Lähi-Infrapuna (NIR) kanal 8 tõstab esile taimkatte ja kanal 2 tuvastab niiskust, mõlemad on olulised pinnamaterjalide eristamisel. Komposiit on kasulik geoloogiliste moodustiste ja omaduste (nt murrang, lõhe) leidmisel, litoloogia (eri tüüpi kivimite nt graniit, basalt) ja kaevanduse valdkonnas.\n\n\n\nRohkem infot [siin](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)"]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":["# Geoloogia 8, 11, 12 komposiit\n\nSee komposiit kasutab erinevate kivimitüüpide eristamiseks lühilaine infrapuna (SWIR) kanaleid 11 ja 12 (kanal on elektromagnetilise spektri piirkond; satelliitsensor suudab Maad kujutada erinevates kanalites). Iga kivimi ja mineraali tüüp peegeldab lühilaine infrapunavalgust erinevalt, võimaldades geoloogiat kaardistada peegeldunud SWIR-valguse võrdlemisel. Lähi-Infrapuna (NIR) kanal 8 tõstab esile taimkatte, aidates kaasa maapinna materjalide eristamisele. Kompositsioonis olev taimestik kuvatakse punasega. Komposiit on kasulik taimestiku ja maapinna tüüpide eristamiseks, tuues välja geoloogilisi erisusi, mis võivad osutuda kasulikuks kaevandamise ja maavarade uurimise juures.\n\n\n\nRohkem infot [siin](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) ja [siin.] (Http://murphygeological.com/new---sentinel-2.html#)"]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":["# Maastikutulekahjud\n\nSee Pierre Markuse loodud skript visualiseerib maastikutulekahjusid Sentinel-2 andmete abil. See ühendab loomulike värvidega tausta mõnede NIR/SWIR andmetega suitsu edasitungimise kohta ning tule kujutamisega punase ja oranži värviga kanalitelt B11 ja B12.\n\n\n\nRohkem infot [siin.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)"]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":[""]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":[""]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":["# Normaliseeritud põlemise suhtarv (NBR)\n\nNormaliseeritud põlemise suhtarvu kasutatakse sageli põlengu raskusastme hindamiseks. See kasutab lähi-infrapuna (NIR) ja lühilaine-infrapuna (SWIR) lainepikkusi. Terve taimestik peegeldab tugevasti spektri lähi-infrapunases spektris ja nõrgalt lühilaine infrapuna spektris. Samas põlenud aladel on vastupidi ning.\n\n\n\nRohkem infot [siin](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":["# Atmosfääri läbipaistvuse suurendamine\n\nSee komposiit kasutab erinevaid kanaleid (kanal on elektromagnetilise spektri piirkond; satelliitsensor suudab Maad kujutada erinevates kanalites) elektromagnetilise spektri mittenähtavas osas, et vähendada atmosfääri mõju kujutisele. Lühilaine-infrapuna kanaleid 11 ja 12 kasutatakse tulekahjude ja põlenud alade kaardistamiseks kuna kõrgema temperatuuriga piirkondades peegeldavad need kanalid tugevalt. Samas taimestikuga alad peegeldavad tugevalt lühilaine-infrapuna kanalit 8, see märgib tulekahju puudumist. Tervet ja tugevat taimestikku kujutatakse helesinisega, stressis taimestikku tuhmi sinisega. Linnaalasid kujutatakse valge, halli, tsüaani või lillaga.\n\n\n\nRohkem infot [siin.](https://eos.com/atmospheric-penetration/)"]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":["# Taimkatteta mullapinna visualiseerimine\n\nTaimkatteta mullapinna visualiseerimine võib olla kasulik mulla kaardistamiseks, maalihete asukoha või erosiooni ulatuse uurimiseks taimkatteta aladel. Visualiseerimisel kujutatakse taimestik rohelisena ja taimkatteta mullapind punasena. Vesi kujutatakse mustana.\n\n\n\nRohkem infot [siin](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) ja [siin.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)"]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":["# Loomulike värvide ja infrapuna komposiit\n\nSee komposiit muudab loomulike värvide kujutise detailid lühilaine-infrapuna lainepikkuste abiga paremini nähtavaks. Kõrgenenud temperatuuriga alad kujutatakse punase või oranžiga.\n\n\n\nRohkem infot [siin.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)"]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":["# Põlenud alade tuvastamine\n\nSeda skripti kasutatakse suurte hiljuti põlenud alade tuvastamiseks. Põlenud alade pikslid kujutatakse punasega, kõik teised alad on loomulikes värvides. Mõnikord skript ülehindab põlenud alade hulka suure veesisaldusega aladel või pilvedega kaetud piirkondades.\n\n\n\nRohkem infot [siin.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":["# Maismaa klorofülli indeks (OTCI)\n\n\n\nMaismaa klorofülli indeks (OTCI) põhineb klorofülli sisaldusel maismaataimestikus ning seda saab kasutada taimestiku seisundi ja tervise jälgimiseks. Madalad OTCI väärtused on seotud tavaliselt vee, liiva või lume pindadega. Väga kõrged väärtused (kujutatakse valgega) viitavad tavaliselt klorofülli puudumisele. Väga kõrged väärtused on seotud kas taimestikuvaba maapinna, kivi või pilvedega. Klorofülli väärtusi vahemikus punasest (madal klorofüll) kuni tumeroheliseni (kõrge klorofülli tase) võib kasutada taimestiku tervise kindlakstegemisel.\n\n\n\nRohkem infot [siin.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":["# Normaliseeritud soolsuse indeks\n\nIndeks näitab mullas esinevate soolade kogust. Mulla sooldumine on üks levinumaid mulla degradatsiooni protsesse, eriti kuivades ja poolkuivades piirkondades.\n\nIndeksi kõrgemad väärtused märgivad kõrgemat soolsust ning madalamad väärtused madalamat soolsust.\n\nRohkem infot [siin,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [siin](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) ja [siin.](https://www.indexdatabase.de/db/i-single.php?id=57)"]},"Disabled":{"msgid":"Disabled","msgstr":[""]},"Yes":{"msgid":"Yes","msgstr":[""]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":["**Sisselogitud kasutajad** saavad kasutada oma kohandatud kujundusi, salvestada ja laadida märgistusi, luua märgistustega lugu, mõõta vahemaasid, luua \naegvõtteid ja kasutada lisavõimalustega piltide allalaadimist.\n\nTasuta konto loomiseks klõpsa lihtsalt ikoonil [siin]\nvõi rakenduses ikoonil **Logi sisse** ja seejärel \"Registreeru\"."]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":["**Avasta** vahekaardil saab:\n\n- Valida **Teema.**\n- **Otsida** andmeid.\n- Vaadata teemat **Esile tõstetud.**\n\nRippmenüü **Teemad** pakub erinevaid eelseadistatud teemasid kui ka kohandatud seadistusega valikuid (peab olema sisse logitud). Valiku loomiseks tuleb klõpsata \nseadete ikoonil ja sisse logida samade tunnustega nagu EO Brauseris.\n\nJaotises **Otsing** saab määrata otsingukriteeriumid:\n - Valida, millistelt satelliitidelt soovitakse saada andmeid, valides selleks soovitud märkeruudud .\n - Valida lisavõimalusi, nt pilvkate.\n - Valida aeg, kas kirjutades ise kuupäev või valides kuupäeva kalendrist.\n\nSatelliitide kohta saab lisainfot klõpsates küsimuse ikoonil,\n mis on infoallika nime kõrval.\n\nKui vajutada ikoonil Otsing, siis tekib tulemustega nimekiri. Iga tulemus esitletakse \neelvaatepildiga ja andmeallikale omaste asjakohaste andmetega. Mõne andmeallika puhul on nähtav ka lingiikoon.\nSellel klõpsates näidatakse otselinke toorpiltidele EO Cloudis või SciHubis. Klõpsides Visualiseeri nupul, avaneb **Visualiseeri** vaheleht valitud tulemustega.\n\nJaotises **Esile tõstetud** on eelvalitud huvitavad asukohad, mis on seotud valitud teemaga."]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":["Vahekaart ** Märgistused** sisaldab sinu kinnitatud (lemmik / salvestatud) üksusi. Kinnitatud üksused sisaldavad teavet\nasukoha, andmeallika ja selle konkreetse kihi, suumi taseme ja aja kohta.\n\nIga märgistuse puhul on sul ühe märgistusega suhtlemiseks mitu võimalust.\n\n- Muuda ** järjekorda ** - klõpsa teisaldamise ikoonil\n\n \n \n\nja lohista märgistus loendis üles või alla.\n- ** Nimeta ümber ** - klõpsa pliiatsiikoonil, mis on märgistuse nime kõrval .\n- Lisa vahekaardile ** Võrdle ** - klõpsa võrdlusikoonil \n- Sisesta ** kirjeldus ** - klõpsa laienduse ikoonil .\n- ** Eemalda ** - klõpsa eemaldamisikoonil .\n- ** Suumi märgistuse asukoht - klõpsa laiuskraad / pikkuskraad\n\nKõigi märgistuste kohal oleval real on erinevad valikud, mis kehtivad kõigi märgistuste jaoks:\n- Loo märgistustest oma lugu - klõpsa nuppu ** Lugu **.\n- Jaga oma märgistusi lingi kaudu teistega - klõpsa nuppu ** Jaga **.\n- Ekspordi märgistused JSON-failina - klõpsa nuppu ** Ekspordi **.\n- Impordi JSON-failist märgistused - klõpsa nuppu ** Impordi **.\n- Kustuta kõik märgistused - klõpsa nuppu ** Kustuta **."]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":["Otsi asukohta kas hiirega kaarti kerides või sisesta asukoht otsingu\nlahtris."]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":["See tööriist võimaldab joonistada kaardile hulknurga ning kuvada selle suurust.\n\nKõik kihid, mis tagastavad üksiku väärstuse (nt NDVI, niiskusindeks, NDWI,…) toetavad indeksi\najalise muutlikkuse näitamist. Diagrammi ikoonil klõpsates \nkuvatakse diagramm. Hulknurga saab eemaldada klõpsates ikooni 'remove' .\n\nVõite üles laadida ka hulknurga geomeetriaga KML/KMZ, GPX või GEOJSON/JSON faile.\n\nKahe lehe ikoon võimaldab kopeerida hulknurga koordinaadid nagu GEOJSON-i, niitrist \ntsentreerib kaardi joonistatud hulknurgale.\n\nAnalüütilisel allalaadimisel kärbitakse eksporditud pildid huvipakkuva piirkonnani."]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":["Selle tööriista abil saad kaardil märkida punkti.\n\nMõne kihi statistilisi andmeid saad vaadata ka diagrammiikoonil klõpsates\n.\nMärgistuse saad eemaldada, klõpsates eemaldamisikoonil .\n"]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":["Selle tööriista abil saad kaardil mõõta kaugusi ja pindalasid.\n\nIga hiireklõps teeb uue punkti. Punktide lisamise lõpetamiseks vajutage klahvi Esc
\nvõi tehke kaardil topeltklõps.\nMõõtmise saad eemaldada, kui klõpsad eemaldamisikoonil ."]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":["Selle tööriista abil saad alla laadida visualiseeritud andmetega pildi kuvatud asukoha kohta. Sa võid valida\npealdiste kuvamise ja saad lisada oma kirjelduse.\nAnalüüsirežiimi lubades saad valida erinevate pildivormingute, eraldusvõime ja\nkoordinaatsüsteemide vahel. Võid valida ka mitu kihti ja alla laadida need failina .zip
.\n\nKlõpsa allalaadimisnupul\n Lae alla \nja sinu pilti (pilte) hakatakse alla laadima. Protsess võib kesta mõni sekund, olenevalt valitud\neraldusvõimest ja valitud kihtide arvust.\n\nEnne allalaadimist saad määratleda huvipakkuva piirkonna (AOI), klõpsa selleks ikoonil ala valimine.\nSinu andmed lõigatakse selle alaga sobivaks."]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":["Selle tööriista abil saad luua aegvõtte visualiseeritud kihi ja kuvatud asukoha animatsiooni.\n\nKõigepealt vali ajavahemik. Otsingutulemusi saad täpsemalt määratleda, filtreerides need kuude kaupa\n(filtreeri kuude järgi märkeruutude abil) ja/või vali üks pilt, määrates perioodi (orbiit, päev, nädal, kuu,\naasta).\n\nSeejärel vajuta Otsi ja vali oma pildid.\nPilte saab valida märkeruudu märkimisel või filtreerida pilte pilvisuse järgi, liigutades selleks liugurit. Samuti saad pilte valida ükshaaval,\nsirvides loendit ja valides pildid. Märkekasti ** Piirid ** kaudu saad oma pildil olevad piirid lubada või keelata.\n\nAegvõtet saad eelvaadata, kui vajutad allosas asuvat nuppu esita. Samuti saad määrata kiiruse\n(kaadrit sekundis).\n\nKui oled tulemusega rahul, klõpsa allalaadimisnupul ja aegvõte\nlaetakse alla .gif
failina."]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":["Oled jõudnud õpetuse lõppu. Kui sul on muid küsimusi, küsi meilt [foorumis] (https://forum.sentinel-hub.com/)\nvõi võta meiega ühendust [e-posti teel] (mailto: info@sentinel-hub.com? Subject = EO% 20Browser% 20Feedback).\n\n\nKui soovid õpetust tulevikus vaadata, saad seda alati vaadata, klõpsates info ikoonil\n\n\n\nparemal ülanurgas."]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":[""]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":[""]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":["Kanal 2 - klorofülli neeldumimse maksimum - 442 nm"]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":[""]},"Level 1":{"msgid":"Level 1","msgstr":[""]},"Level 2":{"msgid":"Level 2","msgstr":[""]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":[""]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (atmosfääri korrektsioon tehtud)"]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":[""]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":[""]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":[""]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.":{"msgid":"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.","msgstr":[""]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":[""]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":[""]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":[""]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":[""]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":[""]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":[""]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":[""]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":[""]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":[""]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":[""]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":[""]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":[""]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":[""]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":[""]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":[""]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":[""]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":[""]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":[""]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":[""]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":[""]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":[""]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":[""]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":[""]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":[""]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":[""]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Built-up probability [0 - 100 %] at 10 m spatial resolution":{"msgid":"Built-up probability [0 - 100 %] at 10 m spatial resolution","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":[""]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":[""]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":[""]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":[""]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":[""]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":[""]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":[""]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":[""]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":[""]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":[""]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":[""]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":[""]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Zoom to location":{"msgid":"Zoom to location","msgstr":[""]},"Remove layer":{"msgid":"Remove layer","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Help":{"msgid":"Help","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cancel":{"msgid":"Cancel","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"More information":{"msgid":"More information","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)":{"msgid":"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"4x4 cubic convolution":{"msgid":"4x4 cubic convolution","msgstr":[""]},"nearest neighbour":{"msgid":"nearest neighbour","msgstr":[""]},"proprietary MTF kernel":{"msgid":"proprietary MTF kernel","msgstr":[""]},"Resampling kernel":{"msgid":"Resampling kernel","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"User Instances":{"msgid":"User Instances","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Shading":{"msgid":"Shading","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Eye height":{"msgid":"Eye height","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Lae KML/KMZ, GPX või GEOJSON/JSON fail, et luua huvipakkuv piirkond. Vastavalt huvipakkuvale piirkonnale lõigatakse eksporditud pilt parajaks."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":["Lohistage KML / KMZ, GPX, GEOJSON/JSON-fail või otsi arvutist"]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Orthorectification":{"msgid":"Orthorectification","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":[""]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":[""]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":[""]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"Please select a layer":{"msgid":"Please select a layer","msgstr":[""]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":[""]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":[""]},"Recalculate":{"msgid":"Recalculate","msgstr":[""]},"Histogram":{"msgid":"Histogram","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":[""]},"Error":{"msgid":"Error","msgstr":[""]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":[""]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":[""]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":[""]},"Home":{"msgid":"Home","msgstr":[""]},"Sphere mode":{"msgid":"Sphere mode","msgstr":[""]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":[""]},"Geometries":{"msgid":"Geometries","msgstr":[""]},"Now":{"msgid":"Now","msgstr":[""]},"Terrain":{"msgid":"Terrain","msgstr":[""]},"Time":{"msgid":"Time","msgstr":[""]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":[""]},"Left button":{"msgid":"Left button","msgstr":[""]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":[""]},"Right button":{"msgid":"Right button","msgstr":[""]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":[""]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":[""]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":[""]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":[""]},"Arrow keys":{"msgid":"Arrow keys","msgstr":[""]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":[""]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":[""]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":[""]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":[""]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":[""]},"Map navigation":{"msgid":"Map navigation","msgstr":[""]},"Pan console":{"msgid":"Pan console","msgstr":[""]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":[""]},"Camera console":{"msgid":"Camera console","msgstr":[""]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":[""]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":[""]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":["Modifitseeritud antotsüaniini peegeldusindeks"]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":[""]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":["# Loomulike värvide komposiit\n\nSatelliidil olevad andurid võivad Maad kujutada elektromagnetilise spektri eri piirkondades. Iga piirkonda nimetatakse kanaliks. Satelliidil Sentinel-2 on 13 kanalit. Loomulike värvide komposiit kasutab nähtava spektri punast, rohelist ja sinist vastavates punase, rohelise ja sinise värvi kanalites. Tulemuseks on Maa kujutis loomulikes värvides, mida on inimestel lihtne mõista.\n\n\n\nRohkem infot [siin](https://custom-scripts.sentinel-hub.com/sentinel-2/composites/) ja [siin](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy)."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":["# Linnaalade pseudovärvidega komposiit\n\nSeda komposiiti kasutatakse linnastunud piirkondade selgemaks visualiseerimiseks. Taimestik antakse edasi rohelistes toonides, linnastunud alad kuvatakse valge, halli või lillana. Muld, liiv ja mineraalne materjal kuvatakse erinevate värvidega. Lumi ja jää kuvatakse tumesinisena ning vesi musta või sinisena. Üleujutatud alad kuvatakse mustjassinisena. Komposiit on kasulik metsatulekahjude ja aktiivsete vulkaanide avastamiseks, kuna need on esitatud punastes ja kollastes toonides.\n\\\n\n\nRohkem infot [siin](https://eos.com/false-color/) ja [siin.](https://eos.com/false-color/)"]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":[""]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":[""]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":[""]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":[""]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":[""]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).":{"msgid":"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).","msgstr":[""]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":[""]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":[""]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":[""]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":[""]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":[""]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":[""]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":[""]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"Could not generate timelapse animation file. Try using lower resolution or fewer frames.":{"msgid":"Could not generate timelapse animation file. Try using lower resolution or fewer frames.","msgstr":[""]},"Width":{"msgid":"Width","msgstr":[""]},"Height":{"msgid":"Height","msgstr":[""]},"Format":{"msgid":"Format","msgstr":[""]},"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.":{"msgid":"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.","msgstr":[""]}}}}
\ No newline at end of file
diff --git a/src/translations/pl.po b/src/translations/pl.po
index 16b6f084..6bec4fc3 100644
--- a/src/translations/pl.po
+++ b/src/translations/pl.po
@@ -7004,4 +7004,82 @@ msgid ""
msgstr ""
msgid "Currently only collections on services.sentinel-hub are supported."
+msgstr ""
+
+msgid "Built-up probability [0 - 100 %] at 10 m spatial resolution"
+msgstr ""
+
+msgid ""
+"# The Global Human Settlement Layer GHS-BUILT-S2 \n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"The Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up "
+"areas (expressed as probabilities from 0 to 100 %) at 10 m spatial "
+"resolution. It was derived from a Sentinel-2 global image composite for the "
+"reference year 2018 using Convolutional Neural Networks.\n"
+"\n"
+"This script visualises the built-up probabilities stretched to 0-255.\n"
+"\n"
+"For more information about the layer, visit [this "
+"website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-"
+"human-settlement-layer-ghs-built-s2/)."
+msgstr ""
+
+msgid ""
+"The **Global Human Settlement** (GHS) framework produces global maps of "
+"built-up areas, population density and settlements to monitor human "
+"presence on Earth over time.\n"
+"\n"
+"**Coverage**: Global coverage with longitude from 180°W to 180°E and "
+"latitude from 72°N to 56°S\n"
+"\n"
+"**Data Availability**: Reference year 2018\n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Knowledge of population distribution and density has a "
+"number of applications, including disaster risk management or the study and "
+"management of urbanisation processes, not only but also in relation to the "
+"challenges of climate change and environmental degradation."
+msgstr ""
+
+msgid "Resampling kernel"
+msgstr ""
+
+msgid ""
+"Select the resampling kernel to use: \n"
+"- 4x4 cubic convolution (CC), \n"
+"- nearest neighbour (NN), \n"
+"- or the proprietary MTF kernel (MTF)"
+msgstr ""
+
+msgid "4x4 cubic convolution"
+msgstr ""
+
+msgid "nearest neighbour"
+msgstr ""
+
+msgid "proprietary MTF kernel"
+msgstr ""
+
+msgid "Width"
+msgstr ""
+
+msgid "Height"
+msgstr ""
+
+msgid "Format"
+msgstr ""
+
+msgid ""
+"For optimisation reasons MPEG4 output format will always be used for "
+"generating a timelapse with transition \"fade\", even when GIF is selected."
+msgstr ""
+
+msgid ""
+"Could not generate timelapse animation file. Try using lower resolution or "
+"fewer frames."
msgstr ""
\ No newline at end of file
diff --git a/src/translations/pl.po.json b/src/translations/pl.po.json
index ffc4df90..f3cb88df 100644
--- a/src/translations/pl.po.json
+++ b/src/translations/pl.po.json
@@ -1 +1 @@
-{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","plural-forms":"nplurals=3; plural=(n==1 ? 0 : n%10>=2 && n%10<=4 && (n%100<12 || n%100>14) ? 1 : 2);","language":"pl","mime-version":"1.0","content-transfer-encoding":"8bit","project-id-version":"","pot-creation-date":"","po-revision-date":"","last-translator":"","language-team":"","x-generator":"Poedit 2.4.1"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nPlural-Forms: nplurals=3; plural=(n==1 ? 0 : n%10>=2 && n%10<=4 && (n%100<12 || n%100>14) ? 1 : 2);\nLanguage: pl\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\nProject-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLast-Translator: \nLanguage-Team: \nx-generator: Poedit 2.4.1\n"]},"Education":{"msgid":"Education","msgstr":["Edukacja"]},"Normal":{"msgid":"Normal","msgstr":["Normalny"]},"Close":{"msgid":"Close","msgstr":["Zamknij"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Zamknij i nie pokazuj więcej"]},"Previous":{"msgid":"Previous","msgstr":["Wstecz"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Zakończ instruktaż"]},"Next":{"msgid":"Next","msgstr":["Dalej"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Kontynuuj z samouczkiem"]},"Don't show again":{"msgid":"Don't show again","msgstr":["Nie pokazuj więcej"]},"Show info":{"msgid":"Show info","msgstr":["Pokaż informacje"]},"Discover":{"msgid":"Discover","msgstr":["Odkrywaj"]},"Visualize":{"msgid":"Visualize","msgstr":["Zobrazuj"]},"Compare":{"msgid":"Compare","msgstr":["Porównaj"]},"Pins":{"msgid":"Pins","msgstr":["Pinezki"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Wystąpił błąd podczas pobierania obrazów:"]},"No tile found":{"msgid":"No tile found","msgstr":["Nie znaleziono kafelka"]},"Dataset":{"msgid":"Dataset","msgstr":["Zbiór danych"]},"Show":{"msgid":"Show","msgstr":["Pokaż"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Pokaż efekty i opcje zaawansowane"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Pokaż wizualizację"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Dodaj do Pinezek"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Dodaj do porównania"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Powiększ do kafelka"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Ukryj warstwę"]},"Show layer":{"msgid":"Show layer","msgstr":["Pokaż warstwę"]},"Share":{"msgid":"Share","msgstr":["Udostępnij"]},"Custom":{"msgid":"Custom","msgstr":["Indywidualne dostosowanie"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Indywidualnie dostosuj wizualizację"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Powiększ, aby wyświetlić dane"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Darmowa rejestracja"]},"for all features":{"msgid":"for all features","msgstr":["na wszystkie funkcje"]},"Powered by":{"msgid":"Powered by","msgstr":["Dostarczone przez"]},"with contributions by":{"msgid":"with contributions by","msgstr":["we współpracy z"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["Wybierz źródło/a danych!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["Nieprawidłowy zakres czasu!"]},"No results found":{"msgid":"No results found","msgstr":["Brak wyników"]},"Theme":{"msgid":"Theme","msgstr":["Motyw"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Zarządzaj instancjami konfiguracji"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Zaloguj się, aby skorzystać z niestandardowych instancji konfiguracji."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["Błąd podczas pobierania dodatkowych danych!"]},"Search":{"msgid":"Search","msgstr":["Wyszukaj"]},"Highlights":{"msgid":"Highlights","msgstr":["Najważniejsze informacje"]},"Data sources":{"msgid":"Data sources","msgstr":["Źródła danych"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Wybierz motyw"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Zakres czasu [UTC]"]},"Date":{"msgid":"Date","msgstr":["Data"]},"Hide description":{"msgid":"Hide description","msgstr":["Ukryj opis"]},"Show description":{"msgid":"Show description","msgstr":["Wyświetl opis"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Ten motyw nie ma żadnych wyróżnień"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Na podstawie: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["1 dzień (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["5 dni (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["10 dni (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["O3 (Ozon)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (dwutlenek azotu)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (dwutlenek siarki)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (tlenek węgla)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (formaldehyd)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (metan)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER AI (Indeks Aerozolu)"]},"Cloud":{"msgid":"Cloud","msgstr":["Chmura"]},"Other":{"msgid":"Other","msgstr":["Inne"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Maks. zachmurzenie"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Zaawansowane wyszukiwanie"]},"Data location":{"msgid":"Data location","msgstr":["Lokalizacja danych"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Wybierz przynajmniej jedną lokalizację!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Tryb zbierania danych"]},"Polarization":{"msgid":"Polarization","msgstr":["Polaryzacja"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Wybierz co najmniej jeden tryb zbierania danych!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Wybierz przynajmniej jedną polaryzację!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Kierunek orbity"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Wybierz co najmniej jeden kierunek orbity!"]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["**Instrument do Badań Kolorów Oceanów i Lądu (OLCI)** na pokładzie Sentinel-3 to spektrometr, który \nmierzy promieniowanie słoneczne odbijane przez Ziemię i monitoruje ocean, środowisko \ni klimat.Dostarcza widzialnych obrazów z większą częstotliwością niż Sentinel-2, ale w niższej rozdzielczości obejmując\n większą ilości fal. Instrument Sentinel-3 OLCI kontynuuje pomiary przeprowadzone wcześniej przez instrument MERIS na pokładzie Envisat, którego misja dobiegła końca.\n\n**Rozdzielczość przestrzenna:** 300 m (oznacza to, że widoczne są tylko obiekty większe niż 300 m).\n\n**Czas rewizyty:** Maksymalnie 2 dni na rewizytę tego samego obszaru przy użyciu obydwu satelitów.\n\n**Dostępność danych:** Od maja 2016 r.\n\n**Typowe zastosowanie:** Topografia powierzchni, obserwacja i monitorowanie kolorów powierzchni oceanów i lądów."]},"Copied":{"msgid":"Copied","msgstr":["Skopiowano"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["Skopiuj do schowka"]},"Data source name":{"msgid":"Data source name","msgstr":["Nazwa źródła danych"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Czas pomiaru"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Zachmurzenie"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Wysokość słońca"]},"MGRS location":{"msgid":"MGRS location","msgstr":["Lokalizacja MGRS"]},"AWS path":{"msgid":"AWS path","msgstr":["Ścieżka AWS"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["Ścieżka EO Cloud"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["Ścieżka CreoDIAS"]},"SciHub link":{"msgid":"SciHub link","msgstr":["Link do SciHub"]},"Back to search":{"msgid":"Back to search","msgstr":["Powrót do wyszukiwania"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Wyświetlanie wyniku $ { this.state.results.length }","Wyświetlanie wyników $ { this.state.results.length }",""]},"Load more":{"msgid":"Load more","msgstr":["Załaduj więcej"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Ładowanie większej liczby wyników ..."]},"Results":{"msgid":"Results","msgstr":["Wyniki"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Wyświetlanie wyniku $ { this.state.selectedTiles.length }.","Wyświetlanie wyników $ { this.state.selectedTiles.length }.",""]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Edytuj opis pinezki"]},"Reject changes":{"msgid":"Reject changes","msgstr":["Odrzuć zmiany"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Potwierdź zmiany"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Zmień nazwę pinezki"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Usuń pinezkę"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Przybliż do lokalizacji z pinezką"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Szer./Dł."]},"Zoom":{"msgid":"Zoom","msgstr":["Powiększenie"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["Za chwilę dodasz pinezkę(pinezki) $ { N_PINS } do swojej kolekcji pinezek. Czy chcesz kontynuować?"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["OSTRZEŻENIE: Zamierzasz usunąć pinezkę. Czy chcesz kontynuować?"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":["OSTRZEŻENIE: Za chwilę usuniesz wszystkie pinezki. Czy chcesz kontynuować?"]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["Brak pinezek. Przejdź do zakładki Wizualizuj, aby zapisać pinezkę lub załadować plik JSON z zapisanymi pinezkami."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Pamiętaj, że pinezki zostaną zapisane tylko po zalogowaniu się. W przeciwnym razie pinezki zostaną utracone po zamknięciu aplikacji."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Odznacz wszystko"]},"Select all":{"msgid":"Select all","msgstr":["Zaznacz wszystko"]},"No pins.":{"msgid":"No pins.","msgstr":["Brak pinezek."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Utwórz link (wybrano pinezkę $ { selectedPins.length })","Utwórz link (wybrane pinezki $ { selectedPins.length })",""]},"File type not supported":{"msgid":"File type not supported","msgstr":["Nieobsługiwany rodzaj pliku"]},"not supported":{"msgid":"not supported","msgstr":["nieobsługiwany"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["Nie znaleziono żadnych pinezek."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Błąd parsowania pliku:"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":["Prześlij plik JSON z zapisanymi pinezkami."]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["Upuść plik JSON lub przeszukaj swój komputer"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Zachowaj istniejące pinezki"]},"Share pins":{"msgid":"Share pins","msgstr":["Udostępnij pinezki"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Stwórz historię z pinezek"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Eksportuj pinezki do komputera"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Importuj pinezki z zapisanego pliku"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Usuń wszystkie pinezki"]},"Story":{"msgid":"Story","msgstr":["Historia"]},"Export":{"msgid":"Export","msgstr":["Eksportuj"]},"Import":{"msgid":"Import","msgstr":["Importuj"]},"Clear":{"msgid":"Clear","msgstr":["Wyczyść"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Udostępnij link do pinezek"]},"Creating link...":{"msgid":"Creating link...","msgstr":["Tworzę link..."]},"OK":{"msgid":"OK","msgstr":["OK"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Aktualizuję zbiór pinezek."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Wystąpił problem z trwałą aktualizacją zbioru pinezek: ${ updatePinsError }."]},"Opacity":{"msgid":"Opacity","msgstr":["Nieprzezroczystość"]},"Split position":{"msgid":"Split position","msgstr":["Pozycja podziału"]},"split":{"msgid":"split","msgstr":["podziel"]},"opacity":{"msgid":"opacity","msgstr":["nieprzezroczystość"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["Brak warstw do porównania."]},"Remove all":{"msgid":"Remove all","msgstr":["Usuń wszystko"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Dodaj wszystkie pinezki"]},"Split":{"msgid":"Split","msgstr":["Podziel"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Wystąpił problem z pobieraniem Twoich przykładów"]},"Download":{"msgid":"Download","msgstr":["Pobierz"]},"Go to Place":{"msgid":"Go to Place","msgstr":["Przejdź do wybranego Miejsca"]},"Labels":{"msgid":"Labels","msgstr":["Oznaczenia"]},"Borders":{"msgid":"Borders","msgstr":["Granice"]},"Roads":{"msgid":"Roads","msgstr":["Drogi"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Powiększ"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Zmniejsz"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["Informacje o aplikacji EO Browser"]},"Contact us":{"msgid":"Contact us","msgstr":["Skontaktuj się z nami"]},"Get data":{"msgid":"Get data","msgstr":["Pobierz dane"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Aby skorzystać z tej funkcji, musisz się zalogować."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Wybierz warstwę."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["Pobieranie obrazu w trybie porównania nie jest możliwe."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["To źródło danych nie jest obsługiwane."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":["Informacje Statystyczne / Wykres Serwisowy z Informacjami o Funkcjach"]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Informacje Statystyczne / Wykres Serwisowy z Informacjami o Funkcjach - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["wybierz warstwę"]},"not available for ":{"msgid":"not available for ","msgstr":["niedostępne dla "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["niedostępne dla \"${ props.presetLayerName }\" (warstwa z wartością nie jest skonfigurowana)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Najpierw wyszukaj dane."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Utwórz animację poklatkową"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Zaznacz interesujące cię miejsce"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Wyśrodkuj mapę na funkcji"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Usuń geometrię"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Obszar zainteresowania"]},"Select mode":{"msgid":"Select mode","msgstr":["Wybierz tryb"]},"Mode:":{"msgid":"Mode:","msgstr":["Tryb:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Usuń pomiar"]},"km":{"msgid":"km","msgstr":["km"]},"m":{"msgid":"m","msgstr":["m"]},"Gain":{"msgid":"Gain","msgstr":["Wzmocnienie"]},"Gamma":{"msgid":"Gamma","msgstr":["Gamma"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Min. jakość danych"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Próbkowanie w górę"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Próbkowanie w dół"]},"Reset all":{"msgid":"Reset all","msgstr":["Zresetuj wszystko"]},"filter by months":{"msgid":"filter by months","msgstr":["filtruj według miesięcy"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Skopiuj geometrię do schowka"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Anuluj zmiany."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Zaznacz obszar zainteresowania"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Najmniejsze zachmurzenie"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Użyj dodatkowych zbiorów danych (zaawansowane)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Kolejność mozaikowania"]},"Most recent":{"msgid":"Most recent","msgstr":["Od najnowszych"]},"Least recent":{"msgid":"Least recent","msgstr":["Od najstarszych"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Dostosuj przedział czasu"]},"Back":{"msgid":"Back","msgstr":["Powrót"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Błąd podczas ładowania skryptu. Sprawdź swój adres URL."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Usuń zaznaczenie opcji Załaduj skrypt z adresu URL, aby edytować kod"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Załaduj skrypt z adresu URL"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["Wprowadź adres URL do swojego skryptu"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Wczytano skrypt."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Dozwolone są tylko domeny HTTPS."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Załaduj skrypt do edytora kodu"]},"Refresh":{"msgid":"Refresh","msgstr":["Odśwież"]},"orbit":{"msgid":"orbit","msgstr":["orbita"]},"day":{"msgid":"day","msgstr":["dzień"]},"week":{"msgid":"week","msgstr":["tydzień"]},"month":{"msgid":"month","msgstr":["miesiąc"]},"year":{"msgid":"year","msgstr":["rok"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Wybierz 1 obraz na:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Film poklatkowy"]},"Select All":{"msgid":"Select All","msgstr":["Zaznacz Wszystko"]},"Speed:":{"msgid":"Speed:","msgstr":["Szybkość:"]},"frames / s":{"msgid":"frames / s","msgstr":["klatka/i"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Przygotowywanie…"]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Nie można pobrać plików:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Nie można pobrać przez canvas"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["Nie można skompresować plików:"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Wystąpił problem podczas pobierania obrazu"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Błąd podczas pobierania obrazu: adres URL jest pusty!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Podczas pobierania obrazu wystąpił błąd:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["Nie można załadować obrazu z obiektu BLOB"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Przeciągnij pasma na pola RGB."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Przeciągnij pasma do równania wskaźnikowego"]},"Index ":{"msgid":"Index ","msgstr":["Wskaźnik "]},"Threshold":{"msgid":"Threshold","msgstr":["Wartość graniczna"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Usuń selektor kolorów"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Wybierz selektor kolorów"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Naciśnij, aby wstawić znacznik"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Naciśnij, aby wstawić pierwszy wierzchołek"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Naciśnij, aby kontynuować rysowanie"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Naciśnij pierwszy znacznik, aby zakończyć"]},"Show captions":{"msgid":"Show captions","msgstr":["Pokaż napisy"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Pokaż tytuł slajdu"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Dodaj nakładki mapy"]},"Show legend":{"msgid":"Show legend","msgstr":["Pokaż legendę"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["W obecnym polu widzenia nie znaleziono żadnych pinezek."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Niektóre pinezki (${ N_PINS_OUTSIDE_BOUNDS }) są ignorowane, ponieważ nie znajdują się w wybranym obszarze."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["Aby utworzyć historię pinezki, przejdź do żądanej pozycji na mapie.\n\nWszystkie pinezki w bieżącym polu widzenia zostaną użyte do stworzenia historii, reszta zostanie zignorowana."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["Plik będzie miał dołączone logo."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["Pasmo dataMask zostanie włączone do pobranych pasm nieprzetworzonych jako drugie pasmo."]},"Show logo":{"msgid":"Show logo","msgstr":["Wyświetl logo"]},"Image format":{"msgid":"Image format","msgstr":["Format obrazu"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Rozdzielczość zdjęcia"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Układ współrzędnych"]},"Layers":{"msgid":"Layers","msgstr":["Warstwy"]},"Visualized":{"msgid":"Visualized","msgstr":["Zwizualizowane"]},"Raw":{"msgid":"Raw","msgstr":["Nieprzetworzone"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["Do obrazu zostaną dodane warstwy nakładające mapy (etykiety miejsc, ulice i granice polityczne)."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Eksportowane obrazy będą zawierać źródło danych oraz datę, skalę powiększenia i oznaczenie marki"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Dodaj krótki opis do eksportowanego obrazu"]},"Description":{"msgid":"Description","msgstr":["Opis"]},"Image format:":{"msgid":"Image format:","msgstr":["Format obrazu:"]},"Basic":{"msgid":"Basic","msgstr":["Podstawowy"]},"Analytical":{"msgid":"Analytical","msgstr":["Analityczny"]},"High-res print":{"msgid":"High-res print","msgstr":["Druk w wysokiej rozdzielczości"]},"Download image":{"msgid":"Download image","msgstr":["Pobierz obraz"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["Wystąpił błąd podczas pobierania niektórych obrazów:"]},"min/px":{"msgid":"min/px","msgstr":["min /px"]},"sec/px":{"msgid":"sec/px","msgstr":["sek/px"]},"Resolution":{"msgid":"Resolution","msgstr":["Rozdzielczość"]},"lat.":{"msgid":"lat.","msgstr":["szer."]},"deg/px":{"msgid":"deg/px","msgstr":["st./px"]},"long.":{"msgid":"long.","msgstr":["dł."]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Przewidywana rozdzielczość: ${ formattedResolution } m/px"]},"Image download":{"msgid":"Image download","msgstr":["Pobieranie obrazu"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Szerokość obrazu [cale]:"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Wysokość obrazu [cale]:"]},"DPI:":{"msgid":"DPI:","msgstr":["DPI:"]},"5 years":{"msgid":"5 years","msgstr":["5 lat"]},"2 years":{"msgid":"2 years","msgstr":["2 lata"]},"1 year":{"msgid":"1 year","msgstr":["1 rok"]},"6 months":{"msgid":"6 months","msgstr":["6 miesięcy"]},"3 months":{"msgid":"3 months","msgstr":["3 miesiące"]},"1 month":{"msgid":"1 month","msgstr":["1 miesiąc"]},"Retry":{"msgid":"Retry","msgstr":["Spróbuj ponownie"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Wczytuję, proszę czekać"]},"mean":{"msgid":"mean","msgstr":["średnia"]},"median":{"msgid":"median","msgstr":["mediana"]},"st. dev.":{"msgid":"st. dev.","msgstr":["odch. stand."]},"min / max":{"msgid":"min / max","msgstr":["min/maks"]},"Export CSV":{"msgid":"Export CSV","msgstr":["Eksportuj CSV"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Czas trwania:"]},"Date:":{"msgid":"Date:","msgstr":["Data:"]},"Single date":{"msgid":"Single date","msgstr":["Pojedyncza data"]},"Timespan":{"msgid":"Timespan","msgstr":["Zakres czasu"]},"hh":{"msgid":"hh","msgstr":["hh"]},"mm":{"msgid":"mm","msgstr":["mm"]},"From:":{"msgid":"From:","msgstr":["Od:"]},"Until:":{"msgid":"Until:","msgstr":["Do:"]},"Apply":{"msgid":"Apply","msgstr":["Zastosuj"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Udostępnij na Facebooku"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Udostępnij na Twitterze"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Sprawdź to "]},"Logout":{"msgid":"Logout","msgstr":["Wyloguj się"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["Zaloguj się, aby odblokować zaawansowane funkcje, takie jak film poklatkowy, pobieranie analityczne, własne konfiguracje i inne."]},"Login":{"msgid":"Login","msgstr":["Zaloguj się"]},"Default":{"msgid":"Default","msgstr":["Domyślnie"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Monitorowanie Ziemi z Kosmosu"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Rolnictwo"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Atmosfera i Zanieczyszczenie Powietrza"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Wykrywanie Zmian w Czasie"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Powodzie i Susze"]},"Geology":{"msgid":"Geology","msgstr":["Geologia"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Oceany i Zbiorniki Wodne"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Śnieg i Lodowce"]},"Urban":{"msgid":"Urban","msgstr":["Obszar Miejski"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Roślinność i Leśnictwo"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Wulkany"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Pożary lasu"]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":["# Witamy w przeglądarce EO Browser!\n\nPełne archiwum satelitów Sentinel-1, Sentinel-2, Sentinel-3, Sentinel5P, archiwum ESA \ndla satelitów Landsat 5, 7 i 8, globalny zasięg obserwacji Landsat 8, Envisat Meris, \nMODIS, produkty Proba-V oraz GIBS w jednym miejscu.\n\n[Strona z prezentacją przeglądarki EO Browser](https://www.sentinel-hub.com/explore/eobrowser/) \n[Poradnik użytkownika EO Browser](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":["#### Krótki przegląd funkcji przeglądarki EO Browser\n\nPrzeglądarka EO Browser to połączenie pełnego archiwum satelitów Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, archiwum ESA (Europejskiej Agencji Kosmicznej) dla satelitów Landsat 5, 7 i 8, globalny zasięg obserwacji Landsat 8, Envisat Meris oraz produkty MODIS, Proba-V i GIBS w jednym miejscu i umożliwia przeglądanie i porównywanie obrazów z tych źródeł w pełnej rozdzielczości. Po prostu przejdź do obszaru, który Cię interesuje, wybierz źródła danych, zakres czasowy i stopień zachmurzenia oraz sprawdź dane wynikowe.\n\nMożesz kontynuować instruktaż, klikając przycisk \\„Dalej\\” lub możesz go zamknąć. Klikając na ikonę informacji w prawym górnym rogu, zawsze możesz wznowić instruktaż na wypadek, gdybyś zamknął go przez pomyłkę lub chciał coś wypróbować."]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":["W zakładce ** Odkrywaj** możesz:\n\n- Wybrać **Motyw.**\n- **Szukaj** dla wyszukania danych.\n- Wyświetlić motyw **Wyróżnienia.**\n\nLista rozwijana **Motyw** zawiera różne wstępnie skonfigurowane motywy, a także własne niestandardowo skonfigurowane instancje, jeśli jesteś Zalogowany. Aby utworzyć instancję, kliknij\nikonę ustawień i zaloguj się przy użyciu tych samych poświadczeń, które były używane w EO Browser.\n\nW zakładce **Szukaj** możesz ustawić kryteria wyszukiwania:\n - Wybrać satelity, z których chcesz otrzymywać dane, zaznaczając pola wyboru.\n- W stosownych przypadkach można wybrać dodatkowe opcje, na przykład poziom zachmurzenia za pomocą suwaka.\n - Wybierz przedział czasu przez wpisanie daty lub wybierz datę z kalendarza.\n\nMożesz przeczytać objaśnienia dotyczące satelitów, klikając ikonę znaku zapytania\n obok nazwy danych źródła.\n\nPo kliknięciu Search, otrzymasz listę wyników. Każdy wynik jest przedstawiany \nz obrazem podglądu i odpowiednimi danymi sprecyzowanymi dla źródła danych. W przypadku niektórych źródeł danych, ikona linku jest również widoczna dla każdego wyniku.\nJej kliknięcie powoduje wyświetlenie bezpośrednich linków do surowego obrazu wynikowego w EO Cloud lub SciHub. Kliknięcie na przyciskVisualize otworzy zakładkę **Visualize** dla wybranego wyniku.\n\nPod zakładką **Wyróżnij** znajdziesz wstępnie wybrane interesujące lokalizacje powiązane z wybranym motywem."]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":["W zakładce Wizualizuj można wybrać różne wstępnie zainstalowane lub niestandardowe kombinacje pasm widmowych aby zwizualizować dane dla wybranego wyniku.\n\nNiektóre z częstych opcji:\n- **Prawdziwy Kolor** - Wizualna interpretacja pokrycia terenu.\n- **Fałszywy Kolor** - Wizualna interpretacja roślinności.\n- **NDVI** - Indeks roślinności.\n- **Indeks wilgoci** - Indeks wilgoci\n- **SWIR** - Indeks podczerwienie krótkofalowej.\n- **NDWI** - Znormalizowany Różnicowy Indeks Wody.\n- **NDSI** - Znormalizowany Róznicowy Indeks Śniegu.\n\nWiększość wizualizacji otrzymuje opis i legendę, którą można obejrzeć poprzez kliknięcie na ikonę\nrozszerzenia .\n \nDla większości źródeł danych dostępna jest opcja **Skryptu Niestandardowego**. Kliknij na nią aby wybrać niestandardowe\nkombinacje pasm, kombinacje indeksów lub napisać swój własny skrypt klasyfikacyjny do wizualizacji danych. Można również\nużyć skryptów niestandardowych, które są przechowywane gdzieś indziej, na dysku Google, GitHub lub w naszymr [Repozytorium skryptów niestandardowych](https://custom-scripts.sentinel-hub.com/). \nWklejURL skryptu do pola tekstowego w panelu zaawansowanej edycji skryptów i klinknij Odśwież.\n \nMożna zmienić dane bezpośrednio w zakładce Zwizualizuj bez wracania do zakładki **Odkryj**. Wpiszlub wybierz ją z kalendarza .\n\nPowyżej wizualizacji znajduje się linia dodatkowych narzędzi. Zauważże ich dostępność zależy od źródła danych.\n- **Przypnij pinezkę** aby zapisać ją w aplikacji do wykorzystania w przyszłości - poprzez kliknięcie na ikonę pinezki .\n- Wybierz **opcje zaawansowane** takie jak metoda próbkowania lub dodaj różne **efekty** takie jak kontrast (wzmocnienie) i luminancja (gamma) - poprzez kliknięcie na ikonę suwaka efektów .\n- Dodajwarstwę do zakladki **Porównaj** dla późniejszych porównań - poprzez kliknięcie na ikonę porównywania .\n- **Przybliż** do środka kafelka - poprzez kliknięcie na celownik.\n- Abyprzeciągnąć **widoczność warstwy** - poprzez kliknięcie na ikonę widoczności .\n- **Udostępnij** swoje wizualizacje na mediach społecznościowych - poprzez kliknięcie na ikonę udostępniania ."]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":["W zakładce **Porównaj** można znaleźć wszystkie wizualizacje, które dodano poprzez opcję **Porównaj**. \n\nDostępne są dwa tryby:\n - **Nieprzejrzystość** (Przeciągnij suwak nieprzejrzystości w lewo lub w prawo, aby wykonać płynne przejście między porównywanymi obrazami)\n - **Rozdzielanie** (Przeciągnij suwak rozdzielania w lewo lub w prawo, aby ustawić granicę między porównywanymi obrazami)\n\nMożna dodać wszystkie wybrane obrazy do panelu porównania za pomocą **Dodaj wszystkie pinezki** lub usunąć wszystkie wizualizacje\nz zakładki **Porównaj** za pomocą przycisku **Usuń wszystko**."]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":["Zakładka **Pinezki** zawiera przypięte (ulubione/zapisane) elementy. Przypięte elementy zawierają informacje o\nlokalizacji, źródle danych i jego określonej warstwie, poziomie powiększenia oraz czasie.\n\nDla każdego wybranego obrazu istnieje kilka opcji interakcji z pojedynczym obrazem:\n\n- Zmień **kolejność** - klikając ikonę przenoszenia\n\n \n \n \nw górnym lewym rogu wybranego obrazu, przeciągając ten obraz w górę lub w dół listy.\n- **Zmień nazwę** - klikając ikonę ołówka obok nazwy wybranego obrazu.\n- Dodaj do zakładki **Porównaj** - klikając ikonę porównania \n- Naciśnij **description** (opis) - klikając ikonę rozwijania .\n- **Remove** (usuń) - klikającikonę usuwania .\n- **Przybliż** do lokalizacji wybranego obrazu - klikając opcję Lat/Lon.\n\nW lini nad powyższymiwybranymi obrazami znajdują się różne opcje, które mają zastosowanie do wszystkich wybranych obrazów:\n - Stwórz własną historię na podstawie wybranych obrazów - klikając **Opowieść**.\n- Udostępnij swoje wybrane obrazy innym za pomocą linku - klikając **Udostępnij**.\n- Eksportuj wybrane obrazy jako plik JSON - klikając na **Eksportuj**.\n- Importuj wybrane obrazy z pliku JSON - klikając na **Import**.\n- Usuńwszystkie pinezki - klikając na **Wyczyść**."]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":["Wyszukaj lokalizację, przewijając mapę za pomocą myszy lub wprowadź lokalizację w polu\nwyszukiwania."]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":["Tutaj możesz wybrać, która warstwa bazowa i nakładki (drogi, granice, etykiety) mają być wyświetlane na mapie."]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":["Tutaj możesz przełączać się między trybem **normalny** i **edukacja**. Tryb **edukacja** oferuje nieco uproszczoną wersję aplikacji.\nDostęp można również uzyskać bezpośrednio poprzez [dedykowany adres URL] (https://apps.sentinel-hub.com/eo-browser-education/)."]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":["Możesz wyświetlić instruktaż w dowolnym momencie, klikając na tę ikonę informacji\n\n\n."]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":["To narzędzie umożliwia narysowanie wielokąta na mapie i wyświetlenie jego rozmiaru.\n\nWszystkie warstwy, które zwracają pojedynczą wartość (np. NDVI, Wskaźnik wilgoci, NDWI, itd.) obsługują przeglądanie\nindeksu dla wybranego obszaru na przetrzeni czasu. Kliknięcie na ikonę wykresu spowoduje\nwyświetlenie wykresów. Można usunąć wielokąt, klikając ikonę usuwania .\n\nMożna również przesłać plik KML/KMZ, GPX lub GEOJSON/JSON z geometrią wielokąta.\n\nIkona dwóch arkuszy umożliwia skopiowanie współrzędnych wielokąta jako GEOJSON, celownik \n wyśrodkowuje mapę na narysowany wielokąt.\n\nEksportowane obrazy zostaną przycięte do obszaru zainteresowania podczas ich pobierania dla celów analitycznych."]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":["Za pomocą tego narzędzia można zaznaczyć punkt na mapie, a\n\n także wyświetlić dane statystyczne dla niektórych warstw, klikając na ikonę wykresu\n. \nMożna usunąć znak, klikając na ikonę usuwania.\n"]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":["Za pomocą tego narzędzia można mierzyć odległości i powierzchnie na mapie.\n\n Każde kliknięcie myszą tworzy nowy punkt na ścieżce. Aby zatrzymać dodawanie punktów, naciśnij klawisz Esc
\nlub kliknij dwukrotnie mapę. \nMożesz usunąć pomiar, klikając ikonę usuwania."]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":["Za pomocą tego narzędzia można pobrać obraz zawierający wizualizację danych dla wyświetlanej lokalizacji. Można wybrać\nwyświetlenie podpisów lub dodać swój własny opis. \nPoprzez włączenie tryby Analitycznego, można wybierać pomiędzy różnymi formatami obrazu, rozdzielczością obrazu oraz\nukładami współrzędnych. Możesz również wybrać wiele warstw i pobrać je jako plik .zip
.\n\nKliknij przycisk pobierania\nDownload\na rozpocznie się pobieranie twoich obrazów. Proces pobierania może zająć kilka sekund, w zależności od wybranej\nrozdzielczości i liczby wybranych warstw.\n\nPrzed pobraniem, można zdefiniować obszar zainteresowania (AOI) klikając na ikonę narzędzia wyboru\nObszaru. Twoje dane zostaną przycięte tak, aby pasowały do tego obszaru."]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":["Za pomocą tego narzędzia można utworzyć animację poklatkową wizualizowanej warstwy i wyświetlanej lokalizacji.\n\nNajpierw wybierz zakres czasu. Można dodatkowo zdefiniować wyniki wyszukiwania, filtrując je według miesięcy\n(pole wyboru: filtrowanie według miesięcy) i/lub wybierając jeden obraz na określony okres (orbita, dzień, tydzień, miesiąc,\nrok).\n\nNastępnie naciśnij Search (Wyszukaj) i wybierz swoje obrazy.\nMożna wybrać wszystkie zaznaczając pole wyboru lub filtrując obrazy według poziomu zachmurzenia, przesuwając suwak. Można też wybierać obrazy pojedynczo,\nprzewijając listę i zaznaczając je.Za pomocą pola wyboru **Borders** (obramowanie) można włączyć/wyłączyć obramowanie obrazu.\n\nMożna wyświetlić podgląd poklatkowy, naciskając przycisk odtwarzania na dole. Możesz także ustawić prędkość\n(klatki na sekundę).\n\nGdy wynik jest zadowalający, kliknij na przycisk pobierania, a animacja poklatkowa zostanie\n pobrana jako plik .gif
."]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":["Dotarliśmy do końca instruktażu. Jeśli masz inne pytania, nie wahaj się nam ich zadać na [forum] (https://forum.sentinel-hub.com/)\nlub skontaktować się z nami [przez e-mail](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nJeśli będziesz chciał zobaczyć instruktaż w przyszłości, zawsze możesz go wyświetlić, klikając ikonę informacji\n\n\n\nw prawym górnym rogu."]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":["#### Szybki przegląd właściwości przeglądarki EO Browser\n\nJeśli masz mały ekran, przejdź [tutaj] (https://www.sentinel-hub.com/explore/eobrowser/user-guide/) aby zobaczyć przewodnik użytkownika.\n\nZawsze możesz powrócić do tej informacji poprzez\n\n\n\nkliknięcie na ikonę informacji w prawym górnym rogu. \n\n#### Inne zasoby\n- [Strona główna przeglądarki EO Browser] (https://www.sentinel-hub.com/explore/eobrowser/)\n- [Aktualizacje przeglądarki EO Browser z lata 2018 - wideo] (https://www.youtube.com/embed/m3pron0C0kE)"]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":["Co to jest przeglądarka EO Browser?"]},"User Account":{"msgid":"User Account","msgstr":["Konto Użytkownika"]},"Discover Tab":{"msgid":"Discover Tab","msgstr":["Zakładka Odkrywaj"]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":["Zakładka Wizualizuj"]},"Compare Tab":{"msgid":"Compare Tab","msgstr":["Zakładka Porównaj"]},"Pins Tab":{"msgid":"Pins Tab","msgstr":["Zakładka Wybrane Obszary"]},"Search Places":{"msgid":"Search Places","msgstr":["Wyszukaj Miejsca"]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":["Warstwy i Nakładki"]},"Education Mode":{"msgid":"Education Mode","msgstr":["Tryb Edukacji"]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":["Informacje i Instruktaż"]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":["Zaznacz Rejon Zainteresowania"]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":["Zaznacz Interesujące Miejsce"]},"Measure Distances":{"msgid":"Measure Distances","msgstr":["Zmierz Odległości"]},"Download Image":{"msgid":"Download Image","msgstr":["Pobierz Obraz"]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":["Utwórz Animację Poklatkową"]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":["Miłego Przeglądania!"]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":["Witaj w Przeglądarce EO!"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":["Pasmo 1 – Substancja żółta (rozpuszczona materia organiczna o zółtym zabarwieniu) i pigmenty detrytyczne – 412,5 nm"]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":["Pasmo 2 – Maksimum absorpcji chlorofilu – 442 nm"]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":["Pasmo 3 – Chlorofil i inne pigmenty – 490 nm"]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":["Pasmo 4 – Zawieszony osad, czerwone pływy – 510 nm"]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":["Pasmo 5 – Minimum absorpcji chlorofilu – 560 nm"]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":["Pasmo 6 — Zawieszony osad – 620 nm"]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":["Pasmo 7 - Absorpcja chlorofilu i wzorzec fluorescencji – 665 nm"]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":["Pasmo 8 – Szczytowa fluorescencja chlorofilu – 681 nm"]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":["Pasmo 9 – Wzorzec fluorescencji, usuwanie wpływów atmosfery – 709 nm"]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":["Pasmo 10 – Roślinność, chmura – 753 nm"]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":["Pasmo 11 – odgałęzione pasmo absorpcji O2 R – 761 nm"]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":["Pasmo 12 – Usuwanie wpływów atmosfery – 779 nm"]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":["Pasmo 13 – Roślinność, wzorzec pary wodnej – 865 nm"]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":["Pasmo 14 – Usuwanie wpływów atmosfery – 885 nm"]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":["Pasmo 15 - Para wodna, ląd - 900 nm"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Pasmo 1 – Niebieskie – 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Pasmo 2 – Zielone – 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Pasmo 3 – Czerwone – 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Pasmo 4 – NIR – 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Pasmo 5 – SWIR-1 – 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Pasmo 7 – SWIR-2 – 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Pasmo 8 – Panchromatyczne – 520-900 nm"]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":["Pasmo 1 – Przybrzeżne/Aerozolowe – 433-453 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Pasmo 2 – Niebieskie – 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Pasmo 3 – Zielone – 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Pasmo 4 – Czerwone – 630-680 nm"]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":["Pasmo 5 – NIR – 845-885 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Pasmo 6 – SWIR-1 – 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Pasmo 7 – SWIR-2 – 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Pasmo 8 – Panchromatyczne – 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Pasmo 9 – Cirrus – 1360-1390 nm"]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":["Landsat 5 (Archiwum ESA - Europejskiej Agencji Kosmicznej)"]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":["Landsat 7 (Archiwum ESA)"]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":["Landsat 8 (Archiwum ESA)"]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":["Landsat 8 (Archiwum USGS)"]},"Red band":{"msgid":"Red band","msgstr":["Pasmo czerwone"]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":["841 - 876 nm (NIR)"]},"Blue band":{"msgid":"Blue band","msgstr":["Pasmo niebieskie"]},"Green band":{"msgid":"Green band","msgstr":["Pasmo zielone"]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":["1230 - 1250 nm"]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":["1628 - 1652 nm"]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":["2105 - 2155 nm"]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":["Pasmo 1 – Aerozol przybrzeżny – 443 nm"]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":["Pasmo 2 – Niebieskie – 490 nm"]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":["Pasmo 3 – Zielone – 560 nm"]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":["Pasmo 4 – Czerwone – 665 nm"]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":["Pasmo 5 – Roślinność Czerwona Krawędź – 705 nm"]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":["Pasmo 6 - Roślinność Czerwona Krawędź - 740 nm"]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":["Pasmo 7 - Roślinność Czerwona Krawędź - 783 nm"]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":["Pasmo 8 - NIR - 842 nm"]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":["Pasmo 9 - Para wodna - 945 nm"]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":["Pasmo 10 - SWIR - Cirrus - 1375 nm"]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":["Pasmo 11 - SWIR - 1610 nm"]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":["Pasmo 12 - SWIR - 2190 nm"]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":["Pasmo 8A - Roślinność Czerwona Krawędź - 865 nm"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":["Pasmo 1 - Usuwanie wpływu aerozolu, ulepszone postrzeganie składników wody - 400 nm"]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":["Pasmo 2 - Substancja żółta (rozpuszczona materia organiczna o zółtym zabarwieniu) i pigmenty detrytyczne (zmętnienie) -412 nm"]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":["Pasmo 3 - Maks. absorpcja Chl, biogeochemia, roślinność - 442,5 nm"]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":["Pasmo 4 - Wysoki Chl, inne pigmenty - 490 nm"]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":["Pasmo 5 - Chl, osad, zmętnienie, czerwone pływy - 510 nm"]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":["Pasmo 6 - Wzorzec chlorofilu (minimum Chl) - 560 nm"]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":["Pasmo 7 - Obiążenie osadem - 620 nm"]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":["Pasmo 8 - Chl (2 Chl maks. abs.), osad, żółta substancja/roślinność - 665 nm"]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":["Pasmo 9 - Dla ulepszonego postrzegania fluorescencji i uzyskania lepszej podstawy dla misji SMILE razem z pasmami 8 (665 nm) i 10 (681.25 nm) - 673,75 nm "]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":["Pasmo 10 - Szczytowa fluorescencja Chl, czerwona krawędź - 681,25 nm"]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":["Pasmo 11 - linia bazowa fluorescencji Chl, przejście z czerwonej krawędzi - 708,75 nm"]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":["Pasmo 12 - Absorpcja O2/chmury, roślinność - 753,75 nm"]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":["Pasmo 13 - Pasmo absorpcji O2/usuwanie wpływu aerozolu - 761,25 nm"]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":["Pasmo 14 - Usuwanie wpływu atmosfery - 764,375 nm"]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":["Pasmo 15 - O2A używane do pomiaru ciśnienia szczytowego chmur, fluorescencji nad lądem - 767,5 nm"]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":["Pasmo 16 - Korekta atmosferyczna/korekta aerozolu - 778,75 nm"]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":["Pasmo 17 - korekta atmosf./korekta aerozolu, chmury, korejestracja pikseli - 865 nm"]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":["Pasmo 18 - Wzorcowe pasmo absorpcji pary wodnej. Pasmo wzorcowe wspólne z instrumentem SLSTR. Monitorowanie roślinności - 885 nm"]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":["Pasmo 19 - Absorpcja pary wodnej / monitorowanie roślinności (maks. współczynnik odbicia) - 900 nm"]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":["Pasmo 20 - Absorpcja pary wodnej, korekta atmosf./ korekta aerozolu - 940 nm"]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":["Pasmo 21 - korekta atmosf./korekta aerozolu - 1020 nm"]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":["Pasmo F1 - Emisja podczerwieni termalnej z pożaru - Aktywny pożar - 3742,00 nm"]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":["Pasmo F2 - Emisja podczerwieni termalnej z pożaru - Aktywny pożar - 10854,00 nm"]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":["Pasmo S1 - VNIR (promieniowanie widzialne i bliskie podczerwieni) - Wykrywanie wpływu chmur, monitorowanie roślinności, aerozol - 554,27 nm"]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":["Pasmo S2 - VNIR - NDVI, monitorowanie roślinności, aerozol - 659,47 nm"]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":["Pasmo S3 - VNIR - NDVI, oznaczanie chmur, korejestracja pikseli - 868,00 nm"]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":["Pasmo S4 - SWIR - Wykrywanie chmur typu cirrus nad lądem - 1374,80 nm"]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":["Pasmo S5 - SWIR - zanik chmur, lód, śnieg, monitorowanie roślinności - 1613,40 nm"]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":["Pasmo S6 - SWIR - Stan roślinności i zanik chmur - 2255,70 nm"]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":["Pasmo S7 - Podczerwień Termalna Otoczenia - SST, LST, aktywny pożar - 3742,00 nm"]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":["Pasmo S8 - Podczerwień Termalna Otoczenia - SST, LST, aktywny pożar - 10854,00 nm"]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":["Pasmo S9 - Podczerwień Termalna Otoczenia - SST, LST - 12022,50 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Współczynnik odbicia"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Temperatura jasnościowa"]},"Credits:":{"msgid":"Credits:","msgstr":["Podziękowania:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["**GIBS** (Global Imagery Browse Services - Usługi Przeglądania Obrazów Ziemi) zapewnia szybki dostęp do ponad 600 obrazów satelitarnych\n, które obejmują wszystkie części świata. Większość obrazów jest dostępna w ciągu kilku godzin po przejściu\n satelity, niektóre produkty obejmują prawie 30 lat."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["Seria satelitów **Landsat** należących do NASA/ U.S. Geological Survey jest podobna do Sentinel-2 (wychwytują fale widzialne i podczerwone) i\ndodatkowo mogą wychwytywać podczerwień termiczną (Landsat 8). Seria Landsat ma długą historię dostarczania obrazów, obejmującą prawie pięć dekad.\n Ta platforma zapewnia dostęp do obrazów zarejestrowanych przez satelity Landsat 5, 7 i 8.\n\n**Spatial resolution:** (rozdzielność przestrzenna) 15m, 30m i 100m próbkowana ponownie na 30m, w zależności od długości fali (to oznacza, że tylko szczegóły większe niż 10m i 30m mogą być dostrzeżone).Więcej informacji [tutaj] (https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** (Czas rewizyty) Potrzebne jest maksymalnie 8 dni, aby ponownie odwiedzić ten sam obszar używając dwóch satelitów operacyjnych Landsat 7 i Landsat 8.\n\n**Data availability:** (Dostępność danych) Europa i Afryka Północna od 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), od 2013 do chwili obecnej (Landsat 8) z archiwum ESA.Globalne archiwum US Geological Survey (USGS) od kwietnia 2013 r. do dnia dzisiejszego (tylko Landsat 8).\n\n**Common usage:** (Typowe zastosowanie) Monitorowanie roślinności, użytkowania gruntów, mapy pokrycia terenu, monitorowanie zmian itp."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["**MODIS** firmy NASA – (Spektroradiometr Obrazujący Średniej Rozdzielczości) pozyskuje dane w celu\nlepszego zrozumienia procesów globalnych zachodzących na lądzie. Przeglądarka EOdostarcza danych niezbędnych do\nobserwacji terenu (pasma 1-7).\n\n**Rozdzielczość przestrzenna:** 250 m (pasma 1-2), 500 m (pasma 3-7), 1000 m (pasma 8-36).\n\n**Czas rewizyty:** Zasięg globalny w 1 – 2 dni z użyciem satelitów Aqua i Terra.\n\n** Dostępność danych:** Od stycznia 2013 r.\n\n**Typowe użycie:** Monitorowanie lądu, chmur, barwy oceanu w skali globalnej."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["Satelita **Proba-V** to mały satelita zaprojektowany do mapowania pokrycia terenu i wzrostu roślinności\n na całym świecie co dwa dni.Przeglądarka EO Browser dostarcza produktów pochodnych, które minimalizują pokrywę\n chmur dzięki połączeniu pomiarów bezchmurnych w okresie 1 dnia (S1), 5 dni (S5) i 10 dni (S10).\n\n**Spatial resolution:** (rozdzielczość przestrzenna) 100 m dla S1 i S5, 333 m dla S1 i S10, 1000 m dla S1 i S10.\n\n**Revisit time:** (okres rewizyty) 1 dzień dla szerokości geograficznych 35-75°N i 35-56°S, 2 dni dla szerokości geograficznych pomiędzy 35°N\n i 35°S.\n\n** Data availability:** (dostępność danych) Od października 2013 r.\n\n**Typowe zastosowanie:** M Obserwacja pokrycia terenu, wzrost roślinności, ocena wpływu klimatu,\n zarządzanie zasobami wodnymi, monitorowanie rolnictwa i szacowanie bezpieczeństwa żywnościowego,\nmonitorowanie śródlądowych zasobów wodnych oraz śledzenie stałego rozprzestrzeniania się pustyń i wylesiania."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":[""]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Dane poziomu 2A to dane wysokiej jakości, w których wyklucza się wpływ atmosfery na światło odbijane od powierzchni Ziemi i docierające do czujnika.Dane są dostępne na całym świecie od marca 2017 r.\n\n Więcej informacji o korekcji atmosferycznej [tutaj] (http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction)."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Dane z Poziomu 1C to dane o jakości wystarczającej dla większości badań, w których wykonano wszystkie korekty obrazu z wyjątkiem korekcji atmosferycznej. Dane są dostępne na całym świecie od czerwca 2015 r."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["Głównym celem misji **Sentinel-3** jest pomiar topografii powierzchni morza, temperatury powierzchni morza i lądu oraz barwy powierzchni oceanu i lądu. Sentinel-3 ma na pokładzie cztery różne instrumenty.Dane zebrane przez Ocean and Land Colour Instrument (OLCI) (Instrument do Badań Kolorów Oceanów i Lądu) oraz Sea and Land Surface Temperature Instrument (SLSTR) (Instrument do Badań Temperatury Morza i Powierzchni Lądu) są dostępne na tej platformie.\n\n** Data Availability:** (dostępność danych) Od maja 2016 r."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["Instrument **Sea and Land Surface Temperature (SLSTR)** na pokładzie Sentinel-3 mierzy globalną i regionalną \ntemperaturę powierzchni mórz i lądów.SLSTR obejmuje długości fal widzialnych, podczerwieni krótkofalowej i podczerwieni średniej widma elektromagnetycznego. \n\n**Rozdzielczość przestrzenna:** 500 m dla fal widzialnych, bliskich i krótkich fal podczerwonych oraz 1 km dla podczerwieni średniej (to znaczy, że widoczne są tylko szczegóły \nwiększe niż odpowiednio 500 m i 1 km).\n\n**Okres rewizyty:** Maksymalnie 1 dzień na ponowne odwiedzenie tego samego obszaru przy użyciu obydwu satelitów.\n\n** Dostępność danych:** Od maja 2016 r.\n\n**Typowe zastosowanie:** Monitorowanie zmian klimatu, monitorowanie roślinności, aktywne wykrywanie pożarów, monitorowanie temperatury powierzchni lądu i morza."]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":["W oparciu o kombinację pasm 4, 3, 2"]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":["W oparciu o kombinację pasm (B04-B03)/(B04 + B03)"]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":["W oparciu o kombinację pasm 5, 4, 3"]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":["W oparciu o pasma prawdziwych kolorów 4, 3, 2 i pasmo PAN 8"]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":["W oparciu o kombinację pasm (B05-B04)/(B05 + B04)"]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":["VV - liniowa gamma0 - ortorektyfikowana"]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":["VV - liniowa gamma0 - nieortorektyfikowana"]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":["VH - liniowa gamma0 - ortorektyfikowana"]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":["W oparciu o kombinację pasm 3, 2, 1"]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":["VH - liniowa gamma 0 - nieortorektyfikowana"]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":["Obrazowanie w kolorze poprzez mapowanie pasm wejściowych. Wartość [RGB] = [VV, 2 VH, VV / VH / 100,0] - liniowa gamma0 - ortorektyfikacja"]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":["VV - decybel gamma0 [-20,0] - ortorektyfikowana"]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":["VH - decybel gamma0 [-20,0] - ortorektyfikowana"]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":["Zwraca kompozyt składający się z (VH, VV, VH-VV)"]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":["HH - liniowa gamma0 - ortorektyfikowana"]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":["HV - liniowa gamma0 - ortorektyfikowana"]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":["Obrazowanie w kolorze poprzez mapowanie pasm wejściowych. Wartość [RGB] = [VV, 2 VH, VV / VH / 100,0] - liniowa gamma0 - ortorektyfikacja"]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":["HH - decybel gamma0 [-20,0] - ortorektyfikowana"]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":["HV - decybel gamma0 [-20,0] - ortorektyfikowana"]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":["HH - liniowa gamma0 - nieortorektyfikowana"]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":["W oparciu o pasma 4, 3, 2"]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":["W oparciu o pasma 8, 4, 3"]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":["W oparciu o pasma 12, 11, 4"]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":["W oparciu o kombinację pasm (B8 - B4)/(B8 + B4)"]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":["W oparciu o kombinację pasm (B8A - B11)/(B8A + B11)"]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":["W oparciu o pasma 12, 8A, 4"]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":["W oparciu o kombinacje pasm (B3 - B8)/(B3 + B8)"]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":["W oparciu o kombinacje pasm (B3 - B11)/(B3 + B11); wartości powyżej 0,42 są uważane za śnieżne"]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":["Klasyfikacja danych Sentinel2 jako wynik algorytmu klasyfikacji Scene agencji ESA."]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":["Wskaźnik aerozolu UV od 380 do 340 nm"]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":["W oparciu o kombinację pasm (B3 - B11)/(B3 + B11)"]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":["OLCI Wskaźnik Chlorofilu Lądowego, w oparciu o kombinację pasm (B12 - B11)/(B11 - B10)"]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":["Wskaźnik aerozolu UV od 388 i 354 nm"]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":["Uśredniony w kolumnie stosunek mieszania metanu w suchym powietrzu"]},"Cloud base height":{"msgid":"Cloud base height","msgstr":["Wysokość podstawy chmur"]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":["Ciśnienie w podstawie chmury"]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":["Efektywna frakcja chmur radiometrycznych"]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":["Grubość optyczna chmury"]},"Cloud top height":{"msgid":"Cloud top height","msgstr":["Wysokość wierzchołka chmury"]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":["Ciśnienie wierzchołka chmury"]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":["Całkowita zawartość Dwutlenku Węgla w kolumnie powietrza"]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":["Zawartość formaldehydu w pionowej kolumnie troposferycznej"]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":["Zawartość Dwutlenku Azotu w kolumnie troposferycznej"]},"Ozone total column":{"msgid":"Ozone total column","msgstr":["Całkowita zawartość ozonu w kolumnie powietrza"]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":["Całkowita zawartość Dwutlenku Siarki w kolumnie powietrza"]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":["W oparciu o pasma 2, 1, 4"]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":["W oparciu o kombinację pasm (B02 - B01)/(B02 + B01)"]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":["W oparciu o kombinację pasm (B02 - B05)/(B02 + B05)"]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":["W oparciu o kombinację pasm (B06 - B07)/(B06 + B07)"]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":["W oparciu o pasma 1, 4, 3"]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":["W oparciu o kombinację pasm 7, 5, 3"]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":["(B09 - B08)/(B09 + B08)"]},"B09 / B08":{"msgid":"B09 / B08","msgstr":["B09/B08"]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":["W oparicu o kombinację pasm 13, 5, 2"]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":["W oparciu o pasma 13, 4, 1"]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":["W oparciu o pasma 13, 5, 2"]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":["W oparciu o kombinację pasm (B13-B07)/(B13 + B07)"]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":["Wskaźnik Chlorofilu Lądowego"]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V 10-dniowa Synteza\nSzczyt Korony (z korektą atmosferyczną)\nrozdzielczość czasowa: 10 dni\n Rozdzielczość: 333 M (rozmiar piksela)"]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V codzienna Synteza\n Górna Warstwa Atmosfery\n rozdzielczość czasowa: 1 dzień\n Rozdzielczość: 333 M (rozmiar piksela)"]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-dniowa Synteza\n Górna Warstaw Atmosfery\n rozdzielczość czasowa: 5 dni Rozdzielczość\n: 100 M (rozmiar piksela)"]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V Synteza 10-dniowa\n Szczyt Korony (z korektą atmosferyczną)\nrozdzielczość czasowa: 1 dzień\n Rozdzielczość: 333 M (rozmiar piksela)"]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":["OrbitTracks_Aqua_Descending"]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-dniowa Synteza\n Szczyt Korony (z korektą atmosferyczną)\nrozdzielczość czasowa: 5 dni\n Rozdzielczość: 333 M (rozmiar piksela)"]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":["OrbitTracks_Aqua_Ascending"]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":["OrbitTracks_Aura_Ascending"]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":["OrbitTracks_Aura_Descending"]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":["OrbitTracks_CloudSat_Ascending"]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":["OrbitTracks_Calipso_Ascending"]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":["OrbitTracks_Calipso_Descending"]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":["OrbitTracks_CloudSat_Descending"]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":["OrbitTracks_CYGNSS_Ascending"]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":["OrbitTracks_CYGNSS_Descending"]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":["OrbitTracks_GCOM-C_Ascending"]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":["OrbitTracks_GCOM-C_Descending"]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":["OrbitTracks_GCOM-W1_Ascending"]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":["OrbitTracks_GCOM-W1_Descending"]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":["OrbitTracks_GOSAT-2_Ascending"]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":["OrbitTracks_GOSAT-2_Descending"]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":["OrbitTracks_GOSAT_Ascending"]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":["OrbitTracks_GOSAT_Descending"]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":["OrbitTracks_GPM_Ascending"]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":["OrbitTracks_GPM_Descending"]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":["OrbitTracks_ICESAT-2_Ascending"]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":["OrbitTracks_ICESAT-2_Descending"]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":["OrbitTracks_ISS_Ascending"]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":["OrbitTracks_ISS_Descending"]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":["OrbitTracks_Landsat-7_Ascending"]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":["OrbitTracks_Landsat-7_Descending"]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":["OrbitTracks_Landsat-8_Ascending"]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":["OrbitTracks_METOP-A_Ascending"]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":["OrbitTracks_METOP-B_Descending"]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":["OrbitTracks_Landsat-8_Descending"]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":["OrbitTracks_METOP-C_Ascending"]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":["OrbitTracks_METOP-C_Descending"]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":["OrbitTracks_NOAA-20_Ascending"]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":["OrbitTracks_NOAA-20_Descending"]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":["OrbitTracks_METOP-B_Ascending"]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":["OrbitTracks_OCO-2_Ascending"]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":["OrbitTracks_OCO-2_Descending"]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":["OrbitTracks_SAOCOM1-A_Ascending"]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":["OrbitTracks_SAOCOM1-A_Descending"]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":["OrbitTracks_Sentinel-1A_Ascending"]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":["OrbitTracks_Sentinel-1B_Ascending"]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":["OrbitTracks_Sentinel-1A_Descending"]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":["OrbitTracks_METOP-A_Descending"]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":["OrbitTracks_Sentinel-1B_Descending"]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":["OrbitTracks_Sentinel-2A_Ascending"]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":["OrbitTracks_Sentinel-2A_Descending"]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":["OrbitTracks_Sentinel-2B_Ascending"]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":["OrbitTracks_Sentinel-2B_Descending"]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":["OrbitTracks_Sentinel-5P_Ascending"]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":["OrbitTracks_Sentinel-5P_Descending"]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":["OrbitTracks_SMAP_Ascending"]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":["OrbitTracks_SMAP_Descending"]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":["OrbitTracks_Suomi_NPP_Descending"]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":["OrbitTracks_Suomi_NPP_Ascending"]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":["OrbitTracks_Terra_Descending"]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":["OrbitTracks_Terra_Ascending"]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":["W oparciu o pasma 4, 3, 2 wzmocnione pasmami 12 i 11."]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":["W oparciu o pasma B07, B06, B04"]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":["W oparciu o kombinacje pasm (B8 - B4)/(B8 + B4)"]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":["W oparciu o pasmo termalne 10"]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":["W oparciu o pasma B12, B11, B8A"]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":["W oparciu o kombinację pasm (B08 - B12)/(B08 + B12)"]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":["Ulepszona wizualizacja naturalnych barw"]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":["W oparciu o kombinację pasm 8, 6, 4"]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":["Ulepszony Indeks Wegetacji"]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":["W oparciu o kombinację pasm: BSI, B08, B11"]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":["Sklasyfikowany Znormalizowany Różnicowy Wskaźnik Wilgotności (NDMI) dla irygacji"]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":["W oparciu o pasma B11, B08, B02"]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":["Barwa Fałszywa 13, 5, 2"]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":["W oparciu o kombinację pasm (B13-B07)/(B13 + B07)"]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":["W oparciu o pasma 12, 8, 2"]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":["W oparciu o pasma 4, 3, 2"]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":["W oparciu o pasma 8, 4, 3"]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":["W oparciu o pasma 12, 8, 2"]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":["W oparciu o pasma 8, 11, 12"]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":["Sedymentacja wody i zawartość chlorofilu"]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":["W oparciu o pasma 12, 8A, 4"]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":["W oparciu o znormalizowany różnicowy wskaźnik śniegu (NDSI)"]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":["W oparciu o kombinację pasm 11, 8, 2"]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":["W oparciu o pasma B12, B11, B04"]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":["W oparciu o pasma 4, 3, 2"]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":["Wskaźnik roślinności odpornej na warunki atmosferyczne"]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":["Indeks Wegetacji Dostosowany do Charakterystyki Gleby"]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":["Zmodyfikowany Wskaźnik Reflektancji Atrocyjaniny"]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":["# Pasma emisji podczerwieni termalnej z pożaru \n\nInstrument do Badań Temperatury Morza i Powierzchni Lądu (SLSTR) satelity Sentinel-3 ma dwa dedykowane kanały (F1 i F2), których celem jest wykrywanie temperatury powierzchni lądu (LST). Kanał F2 z centralną długością fali 10854 nm mierzy w podczerwieni średniej lub TIR.Jest to bardzo przydatne podczas monitorowania pożaru i wysokiej temperatury z rozdzielczością 1 km.\n\n\n\nWięcej informacji [tutaj]: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature"]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":["# Metan (CH4)\n\n\n\nMetan jest, obok dwutlenku węgla, najważniejszym czynnikiem przyczyniającym się do antropogenicznie (spowodowanego działalnością człowieka) wzmaganego efektu cieplarnianego. Pomiary są podawane w częściach na miliard (ppb) z rozdzielczością przestrzenną 7 km x 3,5 km.\n\n\n\nWięcej informacji [tutaj] (http://www.tropomi.eu/data-products/methane)"]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":["# Formaldehyd (HCHO)\n\n\n\nDługoterminowe obserwacje satelitarne formaldehydu troposferycznego (HCHO) są niezbędne do wspierania badań jakości powietrza i badań chemiczno-klimatycznych w skali regionalnej i globalnej. Sezonowe i międzyroczne wahania rozmieszczenia formaldehydu są związane głównie ze zmianami temperatury i przypadkami pożarów,ale także ze zmianami w działalności antropogenicznej (spowodowanej przez człowieka). Jego żywotność wynosi kilku godzin;stężenia HCHO w warstwie granicznej mogą być bezpośrednio związane z uwalnianiem krótkotrwałych węglowodorów, których w większości przypadków nie można obserwować bezpośrednio z kosmosu.Pomiary są podawane w molach na metr kwadratowy (mol/m^2).\n\n\n\n Więcej informacji [tutaj] (http://www.tropomi.eu/data-products/formaldehyde)"]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":["# Dwutlenek siarki (SO2)\n\n\n\nDwutlenek siarki przedostaje się do atmosfery ziemskiej zarówno poprzez procesy naturalne, jak i antropogeniczne (wytworzone przez człowieka).Odgrywa rolę w chemii w skali lokalnej i globalnej, a jego wpływ waha się od krótkotrwałego zanieczyszczenia po zmiany klimatyczne.Tylko około 30% emitowanego SO2 pochodzi ze źródeł naturalnych; większość ma pochodzenie antropogeniczne.Instrument Sentinel-5P/TROPOMI pobiera próbki powierzchni Ziemi przy okresie rewizyty wynoszącym jeden dzień z rozdzielczością przestrzenną 3,5 x 7 km, która umożliwia obrazowanie drobnych szczegółów, w tym wykrywanie mniejszych smug SO2.Pomiary są podawane w molach na metr kwadratowy (mol/m ^ 2).\n\n\n\nWięcej informacji [tutaj] (http://www.tropomi.eu/data-products/sulphur-dioxide)"]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":["# Ozon (O3)\n\n\n\nOzon ma kluczowe znaczenie dla równowagi atmosfery ziemskiej. W stratosferze warstwa ozonowa chroni biosferę przed niebezpiecznym słonecznym promieniowaniem ultrafioletowym. W troposferze działa jako skuteczny środek oczyszczający, ale w wysokim stężeniu staje się szkodliwy dla zdrowia ludzi, zwierząt i roślinności.Ozon jest również istotnym czynnikiem przyczyniającym się do obecnych zmian klimatycznych. Od czasu odkrycia dziury ozonowej w Antarktydzie w latach 80. i późniejszego Protokołu Montrealskiego regulującego produkcję substancji awierających chlor, zubożających warstwę ozonową,ozon jest przedmiotem rutynowego monitoringu z ziemi i z kosmosu. Pomiary są podawane w molach na metr kwadratowy (mol/m^2)\n\n\n\n Więcej informacji [tutaj] (http://www.tropomi.eu/data-products/total-ozone-column)"]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":["# Dwutlenek azotu (NO2)\n\n\n\nDwutlenek azotu (NO2) i tlenek azotu (NO) razem nazywane są zwykle tlenkami azotu. Są ważnymi gazami śladowymi w atmosferze ziemskiej, obecnymi zarówno w troposferze, jak i stratosferze.Dostają się do atmosfery w wyniku działalności antropogenicznej (w szczególności spalania paliw kopalnych i biomasy) oraz procesów naturalnych (np. procesów mikrobiologicznych w glebie, pożarów, wyładowań atmosferycznych).Pomiary są podane w molach na metr kwadratowy (mol/m^2).\n\n\n\n Więcej informacji [tutaj] (http://www.tropomi.eu/data-products/nitrogen-dioxide)"]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":["# Tlenek węgla (CO)\n\n\n\nTlenek węgla (CO) jest ważnym atmosferycznym gazem śladowym. Na niektórych obszarach miejskich jest głównym składnikiem zanieczyszczeń atmosfery. Główne źródła CO to spalanie paliw kopalnych, spalanie biomasy oraz atmosferyczne utlenianie metanu i innych węglowodorów.Całkowita zawartość tlenku węgla w kolumnie jest mierzona w molach na metr kwadratowy (mol/m^2).\n\n\n\nWięcej informacji [tutaj] (http://www.tropomi.eu/data-products/carbon-monoxide)"]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":["# Indeks Aerozoli\n\nIndeks Aerozoli (AI) jest wskaźnikiem jakościowym wskazującym na obecność podwyższonych warstw aerozoli w atmosferze. Może być używany do wykrywania obecności aerozoli pochłaniających promieniowanie UV, takich jak pył pustynny i chmury popiołu wulkanicznego. Wartości dodatnie (od jasnoniebieskiego do czerwonego) wskazują na obecność aerozolu pochłaniającego promieniowanie UV.Wskaźnik ten jest obliczany dla dwóch par długości fal: 340/380 Nm i 354/388 nm.\n\nWięcej informacji [tutaj] (https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)"]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":["# Wysokość podstawy chmury\n\nWysokość podstawy chmury mierzona w metrach (m)."]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":["# Ciśnienie w podstawie chmury\n\nCiśnienie mierzone przy podstawie chmury w Pascalach (Pa)."]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":["# Grubość optyczna chmury\n\nGrubość chmury jest kluczowym parametrem określającym właściwości optyczne chmury. Jest to miara tego, ile światła słonecznego przechodzi przez chmurę, docierając do powierzchni Ziemi. Im większa grubość optyczna chmury, tym więcej światła słonecznego chmura rozprasza i odbija. Kolor ciemny niebieski wskazuje miejsca, w których występująniskie wartości grubości optycznej chmury, a czerwony wskazuje większą grubość optyczną chmury."]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":["# Wysokość podstawy chmury\n\nWysokość podstawy chmury mierzona w metrach (m)."]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":["# Ciśnienie w podstawie chmury \n\nCiśnienie mierzone przy podstawie chmury w Pascalach (Pa)."]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":["# Znormalizowany Różnicowy Wskaźnik Wegetacji (NDVI)\n\nZnormalizowany Różnicowy Wskaźnik Wegetacji jest prostym, ale efektywnym wskaźnikiem służącym do ilościowego określania roślinności zielonej. Wskazuje on stan zdrowia wegetacji w oparciu o sposób, w jaki rośliny odbijają światło na określonych długościach fal. Zakres wartości NDVI wynosi od -1 do 1. Ujemne wartości NDVI (wartości zbliżone do -1) odpowiadają wodzie.Wartości zbliżone do zera (od -0,1 do 0,1) odpowiadają na ogół jałowym obszarom skał, piasku lub śniegu.Niskie, dodatnie wartości oznaczają krzewy i łąki (ok. 0,2 do 0,4), natomiast wysokie wartości wskazują na umiarkowane i tropikalne lasy deszczowe (wartości zbliżone do 1).\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) oraz [tutaj.] (https://eos.com/ndvi/)"]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":["# Ulepszony Indeks Wegetacji (EVI)\n\nUlepszony Indeks wegetacji (EVI) jest „zoptymalizowanym” wskaźnikiem wegetacji, ponieważ uwzględnia korekcję sygnałów pochodzących z podłoża gleby oraz wpływy atmosferyczne. Jest on bardzo przydatny na obszarach gęsto zalesionych. Zakres wartości dla EVI wynosi od -1 do 1, przy zdrowej roślinności na ogół około 0,20 do 0,80.\n\n\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) i [tutaj.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)"]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Indeks Wegetacji Odpornej na Warunki Atmosferyczne (ARVI)\n\nIndex Wegetacji Odpornej na Warunki Atmosferyczne (ARVI) to wskaźnik roślinności, który minimalizuje skutki rozproszenia atmosferycznego. Jest on najbardziej przydatny w regionach o dużej zawartości aerozolu atmosferycznego (mgła, pył, dym, zanieczyszczenie powietrza). Zakres ARVI wynosi od -1 do 1, gdzie roślinność zielona zazwyczaj mieści się w przedziale od 0,20 do 0,80.\n\n\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) i [tutaj.] (https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Indeks Wegetacji Dostosowanej do Gleby (SAVI)\n\nIndeks Wegetacji Dostosowanej do Gleby jest podobny do Znormalizowanego Różnicowego Indeksu Wegetacji (NDVI), ale jest stosowany na obszarach o niskim poziomie pokrycia wegetacyjnego (<40%). Indeks ten jest techniką transformacji, która minimalizuje wpływ jasności gleby na widmowe indeksy roślinności obejmujące długości fal czerwieni i bliskiej podczerwieni (NIR). Wskaźnik jest pomocny podczas analizy młodych upraw, suchych regionów o rzadkiej roślinności i odsłoniętych powierzchni gleby.\n\n\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) i [tutaj.] (https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":["# Zmodyfikowany Indeks Odbicia Antocyjanów (mARI/ARI2)\n\nAntocyjany to pigmenty powszechne w roślinach wyższych, nadające im czerwone, niebieskie i fioletowe zabarwienie. Dostarczają one cennych informacji o stanie fizjologicznym roślin, gdyż są uważane za wskaźniki określające różne rodzaje stresu roślin. Wskaźnik odbicia antocyjanów jest najwyższy około 550nm. Jednak te same długości fal są również odbijane przez chlorofil. Aby wyodrębnić antocyjany, odejmuje się pasmo widmowe 700nm, które odzwierciedla tylko chlorofil, a nie antocyjany.\n\nAby skorygować gęstość i grubość liści, pasmo widmowe w bliskiej podczerwieni (w zalecanych długościach fal 760-800nm), które jest związane z rozproszeniem przez listowie, jest dodawane do podstawowego indeksu ARI. Nowy indeks nosi nazwę zmodyfikowanego ARI lub mARI (również ARI2).\n\nwartości mARI dla badanych drzew w [tym oryginalnym artykule] (https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) wahały się pod względem wartości od 0 do 8.\n\n\n\n\n\nWięcej informacji [tutaj.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)"]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":["# Skrypt Zielonego Miasta\n\nSkrypt zielonego miasta ma na celu zwiększenie świadomości na temat terenów zielonych w miastach na całym świecie. Skrypt bierze pod uwagę Znormalizowany Różnicowy Indeks Wegetacji (NDVI) i rzeczywiste długości fal kolorów;oddziela obszary zabudowane od roślinnych, dzięki czemu jest przydatny do wykrywania obszarów miejskich. Obszary zabudowane są wyświetlane na szaro, a roślinność na zielono.\n\n\n\n\n\n Więcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":["# Skrypt Miejski Sklasyfikowany\n\nSkrypt Miejski Sklasyfikowany ma na celu wykrycie obszarów zabudowanych poprzez oddzielenie ich od jałowej ziemi, roślinności i wody. Obszary o dużej zawartości wilgoci są zwracane na niebiesko; obszary wskazujące obszary zabudowane są zwracane na biało; obszary porośnięte roślinnością są zwracane na zielono; wszystko inne wskazuje na jałową ziemię i jest wyświetlane w brązowych kolorach.\n\n\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)"]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":["# Skrypt Kolorów w Podczerwieni dla Terenów Miejskich\n\nTen skrypt, stworzony przez Leo Tolari, łączy wizualizację prawdziwych kolorów z długością fal bliskiej podczerwieni (NIR) i podczerwieni krótkofalowej (SWIR). Skrypt podkreśla obszary miejskie lepiej niż prawdziwy kolor, a jednocześnie wygląda naturalnie.\n\n\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)"]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":["# NDMI dla Obciążeń Wilgoci\n\nZnormalizowany Index Różnicowy Wilgotności (NDMI) dla obciążeń wilgoci, może być używany do wykrywania irygacji. Dla wszystkich wartości wskaźnika powyżej 0, znając przeznaczenie i pokrycie terenu, można określić, czy miało miejsce nawadnianie. Znając rodzaj upraw (np. uprawy cytrusów), można określić czy nawadnianie jest skuteczne, czy też nie w kluczowym okresie wegetacyjnym, a także dowiedzieć się, czy niektóre części gospodarstwa są niedostatecznie lub nadmiernie nawadniane.\n\n\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)"]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":["# Znormalizowany Różnicowy Index Wilgotności (NDMI)\n\nZnormalizowany Różnicowy Indeks Wilgotności (NDMI) jest używany do określania zawartości wody w roślinności i monitorowania suszy.Zakres wartości NDMI wynosi od -1 do 1. Ujemne wartości NDMI (wartości zbliżone do -1) odpowiadają jałowej glebie. Wartości około zera (-0,2 do 0,4) generalnie wskazują na stres wodny. Wysokie, dodatnie wartości reprezentują wysoką koronę drzew bez stresu wodnego (około 0,4 do 1).\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":["# Znormalizowany Indeks Różnicowy Wody (NDWI)\n\nZnormalizowany indeks różnicowy wody jest najbardziej odpowiedni do odwzorowania zbiorników wodnych. Wartości zbiorników wodnych są większe niż 0,5. Roślinność ma mniejsze wartości. Zabudowane elementy terenu mają wartości dodatnie od zera do 0,2.\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":["# Znormalizowany Indeks Różnicowy Wody (NDWI)\n\nZnormalizowany indeks różnicowy wody jest najbardziej odpowiedni do odwzorowania zbiorników wodnych. Wartości zbiorników wodnych są większe niż 0,5. Roślinność ma mniejsze wartości. Zabudowane elementy terenu mają wartości dodatnie od zera do 0,2."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Kompozyt fałszywego koloru\n\nKompozyt fałszywego koloru wykorzystuje co najmniej jedną niewidoczną długość fali do zobrazowania Ziemi. Bardzo popularny jest kompozyt fałszywego koloru, wykorzystujący pasma bliskiej podczerwieni, czerwieni i zieleni (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach). Kompozyt fałszywych kolorów jest najczęściej używany do oceny gęstości i zdrowia roślin, ponieważ rośliny odbijają bliską podczerwień i zielone światło, podczas gdy pochłaniają kolor czerwony. Miasta i odsłonięta gleba są szare lub brązowe, a woda wydaje się niebieska lub czarna.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) oraz [tutaj.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Kompozyt fałszywego koloru\n\nKompozyt fałszywego koloru wykorzystuje co najmniej jedną niewidoczną długość fali do zobrazowania Ziemi. Bardzo popularny jest kompozyt fałszywego koloru wykorzystujący pasma bliskiej podczerwieni, czerwieni i zieleni (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach). Kompozyt fałszywego koloru jest najczęściej używany do oceny gęstości i zdrowia roślin, ponieważ rośliny odbijają bliską podczerwień i zielone światło, podczas gdy pochłaniają kolor czerwony. Miasta i odsłonięta gleba są szare lub brązowe, a woda wydaje się niebieska lub czarna.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) oraz [tutaj.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":["# Kompozyt fałszywego koloru \n\nFałszywy kolor wykorzystuje co najmniej jedną niewidoczną długość fali do zobrazowania Ziemi. Bardzo popularny jest kompozyt fałszywego koloru wykorzystujący pasma bliskiej podczerwieni, czerwieni i zieleni (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach). Kompozyt fałszywych kolorów jest najczęściej używany do oceny gęstości i zdrowia roślin, ponieważ rośliny odbijają bliską podczerwień i zielone światło, podczas gdy pochłaniają kolor czerwony. Miasta i odsłonięta gleba są szare lub brązowe, a woda wydaje się niebieska lub czarna.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) oraz [tutaj.](https://earthobservatory.nasa.gov/features/FalseColor)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Kompozyt fałszywego koloru\n\nKompozyt fałszywego koloru wykorzystuje co najmniej jedną niewidoczną długość fali do zobrazowania Ziemi. Bardzo popularny jest kompozyt fałszywego koloru wykorzystujący pasma bliskiej podczerwieni, czerwieni i zieleni (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach). Kompozyt fałszywego koloru jest najczęściej używany do oceny gęstości i zdrowia roślin, ponieważ rośliny odbijają podczerwień i zielone światło, podczas gdy pochłaniają kolor czerwony. Miasta i odsłonięta gleba są szare lub brązowe, a woda wydaje się niebieska lub czarna.\n\n\n\nWięcej informacji [tutaj.] (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":["# Kompozyt prawdziwego koloru\n\nCzujniki przenoszone przez satelity mogą zobrazować Ziemię w różnych obszarach widma elektromagnetycznego. Każdy region widma nazywany jest pasmem. Sentinel-2 ma 13 pasm. Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego, zielonego i niebieskiego. W wyniku czego powstaje produkt o naturalnym kolorze, który odzwierciedla Ziemię tak, jak ludzie by ją zobaczyli w sposób naturalny.\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) oraz [tutaj.] (http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy)."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Kompozyt prawdziwego koloru\n\nCzujniki przenoszone przez satelity mogą zobrazować Ziemię w różnych obszarach widma elektromagnetycznego.Każdy region widma nazywany jest pasmem. Landsat 5 ma 7 pasm. Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego, zielonego i niebieskiego, w wyniku czego powstaje produkt o naturalnym kolorze, który jest odzwierciedleniem Ziemi tak, jak jest widziana w sposób naturalny ludzkim okiem.\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) i [tutaj] (https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Kompozyt prawdziwego koloru\n\n Sensory przenoszone przez satelity mogą zobrazować Ziemię w różnych obszarach widma elektromagnetycznego. Każdy region widma nazywany jest pasmem. Landsat 7 ma 8 pasm.Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego, zielonego i niebieskiego, w wyniku czego powstaje produkt o naturalnym kolorze, który jest odzwierciedleniem Ziemi tak, jak jest widziana w sposób naturalny ludzkim okiem.\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) i [tutaj] (https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Kompozyt prawdziwego koloru\n\nSensory przenoszone przez satelity mogą zobrazować Ziemię w różnych obszarach widma elektromagnetycznego. Każdy region widma nazywany jest pasmem. Landsat 8 ma 11 pasm.Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego, zielonego i niebieskiego, w wyniku czego powstaje produkt o naturalnym kolorze, który jest odzwierciedleniem Ziemi tak, jak jest widziana w sposób naturalny ludzkim okiem.\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/landsat-8/composites/) i [tutaj.] (https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Kompozyt prawdziwego koloru\n\nSensory przenoszone przez satelity mogą zobrazować Ziemię w różnych regionach widma elektromagnetycznego. Każdy region widma nazywany jest pasmem. Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego,zielonego i niebieskiego, w wyniku czego powstaje produkt o naturalnym kolorze, który jest odzwierciedleniem Ziemi tak, jak jest widziana w sposób naturalny ludzkim okiem.\n\n\n\nWięcej informacji [tutaj.] (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":["# Kompozyt prawdziwego koloru\n\nSensory przenoszone przez satelity mogą zobrazować Ziemię w różnych obszarach widma elektromagnetycznego. Każdy region widma nazywany jest pasmem.Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego,zielonego i niebieskiego, w wyniku czego powstaje produkt o naturalnym kolorze, który jest odzwierciedleniem Ziemi tak, jak jest widziana w sposób naturalny ludzkim okiem.\n\n\n\nWięcej informacji [tutaj.] (https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)"]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Wyostrzony Panchromatycznie Prawdziwy Kolor\n\nWyostrzony panchromatycznie kompozyt prawdziwego koloru jest uzyskiwany przy użyciu zwykłych danych o prawdziwych kolorach (czerwony, zielony i niebieski (RGB)) i wzmacniania ich za pomocą pasma panchromatycznego 8 lub pasma panoramicznego (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach).Obraz z pasma panoramicznego jest podobny do filmu czarno-białego: łączy światło z czerwonej, zielonej i niebieskiej części widma w jedną miarę całkowitej widzialnej reflektancji. Obrazy panchromatycznie wyostrzone posiadają cztery razy większą rozdzielczość niż zwykły kompozyt prawdziwego koloru, co znacznie zwiększa użyteczność zdjęć Landsat.\n\n\n\nWięcej informacji [tutaj] (https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) i [tutaj.] (https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":["# Miejski Kompozyt Fałszywego Koloru\n\nTen kompozyt służy do wyraźniejszej wizualizacji obszarów zurbanizowanych. Roślinność jest widoczna w odcieniach zieleni, a obszary zurbanizowane reprezentowane są przez biel, szarość lub fiolet.Gleby, piasek i minerały są ukazywane w różnorodnych kolorach. Śnieg i lód pojawiają się w kolorze ciemnoniebieskim, a woda w kolorze czarnym lub niebieskim.Obszary zalane są głęboko ciemnoniebieskie i prawie czarne.Kompozyt jest przydatny do wykrywania pożarów i kalder wulkanów, ponieważ są one ukazywane w odcieniach czerwieni i żółci.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) i [tutaj.] (https://eos.com/false-color/)"]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Miejski Kompozyt Fałszywego Koloru\n\nTen kompozyt wykorzystuje kombinację pasm w podczerwieni widzialnej i krótkofalowej (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach).Ukazuje on roślinność w odcieniach zieleni. Podczas gdy ciemniejsze odcienie zieleni wskazują na gęstszą roślinność, roślinność rzadsza ma jaśniejsze odcienie.Obszary miejskie są niebieskie, a gleby mają różne odcienie brązu.\n\n\n\nWięcej informacji [tutaj.] (https://gisgeography.com/landsat-8-bands-combinations/)"]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":["# Kompozyt Rolniczy\n\nten kompozyt wykorzystuje krótkofalową podczerwień, bliską podczerwień i pasma koloru niebieskiego do monitorowania stanu upraw (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może obrazować Ziemię w różnych pasmach). Zarówno pasma krótkofalowe, jak i pasma bliskiej podczerwieni są szczególnie skuteczne w uwydatnianiu gęstej roślinności, która w kompozycie jest ukazywana w kolorze ciemnozielonym.Uprawy są ukazuwane w kolorze żywej zieleni, a goła ziemia ma kolor magenta.\n\n\n\nWięcej informacji [tutaj.] (https://earthobservatory.nasa.gov/features/FalseColor/page5.php) i [tutaj.] (https://gisgeography.com/sentinel-2-bands-combinations/)"]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":["# Klasyfikator Śniegu\n\nAlgorytm klasyfikatora śniegu ma na celu wykrycie śniegu poprzez klasyfikację pikseli na podstawie różnych progów jasności i Znormalizowanego Indeksu Różnicowego Śniegu (NDSI).Wartości sklasyfikowane jako śnieg są przedstawiane w kolorze jasnoniebieskim. Skrypt może zbyt wysoko oszacować obszary śnieżne nad chmurami.\n\n\n\nWięcej informacji [tutaj.] (https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":["# Zobrazowanie Jakości Wody Ulyssys (UWQV)\n\nSkrypt ten ma na celu dynamiczną wizualizację stanu chlorofilu i osadów w zbiornikach wodnych, które są głównymi wskaźnikami jakości wody.Zawartość chlorofilu waha się kolorystycznie od ciemnoniebieskiego (niska zawartość chlorofilu) poprzez zielony do czerwonego (wysoka zawartość chlorofilu).Stężenia osadów mają kolor brązowy; nieprzezroczysty brąz wskazuje na wysoką zawartość osadu.\n\n\n\nWięcej informacji [tutaj.] (https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)"]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":["# Naturalne Kolory z Optymalizacją dla Uwydatnienia \n\nTen skrypt ma na celu przedstawienie Ziemi w pięknych, naturalnych barwach. Wykorzystuje on optymalizację uwydatnienia, aby uniknąć wypalenia pikseli i wyrównać ekspozycję.\n\n\n\nWięcej informacji [tutaj.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)"]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":["# Kompozyt Geology 12, 8, 2\n\n Ten kompozyt wykorzystuje pasmo podczerwieni krótkofalowej (SWIR) nr 12 do rozróżnienia różnych typów skał (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach).Każdy rodzaj skały i minerału inaczej odbija krótkofalowe światło podczerwone, pozwalając na uzyskanie odwzorowania struktury geologicznej poprzez porównanie odbitego światła SWIR. Pasmo bliskiej podczerwieni (NIR) nr 8 uwypukla roślinność, a pasmo nr 2 wykrywa wilgoć, umożliwiając rozróżnienie materiałów budujących podłoże. Kompozyt ten jest przydatny do wyszukiwania formacji i cech geologicznych (np. uskoków, pęknięć), skał (np. granitu, bazaltu itp.) oraz znajduje zastosowanie w górnictwie.\n\n\n\nWięcej informacji [tutaj.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)"]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":["# Kompozyt Geology 8, 11, 12 \n\nTen kompozyt wykorzystuje pasma podczerwieni krótkofalowej (SWIR) nr 11 i 12 do rozróżnienia różnych typów skał (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach).Każdy rodzaj skały i minerału inaczej odbija krótkofalowe światło podczerwone, pozwalając na uzyskanie odwzorowania struktury geologicznej poprzez porównanie odbitego światła SWIR. Pasmo bliskiej podczerwieni (NIR) nr 8 uwypukla roślinność, umożliwiając rózróżnienie materiałów budujących podłoże. Roślinność w tym kompozycie ma kolor czerwony. Kompozyt ten jest przydatny do rozróżnienia roślinności i terenu, a zwłaszcza cech geologicznych, które mogą być pomocne w poszukiwaniu i wydobywaniu minerałów.\n\n\n\nWięcej informacji [tutaj] (https://earthobservatory.nasa.gov/features/FalseColor/page5.php) i [tutaj.] (http://murphygeological.com/new---sentinel-2.html#)"]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":["# Pożary\n\nTen skrypt stworzony przez Pierre'a Markusego wizualizuje pożary przy użyciu danych z Sentinel-2. Łączy naturalny kolor tłaz niektórymi danymi NIR/SWIR dotyczącymi penetracji dymu i zawierającymi większą ilość szczegołów, dodając jednocześnie najważniejsze informacje z B11 i B12, aby ukazać pożary w kolorze czerwonym i pomarańczowym.\n\n\n\nWięcej informacji [tutaj.](Https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)"]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":["# Wzmocniony Prawdziwy Kolor\n\nTen skrypt, stworzony przez Pierre Markuse, wykorzystuje wiele pasm (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach) oraz kontrolę nasycenia i jasności, aby poprawić wizualizację prawdziwych kolorów.\n\n\n\nWięcejinformacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)"]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":["# Indeks Wypalonego Obszaru\n\nIndeks Wypalonego Obszaru wykorzystuje szersze spektrum pasm Widocznych, Obrzeża Czeriwni, NIR oraz SWIR\n\nOpis wartości:() => Zakres wartości indeksu wynosi od `-1` do `1` dla śladów po pożarach, oraz `1` - `6` dla aktywnych pożarów.Różne intensywności pożaru mogą skutkować różnymi progami; aktualne wartości zostały skalibrowane, zgodnie z zamysłem autora, głównie pod kątem regionu śródziemnomorskiego.\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)>"]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":["# Znormalizowany Współczynnik Wypalenia (NBR)\n\nZnormalizowany Współczynnik Wypalenia jest często używany do oszacowania stopnia wpływu pożaru. Wykorzystuje fale bliskiej (NIR) i krótkofalowej podczerwieni (SWIR).Zdrowa roślinność ma wysoki współczynnik odbicia w bliskiej podczerwieni i niski współczynnik odbicia w podczerwieni krótkofalowej.Z drugiej strony, obszary wypalone mają wysoki współczynnik odbicia w podczerwieni krótkofalowej, ale niską reflektancję w bliskiej podczerwieni Ciemniejsze piksele wskazują na obszary wypalone.\n\n\n\nWięcej informacji [tutaj](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":["# Przenikanie atmosferyczne\n\nTen kompozyt wykorzystuje różne pasma (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach) w niewidzialnej części widma elektromagnetycznego, aby zmniejszyć wpływ atmosfery na obraz.Pasma podczerwieni krótkofalowej nr 11 i 12 są silnie odbijane przez nagrzane obszary, przez co są przydatne do odwzorowania pożarów i spalonych obszarów. Natomiast pasmo podczerwieni krótkofalowej nr 8 jest silnie odbijane przez roślinność, co oznacza brak pożarów.Roślinność ukazywana jest w kolorze niebieskim, pokazując szczegóły związane z żywotnością roślin. Zdrowa roślinność ukazana jest w kolorze jasnoniebieskim, podczas gdy poddana stresowi, rzadka i/lub sucha roślinność jest matowo-niebieska. Elementy zabudowy miejskiej są koloru białego, szarego, niebieskozielonego lub fioletowego.\n\n\n\nWięcej informacji [tutaj](https://eos.com/atmospheric-penetration/)"]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":["# Wizualizacja Gleb Jałowych\n\nWizualizacja gleby jałowej może być przydatna do mapowania gleby, badania lokalizacji osuwisk lub zakresu erozji na obszarach nieporośnietych roślinnością.W tej wizualizacji roślinność jest oznaczona kolorem zielonym, a jałowy grunt kolorem czerwonym. Woda przyjumje kolor czarny.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)"]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":["# Kompozyt Prawdziwego Koloru z Uwydatnieniem Podczerwieni\n\nTen kompozyt poprawia wizualizację naturalnych barw, dodając fale podczerwieni krótkofalowej dla wzmocnienia szczegółów. Wyświetla nagrzane obszary na czerwono/pomarańczowo.\n\n\n\nWięcej informacji [tutaj.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)"]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":["# Wykrywanie Wypalonych Obszarów\n\nTen skrypt służy do wykrywania niedawno wypalonych dużych obszarów. Piksele w kolorze czerwonym oznaczają spalone obszary, a wszystkie inne piksele mają naturalne barwy. Skrypt czasami źle ocenia spalone obszary nad wodą i chmurami.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":["# Indeks Chlorofilu Lądowego (OTCI)\n\n\n\nIndeks Chlorofilu Lądowego (OTCI) jest oceniany na podstawie zawartości chlorofilu w roślinności lądowej i może być stosowany do monitorowania stanu i zdrowia roślinności.Niskie wartości OTCI zwykle wskazuję na wodę, piasek lub śnieg. Ekstremalnie wysokie wartości wyświetlane na biało zwykle również sugerują brak chlorofilu.Zazwyczaj przedstawiają gołą ziemię, skały lub chmury. Wartości chlorofilu mieszczące się w zakresie od czerwonego (niskie wartości chlorofilu) do ciemnozielonego (wysokie wartości chlorofilu) mogą być wykorzystane do określenia zdrowia roślin.\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":["# Znormalizowany Różnicowy Indeks Zasolenia\n\nIndeks ten obrazuje ilość soli obecnej w glebie.Zasolenie gleby jest jedną z najpowszechniejszych przyczyn degradacji gleby, zwłaszcza na obszarach suchych i półsuchych, gdzie opady przewyższają parowanie. \n\nWyższe wartości wskazują na większe zasolenie, a niższe - na mniejsze zasolenie.\n\nCzytaj więcej [tutaj,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Błąd: Fuzja danych nie obsługuje formatów KMZ/JPG i KMZ/PNG."]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["**MERIS** (Spektrometr o średniej rozdzielczości) był czujnikiem na pokładzie satelity [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat), którego podstawową misją jest obserwacja koloru lądu i oceanu oraz atmosfery. Nie jest już aktywny i został zastąpiony przez Sentinel-3.\n\n**Rozdzielczość przestrzenna:** Pełna rozdzielczość lądu i wybrzeża: 260 m x 290 m (oznacza to, że widoczne są tylko obiekty większe niż 260 m x 290 m).\n\n**Czas rewizyty:** Maksymalnie 3 dni na ponowne zobrazowanie tego samego obszaru.\n\n**Dostępność danych:** Od czerwca 2002 r. do kwietnia 2012 r.\n\n**Typowe zastosowanie:** Monitorowanie oceanów (fitoplankton, zawiesina), atmosfery (para wodna, CO2, chmury, aerozole) i lądów (indeks roślinności, globalne pokrycie terenu, wilgotność)."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["**Sentinel-2** zapewnia obrazy o wysokiej rozdzielczości w zakresie promieniowania widzialnego i podczerwonego w celu monitorowania roślinności, pokrywy glebowej i wodnej, śródlądowych dróg wodnych i obszarów przybrzeżnych.\n\n**Rozdzielczość przestrzenna:** 10 m, 20 m i 60 m w zależności od długości fali (oznacza to, że widoczne są tylko obiekty większe niż 10 m, 20 m i 60 m). Więcej informacji [tutaj](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial).\n\n**Czas rewizyty:** Maksymalnie 5 dni na rewizytę tego samego obszaru przy użyciu obydwu satelitów.\n\n**Dostępność danych:** Od czerwca 2015 r.Pełny zasięg globalny od marca 2017 r.\n\n**Typowe zastosowanie:** Mapy pokrycia terenu, mapy zmian pokrycia terenu, monitoring roślinności, monitoring spalonych terenów."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["**Sentinel-5P** to satelita, który przeprowadza pomiary atmosferyczne niezbędne do oceny jakości powietrza, poziomu ozonu, poziomu promieniowania UV\noraz monitorowania i prognozowania klimatu.\n\n**Rozdzielczość przestrzenna:** 7 x 3,5 km (oznacza to, że widoczne są tylko obiekty większe niż 7 x 3,5 km).\n\n**Czas rewizyty:** Maksymalnie 1 dzień na rewizytę tego samego obszaru.\n\n**Dostępność danych:** Od kwietnia 2018 r.\n\n**Typowe zastosowanie:** Monitorowanie stężenia tlenku węgla (CO), dwutlenku azotu (NO2) i ozonu (O3) w powietrzu. Monitorowanie wskaźnika aerozolu UV (AER_AI) i różnych parametrów geofizycznych chmur (Cloud)."]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Zobrazuj teren w 3D"]},"Measure":{"msgid":"Measure","msgstr":[""]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":[""]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":[""]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":[""]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":[""]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":[""]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":[""]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":[""]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":[""]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":[""]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":[""]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":[""]},"Hello,":{"msgid":"Hello,","msgstr":[""]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (z korektą atmosferyczną)"]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":["**Zalogowani użytkownicy** mogą korzystać ze swoich niestandardowych motywów, zapisywać i ładować zapisane zdjęcia (zwane dalej pinezkami), tworzyć historię pinezek, mierzyć odległości, tworzyć zdjęcia poklatkowe\noraz korzystać z zaawansowanej funkcji pobierania obrazu.\n\nAby utworzyć bezpłatne konto, po prostu kliknij [tutaj]\nlub w aplikacji na **Zaloguj się**, a następnie \"Zarejestruj się\"."]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":[""]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":[""]},"More information":{"msgid":"More information","msgstr":[""]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":[""]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":[""]},"Home":{"msgid":"Home","msgstr":[""]},"Shading":{"msgid":"Shading","msgstr":[""]},"Sphere mode":{"msgid":"Sphere mode","msgstr":[""]},"Eye height":{"msgid":"Eye height","msgstr":[""]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":[""]},"Geometries":{"msgid":"Geometries","msgstr":[""]},"Now":{"msgid":"Now","msgstr":[""]},"Terrain":{"msgid":"Terrain","msgstr":[""]},"Time":{"msgid":"Time","msgstr":[""]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":[""]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":[""]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":[""]},"Left button":{"msgid":"Left button","msgstr":[""]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":[""]},"Right button":{"msgid":"Right button","msgstr":[""]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":[""]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":[""]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":[""]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":[""]},"Arrow keys":{"msgid":"Arrow keys","msgstr":[""]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":[""]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":[""]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":[""]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":[""]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":[""]},"Map navigation":{"msgid":"Map navigation","msgstr":[""]},"Pan console":{"msgid":"Pan console","msgstr":[""]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":[""]},"Camera console":{"msgid":"Camera console","msgstr":[""]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":[""]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":[""]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":[""]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":[""]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":[""]},"Histogram":{"msgid":"Histogram","msgstr":[""]},"Disabled":{"msgid":"Disabled","msgstr":[""]},"Yes":{"msgid":"Yes","msgstr":[""]},"Orthorectification":{"msgid":"Orthorectification","msgstr":[""]},"Cancel":{"msgid":"Cancel","msgstr":[""]},"Error":{"msgid":"Error","msgstr":[""]},"Help":{"msgid":"Help","msgstr":[""]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":[""]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":[""]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":[""]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":[""]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":[""]},"User Instances":{"msgid":"User Instances","msgstr":[""]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":[""]},"Recalculate":{"msgid":"Recalculate","msgstr":[""]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":[""]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":[""]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":[""]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":[""]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":[""]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":[""]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":[""]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":[""]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":[""]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":[""]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":[""]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":[""]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":[""]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":[""]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":[""]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":[""]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":[""]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":[""]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":[""]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":[""]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":[""]},"File upload":{"msgid":"File upload","msgstr":["Przesyłanie plików"]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Prześlij plik KML/KMZ, GPX lub GEOJSON/JSON, aby utworzyć obszar zainteresowania. Obszar ten będzie używany do przycinania podczas eksportowania obrazu."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":["Upuść plik KML/KMZ, GPX, GEOJSON/JSON lub przeszukaj swój komputer"]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":[""]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":[""]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":[""]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":[""]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":[""]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":[""]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":[""]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":[""]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":[""]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":[""]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":[""]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":[""]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":[""]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":[""]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":[""]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":[""]},"Zoom to location":{"msgid":"Zoom to location","msgstr":[""]},"Remove layer":{"msgid":"Remove layer","msgstr":[""]},"Please select a layer":{"msgid":"Please select a layer","msgstr":[""]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":[""]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":[""]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":[""]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":[""]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":[""]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":[""]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":[""]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":[""]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":[""]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":[""]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":[""]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":[""]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":[""]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":[""]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":[""]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":[""]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":[""]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":[""]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":[""]},"Level 1":{"msgid":"Level 1","msgstr":[""]},"Level 2":{"msgid":"Level 2","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":[""]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":[""]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Maks. zachmurzenie:"]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Prześlij dane"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":[""]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]}}}}
\ No newline at end of file
+{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","plural-forms":"nplurals=3; plural=(n==1 ? 0 : n%10>=2 && n%10<=4 && (n%100<12 || n%100>14) ? 1 : 2);","language":"pl","mime-version":"1.0","content-transfer-encoding":"8bit","project-id-version":"","pot-creation-date":"","po-revision-date":"","last-translator":"","language-team":"","x-generator":"Poedit 2.4.1"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nPlural-Forms: nplurals=3; plural=(n==1 ? 0 : n%10>=2 && n%10<=4 && (n%100<12 || n%100>14) ? 1 : 2);\nLanguage: pl\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\nProject-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLast-Translator: \nLanguage-Team: \nx-generator: Poedit 2.4.1\n"]},"Education":{"msgid":"Education","msgstr":["Edukacja"]},"Normal":{"msgid":"Normal","msgstr":["Normalny"]},"Close":{"msgid":"Close","msgstr":["Zamknij"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Zamknij i nie pokazuj więcej"]},"Previous":{"msgid":"Previous","msgstr":["Wstecz"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Zakończ instruktaż"]},"Next":{"msgid":"Next","msgstr":["Dalej"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Kontynuuj z samouczkiem"]},"Don't show again":{"msgid":"Don't show again","msgstr":["Nie pokazuj więcej"]},"Show info":{"msgid":"Show info","msgstr":["Pokaż informacje"]},"Discover":{"msgid":"Discover","msgstr":["Odkrywaj"]},"Visualize":{"msgid":"Visualize","msgstr":["Zobrazuj"]},"Compare":{"msgid":"Compare","msgstr":["Porównaj"]},"Pins":{"msgid":"Pins","msgstr":["Pinezki"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Wystąpił błąd podczas pobierania obrazów:"]},"No tile found":{"msgid":"No tile found","msgstr":["Nie znaleziono kafelka"]},"Dataset":{"msgid":"Dataset","msgstr":["Zbiór danych"]},"Show":{"msgid":"Show","msgstr":["Pokaż"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Pokaż efekty i opcje zaawansowane"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Pokaż wizualizację"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Dodaj do Pinezek"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Dodaj do porównania"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Powiększ do kafelka"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Ukryj warstwę"]},"Show layer":{"msgid":"Show layer","msgstr":["Pokaż warstwę"]},"Share":{"msgid":"Share","msgstr":["Udostępnij"]},"Custom":{"msgid":"Custom","msgstr":["Indywidualne dostosowanie"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Indywidualnie dostosuj wizualizację"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Powiększ, aby wyświetlić dane"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Darmowa rejestracja"]},"for all features":{"msgid":"for all features","msgstr":["na wszystkie funkcje"]},"Powered by":{"msgid":"Powered by","msgstr":["Dostarczone przez"]},"with contributions by":{"msgid":"with contributions by","msgstr":["we współpracy z"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["Wybierz źródło/a danych!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["Nieprawidłowy zakres czasu!"]},"No results found":{"msgid":"No results found","msgstr":["Brak wyników"]},"Theme":{"msgid":"Theme","msgstr":["Motyw"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Zarządzaj instancjami konfiguracji"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Zaloguj się, aby skorzystać z niestandardowych instancji konfiguracji."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["Błąd podczas pobierania dodatkowych danych!"]},"Search":{"msgid":"Search","msgstr":["Wyszukaj"]},"Highlights":{"msgid":"Highlights","msgstr":["Najważniejsze informacje"]},"Data sources":{"msgid":"Data sources","msgstr":["Źródła danych"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Wybierz motyw"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Zakres czasu [UTC]"]},"Date":{"msgid":"Date","msgstr":["Data"]},"Hide description":{"msgid":"Hide description","msgstr":["Ukryj opis"]},"Show description":{"msgid":"Show description","msgstr":["Wyświetl opis"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Ten motyw nie ma żadnych wyróżnień"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Na podstawie: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["1 dzień (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["5 dni (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["10 dni (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["O3 (Ozon)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (dwutlenek azotu)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (dwutlenek siarki)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (tlenek węgla)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (formaldehyd)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (metan)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER AI (Indeks Aerozolu)"]},"Cloud":{"msgid":"Cloud","msgstr":["Chmura"]},"Other":{"msgid":"Other","msgstr":["Inne"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Maks. zachmurzenie"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Zaawansowane wyszukiwanie"]},"Data location":{"msgid":"Data location","msgstr":["Lokalizacja danych"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Wybierz przynajmniej jedną lokalizację!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Tryb zbierania danych"]},"Polarization":{"msgid":"Polarization","msgstr":["Polaryzacja"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Wybierz co najmniej jeden tryb zbierania danych!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Wybierz przynajmniej jedną polaryzację!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Kierunek orbity"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Wybierz co najmniej jeden kierunek orbity!"]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["**Instrument do Badań Kolorów Oceanów i Lądu (OLCI)** na pokładzie Sentinel-3 to spektrometr, który \nmierzy promieniowanie słoneczne odbijane przez Ziemię i monitoruje ocean, środowisko \ni klimat.Dostarcza widzialnych obrazów z większą częstotliwością niż Sentinel-2, ale w niższej rozdzielczości obejmując\n większą ilości fal. Instrument Sentinel-3 OLCI kontynuuje pomiary przeprowadzone wcześniej przez instrument MERIS na pokładzie Envisat, którego misja dobiegła końca.\n\n**Rozdzielczość przestrzenna:** 300 m (oznacza to, że widoczne są tylko obiekty większe niż 300 m).\n\n**Czas rewizyty:** Maksymalnie 2 dni na rewizytę tego samego obszaru przy użyciu obydwu satelitów.\n\n**Dostępność danych:** Od maja 2016 r.\n\n**Typowe zastosowanie:** Topografia powierzchni, obserwacja i monitorowanie kolorów powierzchni oceanów i lądów."]},"Copied":{"msgid":"Copied","msgstr":["Skopiowano"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["Skopiuj do schowka"]},"Data source name":{"msgid":"Data source name","msgstr":["Nazwa źródła danych"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Czas pomiaru"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Zachmurzenie"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Wysokość słońca"]},"MGRS location":{"msgid":"MGRS location","msgstr":["Lokalizacja MGRS"]},"AWS path":{"msgid":"AWS path","msgstr":["Ścieżka AWS"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["Ścieżka EO Cloud"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["Ścieżka CreoDIAS"]},"SciHub link":{"msgid":"SciHub link","msgstr":["Link do SciHub"]},"Back to search":{"msgid":"Back to search","msgstr":["Powrót do wyszukiwania"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Wyświetlanie wyniku $ { this.state.results.length }","Wyświetlanie wyników $ { this.state.results.length }",""]},"Load more":{"msgid":"Load more","msgstr":["Załaduj więcej"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Ładowanie większej liczby wyników ..."]},"Results":{"msgid":"Results","msgstr":["Wyniki"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Wyświetlanie wyniku $ { this.state.selectedTiles.length }.","Wyświetlanie wyników $ { this.state.selectedTiles.length }.",""]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Edytuj opis pinezki"]},"Reject changes":{"msgid":"Reject changes","msgstr":["Odrzuć zmiany"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Potwierdź zmiany"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Zmień nazwę pinezki"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Usuń pinezkę"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Przybliż do lokalizacji z pinezką"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Szer./Dł."]},"Zoom":{"msgid":"Zoom","msgstr":["Powiększenie"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["Za chwilę dodasz pinezkę(pinezki) $ { N_PINS } do swojej kolekcji pinezek. Czy chcesz kontynuować?"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["OSTRZEŻENIE: Zamierzasz usunąć pinezkę. Czy chcesz kontynuować?"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":["OSTRZEŻENIE: Za chwilę usuniesz wszystkie pinezki. Czy chcesz kontynuować?"]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["Brak pinezek. Przejdź do zakładki Wizualizuj, aby zapisać pinezkę lub załadować plik JSON z zapisanymi pinezkami."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Pamiętaj, że pinezki zostaną zapisane tylko po zalogowaniu się. W przeciwnym razie pinezki zostaną utracone po zamknięciu aplikacji."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Odznacz wszystko"]},"Select all":{"msgid":"Select all","msgstr":["Zaznacz wszystko"]},"No pins.":{"msgid":"No pins.","msgstr":["Brak pinezek."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Utwórz link (wybrano pinezkę $ { selectedPins.length })","Utwórz link (wybrane pinezki $ { selectedPins.length })",""]},"File type not supported":{"msgid":"File type not supported","msgstr":["Nieobsługiwany rodzaj pliku"]},"not supported":{"msgid":"not supported","msgstr":["nieobsługiwany"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["Nie znaleziono żadnych pinezek."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Błąd parsowania pliku:"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":["Prześlij plik JSON z zapisanymi pinezkami."]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["Upuść plik JSON lub przeszukaj swój komputer"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Zachowaj istniejące pinezki"]},"Share pins":{"msgid":"Share pins","msgstr":["Udostępnij pinezki"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Stwórz historię z pinezek"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Eksportuj pinezki do komputera"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Importuj pinezki z zapisanego pliku"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Usuń wszystkie pinezki"]},"Story":{"msgid":"Story","msgstr":["Historia"]},"Export":{"msgid":"Export","msgstr":["Eksportuj"]},"Import":{"msgid":"Import","msgstr":["Importuj"]},"Clear":{"msgid":"Clear","msgstr":["Wyczyść"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Udostępnij link do pinezek"]},"Creating link...":{"msgid":"Creating link...","msgstr":["Tworzę link..."]},"OK":{"msgid":"OK","msgstr":["OK"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Aktualizuję zbiór pinezek."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Wystąpił problem z trwałą aktualizacją zbioru pinezek: ${ updatePinsError }."]},"Opacity":{"msgid":"Opacity","msgstr":["Nieprzezroczystość"]},"Split position":{"msgid":"Split position","msgstr":["Pozycja podziału"]},"split":{"msgid":"split","msgstr":["podziel"]},"opacity":{"msgid":"opacity","msgstr":["nieprzezroczystość"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["Brak warstw do porównania."]},"Remove all":{"msgid":"Remove all","msgstr":["Usuń wszystko"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Dodaj wszystkie pinezki"]},"Split":{"msgid":"Split","msgstr":["Podziel"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Wystąpił problem z pobieraniem Twoich przykładów"]},"Download":{"msgid":"Download","msgstr":["Pobierz"]},"Go to Place":{"msgid":"Go to Place","msgstr":["Przejdź do wybranego Miejsca"]},"Labels":{"msgid":"Labels","msgstr":["Oznaczenia"]},"Borders":{"msgid":"Borders","msgstr":["Granice"]},"Roads":{"msgid":"Roads","msgstr":["Drogi"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Powiększ"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Zmniejsz"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["Informacje o aplikacji EO Browser"]},"Contact us":{"msgid":"Contact us","msgstr":["Skontaktuj się z nami"]},"Get data":{"msgid":"Get data","msgstr":["Pobierz dane"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Aby skorzystać z tej funkcji, musisz się zalogować."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Wybierz warstwę."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["Pobieranie obrazu w trybie porównania nie jest możliwe."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["To źródło danych nie jest obsługiwane."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":["Informacje Statystyczne / Wykres Serwisowy z Informacjami o Funkcjach"]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Informacje Statystyczne / Wykres Serwisowy z Informacjami o Funkcjach - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["wybierz warstwę"]},"not available for ":{"msgid":"not available for ","msgstr":["niedostępne dla "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["niedostępne dla \"${ props.presetLayerName }\" (warstwa z wartością nie jest skonfigurowana)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Najpierw wyszukaj dane."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Utwórz animację poklatkową"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Zaznacz interesujące cię miejsce"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Wyśrodkuj mapę na funkcji"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Usuń geometrię"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Obszar zainteresowania"]},"Select mode":{"msgid":"Select mode","msgstr":["Wybierz tryb"]},"Mode:":{"msgid":"Mode:","msgstr":["Tryb:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Usuń pomiar"]},"km":{"msgid":"km","msgstr":["km"]},"m":{"msgid":"m","msgstr":["m"]},"Gain":{"msgid":"Gain","msgstr":["Wzmocnienie"]},"Gamma":{"msgid":"Gamma","msgstr":["Gamma"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Min. jakość danych"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Próbkowanie w górę"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Próbkowanie w dół"]},"Reset all":{"msgid":"Reset all","msgstr":["Zresetuj wszystko"]},"filter by months":{"msgid":"filter by months","msgstr":["filtruj według miesięcy"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Skopiuj geometrię do schowka"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Anuluj zmiany."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Zaznacz obszar zainteresowania"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Najmniejsze zachmurzenie"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Użyj dodatkowych zbiorów danych (zaawansowane)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Kolejność mozaikowania"]},"Most recent":{"msgid":"Most recent","msgstr":["Od najnowszych"]},"Least recent":{"msgid":"Least recent","msgstr":["Od najstarszych"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Dostosuj przedział czasu"]},"Back":{"msgid":"Back","msgstr":["Powrót"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Błąd podczas ładowania skryptu. Sprawdź swój adres URL."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Usuń zaznaczenie opcji Załaduj skrypt z adresu URL, aby edytować kod"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Załaduj skrypt z adresu URL"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["Wprowadź adres URL do swojego skryptu"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Wczytano skrypt."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Dozwolone są tylko domeny HTTPS."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Załaduj skrypt do edytora kodu"]},"Refresh":{"msgid":"Refresh","msgstr":["Odśwież"]},"orbit":{"msgid":"orbit","msgstr":["orbita"]},"day":{"msgid":"day","msgstr":["dzień"]},"week":{"msgid":"week","msgstr":["tydzień"]},"month":{"msgid":"month","msgstr":["miesiąc"]},"year":{"msgid":"year","msgstr":["rok"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Wybierz 1 obraz na:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Film poklatkowy"]},"Select All":{"msgid":"Select All","msgstr":["Zaznacz Wszystko"]},"Speed:":{"msgid":"Speed:","msgstr":["Szybkość:"]},"frames / s":{"msgid":"frames / s","msgstr":["klatka/i"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Przygotowywanie…"]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Nie można pobrać plików:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Nie można pobrać przez canvas"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["Nie można skompresować plików:"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Wystąpił problem podczas pobierania obrazu"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Błąd podczas pobierania obrazu: adres URL jest pusty!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Podczas pobierania obrazu wystąpił błąd:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["Nie można załadować obrazu z obiektu BLOB"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Przeciągnij pasma na pola RGB."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Przeciągnij pasma do równania wskaźnikowego"]},"Index ":{"msgid":"Index ","msgstr":["Wskaźnik "]},"Threshold":{"msgid":"Threshold","msgstr":["Wartość graniczna"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Usuń selektor kolorów"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Wybierz selektor kolorów"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Naciśnij, aby wstawić znacznik"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Naciśnij, aby wstawić pierwszy wierzchołek"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Naciśnij, aby kontynuować rysowanie"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Naciśnij pierwszy znacznik, aby zakończyć"]},"Show captions":{"msgid":"Show captions","msgstr":["Pokaż napisy"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Pokaż tytuł slajdu"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Dodaj nakładki mapy"]},"Show legend":{"msgid":"Show legend","msgstr":["Pokaż legendę"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["W obecnym polu widzenia nie znaleziono żadnych pinezek."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Niektóre pinezki (${ N_PINS_OUTSIDE_BOUNDS }) są ignorowane, ponieważ nie znajdują się w wybranym obszarze."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["Aby utworzyć historię pinezki, przejdź do żądanej pozycji na mapie.\n\nWszystkie pinezki w bieżącym polu widzenia zostaną użyte do stworzenia historii, reszta zostanie zignorowana."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["Plik będzie miał dołączone logo."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["Pasmo dataMask zostanie włączone do pobranych pasm nieprzetworzonych jako drugie pasmo."]},"Show logo":{"msgid":"Show logo","msgstr":["Wyświetl logo"]},"Image format":{"msgid":"Image format","msgstr":["Format obrazu"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Rozdzielczość zdjęcia"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Układ współrzędnych"]},"Layers":{"msgid":"Layers","msgstr":["Warstwy"]},"Visualized":{"msgid":"Visualized","msgstr":["Zwizualizowane"]},"Raw":{"msgid":"Raw","msgstr":["Nieprzetworzone"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["Do obrazu zostaną dodane warstwy nakładające mapy (etykiety miejsc, ulice i granice polityczne)."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Eksportowane obrazy będą zawierać źródło danych oraz datę, skalę powiększenia i oznaczenie marki"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Dodaj krótki opis do eksportowanego obrazu"]},"Description":{"msgid":"Description","msgstr":["Opis"]},"Image format:":{"msgid":"Image format:","msgstr":["Format obrazu:"]},"Basic":{"msgid":"Basic","msgstr":["Podstawowy"]},"Analytical":{"msgid":"Analytical","msgstr":["Analityczny"]},"High-res print":{"msgid":"High-res print","msgstr":["Druk w wysokiej rozdzielczości"]},"Download image":{"msgid":"Download image","msgstr":["Pobierz obraz"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["Wystąpił błąd podczas pobierania niektórych obrazów:"]},"min/px":{"msgid":"min/px","msgstr":["min /px"]},"sec/px":{"msgid":"sec/px","msgstr":["sek/px"]},"Resolution":{"msgid":"Resolution","msgstr":["Rozdzielczość"]},"lat.":{"msgid":"lat.","msgstr":["szer."]},"deg/px":{"msgid":"deg/px","msgstr":["st./px"]},"long.":{"msgid":"long.","msgstr":["dł."]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Przewidywana rozdzielczość: ${ formattedResolution } m/px"]},"Image download":{"msgid":"Image download","msgstr":["Pobieranie obrazu"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Szerokość obrazu [cale]:"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Wysokość obrazu [cale]:"]},"DPI:":{"msgid":"DPI:","msgstr":["DPI:"]},"5 years":{"msgid":"5 years","msgstr":["5 lat"]},"2 years":{"msgid":"2 years","msgstr":["2 lata"]},"1 year":{"msgid":"1 year","msgstr":["1 rok"]},"6 months":{"msgid":"6 months","msgstr":["6 miesięcy"]},"3 months":{"msgid":"3 months","msgstr":["3 miesiące"]},"1 month":{"msgid":"1 month","msgstr":["1 miesiąc"]},"Retry":{"msgid":"Retry","msgstr":["Spróbuj ponownie"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Wczytuję, proszę czekać"]},"mean":{"msgid":"mean","msgstr":["średnia"]},"median":{"msgid":"median","msgstr":["mediana"]},"st. dev.":{"msgid":"st. dev.","msgstr":["odch. stand."]},"min / max":{"msgid":"min / max","msgstr":["min/maks"]},"Export CSV":{"msgid":"Export CSV","msgstr":["Eksportuj CSV"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Czas trwania:"]},"Date:":{"msgid":"Date:","msgstr":["Data:"]},"Single date":{"msgid":"Single date","msgstr":["Pojedyncza data"]},"Timespan":{"msgid":"Timespan","msgstr":["Zakres czasu"]},"hh":{"msgid":"hh","msgstr":["hh"]},"mm":{"msgid":"mm","msgstr":["mm"]},"From:":{"msgid":"From:","msgstr":["Od:"]},"Until:":{"msgid":"Until:","msgstr":["Do:"]},"Apply":{"msgid":"Apply","msgstr":["Zastosuj"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Udostępnij na Facebooku"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Udostępnij na Twitterze"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Sprawdź to "]},"Logout":{"msgid":"Logout","msgstr":["Wyloguj się"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["Zaloguj się, aby odblokować zaawansowane funkcje, takie jak film poklatkowy, pobieranie analityczne, własne konfiguracje i inne."]},"Login":{"msgid":"Login","msgstr":["Zaloguj się"]},"Default":{"msgid":"Default","msgstr":["Domyślnie"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Monitorowanie Ziemi z Kosmosu"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Rolnictwo"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Atmosfera i Zanieczyszczenie Powietrza"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Wykrywanie Zmian w Czasie"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Powodzie i Susze"]},"Geology":{"msgid":"Geology","msgstr":["Geologia"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Oceany i Zbiorniki Wodne"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Śnieg i Lodowce"]},"Urban":{"msgid":"Urban","msgstr":["Obszar Miejski"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Roślinność i Leśnictwo"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Wulkany"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Pożary lasu"]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":["# Witamy w przeglądarce EO Browser!\n\nPełne archiwum satelitów Sentinel-1, Sentinel-2, Sentinel-3, Sentinel5P, archiwum ESA \ndla satelitów Landsat 5, 7 i 8, globalny zasięg obserwacji Landsat 8, Envisat Meris, \nMODIS, produkty Proba-V oraz GIBS w jednym miejscu.\n\n[Strona z prezentacją przeglądarki EO Browser](https://www.sentinel-hub.com/explore/eobrowser/) \n[Poradnik użytkownika EO Browser](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":["#### Krótki przegląd funkcji przeglądarki EO Browser\n\nPrzeglądarka EO Browser to połączenie pełnego archiwum satelitów Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, archiwum ESA (Europejskiej Agencji Kosmicznej) dla satelitów Landsat 5, 7 i 8, globalny zasięg obserwacji Landsat 8, Envisat Meris oraz produkty MODIS, Proba-V i GIBS w jednym miejscu i umożliwia przeglądanie i porównywanie obrazów z tych źródeł w pełnej rozdzielczości. Po prostu przejdź do obszaru, który Cię interesuje, wybierz źródła danych, zakres czasowy i stopień zachmurzenia oraz sprawdź dane wynikowe.\n\nMożesz kontynuować instruktaż, klikając przycisk \\„Dalej\\” lub możesz go zamknąć. Klikając na ikonę informacji w prawym górnym rogu, zawsze możesz wznowić instruktaż na wypadek, gdybyś zamknął go przez pomyłkę lub chciał coś wypróbować."]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":["W zakładce ** Odkrywaj** możesz:\n\n- Wybrać **Motyw.**\n- **Szukaj** dla wyszukania danych.\n- Wyświetlić motyw **Wyróżnienia.**\n\nLista rozwijana **Motyw** zawiera różne wstępnie skonfigurowane motywy, a także własne niestandardowo skonfigurowane instancje, jeśli jesteś Zalogowany. Aby utworzyć instancję, kliknij\nikonę ustawień i zaloguj się przy użyciu tych samych poświadczeń, które były używane w EO Browser.\n\nW zakładce **Szukaj** możesz ustawić kryteria wyszukiwania:\n - Wybrać satelity, z których chcesz otrzymywać dane, zaznaczając pola wyboru.\n- W stosownych przypadkach można wybrać dodatkowe opcje, na przykład poziom zachmurzenia za pomocą suwaka.\n - Wybierz przedział czasu przez wpisanie daty lub wybierz datę z kalendarza.\n\nMożesz przeczytać objaśnienia dotyczące satelitów, klikając ikonę znaku zapytania\n obok nazwy danych źródła.\n\nPo kliknięciu Search, otrzymasz listę wyników. Każdy wynik jest przedstawiany \nz obrazem podglądu i odpowiednimi danymi sprecyzowanymi dla źródła danych. W przypadku niektórych źródeł danych, ikona linku jest również widoczna dla każdego wyniku.\nJej kliknięcie powoduje wyświetlenie bezpośrednich linków do surowego obrazu wynikowego w EO Cloud lub SciHub. Kliknięcie na przyciskVisualize otworzy zakładkę **Visualize** dla wybranego wyniku.\n\nPod zakładką **Wyróżnij** znajdziesz wstępnie wybrane interesujące lokalizacje powiązane z wybranym motywem."]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":["W zakładce Wizualizuj można wybrać różne wstępnie zainstalowane lub niestandardowe kombinacje pasm widmowych aby zwizualizować dane dla wybranego wyniku.\n\nNiektóre z częstych opcji:\n- **Prawdziwy Kolor** - Wizualna interpretacja pokrycia terenu.\n- **Fałszywy Kolor** - Wizualna interpretacja roślinności.\n- **NDVI** - Indeks roślinności.\n- **Indeks wilgoci** - Indeks wilgoci\n- **SWIR** - Indeks podczerwienie krótkofalowej.\n- **NDWI** - Znormalizowany Różnicowy Indeks Wody.\n- **NDSI** - Znormalizowany Róznicowy Indeks Śniegu.\n\nWiększość wizualizacji otrzymuje opis i legendę, którą można obejrzeć poprzez kliknięcie na ikonę\nrozszerzenia .\n \nDla większości źródeł danych dostępna jest opcja **Skryptu Niestandardowego**. Kliknij na nią aby wybrać niestandardowe\nkombinacje pasm, kombinacje indeksów lub napisać swój własny skrypt klasyfikacyjny do wizualizacji danych. Można również\nużyć skryptów niestandardowych, które są przechowywane gdzieś indziej, na dysku Google, GitHub lub w naszymr [Repozytorium skryptów niestandardowych](https://custom-scripts.sentinel-hub.com/). \nWklejURL skryptu do pola tekstowego w panelu zaawansowanej edycji skryptów i klinknij Odśwież.\n \nMożna zmienić dane bezpośrednio w zakładce Zwizualizuj bez wracania do zakładki **Odkryj**. Wpiszlub wybierz ją z kalendarza .\n\nPowyżej wizualizacji znajduje się linia dodatkowych narzędzi. Zauważże ich dostępność zależy od źródła danych.\n- **Przypnij pinezkę** aby zapisać ją w aplikacji do wykorzystania w przyszłości - poprzez kliknięcie na ikonę pinezki .\n- Wybierz **opcje zaawansowane** takie jak metoda próbkowania lub dodaj różne **efekty** takie jak kontrast (wzmocnienie) i luminancja (gamma) - poprzez kliknięcie na ikonę suwaka efektów .\n- Dodajwarstwę do zakladki **Porównaj** dla późniejszych porównań - poprzez kliknięcie na ikonę porównywania .\n- **Przybliż** do środka kafelka - poprzez kliknięcie na celownik.\n- Abyprzeciągnąć **widoczność warstwy** - poprzez kliknięcie na ikonę widoczności .\n- **Udostępnij** swoje wizualizacje na mediach społecznościowych - poprzez kliknięcie na ikonę udostępniania ."]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":["W zakładce **Porównaj** można znaleźć wszystkie wizualizacje, które dodano poprzez opcję **Porównaj**. \n\nDostępne są dwa tryby:\n - **Nieprzejrzystość** (Przeciągnij suwak nieprzejrzystości w lewo lub w prawo, aby wykonać płynne przejście między porównywanymi obrazami)\n - **Rozdzielanie** (Przeciągnij suwak rozdzielania w lewo lub w prawo, aby ustawić granicę między porównywanymi obrazami)\n\nMożna dodać wszystkie wybrane obrazy do panelu porównania za pomocą **Dodaj wszystkie pinezki** lub usunąć wszystkie wizualizacje\nz zakładki **Porównaj** za pomocą przycisku **Usuń wszystko**."]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":["Zakładka **Pinezki** zawiera przypięte (ulubione/zapisane) elementy. Przypięte elementy zawierają informacje o\nlokalizacji, źródle danych i jego określonej warstwie, poziomie powiększenia oraz czasie.\n\nDla każdego wybranego obrazu istnieje kilka opcji interakcji z pojedynczym obrazem:\n\n- Zmień **kolejność** - klikając ikonę przenoszenia\n\n \n \n \nw górnym lewym rogu wybranego obrazu, przeciągając ten obraz w górę lub w dół listy.\n- **Zmień nazwę** - klikając ikonę ołówka obok nazwy wybranego obrazu.\n- Dodaj do zakładki **Porównaj** - klikając ikonę porównania \n- Naciśnij **description** (opis) - klikając ikonę rozwijania .\n- **Remove** (usuń) - klikającikonę usuwania .\n- **Przybliż** do lokalizacji wybranego obrazu - klikając opcję Lat/Lon.\n\nW lini nad powyższymiwybranymi obrazami znajdują się różne opcje, które mają zastosowanie do wszystkich wybranych obrazów:\n - Stwórz własną historię na podstawie wybranych obrazów - klikając **Opowieść**.\n- Udostępnij swoje wybrane obrazy innym za pomocą linku - klikając **Udostępnij**.\n- Eksportuj wybrane obrazy jako plik JSON - klikając na **Eksportuj**.\n- Importuj wybrane obrazy z pliku JSON - klikając na **Import**.\n- Usuńwszystkie pinezki - klikając na **Wyczyść**."]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":["Wyszukaj lokalizację, przewijając mapę za pomocą myszy lub wprowadź lokalizację w polu\nwyszukiwania."]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":["Tutaj możesz wybrać, która warstwa bazowa i nakładki (drogi, granice, etykiety) mają być wyświetlane na mapie."]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":["Tutaj możesz przełączać się między trybem **normalny** i **edukacja**. Tryb **edukacja** oferuje nieco uproszczoną wersję aplikacji.\nDostęp można również uzyskać bezpośrednio poprzez [dedykowany adres URL] (https://apps.sentinel-hub.com/eo-browser-education/)."]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":["Możesz wyświetlić instruktaż w dowolnym momencie, klikając na tę ikonę informacji\n\n\n."]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":["To narzędzie umożliwia narysowanie wielokąta na mapie i wyświetlenie jego rozmiaru.\n\nWszystkie warstwy, które zwracają pojedynczą wartość (np. NDVI, Wskaźnik wilgoci, NDWI, itd.) obsługują przeglądanie\nindeksu dla wybranego obszaru na przetrzeni czasu. Kliknięcie na ikonę wykresu spowoduje\nwyświetlenie wykresów. Można usunąć wielokąt, klikając ikonę usuwania .\n\nMożna również przesłać plik KML/KMZ, GPX lub GEOJSON/JSON z geometrią wielokąta.\n\nIkona dwóch arkuszy umożliwia skopiowanie współrzędnych wielokąta jako GEOJSON, celownik \n wyśrodkowuje mapę na narysowany wielokąt.\n\nEksportowane obrazy zostaną przycięte do obszaru zainteresowania podczas ich pobierania dla celów analitycznych."]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":["Za pomocą tego narzędzia można zaznaczyć punkt na mapie, a\n\n także wyświetlić dane statystyczne dla niektórych warstw, klikając na ikonę wykresu\n. \nMożna usunąć znak, klikając na ikonę usuwania.\n"]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":["Za pomocą tego narzędzia można mierzyć odległości i powierzchnie na mapie.\n\n Każde kliknięcie myszą tworzy nowy punkt na ścieżce. Aby zatrzymać dodawanie punktów, naciśnij klawisz Esc
\nlub kliknij dwukrotnie mapę. \nMożesz usunąć pomiar, klikając ikonę usuwania."]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":["Za pomocą tego narzędzia można pobrać obraz zawierający wizualizację danych dla wyświetlanej lokalizacji. Można wybrać\nwyświetlenie podpisów lub dodać swój własny opis. \nPoprzez włączenie tryby Analitycznego, można wybierać pomiędzy różnymi formatami obrazu, rozdzielczością obrazu oraz\nukładami współrzędnych. Możesz również wybrać wiele warstw i pobrać je jako plik .zip
.\n\nKliknij przycisk pobierania\nDownload\na rozpocznie się pobieranie twoich obrazów. Proces pobierania może zająć kilka sekund, w zależności od wybranej\nrozdzielczości i liczby wybranych warstw.\n\nPrzed pobraniem, można zdefiniować obszar zainteresowania (AOI) klikając na ikonę narzędzia wyboru\nObszaru. Twoje dane zostaną przycięte tak, aby pasowały do tego obszaru."]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":["Za pomocą tego narzędzia można utworzyć animację poklatkową wizualizowanej warstwy i wyświetlanej lokalizacji.\n\nNajpierw wybierz zakres czasu. Można dodatkowo zdefiniować wyniki wyszukiwania, filtrując je według miesięcy\n(pole wyboru: filtrowanie według miesięcy) i/lub wybierając jeden obraz na określony okres (orbita, dzień, tydzień, miesiąc,\nrok).\n\nNastępnie naciśnij Search (Wyszukaj) i wybierz swoje obrazy.\nMożna wybrać wszystkie zaznaczając pole wyboru lub filtrując obrazy według poziomu zachmurzenia, przesuwając suwak. Można też wybierać obrazy pojedynczo,\nprzewijając listę i zaznaczając je.Za pomocą pola wyboru **Borders** (obramowanie) można włączyć/wyłączyć obramowanie obrazu.\n\nMożna wyświetlić podgląd poklatkowy, naciskając przycisk odtwarzania na dole. Możesz także ustawić prędkość\n(klatki na sekundę).\n\nGdy wynik jest zadowalający, kliknij na przycisk pobierania, a animacja poklatkowa zostanie\n pobrana jako plik .gif
."]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":["Dotarliśmy do końca instruktażu. Jeśli masz inne pytania, nie wahaj się nam ich zadać na [forum] (https://forum.sentinel-hub.com/)\nlub skontaktować się z nami [przez e-mail](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nJeśli będziesz chciał zobaczyć instruktaż w przyszłości, zawsze możesz go wyświetlić, klikając ikonę informacji\n\n\n\nw prawym górnym rogu."]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":["#### Szybki przegląd właściwości przeglądarki EO Browser\n\nJeśli masz mały ekran, przejdź [tutaj] (https://www.sentinel-hub.com/explore/eobrowser/user-guide/) aby zobaczyć przewodnik użytkownika.\n\nZawsze możesz powrócić do tej informacji poprzez\n\n\n\nkliknięcie na ikonę informacji w prawym górnym rogu. \n\n#### Inne zasoby\n- [Strona główna przeglądarki EO Browser] (https://www.sentinel-hub.com/explore/eobrowser/)\n- [Aktualizacje przeglądarki EO Browser z lata 2018 - wideo] (https://www.youtube.com/embed/m3pron0C0kE)"]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":["Co to jest przeglądarka EO Browser?"]},"User Account":{"msgid":"User Account","msgstr":["Konto Użytkownika"]},"Discover Tab":{"msgid":"Discover Tab","msgstr":["Zakładka Odkrywaj"]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":["Zakładka Wizualizuj"]},"Compare Tab":{"msgid":"Compare Tab","msgstr":["Zakładka Porównaj"]},"Pins Tab":{"msgid":"Pins Tab","msgstr":["Zakładka Wybrane Obszary"]},"Search Places":{"msgid":"Search Places","msgstr":["Wyszukaj Miejsca"]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":["Warstwy i Nakładki"]},"Education Mode":{"msgid":"Education Mode","msgstr":["Tryb Edukacji"]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":["Informacje i Instruktaż"]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":["Zaznacz Rejon Zainteresowania"]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":["Zaznacz Interesujące Miejsce"]},"Measure Distances":{"msgid":"Measure Distances","msgstr":["Zmierz Odległości"]},"Download Image":{"msgid":"Download Image","msgstr":["Pobierz Obraz"]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":["Utwórz Animację Poklatkową"]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":["Miłego Przeglądania!"]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":["Witaj w Przeglądarce EO!"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":["Pasmo 1 – Substancja żółta (rozpuszczona materia organiczna o zółtym zabarwieniu) i pigmenty detrytyczne – 412,5 nm"]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":["Pasmo 2 – Maksimum absorpcji chlorofilu – 442 nm"]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":["Pasmo 3 – Chlorofil i inne pigmenty – 490 nm"]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":["Pasmo 4 – Zawieszony osad, czerwone pływy – 510 nm"]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":["Pasmo 5 – Minimum absorpcji chlorofilu – 560 nm"]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":["Pasmo 6 — Zawieszony osad – 620 nm"]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":["Pasmo 7 - Absorpcja chlorofilu i wzorzec fluorescencji – 665 nm"]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":["Pasmo 8 – Szczytowa fluorescencja chlorofilu – 681 nm"]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":["Pasmo 9 – Wzorzec fluorescencji, usuwanie wpływów atmosfery – 709 nm"]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":["Pasmo 10 – Roślinność, chmura – 753 nm"]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":["Pasmo 11 – odgałęzione pasmo absorpcji O2 R – 761 nm"]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":["Pasmo 12 – Usuwanie wpływów atmosfery – 779 nm"]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":["Pasmo 13 – Roślinność, wzorzec pary wodnej – 865 nm"]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":["Pasmo 14 – Usuwanie wpływów atmosfery – 885 nm"]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":["Pasmo 15 - Para wodna, ląd - 900 nm"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Pasmo 1 – Niebieskie – 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Pasmo 2 – Zielone – 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Pasmo 3 – Czerwone – 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Pasmo 4 – NIR – 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Pasmo 5 – SWIR-1 – 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Pasmo 7 – SWIR-2 – 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Pasmo 8 – Panchromatyczne – 520-900 nm"]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":["Pasmo 1 – Przybrzeżne/Aerozolowe – 433-453 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Pasmo 2 – Niebieskie – 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Pasmo 3 – Zielone – 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Pasmo 4 – Czerwone – 630-680 nm"]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":["Pasmo 5 – NIR – 845-885 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Pasmo 6 – SWIR-1 – 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Pasmo 7 – SWIR-2 – 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Pasmo 8 – Panchromatyczne – 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Pasmo 9 – Cirrus – 1360-1390 nm"]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":["Landsat 5 (Archiwum ESA - Europejskiej Agencji Kosmicznej)"]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":["Landsat 7 (Archiwum ESA)"]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":["Landsat 8 (Archiwum ESA)"]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":["Landsat 8 (Archiwum USGS)"]},"Red band":{"msgid":"Red band","msgstr":["Pasmo czerwone"]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":["841 - 876 nm (NIR)"]},"Blue band":{"msgid":"Blue band","msgstr":["Pasmo niebieskie"]},"Green band":{"msgid":"Green band","msgstr":["Pasmo zielone"]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":["1230 - 1250 nm"]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":["1628 - 1652 nm"]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":["2105 - 2155 nm"]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":["Pasmo 1 – Aerozol przybrzeżny – 443 nm"]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":["Pasmo 2 – Niebieskie – 490 nm"]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":["Pasmo 3 – Zielone – 560 nm"]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":["Pasmo 4 – Czerwone – 665 nm"]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":["Pasmo 5 – Roślinność Czerwona Krawędź – 705 nm"]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":["Pasmo 6 - Roślinność Czerwona Krawędź - 740 nm"]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":["Pasmo 7 - Roślinność Czerwona Krawędź - 783 nm"]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":["Pasmo 8 - NIR - 842 nm"]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":["Pasmo 9 - Para wodna - 945 nm"]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":["Pasmo 10 - SWIR - Cirrus - 1375 nm"]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":["Pasmo 11 - SWIR - 1610 nm"]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":["Pasmo 12 - SWIR - 2190 nm"]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":["Pasmo 8A - Roślinność Czerwona Krawędź - 865 nm"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":["Pasmo 1 - Usuwanie wpływu aerozolu, ulepszone postrzeganie składników wody - 400 nm"]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":["Pasmo 2 - Substancja żółta (rozpuszczona materia organiczna o zółtym zabarwieniu) i pigmenty detrytyczne (zmętnienie) -412 nm"]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":["Pasmo 3 - Maks. absorpcja Chl, biogeochemia, roślinność - 442,5 nm"]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":["Pasmo 4 - Wysoki Chl, inne pigmenty - 490 nm"]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":["Pasmo 5 - Chl, osad, zmętnienie, czerwone pływy - 510 nm"]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":["Pasmo 6 - Wzorzec chlorofilu (minimum Chl) - 560 nm"]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":["Pasmo 7 - Obiążenie osadem - 620 nm"]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":["Pasmo 8 - Chl (2 Chl maks. abs.), osad, żółta substancja/roślinność - 665 nm"]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":["Pasmo 9 - Dla ulepszonego postrzegania fluorescencji i uzyskania lepszej podstawy dla misji SMILE razem z pasmami 8 (665 nm) i 10 (681.25 nm) - 673,75 nm "]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":["Pasmo 10 - Szczytowa fluorescencja Chl, czerwona krawędź - 681,25 nm"]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":["Pasmo 11 - linia bazowa fluorescencji Chl, przejście z czerwonej krawędzi - 708,75 nm"]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":["Pasmo 12 - Absorpcja O2/chmury, roślinność - 753,75 nm"]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":["Pasmo 13 - Pasmo absorpcji O2/usuwanie wpływu aerozolu - 761,25 nm"]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":["Pasmo 14 - Usuwanie wpływu atmosfery - 764,375 nm"]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":["Pasmo 15 - O2A używane do pomiaru ciśnienia szczytowego chmur, fluorescencji nad lądem - 767,5 nm"]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":["Pasmo 16 - Korekta atmosferyczna/korekta aerozolu - 778,75 nm"]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":["Pasmo 17 - korekta atmosf./korekta aerozolu, chmury, korejestracja pikseli - 865 nm"]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":["Pasmo 18 - Wzorcowe pasmo absorpcji pary wodnej. Pasmo wzorcowe wspólne z instrumentem SLSTR. Monitorowanie roślinności - 885 nm"]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":["Pasmo 19 - Absorpcja pary wodnej / monitorowanie roślinności (maks. współczynnik odbicia) - 900 nm"]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":["Pasmo 20 - Absorpcja pary wodnej, korekta atmosf./ korekta aerozolu - 940 nm"]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":["Pasmo 21 - korekta atmosf./korekta aerozolu - 1020 nm"]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":["Pasmo F1 - Emisja podczerwieni termalnej z pożaru - Aktywny pożar - 3742,00 nm"]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":["Pasmo F2 - Emisja podczerwieni termalnej z pożaru - Aktywny pożar - 10854,00 nm"]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":["Pasmo S1 - VNIR (promieniowanie widzialne i bliskie podczerwieni) - Wykrywanie wpływu chmur, monitorowanie roślinności, aerozol - 554,27 nm"]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":["Pasmo S2 - VNIR - NDVI, monitorowanie roślinności, aerozol - 659,47 nm"]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":["Pasmo S3 - VNIR - NDVI, oznaczanie chmur, korejestracja pikseli - 868,00 nm"]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":["Pasmo S4 - SWIR - Wykrywanie chmur typu cirrus nad lądem - 1374,80 nm"]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":["Pasmo S5 - SWIR - zanik chmur, lód, śnieg, monitorowanie roślinności - 1613,40 nm"]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":["Pasmo S6 - SWIR - Stan roślinności i zanik chmur - 2255,70 nm"]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":["Pasmo S7 - Podczerwień Termalna Otoczenia - SST, LST, aktywny pożar - 3742,00 nm"]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":["Pasmo S8 - Podczerwień Termalna Otoczenia - SST, LST, aktywny pożar - 10854,00 nm"]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":["Pasmo S9 - Podczerwień Termalna Otoczenia - SST, LST - 12022,50 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Współczynnik odbicia"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Temperatura jasnościowa"]},"Credits:":{"msgid":"Credits:","msgstr":["Podziękowania:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["**GIBS** (Global Imagery Browse Services - Usługi Przeglądania Obrazów Ziemi) zapewnia szybki dostęp do ponad 600 obrazów satelitarnych\n, które obejmują wszystkie części świata. Większość obrazów jest dostępna w ciągu kilku godzin po przejściu\n satelity, niektóre produkty obejmują prawie 30 lat."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["Seria satelitów **Landsat** należących do NASA/ U.S. Geological Survey jest podobna do Sentinel-2 (wychwytują fale widzialne i podczerwone) i\ndodatkowo mogą wychwytywać podczerwień termiczną (Landsat 8). Seria Landsat ma długą historię dostarczania obrazów, obejmującą prawie pięć dekad.\n Ta platforma zapewnia dostęp do obrazów zarejestrowanych przez satelity Landsat 5, 7 i 8.\n\n**Spatial resolution:** (rozdzielność przestrzenna) 15m, 30m i 100m próbkowana ponownie na 30m, w zależności od długości fali (to oznacza, że tylko szczegóły większe niż 10m i 30m mogą być dostrzeżone).Więcej informacji [tutaj] (https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** (Czas rewizyty) Potrzebne jest maksymalnie 8 dni, aby ponownie odwiedzić ten sam obszar używając dwóch satelitów operacyjnych Landsat 7 i Landsat 8.\n\n**Data availability:** (Dostępność danych) Europa i Afryka Północna od 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), od 2013 do chwili obecnej (Landsat 8) z archiwum ESA.Globalne archiwum US Geological Survey (USGS) od kwietnia 2013 r. do dnia dzisiejszego (tylko Landsat 8).\n\n**Common usage:** (Typowe zastosowanie) Monitorowanie roślinności, użytkowania gruntów, mapy pokrycia terenu, monitorowanie zmian itp."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["**MODIS** firmy NASA – (Spektroradiometr Obrazujący Średniej Rozdzielczości) pozyskuje dane w celu\nlepszego zrozumienia procesów globalnych zachodzących na lądzie. Przeglądarka EOdostarcza danych niezbędnych do\nobserwacji terenu (pasma 1-7).\n\n**Rozdzielczość przestrzenna:** 250 m (pasma 1-2), 500 m (pasma 3-7), 1000 m (pasma 8-36).\n\n**Czas rewizyty:** Zasięg globalny w 1 – 2 dni z użyciem satelitów Aqua i Terra.\n\n** Dostępność danych:** Od stycznia 2013 r.\n\n**Typowe użycie:** Monitorowanie lądu, chmur, barwy oceanu w skali globalnej."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["Satelita **Proba-V** to mały satelita zaprojektowany do mapowania pokrycia terenu i wzrostu roślinności\n na całym świecie co dwa dni.Przeglądarka EO Browser dostarcza produktów pochodnych, które minimalizują pokrywę\n chmur dzięki połączeniu pomiarów bezchmurnych w okresie 1 dnia (S1), 5 dni (S5) i 10 dni (S10).\n\n**Spatial resolution:** (rozdzielczość przestrzenna) 100 m dla S1 i S5, 333 m dla S1 i S10, 1000 m dla S1 i S10.\n\n**Revisit time:** (okres rewizyty) 1 dzień dla szerokości geograficznych 35-75°N i 35-56°S, 2 dni dla szerokości geograficznych pomiędzy 35°N\n i 35°S.\n\n** Data availability:** (dostępność danych) Od października 2013 r.\n\n**Typowe zastosowanie:** M Obserwacja pokrycia terenu, wzrost roślinności, ocena wpływu klimatu,\n zarządzanie zasobami wodnymi, monitorowanie rolnictwa i szacowanie bezpieczeństwa żywnościowego,\nmonitorowanie śródlądowych zasobów wodnych oraz śledzenie stałego rozprzestrzeniania się pustyń i wylesiania."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":[""]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Dane poziomu 2A to dane wysokiej jakości, w których wyklucza się wpływ atmosfery na światło odbijane od powierzchni Ziemi i docierające do czujnika.Dane są dostępne na całym świecie od marca 2017 r.\n\n Więcej informacji o korekcji atmosferycznej [tutaj] (http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction)."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Dane z Poziomu 1C to dane o jakości wystarczającej dla większości badań, w których wykonano wszystkie korekty obrazu z wyjątkiem korekcji atmosferycznej. Dane są dostępne na całym świecie od czerwca 2015 r."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["Głównym celem misji **Sentinel-3** jest pomiar topografii powierzchni morza, temperatury powierzchni morza i lądu oraz barwy powierzchni oceanu i lądu. Sentinel-3 ma na pokładzie cztery różne instrumenty.Dane zebrane przez Ocean and Land Colour Instrument (OLCI) (Instrument do Badań Kolorów Oceanów i Lądu) oraz Sea and Land Surface Temperature Instrument (SLSTR) (Instrument do Badań Temperatury Morza i Powierzchni Lądu) są dostępne na tej platformie.\n\n** Data Availability:** (dostępność danych) Od maja 2016 r."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["Instrument **Sea and Land Surface Temperature (SLSTR)** na pokładzie Sentinel-3 mierzy globalną i regionalną \ntemperaturę powierzchni mórz i lądów.SLSTR obejmuje długości fal widzialnych, podczerwieni krótkofalowej i podczerwieni średniej widma elektromagnetycznego. \n\n**Rozdzielczość przestrzenna:** 500 m dla fal widzialnych, bliskich i krótkich fal podczerwonych oraz 1 km dla podczerwieni średniej (to znaczy, że widoczne są tylko szczegóły \nwiększe niż odpowiednio 500 m i 1 km).\n\n**Okres rewizyty:** Maksymalnie 1 dzień na ponowne odwiedzenie tego samego obszaru przy użyciu obydwu satelitów.\n\n** Dostępność danych:** Od maja 2016 r.\n\n**Typowe zastosowanie:** Monitorowanie zmian klimatu, monitorowanie roślinności, aktywne wykrywanie pożarów, monitorowanie temperatury powierzchni lądu i morza."]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":["W oparciu o kombinację pasm 4, 3, 2"]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":["W oparciu o kombinację pasm (B04-B03)/(B04 + B03)"]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":["W oparciu o kombinację pasm 5, 4, 3"]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":["W oparciu o pasma prawdziwych kolorów 4, 3, 2 i pasmo PAN 8"]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":["W oparciu o kombinację pasm (B05-B04)/(B05 + B04)"]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":["VV - liniowa gamma0 - ortorektyfikowana"]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":["VV - liniowa gamma0 - nieortorektyfikowana"]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":["VH - liniowa gamma0 - ortorektyfikowana"]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":["W oparciu o kombinację pasm 3, 2, 1"]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":["VH - liniowa gamma 0 - nieortorektyfikowana"]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":["Obrazowanie w kolorze poprzez mapowanie pasm wejściowych. Wartość [RGB] = [VV, 2 VH, VV / VH / 100,0] - liniowa gamma0 - ortorektyfikacja"]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":["VV - decybel gamma0 [-20,0] - ortorektyfikowana"]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":["VH - decybel gamma0 [-20,0] - ortorektyfikowana"]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":["Zwraca kompozyt składający się z (VH, VV, VH-VV)"]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":["HH - liniowa gamma0 - ortorektyfikowana"]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":["HV - liniowa gamma0 - ortorektyfikowana"]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":["Obrazowanie w kolorze poprzez mapowanie pasm wejściowych. Wartość [RGB] = [VV, 2 VH, VV / VH / 100,0] - liniowa gamma0 - ortorektyfikacja"]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":["HH - decybel gamma0 [-20,0] - ortorektyfikowana"]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":["HV - decybel gamma0 [-20,0] - ortorektyfikowana"]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":["HH - liniowa gamma0 - nieortorektyfikowana"]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":["W oparciu o pasma 4, 3, 2"]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":["W oparciu o pasma 8, 4, 3"]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":["W oparciu o pasma 12, 11, 4"]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":["W oparciu o kombinację pasm (B8 - B4)/(B8 + B4)"]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":["W oparciu o kombinację pasm (B8A - B11)/(B8A + B11)"]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":["W oparciu o pasma 12, 8A, 4"]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":["W oparciu o kombinacje pasm (B3 - B8)/(B3 + B8)"]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":["W oparciu o kombinacje pasm (B3 - B11)/(B3 + B11); wartości powyżej 0,42 są uważane za śnieżne"]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":["Klasyfikacja danych Sentinel2 jako wynik algorytmu klasyfikacji Scene agencji ESA."]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":["Wskaźnik aerozolu UV od 380 do 340 nm"]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":["W oparciu o kombinację pasm (B3 - B11)/(B3 + B11)"]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":["OLCI Wskaźnik Chlorofilu Lądowego, w oparciu o kombinację pasm (B12 - B11)/(B11 - B10)"]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":["Wskaźnik aerozolu UV od 388 i 354 nm"]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":["Uśredniony w kolumnie stosunek mieszania metanu w suchym powietrzu"]},"Cloud base height":{"msgid":"Cloud base height","msgstr":["Wysokość podstawy chmur"]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":["Ciśnienie w podstawie chmury"]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":["Efektywna frakcja chmur radiometrycznych"]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":["Grubość optyczna chmury"]},"Cloud top height":{"msgid":"Cloud top height","msgstr":["Wysokość wierzchołka chmury"]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":["Ciśnienie wierzchołka chmury"]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":["Całkowita zawartość Dwutlenku Węgla w kolumnie powietrza"]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":["Zawartość formaldehydu w pionowej kolumnie troposferycznej"]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":["Zawartość Dwutlenku Azotu w kolumnie troposferycznej"]},"Ozone total column":{"msgid":"Ozone total column","msgstr":["Całkowita zawartość ozonu w kolumnie powietrza"]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":["Całkowita zawartość Dwutlenku Siarki w kolumnie powietrza"]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":["W oparciu o pasma 2, 1, 4"]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":["W oparciu o kombinację pasm (B02 - B01)/(B02 + B01)"]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":["W oparciu o kombinację pasm (B02 - B05)/(B02 + B05)"]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":["W oparciu o kombinację pasm (B06 - B07)/(B06 + B07)"]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":["W oparciu o pasma 1, 4, 3"]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":["W oparciu o kombinację pasm 7, 5, 3"]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":["(B09 - B08)/(B09 + B08)"]},"B09 / B08":{"msgid":"B09 / B08","msgstr":["B09/B08"]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":["W oparicu o kombinację pasm 13, 5, 2"]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":["W oparciu o pasma 13, 4, 1"]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":["W oparciu o pasma 13, 5, 2"]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":["W oparciu o kombinację pasm (B13-B07)/(B13 + B07)"]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":["Wskaźnik Chlorofilu Lądowego"]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V 10-dniowa Synteza\nSzczyt Korony (z korektą atmosferyczną)\nrozdzielczość czasowa: 10 dni\n Rozdzielczość: 333 M (rozmiar piksela)"]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V codzienna Synteza\n Górna Warstwa Atmosfery\n rozdzielczość czasowa: 1 dzień\n Rozdzielczość: 333 M (rozmiar piksela)"]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-dniowa Synteza\n Górna Warstaw Atmosfery\n rozdzielczość czasowa: 5 dni Rozdzielczość\n: 100 M (rozmiar piksela)"]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V Synteza 10-dniowa\n Szczyt Korony (z korektą atmosferyczną)\nrozdzielczość czasowa: 1 dzień\n Rozdzielczość: 333 M (rozmiar piksela)"]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":["OrbitTracks_Aqua_Descending"]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-dniowa Synteza\n Szczyt Korony (z korektą atmosferyczną)\nrozdzielczość czasowa: 5 dni\n Rozdzielczość: 333 M (rozmiar piksela)"]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":["OrbitTracks_Aqua_Ascending"]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":["OrbitTracks_Aura_Ascending"]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":["OrbitTracks_Aura_Descending"]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":["OrbitTracks_CloudSat_Ascending"]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":["OrbitTracks_Calipso_Ascending"]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":["OrbitTracks_Calipso_Descending"]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":["OrbitTracks_CloudSat_Descending"]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":["OrbitTracks_CYGNSS_Ascending"]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":["OrbitTracks_CYGNSS_Descending"]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":["OrbitTracks_GCOM-C_Ascending"]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":["OrbitTracks_GCOM-C_Descending"]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":["OrbitTracks_GCOM-W1_Ascending"]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":["OrbitTracks_GCOM-W1_Descending"]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":["OrbitTracks_GOSAT-2_Ascending"]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":["OrbitTracks_GOSAT-2_Descending"]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":["OrbitTracks_GOSAT_Ascending"]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":["OrbitTracks_GOSAT_Descending"]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":["OrbitTracks_GPM_Ascending"]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":["OrbitTracks_GPM_Descending"]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":["OrbitTracks_ICESAT-2_Ascending"]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":["OrbitTracks_ICESAT-2_Descending"]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":["OrbitTracks_ISS_Ascending"]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":["OrbitTracks_ISS_Descending"]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":["OrbitTracks_Landsat-7_Ascending"]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":["OrbitTracks_Landsat-7_Descending"]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":["OrbitTracks_Landsat-8_Ascending"]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":["OrbitTracks_METOP-A_Ascending"]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":["OrbitTracks_METOP-B_Descending"]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":["OrbitTracks_Landsat-8_Descending"]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":["OrbitTracks_METOP-C_Ascending"]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":["OrbitTracks_METOP-C_Descending"]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":["OrbitTracks_NOAA-20_Ascending"]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":["OrbitTracks_NOAA-20_Descending"]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":["OrbitTracks_METOP-B_Ascending"]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":["OrbitTracks_OCO-2_Ascending"]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":["OrbitTracks_OCO-2_Descending"]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":["OrbitTracks_SAOCOM1-A_Ascending"]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":["OrbitTracks_SAOCOM1-A_Descending"]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":["OrbitTracks_Sentinel-1A_Ascending"]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":["OrbitTracks_Sentinel-1B_Ascending"]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":["OrbitTracks_Sentinel-1A_Descending"]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":["OrbitTracks_METOP-A_Descending"]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":["OrbitTracks_Sentinel-1B_Descending"]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":["OrbitTracks_Sentinel-2A_Ascending"]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":["OrbitTracks_Sentinel-2A_Descending"]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":["OrbitTracks_Sentinel-2B_Ascending"]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":["OrbitTracks_Sentinel-2B_Descending"]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":["OrbitTracks_Sentinel-5P_Ascending"]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":["OrbitTracks_Sentinel-5P_Descending"]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":["OrbitTracks_SMAP_Ascending"]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":["OrbitTracks_SMAP_Descending"]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":["OrbitTracks_Suomi_NPP_Descending"]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":["OrbitTracks_Suomi_NPP_Ascending"]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":["OrbitTracks_Terra_Descending"]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":["OrbitTracks_Terra_Ascending"]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":["W oparciu o pasma 4, 3, 2 wzmocnione pasmami 12 i 11."]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":["W oparciu o pasma B07, B06, B04"]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":["W oparciu o kombinacje pasm (B8 - B4)/(B8 + B4)"]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":["W oparciu o pasmo termalne 10"]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":["W oparciu o pasma B12, B11, B8A"]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":["W oparciu o kombinację pasm (B08 - B12)/(B08 + B12)"]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":["Ulepszona wizualizacja naturalnych barw"]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":["W oparciu o kombinację pasm 8, 6, 4"]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":["Ulepszony Indeks Wegetacji"]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":["W oparciu o kombinację pasm: BSI, B08, B11"]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":["Sklasyfikowany Znormalizowany Różnicowy Wskaźnik Wilgotności (NDMI) dla irygacji"]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":["W oparciu o pasma B11, B08, B02"]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":["Barwa Fałszywa 13, 5, 2"]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":["W oparciu o kombinację pasm (B13-B07)/(B13 + B07)"]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":["W oparciu o pasma 12, 8, 2"]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":["W oparciu o pasma 4, 3, 2"]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":["W oparciu o pasma 8, 4, 3"]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":["W oparciu o pasma 12, 8, 2"]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":["W oparciu o pasma 8, 11, 12"]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":["Sedymentacja wody i zawartość chlorofilu"]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":["W oparciu o pasma 12, 8A, 4"]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":["W oparciu o znormalizowany różnicowy wskaźnik śniegu (NDSI)"]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":["W oparciu o kombinację pasm 11, 8, 2"]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":["W oparciu o pasma B12, B11, B04"]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":["W oparciu o pasma 4, 3, 2"]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":["Wskaźnik roślinności odpornej na warunki atmosferyczne"]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":["Indeks Wegetacji Dostosowany do Charakterystyki Gleby"]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":["Zmodyfikowany Wskaźnik Reflektancji Atrocyjaniny"]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":["# Pasma emisji podczerwieni termalnej z pożaru \n\nInstrument do Badań Temperatury Morza i Powierzchni Lądu (SLSTR) satelity Sentinel-3 ma dwa dedykowane kanały (F1 i F2), których celem jest wykrywanie temperatury powierzchni lądu (LST). Kanał F2 z centralną długością fali 10854 nm mierzy w podczerwieni średniej lub TIR.Jest to bardzo przydatne podczas monitorowania pożaru i wysokiej temperatury z rozdzielczością 1 km.\n\n\n\nWięcej informacji [tutaj]: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature"]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":["# Metan (CH4)\n\n\n\nMetan jest, obok dwutlenku węgla, najważniejszym czynnikiem przyczyniającym się do antropogenicznie (spowodowanego działalnością człowieka) wzmaganego efektu cieplarnianego. Pomiary są podawane w częściach na miliard (ppb) z rozdzielczością przestrzenną 7 km x 3,5 km.\n\n\n\nWięcej informacji [tutaj] (http://www.tropomi.eu/data-products/methane)"]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":["# Formaldehyd (HCHO)\n\n\n\nDługoterminowe obserwacje satelitarne formaldehydu troposferycznego (HCHO) są niezbędne do wspierania badań jakości powietrza i badań chemiczno-klimatycznych w skali regionalnej i globalnej. Sezonowe i międzyroczne wahania rozmieszczenia formaldehydu są związane głównie ze zmianami temperatury i przypadkami pożarów,ale także ze zmianami w działalności antropogenicznej (spowodowanej przez człowieka). Jego żywotność wynosi kilku godzin;stężenia HCHO w warstwie granicznej mogą być bezpośrednio związane z uwalnianiem krótkotrwałych węglowodorów, których w większości przypadków nie można obserwować bezpośrednio z kosmosu.Pomiary są podawane w molach na metr kwadratowy (mol/m^2).\n\n\n\n Więcej informacji [tutaj] (http://www.tropomi.eu/data-products/formaldehyde)"]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":["# Dwutlenek siarki (SO2)\n\n\n\nDwutlenek siarki przedostaje się do atmosfery ziemskiej zarówno poprzez procesy naturalne, jak i antropogeniczne (wytworzone przez człowieka).Odgrywa rolę w chemii w skali lokalnej i globalnej, a jego wpływ waha się od krótkotrwałego zanieczyszczenia po zmiany klimatyczne.Tylko około 30% emitowanego SO2 pochodzi ze źródeł naturalnych; większość ma pochodzenie antropogeniczne.Instrument Sentinel-5P/TROPOMI pobiera próbki powierzchni Ziemi przy okresie rewizyty wynoszącym jeden dzień z rozdzielczością przestrzenną 3,5 x 7 km, która umożliwia obrazowanie drobnych szczegółów, w tym wykrywanie mniejszych smug SO2.Pomiary są podawane w molach na metr kwadratowy (mol/m ^ 2).\n\n\n\nWięcej informacji [tutaj] (http://www.tropomi.eu/data-products/sulphur-dioxide)"]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":["# Ozon (O3)\n\n\n\nOzon ma kluczowe znaczenie dla równowagi atmosfery ziemskiej. W stratosferze warstwa ozonowa chroni biosferę przed niebezpiecznym słonecznym promieniowaniem ultrafioletowym. W troposferze działa jako skuteczny środek oczyszczający, ale w wysokim stężeniu staje się szkodliwy dla zdrowia ludzi, zwierząt i roślinności.Ozon jest również istotnym czynnikiem przyczyniającym się do obecnych zmian klimatycznych. Od czasu odkrycia dziury ozonowej w Antarktydzie w latach 80. i późniejszego Protokołu Montrealskiego regulującego produkcję substancji awierających chlor, zubożających warstwę ozonową,ozon jest przedmiotem rutynowego monitoringu z ziemi i z kosmosu. Pomiary są podawane w molach na metr kwadratowy (mol/m^2)\n\n\n\n Więcej informacji [tutaj] (http://www.tropomi.eu/data-products/total-ozone-column)"]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":["# Dwutlenek azotu (NO2)\n\n\n\nDwutlenek azotu (NO2) i tlenek azotu (NO) razem nazywane są zwykle tlenkami azotu. Są ważnymi gazami śladowymi w atmosferze ziemskiej, obecnymi zarówno w troposferze, jak i stratosferze.Dostają się do atmosfery w wyniku działalności antropogenicznej (w szczególności spalania paliw kopalnych i biomasy) oraz procesów naturalnych (np. procesów mikrobiologicznych w glebie, pożarów, wyładowań atmosferycznych).Pomiary są podane w molach na metr kwadratowy (mol/m^2).\n\n\n\n Więcej informacji [tutaj] (http://www.tropomi.eu/data-products/nitrogen-dioxide)"]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":["# Tlenek węgla (CO)\n\n\n\nTlenek węgla (CO) jest ważnym atmosferycznym gazem śladowym. Na niektórych obszarach miejskich jest głównym składnikiem zanieczyszczeń atmosfery. Główne źródła CO to spalanie paliw kopalnych, spalanie biomasy oraz atmosferyczne utlenianie metanu i innych węglowodorów.Całkowita zawartość tlenku węgla w kolumnie jest mierzona w molach na metr kwadratowy (mol/m^2).\n\n\n\nWięcej informacji [tutaj] (http://www.tropomi.eu/data-products/carbon-monoxide)"]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":["# Indeks Aerozoli\n\nIndeks Aerozoli (AI) jest wskaźnikiem jakościowym wskazującym na obecność podwyższonych warstw aerozoli w atmosferze. Może być używany do wykrywania obecności aerozoli pochłaniających promieniowanie UV, takich jak pył pustynny i chmury popiołu wulkanicznego. Wartości dodatnie (od jasnoniebieskiego do czerwonego) wskazują na obecność aerozolu pochłaniającego promieniowanie UV.Wskaźnik ten jest obliczany dla dwóch par długości fal: 340/380 Nm i 354/388 nm.\n\nWięcej informacji [tutaj] (https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)"]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":["# Wysokość podstawy chmury\n\nWysokość podstawy chmury mierzona w metrach (m)."]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":["# Ciśnienie w podstawie chmury\n\nCiśnienie mierzone przy podstawie chmury w Pascalach (Pa)."]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":["# Grubość optyczna chmury\n\nGrubość chmury jest kluczowym parametrem określającym właściwości optyczne chmury. Jest to miara tego, ile światła słonecznego przechodzi przez chmurę, docierając do powierzchni Ziemi. Im większa grubość optyczna chmury, tym więcej światła słonecznego chmura rozprasza i odbija. Kolor ciemny niebieski wskazuje miejsca, w których występująniskie wartości grubości optycznej chmury, a czerwony wskazuje większą grubość optyczną chmury."]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":["# Wysokość podstawy chmury\n\nWysokość podstawy chmury mierzona w metrach (m)."]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":["# Ciśnienie w podstawie chmury \n\nCiśnienie mierzone przy podstawie chmury w Pascalach (Pa)."]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":["# Znormalizowany Różnicowy Wskaźnik Wegetacji (NDVI)\n\nZnormalizowany Różnicowy Wskaźnik Wegetacji jest prostym, ale efektywnym wskaźnikiem służącym do ilościowego określania roślinności zielonej. Wskazuje on stan zdrowia wegetacji w oparciu o sposób, w jaki rośliny odbijają światło na określonych długościach fal. Zakres wartości NDVI wynosi od -1 do 1. Ujemne wartości NDVI (wartości zbliżone do -1) odpowiadają wodzie.Wartości zbliżone do zera (od -0,1 do 0,1) odpowiadają na ogół jałowym obszarom skał, piasku lub śniegu.Niskie, dodatnie wartości oznaczają krzewy i łąki (ok. 0,2 do 0,4), natomiast wysokie wartości wskazują na umiarkowane i tropikalne lasy deszczowe (wartości zbliżone do 1).\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) oraz [tutaj.] (https://eos.com/ndvi/)"]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":["# Ulepszony Indeks Wegetacji (EVI)\n\nUlepszony Indeks wegetacji (EVI) jest „zoptymalizowanym” wskaźnikiem wegetacji, ponieważ uwzględnia korekcję sygnałów pochodzących z podłoża gleby oraz wpływy atmosferyczne. Jest on bardzo przydatny na obszarach gęsto zalesionych. Zakres wartości dla EVI wynosi od -1 do 1, przy zdrowej roślinności na ogół około 0,20 do 0,80.\n\n\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) i [tutaj.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)"]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Indeks Wegetacji Odpornej na Warunki Atmosferyczne (ARVI)\n\nIndex Wegetacji Odpornej na Warunki Atmosferyczne (ARVI) to wskaźnik roślinności, który minimalizuje skutki rozproszenia atmosferycznego. Jest on najbardziej przydatny w regionach o dużej zawartości aerozolu atmosferycznego (mgła, pył, dym, zanieczyszczenie powietrza). Zakres ARVI wynosi od -1 do 1, gdzie roślinność zielona zazwyczaj mieści się w przedziale od 0,20 do 0,80.\n\n\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) i [tutaj.] (https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Indeks Wegetacji Dostosowanej do Gleby (SAVI)\n\nIndeks Wegetacji Dostosowanej do Gleby jest podobny do Znormalizowanego Różnicowego Indeksu Wegetacji (NDVI), ale jest stosowany na obszarach o niskim poziomie pokrycia wegetacyjnego (<40%). Indeks ten jest techniką transformacji, która minimalizuje wpływ jasności gleby na widmowe indeksy roślinności obejmujące długości fal czerwieni i bliskiej podczerwieni (NIR). Wskaźnik jest pomocny podczas analizy młodych upraw, suchych regionów o rzadkiej roślinności i odsłoniętych powierzchni gleby.\n\n\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) i [tutaj.] (https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":["# Zmodyfikowany Indeks Odbicia Antocyjanów (mARI/ARI2)\n\nAntocyjany to pigmenty powszechne w roślinach wyższych, nadające im czerwone, niebieskie i fioletowe zabarwienie. Dostarczają one cennych informacji o stanie fizjologicznym roślin, gdyż są uważane za wskaźniki określające różne rodzaje stresu roślin. Wskaźnik odbicia antocyjanów jest najwyższy około 550nm. Jednak te same długości fal są również odbijane przez chlorofil. Aby wyodrębnić antocyjany, odejmuje się pasmo widmowe 700nm, które odzwierciedla tylko chlorofil, a nie antocyjany.\n\nAby skorygować gęstość i grubość liści, pasmo widmowe w bliskiej podczerwieni (w zalecanych długościach fal 760-800nm), które jest związane z rozproszeniem przez listowie, jest dodawane do podstawowego indeksu ARI. Nowy indeks nosi nazwę zmodyfikowanego ARI lub mARI (również ARI2).\n\nwartości mARI dla badanych drzew w [tym oryginalnym artykule] (https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) wahały się pod względem wartości od 0 do 8.\n\n\n\n\n\nWięcej informacji [tutaj.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)"]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":["# Skrypt Zielonego Miasta\n\nSkrypt zielonego miasta ma na celu zwiększenie świadomości na temat terenów zielonych w miastach na całym świecie. Skrypt bierze pod uwagę Znormalizowany Różnicowy Indeks Wegetacji (NDVI) i rzeczywiste długości fal kolorów;oddziela obszary zabudowane od roślinnych, dzięki czemu jest przydatny do wykrywania obszarów miejskich. Obszary zabudowane są wyświetlane na szaro, a roślinność na zielono.\n\n\n\n\n\n Więcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":["# Skrypt Miejski Sklasyfikowany\n\nSkrypt Miejski Sklasyfikowany ma na celu wykrycie obszarów zabudowanych poprzez oddzielenie ich od jałowej ziemi, roślinności i wody. Obszary o dużej zawartości wilgoci są zwracane na niebiesko; obszary wskazujące obszary zabudowane są zwracane na biało; obszary porośnięte roślinnością są zwracane na zielono; wszystko inne wskazuje na jałową ziemię i jest wyświetlane w brązowych kolorach.\n\n\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)"]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":["# Skrypt Kolorów w Podczerwieni dla Terenów Miejskich\n\nTen skrypt, stworzony przez Leo Tolari, łączy wizualizację prawdziwych kolorów z długością fal bliskiej podczerwieni (NIR) i podczerwieni krótkofalowej (SWIR). Skrypt podkreśla obszary miejskie lepiej niż prawdziwy kolor, a jednocześnie wygląda naturalnie.\n\n\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)"]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":["# NDMI dla Obciążeń Wilgoci\n\nZnormalizowany Index Różnicowy Wilgotności (NDMI) dla obciążeń wilgoci, może być używany do wykrywania irygacji. Dla wszystkich wartości wskaźnika powyżej 0, znając przeznaczenie i pokrycie terenu, można określić, czy miało miejsce nawadnianie. Znając rodzaj upraw (np. uprawy cytrusów), można określić czy nawadnianie jest skuteczne, czy też nie w kluczowym okresie wegetacyjnym, a także dowiedzieć się, czy niektóre części gospodarstwa są niedostatecznie lub nadmiernie nawadniane.\n\n\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)"]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":["# Znormalizowany Różnicowy Index Wilgotności (NDMI)\n\nZnormalizowany Różnicowy Indeks Wilgotności (NDMI) jest używany do określania zawartości wody w roślinności i monitorowania suszy.Zakres wartości NDMI wynosi od -1 do 1. Ujemne wartości NDMI (wartości zbliżone do -1) odpowiadają jałowej glebie. Wartości około zera (-0,2 do 0,4) generalnie wskazują na stres wodny. Wysokie, dodatnie wartości reprezentują wysoką koronę drzew bez stresu wodnego (około 0,4 do 1).\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":["# Znormalizowany Indeks Różnicowy Wody (NDWI)\n\nZnormalizowany indeks różnicowy wody jest najbardziej odpowiedni do odwzorowania zbiorników wodnych. Wartości zbiorników wodnych są większe niż 0,5. Roślinność ma mniejsze wartości. Zabudowane elementy terenu mają wartości dodatnie od zera do 0,2.\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":["# Znormalizowany Indeks Różnicowy Wody (NDWI)\n\nZnormalizowany indeks różnicowy wody jest najbardziej odpowiedni do odwzorowania zbiorników wodnych. Wartości zbiorników wodnych są większe niż 0,5. Roślinność ma mniejsze wartości. Zabudowane elementy terenu mają wartości dodatnie od zera do 0,2."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Kompozyt fałszywego koloru\n\nKompozyt fałszywego koloru wykorzystuje co najmniej jedną niewidoczną długość fali do zobrazowania Ziemi. Bardzo popularny jest kompozyt fałszywego koloru, wykorzystujący pasma bliskiej podczerwieni, czerwieni i zieleni (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach). Kompozyt fałszywych kolorów jest najczęściej używany do oceny gęstości i zdrowia roślin, ponieważ rośliny odbijają bliską podczerwień i zielone światło, podczas gdy pochłaniają kolor czerwony. Miasta i odsłonięta gleba są szare lub brązowe, a woda wydaje się niebieska lub czarna.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) oraz [tutaj.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Kompozyt fałszywego koloru\n\nKompozyt fałszywego koloru wykorzystuje co najmniej jedną niewidoczną długość fali do zobrazowania Ziemi. Bardzo popularny jest kompozyt fałszywego koloru wykorzystujący pasma bliskiej podczerwieni, czerwieni i zieleni (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach). Kompozyt fałszywego koloru jest najczęściej używany do oceny gęstości i zdrowia roślin, ponieważ rośliny odbijają bliską podczerwień i zielone światło, podczas gdy pochłaniają kolor czerwony. Miasta i odsłonięta gleba są szare lub brązowe, a woda wydaje się niebieska lub czarna.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) oraz [tutaj.](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":["# Kompozyt fałszywego koloru \n\nFałszywy kolor wykorzystuje co najmniej jedną niewidoczną długość fali do zobrazowania Ziemi. Bardzo popularny jest kompozyt fałszywego koloru wykorzystujący pasma bliskiej podczerwieni, czerwieni i zieleni (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach). Kompozyt fałszywych kolorów jest najczęściej używany do oceny gęstości i zdrowia roślin, ponieważ rośliny odbijają bliską podczerwień i zielone światło, podczas gdy pochłaniają kolor czerwony. Miasta i odsłonięta gleba są szare lub brązowe, a woda wydaje się niebieska lub czarna.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) oraz [tutaj.](https://earthobservatory.nasa.gov/features/FalseColor)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Kompozyt fałszywego koloru\n\nKompozyt fałszywego koloru wykorzystuje co najmniej jedną niewidoczną długość fali do zobrazowania Ziemi. Bardzo popularny jest kompozyt fałszywego koloru wykorzystujący pasma bliskiej podczerwieni, czerwieni i zieleni (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach). Kompozyt fałszywego koloru jest najczęściej używany do oceny gęstości i zdrowia roślin, ponieważ rośliny odbijają podczerwień i zielone światło, podczas gdy pochłaniają kolor czerwony. Miasta i odsłonięta gleba są szare lub brązowe, a woda wydaje się niebieska lub czarna.\n\n\n\nWięcej informacji [tutaj.] (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":["# Kompozyt prawdziwego koloru\n\nCzujniki przenoszone przez satelity mogą zobrazować Ziemię w różnych obszarach widma elektromagnetycznego. Każdy region widma nazywany jest pasmem. Sentinel-2 ma 13 pasm. Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego, zielonego i niebieskiego. W wyniku czego powstaje produkt o naturalnym kolorze, który odzwierciedla Ziemię tak, jak ludzie by ją zobaczyli w sposób naturalny.\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) oraz [tutaj.] (http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy)."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Kompozyt prawdziwego koloru\n\nCzujniki przenoszone przez satelity mogą zobrazować Ziemię w różnych obszarach widma elektromagnetycznego.Każdy region widma nazywany jest pasmem. Landsat 5 ma 7 pasm. Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego, zielonego i niebieskiego, w wyniku czego powstaje produkt o naturalnym kolorze, który jest odzwierciedleniem Ziemi tak, jak jest widziana w sposób naturalny ludzkim okiem.\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) i [tutaj] (https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Kompozyt prawdziwego koloru\n\n Sensory przenoszone przez satelity mogą zobrazować Ziemię w różnych obszarach widma elektromagnetycznego. Każdy region widma nazywany jest pasmem. Landsat 7 ma 8 pasm.Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego, zielonego i niebieskiego, w wyniku czego powstaje produkt o naturalnym kolorze, który jest odzwierciedleniem Ziemi tak, jak jest widziana w sposób naturalny ludzkim okiem.\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) i [tutaj] (https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Kompozyt prawdziwego koloru\n\nSensory przenoszone przez satelity mogą zobrazować Ziemię w różnych obszarach widma elektromagnetycznego. Każdy region widma nazywany jest pasmem. Landsat 8 ma 11 pasm.Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego, zielonego i niebieskiego, w wyniku czego powstaje produkt o naturalnym kolorze, który jest odzwierciedleniem Ziemi tak, jak jest widziana w sposób naturalny ludzkim okiem.\n\n\n\nWięcej informacji [tutaj] (https://custom-scripts.sentinel-hub.com/landsat-8/composites/) i [tutaj.] (https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Kompozyt prawdziwego koloru\n\nSensory przenoszone przez satelity mogą zobrazować Ziemię w różnych regionach widma elektromagnetycznego. Każdy region widma nazywany jest pasmem. Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego,zielonego i niebieskiego, w wyniku czego powstaje produkt o naturalnym kolorze, który jest odzwierciedleniem Ziemi tak, jak jest widziana w sposób naturalny ludzkim okiem.\n\n\n\nWięcej informacji [tutaj.] (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":["# Kompozyt prawdziwego koloru\n\nSensory przenoszone przez satelity mogą zobrazować Ziemię w różnych obszarach widma elektromagnetycznego. Każdy region widma nazywany jest pasmem.Kompozyt prawdziwego koloru wykorzystuje widzialne pasma światła czerwonego, zielonego i niebieskiego w odpowiednich kanałach koloru czerwonego,zielonego i niebieskiego, w wyniku czego powstaje produkt o naturalnym kolorze, który jest odzwierciedleniem Ziemi tak, jak jest widziana w sposób naturalny ludzkim okiem.\n\n\n\nWięcej informacji [tutaj.] (https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)"]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Wyostrzony Panchromatycznie Prawdziwy Kolor\n\nWyostrzony panchromatycznie kompozyt prawdziwego koloru jest uzyskiwany przy użyciu zwykłych danych o prawdziwych kolorach (czerwony, zielony i niebieski (RGB)) i wzmacniania ich za pomocą pasma panchromatycznego 8 lub pasma panoramicznego (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach).Obraz z pasma panoramicznego jest podobny do filmu czarno-białego: łączy światło z czerwonej, zielonej i niebieskiej części widma w jedną miarę całkowitej widzialnej reflektancji. Obrazy panchromatycznie wyostrzone posiadają cztery razy większą rozdzielczość niż zwykły kompozyt prawdziwego koloru, co znacznie zwiększa użyteczność zdjęć Landsat.\n\n\n\nWięcej informacji [tutaj] (https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) i [tutaj.] (https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":["# Miejski Kompozyt Fałszywego Koloru\n\nTen kompozyt służy do wyraźniejszej wizualizacji obszarów zurbanizowanych. Roślinność jest widoczna w odcieniach zieleni, a obszary zurbanizowane reprezentowane są przez biel, szarość lub fiolet.Gleby, piasek i minerały są ukazywane w różnorodnych kolorach. Śnieg i lód pojawiają się w kolorze ciemnoniebieskim, a woda w kolorze czarnym lub niebieskim.Obszary zalane są głęboko ciemnoniebieskie i prawie czarne.Kompozyt jest przydatny do wykrywania pożarów i kalder wulkanów, ponieważ są one ukazywane w odcieniach czerwieni i żółci.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) i [tutaj.] (https://eos.com/false-color/)"]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Miejski Kompozyt Fałszywego Koloru\n\nTen kompozyt wykorzystuje kombinację pasm w podczerwieni widzialnej i krótkofalowej (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach).Ukazuje on roślinność w odcieniach zieleni. Podczas gdy ciemniejsze odcienie zieleni wskazują na gęstszą roślinność, roślinność rzadsza ma jaśniejsze odcienie.Obszary miejskie są niebieskie, a gleby mają różne odcienie brązu.\n\n\n\nWięcej informacji [tutaj.] (https://gisgeography.com/landsat-8-bands-combinations/)"]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":["# Kompozyt Rolniczy\n\nten kompozyt wykorzystuje krótkofalową podczerwień, bliską podczerwień i pasma koloru niebieskiego do monitorowania stanu upraw (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może obrazować Ziemię w różnych pasmach). Zarówno pasma krótkofalowe, jak i pasma bliskiej podczerwieni są szczególnie skuteczne w uwydatnianiu gęstej roślinności, która w kompozycie jest ukazywana w kolorze ciemnozielonym.Uprawy są ukazuwane w kolorze żywej zieleni, a goła ziemia ma kolor magenta.\n\n\n\nWięcej informacji [tutaj.] (https://earthobservatory.nasa.gov/features/FalseColor/page5.php) i [tutaj.] (https://gisgeography.com/sentinel-2-bands-combinations/)"]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":["# Klasyfikator Śniegu\n\nAlgorytm klasyfikatora śniegu ma na celu wykrycie śniegu poprzez klasyfikację pikseli na podstawie różnych progów jasności i Znormalizowanego Indeksu Różnicowego Śniegu (NDSI).Wartości sklasyfikowane jako śnieg są przedstawiane w kolorze jasnoniebieskim. Skrypt może zbyt wysoko oszacować obszary śnieżne nad chmurami.\n\n\n\nWięcej informacji [tutaj.] (https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":["# Zobrazowanie Jakości Wody Ulyssys (UWQV)\n\nSkrypt ten ma na celu dynamiczną wizualizację stanu chlorofilu i osadów w zbiornikach wodnych, które są głównymi wskaźnikami jakości wody.Zawartość chlorofilu waha się kolorystycznie od ciemnoniebieskiego (niska zawartość chlorofilu) poprzez zielony do czerwonego (wysoka zawartość chlorofilu).Stężenia osadów mają kolor brązowy; nieprzezroczysty brąz wskazuje na wysoką zawartość osadu.\n\n\n\nWięcej informacji [tutaj.] (https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)"]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":["# Naturalne Kolory z Optymalizacją dla Uwydatnienia \n\nTen skrypt ma na celu przedstawienie Ziemi w pięknych, naturalnych barwach. Wykorzystuje on optymalizację uwydatnienia, aby uniknąć wypalenia pikseli i wyrównać ekspozycję.\n\n\n\nWięcej informacji [tutaj.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)"]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":["# Kompozyt Geology 12, 8, 2\n\n Ten kompozyt wykorzystuje pasmo podczerwieni krótkofalowej (SWIR) nr 12 do rozróżnienia różnych typów skał (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach).Każdy rodzaj skały i minerału inaczej odbija krótkofalowe światło podczerwone, pozwalając na uzyskanie odwzorowania struktury geologicznej poprzez porównanie odbitego światła SWIR. Pasmo bliskiej podczerwieni (NIR) nr 8 uwypukla roślinność, a pasmo nr 2 wykrywa wilgoć, umożliwiając rozróżnienie materiałów budujących podłoże. Kompozyt ten jest przydatny do wyszukiwania formacji i cech geologicznych (np. uskoków, pęknięć), skał (np. granitu, bazaltu itp.) oraz znajduje zastosowanie w górnictwie.\n\n\n\nWięcej informacji [tutaj.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)"]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":["# Kompozyt Geology 8, 11, 12 \n\nTen kompozyt wykorzystuje pasma podczerwieni krótkofalowej (SWIR) nr 11 i 12 do rozróżnienia różnych typów skał (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach).Każdy rodzaj skały i minerału inaczej odbija krótkofalowe światło podczerwone, pozwalając na uzyskanie odwzorowania struktury geologicznej poprzez porównanie odbitego światła SWIR. Pasmo bliskiej podczerwieni (NIR) nr 8 uwypukla roślinność, umożliwiając rózróżnienie materiałów budujących podłoże. Roślinność w tym kompozycie ma kolor czerwony. Kompozyt ten jest przydatny do rozróżnienia roślinności i terenu, a zwłaszcza cech geologicznych, które mogą być pomocne w poszukiwaniu i wydobywaniu minerałów.\n\n\n\nWięcej informacji [tutaj] (https://earthobservatory.nasa.gov/features/FalseColor/page5.php) i [tutaj.] (http://murphygeological.com/new---sentinel-2.html#)"]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":["# Pożary\n\nTen skrypt stworzony przez Pierre'a Markusego wizualizuje pożary przy użyciu danych z Sentinel-2. Łączy naturalny kolor tłaz niektórymi danymi NIR/SWIR dotyczącymi penetracji dymu i zawierającymi większą ilość szczegołów, dodając jednocześnie najważniejsze informacje z B11 i B12, aby ukazać pożary w kolorze czerwonym i pomarańczowym.\n\n\n\nWięcej informacji [tutaj.](Https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)"]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":["# Wzmocniony Prawdziwy Kolor\n\nTen skrypt, stworzony przez Pierre Markuse, wykorzystuje wiele pasm (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach) oraz kontrolę nasycenia i jasności, aby poprawić wizualizację prawdziwych kolorów.\n\n\n\nWięcejinformacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)"]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":["# Indeks Wypalonego Obszaru\n\nIndeks Wypalonego Obszaru wykorzystuje szersze spektrum pasm Widocznych, Obrzeża Czeriwni, NIR oraz SWIR\n\nOpis wartości:() => Zakres wartości indeksu wynosi od `-1` do `1` dla śladów po pożarach, oraz `1` - `6` dla aktywnych pożarów.Różne intensywności pożaru mogą skutkować różnymi progami; aktualne wartości zostały skalibrowane, zgodnie z zamysłem autora, głównie pod kątem regionu śródziemnomorskiego.\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)>"]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":["# Znormalizowany Współczynnik Wypalenia (NBR)\n\nZnormalizowany Współczynnik Wypalenia jest często używany do oszacowania stopnia wpływu pożaru. Wykorzystuje fale bliskiej (NIR) i krótkofalowej podczerwieni (SWIR).Zdrowa roślinność ma wysoki współczynnik odbicia w bliskiej podczerwieni i niski współczynnik odbicia w podczerwieni krótkofalowej.Z drugiej strony, obszary wypalone mają wysoki współczynnik odbicia w podczerwieni krótkofalowej, ale niską reflektancję w bliskiej podczerwieni Ciemniejsze piksele wskazują na obszary wypalone.\n\n\n\nWięcej informacji [tutaj](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":["# Przenikanie atmosferyczne\n\nTen kompozyt wykorzystuje różne pasma (pasmo to obszar widma elektromagnetycznego; czujnik satelitarny może zobrazować Ziemię w różnych pasmach) w niewidzialnej części widma elektromagnetycznego, aby zmniejszyć wpływ atmosfery na obraz.Pasma podczerwieni krótkofalowej nr 11 i 12 są silnie odbijane przez nagrzane obszary, przez co są przydatne do odwzorowania pożarów i spalonych obszarów. Natomiast pasmo podczerwieni krótkofalowej nr 8 jest silnie odbijane przez roślinność, co oznacza brak pożarów.Roślinność ukazywana jest w kolorze niebieskim, pokazując szczegóły związane z żywotnością roślin. Zdrowa roślinność ukazana jest w kolorze jasnoniebieskim, podczas gdy poddana stresowi, rzadka i/lub sucha roślinność jest matowo-niebieska. Elementy zabudowy miejskiej są koloru białego, szarego, niebieskozielonego lub fioletowego.\n\n\n\nWięcej informacji [tutaj](https://eos.com/atmospheric-penetration/)"]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":["# Wizualizacja Gleb Jałowych\n\nWizualizacja gleby jałowej może być przydatna do mapowania gleby, badania lokalizacji osuwisk lub zakresu erozji na obszarach nieporośnietych roślinnością.W tej wizualizacji roślinność jest oznaczona kolorem zielonym, a jałowy grunt kolorem czerwonym. Woda przyjumje kolor czarny.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)"]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":["# Kompozyt Prawdziwego Koloru z Uwydatnieniem Podczerwieni\n\nTen kompozyt poprawia wizualizację naturalnych barw, dodając fale podczerwieni krótkofalowej dla wzmocnienia szczegółów. Wyświetla nagrzane obszary na czerwono/pomarańczowo.\n\n\n\nWięcej informacji [tutaj.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)"]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":["# Wykrywanie Wypalonych Obszarów\n\nTen skrypt służy do wykrywania niedawno wypalonych dużych obszarów. Piksele w kolorze czerwonym oznaczają spalone obszary, a wszystkie inne piksele mają naturalne barwy. Skrypt czasami źle ocenia spalone obszary nad wodą i chmurami.\n\n\n\nWięcej informacji [tutaj](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":["# Indeks Chlorofilu Lądowego (OTCI)\n\n\n\nIndeks Chlorofilu Lądowego (OTCI) jest oceniany na podstawie zawartości chlorofilu w roślinności lądowej i może być stosowany do monitorowania stanu i zdrowia roślinności.Niskie wartości OTCI zwykle wskazuję na wodę, piasek lub śnieg. Ekstremalnie wysokie wartości wyświetlane na biało zwykle również sugerują brak chlorofilu.Zazwyczaj przedstawiają gołą ziemię, skały lub chmury. Wartości chlorofilu mieszczące się w zakresie od czerwonego (niskie wartości chlorofilu) do ciemnozielonego (wysokie wartości chlorofilu) mogą być wykorzystane do określenia zdrowia roślin.\n\n\n\nWięcej informacji [tutaj.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":["# Znormalizowany Różnicowy Indeks Zasolenia\n\nIndeks ten obrazuje ilość soli obecnej w glebie.Zasolenie gleby jest jedną z najpowszechniejszych przyczyn degradacji gleby, zwłaszcza na obszarach suchych i półsuchych, gdzie opady przewyższają parowanie. \n\nWyższe wartości wskazują na większe zasolenie, a niższe - na mniejsze zasolenie.\n\nCzytaj więcej [tutaj,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Błąd: Fuzja danych nie obsługuje formatów KMZ/JPG i KMZ/PNG."]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["**MERIS** (Spektrometr o średniej rozdzielczości) był czujnikiem na pokładzie satelity [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat), którego podstawową misją jest obserwacja koloru lądu i oceanu oraz atmosfery. Nie jest już aktywny i został zastąpiony przez Sentinel-3.\n\n**Rozdzielczość przestrzenna:** Pełna rozdzielczość lądu i wybrzeża: 260 m x 290 m (oznacza to, że widoczne są tylko obiekty większe niż 260 m x 290 m).\n\n**Czas rewizyty:** Maksymalnie 3 dni na ponowne zobrazowanie tego samego obszaru.\n\n**Dostępność danych:** Od czerwca 2002 r. do kwietnia 2012 r.\n\n**Typowe zastosowanie:** Monitorowanie oceanów (fitoplankton, zawiesina), atmosfery (para wodna, CO2, chmury, aerozole) i lądów (indeks roślinności, globalne pokrycie terenu, wilgotność)."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["**Sentinel-2** zapewnia obrazy o wysokiej rozdzielczości w zakresie promieniowania widzialnego i podczerwonego w celu monitorowania roślinności, pokrywy glebowej i wodnej, śródlądowych dróg wodnych i obszarów przybrzeżnych.\n\n**Rozdzielczość przestrzenna:** 10 m, 20 m i 60 m w zależności od długości fali (oznacza to, że widoczne są tylko obiekty większe niż 10 m, 20 m i 60 m). Więcej informacji [tutaj](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial).\n\n**Czas rewizyty:** Maksymalnie 5 dni na rewizytę tego samego obszaru przy użyciu obydwu satelitów.\n\n**Dostępność danych:** Od czerwca 2015 r.Pełny zasięg globalny od marca 2017 r.\n\n**Typowe zastosowanie:** Mapy pokrycia terenu, mapy zmian pokrycia terenu, monitoring roślinności, monitoring spalonych terenów."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["**Sentinel-5P** to satelita, który przeprowadza pomiary atmosferyczne niezbędne do oceny jakości powietrza, poziomu ozonu, poziomu promieniowania UV\noraz monitorowania i prognozowania klimatu.\n\n**Rozdzielczość przestrzenna:** 7 x 3,5 km (oznacza to, że widoczne są tylko obiekty większe niż 7 x 3,5 km).\n\n**Czas rewizyty:** Maksymalnie 1 dzień na rewizytę tego samego obszaru.\n\n**Dostępność danych:** Od kwietnia 2018 r.\n\n**Typowe zastosowanie:** Monitorowanie stężenia tlenku węgla (CO), dwutlenku azotu (NO2) i ozonu (O3) w powietrzu. Monitorowanie wskaźnika aerozolu UV (AER_AI) i różnych parametrów geofizycznych chmur (Cloud)."]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Zobrazuj teren w 3D"]},"Measure":{"msgid":"Measure","msgstr":[""]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":[""]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":[""]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":[""]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":[""]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":[""]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":[""]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":[""]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":[""]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":[""]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":[""]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":[""]},"Hello,":{"msgid":"Hello,","msgstr":[""]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (z korektą atmosferyczną)"]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":["**Zalogowani użytkownicy** mogą korzystać ze swoich niestandardowych motywów, zapisywać i ładować zapisane zdjęcia (zwane dalej pinezkami), tworzyć historię pinezek, mierzyć odległości, tworzyć zdjęcia poklatkowe\noraz korzystać z zaawansowanej funkcji pobierania obrazu.\n\nAby utworzyć bezpłatne konto, po prostu kliknij [tutaj]\nlub w aplikacji na **Zaloguj się**, a następnie \"Zarejestruj się\"."]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":[""]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":[""]},"More information":{"msgid":"More information","msgstr":[""]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":[""]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":[""]},"Home":{"msgid":"Home","msgstr":[""]},"Shading":{"msgid":"Shading","msgstr":[""]},"Sphere mode":{"msgid":"Sphere mode","msgstr":[""]},"Eye height":{"msgid":"Eye height","msgstr":[""]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":[""]},"Geometries":{"msgid":"Geometries","msgstr":[""]},"Now":{"msgid":"Now","msgstr":[""]},"Terrain":{"msgid":"Terrain","msgstr":[""]},"Time":{"msgid":"Time","msgstr":[""]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":[""]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":[""]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":[""]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":[""]},"Left button":{"msgid":"Left button","msgstr":[""]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":[""]},"Right button":{"msgid":"Right button","msgstr":[""]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":[""]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":[""]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":[""]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":[""]},"Arrow keys":{"msgid":"Arrow keys","msgstr":[""]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":[""]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":[""]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":[""]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":[""]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":[""]},"Map navigation":{"msgid":"Map navigation","msgstr":[""]},"Pan console":{"msgid":"Pan console","msgstr":[""]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":[""]},"Camera console":{"msgid":"Camera console","msgstr":[""]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":[""]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":[""]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":[""]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":[""]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":[""]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":[""]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":[""]},"Histogram":{"msgid":"Histogram","msgstr":[""]},"Disabled":{"msgid":"Disabled","msgstr":[""]},"Yes":{"msgid":"Yes","msgstr":[""]},"Orthorectification":{"msgid":"Orthorectification","msgstr":[""]},"Cancel":{"msgid":"Cancel","msgstr":[""]},"Error":{"msgid":"Error","msgstr":[""]},"Help":{"msgid":"Help","msgstr":[""]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":[""]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":[""]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":[""]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":[""]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":[""]},"User Instances":{"msgid":"User Instances","msgstr":[""]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":[""]},"Recalculate":{"msgid":"Recalculate","msgstr":[""]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":[""]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":[""]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":[""]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":[""]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":[""]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":[""]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":[""]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":[""]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":[""]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":[""]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":[""]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":[""]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":[""]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":[""]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":[""]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":[""]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":[""]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":[""]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":[""]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":[""]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":[""]},"File upload":{"msgid":"File upload","msgstr":["Przesyłanie plików"]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Prześlij plik KML/KMZ, GPX lub GEOJSON/JSON, aby utworzyć obszar zainteresowania. Obszar ten będzie używany do przycinania podczas eksportowania obrazu."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":["Upuść plik KML/KMZ, GPX, GEOJSON/JSON lub przeszukaj swój komputer"]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":[""]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":[""]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":[""]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":[""]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":[""]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":[""]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":[""]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":[""]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":[""]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":[""]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":[""]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":[""]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":[""]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":[""]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":[""]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":[""]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":[""]},"Zoom to location":{"msgid":"Zoom to location","msgstr":[""]},"Remove layer":{"msgid":"Remove layer","msgstr":[""]},"Please select a layer":{"msgid":"Please select a layer","msgstr":[""]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":[""]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":[""]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":[""]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":[""]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":[""]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":[""]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":[""]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":[""]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":[""]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":[""]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":[""]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":[""]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":[""]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":[""]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":[""]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":[""]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":[""]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":[""]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":[""]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":[""]},"Level 1":{"msgid":"Level 1","msgstr":[""]},"Level 2":{"msgid":"Level 2","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":[""]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":[""]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":[""]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":[""]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":[""]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Maks. zachmurzenie:"]},"Order name":{"msgid":"Order name","msgstr":[""]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":[""]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":[""]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":[""]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":[""]},"Order options":{"msgid":"Order options","msgstr":[""]},"My orders":{"msgid":"My orders","msgstr":[""]},"My quotas":{"msgid":"My quotas","msgstr":[""]},"Use current display area":{"msgid":"Use current display area","msgstr":[""]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":[""]},"Constellation":{"msgid":"Constellation","msgstr":[""]},"Processing level":{"msgid":"Processing level","msgstr":[""]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":[""]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":[""]},"Hide details":{"msgid":"Hide details","msgstr":[""]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":[""]},"From":{"msgid":"From","msgstr":[""]},"To":{"msgid":"To","msgstr":[""]},"Provider":{"msgid":"Provider","msgstr":[""]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":[""]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":[""]},"Processing Level":{"msgid":"Processing Level","msgstr":[""]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":[""]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":[""]},"remove":{"msgid":"remove","msgstr":[""]},"Show results on map":{"msgid":"Show results on map","msgstr":[""]},"Order type":{"msgid":"Order type","msgstr":[""]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":[""]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":[""]},"Created at":{"msgid":"Created at","msgstr":[""]},"Confirmed at":{"msgid":"Confirmed at","msgstr":[""]},"Size":{"msgid":"Size","msgstr":[""]},"Status":{"msgid":"Status","msgstr":[""]},"All input parameters":{"msgid":"All input parameters","msgstr":[""]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":[""]},"Delete":{"msgid":"Delete","msgstr":[""]},"Show coverage":{"msgid":"Show coverage","msgstr":[""]},"Show data":{"msgid":"Show data","msgstr":[""]},"No orders found":{"msgid":"No orders found","msgstr":[""]},"Error confirming order":{"msgid":"Error confirming order","msgstr":[""]},"Error deleting order":{"msgid":"Error deleting order","msgstr":[""]},"Confirm order":{"msgid":"Confirm order","msgstr":[""]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":[""]},"Delete order":{"msgid":"Delete order","msgstr":[""]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":[""]},"Refresh orders":{"msgid":"Refresh orders","msgstr":[""]},"Creating order":{"msgid":"Creating order","msgstr":[""]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":[""]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Prześlij dane"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":[""]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":[""]},"Sun":{"msgid":"Sun","msgstr":[""]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":[""]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":[""]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":[""]},"Ambient factor":{"msgid":"Ambient factor","msgstr":[""]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":[""]},"Specular factor":{"msgid":"Specular factor","msgstr":[""]},"Specular power":{"msgid":"Specular power","msgstr":[""]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":[""]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":[""]},"Shadow map size":{"msgid":"Shadow map size","msgstr":[""]},"Parameters":{"msgid":"Parameters","msgstr":[""]},"Local time on computer":{"msgid":"Local time on computer","msgstr":[""]},"Edit":{"msgid":"Edit","msgstr":[""]},"Reset values":{"msgid":"Reset values","msgstr":[""]},"Current time":{"msgid":"Current time","msgstr":[""]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":[""]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":[""]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":[""]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":[""]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":[""]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":[""]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":[""]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]},"Built-up probability [0 - 100 %] at 10 m spatial resolution":{"msgid":"Built-up probability [0 - 100 %] at 10 m spatial resolution","msgstr":[""]},"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).":{"msgid":"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).","msgstr":[""]},"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.":{"msgid":"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.","msgstr":[""]},"Resampling kernel":{"msgid":"Resampling kernel","msgstr":[""]},"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)":{"msgid":"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)","msgstr":[""]},"4x4 cubic convolution":{"msgid":"4x4 cubic convolution","msgstr":[""]},"nearest neighbour":{"msgid":"nearest neighbour","msgstr":[""]},"proprietary MTF kernel":{"msgid":"proprietary MTF kernel","msgstr":[""]},"Width":{"msgid":"Width","msgstr":[""]},"Height":{"msgid":"Height","msgstr":[""]},"Format":{"msgid":"Format","msgstr":[""]},"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.":{"msgid":"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.","msgstr":[""]},"Could not generate timelapse animation file. Try using lower resolution or fewer frames.":{"msgid":"Could not generate timelapse animation file. Try using lower resolution or fewer frames.","msgstr":[""]}}}}
\ No newline at end of file
diff --git a/src/translations/sl.po b/src/translations/sl.po
index 690903c3..1000a585 100644
--- a/src/translations/sl.po
+++ b/src/translations/sl.po
@@ -7578,4 +7578,82 @@ msgid ""
msgstr ""
msgid "Currently only collections on services.sentinel-hub are supported."
+msgstr ""
+
+msgid "Built-up probability [0 - 100 %] at 10 m spatial resolution"
+msgstr ""
+
+msgid ""
+"# The Global Human Settlement Layer GHS-BUILT-S2 \n"
+"\n"
+"\n"
+"\n"
+"\n"
+"\n"
+"The Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up "
+"areas (expressed as probabilities from 0 to 100 %) at 10 m spatial "
+"resolution. It was derived from a Sentinel-2 global image composite for the "
+"reference year 2018 using Convolutional Neural Networks.\n"
+"\n"
+"This script visualises the built-up probabilities stretched to 0-255.\n"
+"\n"
+"For more information about the layer, visit [this "
+"website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-"
+"human-settlement-layer-ghs-built-s2/)."
+msgstr ""
+
+msgid ""
+"The **Global Human Settlement** (GHS) framework produces global maps of "
+"built-up areas, population density and settlements to monitor human "
+"presence on Earth over time.\n"
+"\n"
+"**Coverage**: Global coverage with longitude from 180°W to 180°E and "
+"latitude from 72°N to 56°S\n"
+"\n"
+"**Data Availability**: Reference year 2018\n"
+"\n"
+"**Spatial resolution**: 10 meters.\n"
+"\n"
+"**Common Usage**: Knowledge of population distribution and density has a "
+"number of applications, including disaster risk management or the study and "
+"management of urbanisation processes, not only but also in relation to the "
+"challenges of climate change and environmental degradation."
+msgstr ""
+
+msgid "Resampling kernel"
+msgstr ""
+
+msgid ""
+"Select the resampling kernel to use: \n"
+"- 4x4 cubic convolution (CC), \n"
+"- nearest neighbour (NN), \n"
+"- or the proprietary MTF kernel (MTF)"
+msgstr ""
+
+msgid "4x4 cubic convolution"
+msgstr ""
+
+msgid "nearest neighbour"
+msgstr ""
+
+msgid "proprietary MTF kernel"
+msgstr ""
+
+msgid "Width"
+msgstr ""
+
+msgid "Height"
+msgstr ""
+
+msgid "Format"
+msgstr ""
+
+msgid ""
+"For optimisation reasons MPEG4 output format will always be used for "
+"generating a timelapse with transition \"fade\", even when GIF is selected."
+msgstr ""
+
+msgid ""
+"Could not generate timelapse animation file. Try using lower resolution or "
+"fewer frames."
msgstr ""
\ No newline at end of file
diff --git a/src/translations/sl.po.json b/src/translations/sl.po.json
index 40daa77e..35fbc4b9 100644
--- a/src/translations/sl.po.json
+++ b/src/translations/sl.po.json
@@ -1 +1 @@
-{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","plural-forms":"nplurals=4; plural=(n%100==1 ? 0 : n%100==2 ? 1 : n%100>=3 && n%100<=4 ? 2 : 3);","mime-version":"1.0","content-transfer-encoding":"8bit","project-id-version":"","pot-creation-date":"","po-revision-date":"","language-team":"","x-generator":"Poedit 3.0","last-translator":"","language":"sl_SI"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nPlural-Forms: nplurals=4; plural=(n%100==1 ? 0 : n%100==2 ? 1 : n%100>=3 && n%100<=4 ? 2 : 3);\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\nProject-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLanguage-Team: \nx-generator: Poedit 3.0\nLast-Translator: \nLanguage: sl_SI\n"]},"Education":{"msgid":"Education","msgstr":["Izobraževanje"]},"Normal":{"msgid":"Normal","msgstr":["Običajno"]},"Close":{"msgid":"Close","msgstr":["Zapri"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Zapri in ne pokaži več"]},"Previous":{"msgid":"Previous","msgstr":["Nazaj"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Konec"]},"Next":{"msgid":"Next","msgstr":["Naprej"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Nadaljujte z vodičem"]},"Don't show again":{"msgid":"Don't show again","msgstr":["Ne pokaži več"]},"Show info":{"msgid":"Show info","msgstr":["Pokaži informacije"]},"Discover":{"msgid":"Discover","msgstr":["Odkrij"]},"Visualize":{"msgid":"Visualize","msgstr":["Prikaži"]},"Compare":{"msgid":"Compare","msgstr":["Primerjaj"]},"Pins":{"msgid":"Pins","msgstr":["Oznake"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Med dodajanjem posnetkov je prišlo do napake:"]},"No tile found":{"msgid":"No tile found","msgstr":["Noben del ni bil najden"]},"Dataset":{"msgid":"Dataset","msgstr":["Zbirka podatkov"]},"Show":{"msgid":"Show","msgstr":["Prikaži"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Pokaži učinke in napredne možnosti"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Pokaži prikaz"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Dodaj med oznake"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Dodaj za primerjavo"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Povečanje na del"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Skrij plast"]},"Show layer":{"msgid":"Show layer","msgstr":["Pokaži plast"]},"Share":{"msgid":"Share","msgstr":["Deli"]},"Custom":{"msgid":"Custom","msgstr":["Po meri"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Ustvari prikaz po meri"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Povečaj, da boš videl podatke"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Brezplačna registracija"]},"for all features":{"msgid":"for all features","msgstr":["za vse funkcije"]},"Powered by":{"msgid":"Powered by","msgstr":["Poganja ga"]},"with contributions by":{"msgid":"with contributions by","msgstr":["s prispevki"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["Izberi vir(e) podatkov!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["Neveljaven časovni obseg!"]},"No results found":{"msgid":"No results found","msgstr":["Ni zadetkov"]},"Theme":{"msgid":"Theme","msgstr":["Tema"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Upravljanje konfiguracij"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Prijava za uporabo konfiguracij po meri."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["Napaka pri pridobivanju dodatnih podatkov!"]},"Search":{"msgid":"Search","msgstr":["Poišči"]},"Highlights":{"msgid":"Highlights","msgstr":["Poudarki"]},"Data sources":{"msgid":"Data sources","msgstr":["Viri podatkov"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Izberi temo"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Časovni razpon [UTC]"]},"Date":{"msgid":"Date","msgstr":["Datum"]},"Hide description":{"msgid":"Hide description","msgstr":["Skrij opis"]},"Show description":{"msgid":"Show description","msgstr":["Prikaži opis"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Tema nima poudarkov"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Na podlagi: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["1 dan (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["5 dni (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["10 dni (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["O3 (Ozon)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (dušikov dioksid)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (žveplov dioksid)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (ogljikov monoksid)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (formaldehid)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (metan)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER AI (indeks aerosolov)"]},"Cloud":{"msgid":"Cloud","msgstr":["Oblačnost"]},"Other":{"msgid":"Other","msgstr":["Drugo"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Maksimalna oblačnost"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Napredno iskanje"]},"Data location":{"msgid":"Data location","msgstr":["Viri podatkov"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Izberi vsaj eno lokacijo!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Način zajema"]},"Polarization":{"msgid":"Polarization","msgstr":["Polarizacija"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Izberi vsaj en način pridobivanja podatkov!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Izberi vsaj eno polarizacijo!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Smer orbite"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Izberi vsaj eno smer orbite!"]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["**MERIS** (Medium-resolution spectrometer) je bil senzor na krovu satelita [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) s primarno misijo opazovanja barve kopnega in oceana ter atmosfere. Ni več aktiven in ga je naselil Sentinel-3.\n\n**Prostorska ločljivost:** Polna ločljivost kopno in obala: 260 m x 290 m (videti je mogoče le podrobnosti večje od 260 m x 290 m).\n\n**Čas ponovnega obiska:** največ 3 dni za ponoven obisk istega območja.\n\n**Razpoložljivost podatkov:** Od junija 2002 do aprila 2012.\n\n**Običajna raba:** Spremljanje oceana (fitoplankton, suspendirana snov), ozračja (vodna para, CO2, oblaki, aerosoli) in kopno (vegetacijski indeks, globalna pokritost, vlaga)."]},"Credits:":{"msgid":"Credits:","msgstr":["Zahvala:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["**GIBS** (Global Imagery Browse Services) omogoča hiter dostop do več kot 600 satelitskih \nizdelkov, ki pokrivajo vse dele sveta. Večina posnetkov je na voljo v nekaj urah po\npreletu satelita, nekateri izdelki zajemajo skoraj 30 let."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["Serija satelitov **Landsat** agencije NASA/ Geološkega zavoda ZDA je podobna satelitu Sentinel-2 (zajemajo vidne in infrardeče valovne dolžine)\ndodatno pa lahko zajamejo toplotno infrardečo svetlobo (Landsat 8). Serija satelitov Landsat ima dolgo zgodovino slikanja, ki zajema skoraj pet desetletij.\n Platforma omogoča dostop do posnetkov, pridobljenih s sateliti Landsat 5, 7 in 8.\n\n**Prostorska ločljivost:** 15 m, 30 m in 100 m, prevzorčeno na 30 m, odvisno od valovne dolžine (to pomeni, da so vidne le podrobnosti, večje od 10 m in 30 m). Več informacij je [tukaj](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Določen čas ponovnega obiska:** Največ 8 dni za ponovni obisk istega območja z uporabo dveh operativnih satelitov Landsat 7 in Landsat 8.\n\n**Dostopnost podatkov:** Evropa in severna Afrika od leta 1984 do 2011 (Landsat 5), od leta 1999 do 2003 (Landsat 7), od leta 2013 do danes (Landsat 8) iz arhiva ESA. Globalni arhiv U.S. Geological Survey (USGS) od aprila 2013 do danes (samo Landsat 8) .\n\n**Pogosta uporaba:** Spremljanje vegetacije, raba tal, karte pokrovnosti tal, spremljanje sprememb itd."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["NASA **MODIS** (spektroradiometer za slikanje z zmerno ločljivostjo) pridobiva podatke, da bi\nizboljšali razumevanje globalnih procesov, ki se odvijajo na kopnem. Pregledovalnik EO Browser zagotavlja \npodatke za opazovanje kopnega (kanali 1-7).\n\n**Prostorska ločljivost:** 250 m (kanali 1-2), 500 m (kanali 3-7), 1000 m (kanali 8-36).\n\n**Čas ponovnega obiska:** globalna pokritost v 1 do 2 dneh s satelitoma Aqua in Terra.\n\n** Razpoložljivost podatkov:** od januarja 2013.\n\n**Običajna uporaba:** Spremljanje barve kopnega, oblakov in oceanov na svetovni ravni."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["Satelit **Proba-V** je majhen satelit, namenjen kartiranju pokrovnosti tal in rasti vegetacije\npo vsem svetu vsaka dva dni. EO Browser zagotavlja izpeljane izdelke, ki čim bolj zmanjšujejo količino oblakov\ntako, da združujejo meritve brez oblakov v obdobju enega dneva (S1), petih dni (S5) in desetih dni (S10).\n\n**Prostorska ločljivost:** 100 m za S1 in S5, 333 m za S1 in S10, 1000 m za S1 in S10.\n\n**Čas ponovnega obiska:** 1 dan za zemljepisne širine 35-75° S in 35-56° J, 2 dni za zemljepisne širine med 35° S\nin 35° j. š.\n\n**Dostopnost podatkov:** od oktobra 2013.\n\n**Običajna uporaba:** opazovanje pokrovnosti tal, rasti vegetacije, ocena vpliva podnebja,\nupravljanje vodnih virov, spremljanje kmetijstva in ocene prehranske varnosti, celinske vode\nvirov ter spremljanje stalnega širjenja puščav in krčenja gozdov."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":["**Sentinel-1** zagotavlja dnevne in nočne radarske posnetke v vsakem vremenu za kopenske in oceanske storitve. EO\nBrowser zagotavlja podatke, pridobljene v interferometričnih načinih Wide Swath (IW) in Extra Wide Swath (EW)\nobdelani do ravni 1 Ground Range Detected (GRD).\n\n** Velikost piksla:** 10 m (IW), 40 m (EW).\n\n**Čas ponovnega obiska:** <= 5 dni z uporabo obeh satelitov.\n\n**Čas ponovnega obiska** (za asc/desc in prekrivanje z uporabo obeh satelitov): <= 3 dni, glej [scenarij opazovanja](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Dostopnost podatkov:** od oktobra 2014.\n\n**Običajna uporaba:** spremljanje morja in kopnega, odzivanje na izredne razmere, podnebne spremembe."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["**Sentinel-2** zagotavlja posnetke visoke ločljivosti v vidni in infrardeči valovni dolžini za spremljanje vegetacije, tal in vodnih površin, celinskih vodnih poti in obalnih območij. .\n\n**Prostorska ločljivost:** 10 m, 20 m in 60 m, odvisno od valovne dolžine (to pomeni, da so vidne le podrobnosti, večje od 10 m, 20 m in 60 m). Več informacij je [tukaj](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial)\n\n**Čas ponovnega obiska:** največ 5 dni za ponovni obisk istega območja z uporabo obeh satelitov.\n\n** Razpoložljivost podatkov:** od junija 2015. Celotna globalna pokritost od marca 2017.\n\n**Običajna uporaba:** karte pokrovnosti tal, zaznavanja sprememb tal, spremljanje vegetacije, spremljanje požganih območij."]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Podatki stopnje 2A so visokokakovostni podatki, pri katerih so odstranjeni učinki atmosfere na svetlobo, ki se odbije od zemeljske površine in doseže senzor. Podatki so na voljo po vsem svetu od marca 2017.\n\nVeč informacij o atmosferskih popravkih [tukaj] (http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction)."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Podatki stopnje 1C so podatki zadostne kakovosti za večino raziskav. Opravljeni so vsi popravki, razen atmosferskih. Podatki so na voljo po vsem svetu od junija 2015 dalje."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["Glavni cilj misije **Sentinel-3** je merjenje topografije morske površine, temperature morske in kopenske površine ter barve oceana in kopenske površine. Sentinel-3 ima na krovu štiri različne instrumente. Na tej platformi so na voljo podatki, pridobljeni s senzorjema Ocean and Land Colour Instrument (OLCI) in Sea and Land Surface Temperature Instrument (SLSTR).\n\n** Razpoložljivost podatkov:** od maja 2016 dalje."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["Instrument **Sea and Land Surface Temperature (SLSTR)** na krovu Sentinel-3 meri globalno in regionalno temperaturo morja in površja kopnega\ntemperaturo tal in kopnega. SLSTR pokriva vidno, kratkovalovno infrardeče in toplotno infrardeče valovne dolžine elektromagnetnega spektra\n\n**Prostorska ločljivost:** 500 m za vidne, bližnje in kratkovalovne infrardeče valovne dolžine ter 1 km za toplotne infrardeče valovne dolžine (to pomeni le podrobnosti\nvečje od 500 m oziroma 1 km).\n\n**Čas ponovnega obiska:** največ 1 dan za ponovni ogled istega območja z uporabo obeh satelitov.\n\n**Dostopnost podatkov:** od maja 2016 dalje.\n\n**Običajna uporaba:** spremljanje podnebnih sprememb, spremljanje vegetacije, odkrivanje aktivnih požarov, spremljanje temperature zemeljske in morske površine."]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["**Ocean and Land Colour Instrument (OLCI)** na krovu Sentinel-3 je spektrometer, ki\nmeri sončno sevanje, ki se odbija od Zemlje, in spremlja oceane, okolje,\nin podnebje. Zagotavlja pogostejše vidne posnetke kot Sentinel-2, vendar z nižjo ločljivostjo\nin z več zajetimi valovnimi dolžinami. Instrument Sentinel-3 OLCI nadaljuje meritve, ki jih je prej opravljal instrument MERIS na krovu satelita Envisat, katerega misija se je končala.\n\n**Prostorska ločljivost:** 300 m (to pomeni, da so vidne le podrobnosti, večje od 300 m).\n\n**Čas ponovnega obiska:** največ 2 dni za ponovni obisk istega območja z uporabo obeh satelitov.\n\n** Razpoložljivost podatkov:** od maja 2016 dalje.\n\n**Običajna uporaba:** opazovanje in spremljanje topografije površja, barve oceanov in kopnega."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["**Sentinel-5P** je satelit, ki zagotavlja meritve ozračja za spremljanje kakovosti zraka, koncentracij ozona in jakost UV-sevanja,\nter spremljanje in napovedovanje podnebja.\n\n**Prostorska ločljivost:** 7 x 3,5 km (to pomeni, da so vidne le podrobnosti, večje od 7 x 3,5 km).\n\n**Čas ponovnega obiska: ** največ en dan za ponovni ogled istega območja.\n\n** Razpoložljivost podatkov:** od aprila 2018 dalje.\n\n**Običajna uporaba:** spremljanje koncentracije ogljikovega monoksida (CO), dušikovega dioksida (NO2) in ozona (O3) v zraku. Spremljanje aerosolnega indeksa UV (AER_AI) in različnih geofizikalnih parametrov oblakov (Cloud)."]},"Copied":{"msgid":"Copied","msgstr":["Kopirano"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["Kopiraj v odložišče"]},"Data source name":{"msgid":"Data source name","msgstr":["Ime vira podatkov"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Čas zajema"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Pokritost z oblaki"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Višina Sonca"]},"MGRS location":{"msgid":"MGRS location","msgstr":["Lokacija MGRS"]},"AWS path":{"msgid":"AWS path","msgstr":["Pot AWS"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["Pot EO Cloud"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["Pot CreoDIAS"]},"SciHub link":{"msgid":"SciHub link","msgstr":["Povezava SciHub"]},"Back to search":{"msgid":"Back to search","msgstr":["Nazaj na iskanje"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Prikaz ${ this.state.results.length } rezultatov","Prikaz ${ this.state.results.length } rezultatov","Prikaz ${ this.state.results.length } rezultatov","Prikaz ${ this.state.results.length } rezultatov"]},"Load more":{"msgid":"Load more","msgstr":["Naloži več"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Nalaganje več rezultatov …"]},"Results":{"msgid":"Results","msgstr":["Rezultati"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Prikaz ${ this.state.selectedTiles.length } rezultatov.","Prikaz ${ this.state.selectedTiles.length } rezultatov.","Prikaz ${ this.state.selectedTiles.length } rezultatov.","Prikaz ${ this.state.selectedTiles.length } rezultatov."]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Urejanje opisa oznake"]},"Reject changes":{"msgid":"Reject changes","msgstr":["Zavrni spremembe"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Potrdi spremembe"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Preimenuj oznako"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Odstrani oznako"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Povečanje na označeno lokacijo"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Lat/Lon"]},"Zoom":{"msgid":"Zoom","msgstr":["Povečava"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["V svojo zbirko boš dodal ${ N_PINS } oznak(-o). Želiš nadaljevati?"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["OPOZORILO: izbrisali boste oznako. Ali želiš nadaljevati?"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":["OPOZORILO: izbrisali boste vse oznake. Ali želiš nadaljevati?"]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["Brez oznak. Pojdite na zavihek Prikaži in shranite oznako ali naložite datoteko JSON s shranjenimi oznakami."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Upoštevajte, da bodo oznake shranjene le, če se prijavite. V nasprotnem primeru se bodo po zaprtju aplikacije izgubile."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Odznači vse"]},"Select all":{"msgid":"Select all","msgstr":["Izberi vse"]},"No pins.":{"msgid":"No pins.","msgstr":["Brez oznak."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Ustvari povezavo (${ selectedPins.length } izbranih oznak)","Ustvari povezavo (${ selectedPins.length } izbrana oznaka)","Ustvari povezavo (${ selectedPins.length } izbrani oznaki)","Ustvari povezavo (${ selectedPins.length } izbrane oznake)"]},"File type not supported":{"msgid":"File type not supported","msgstr":["Vrsta datoteke ni podprta"]},"not supported":{"msgid":"not supported","msgstr":["ni podprto"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["Niso bilo najdenih oznak."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Napaka pri razčlenjevanju datoteke:"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":["Prenesite datoteko JSON s shranjenimi oznakami."]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["Spustite datoteko JSON ali poiščite v računalniku"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Ohrani obstoječe oznake"]},"Share pins":{"msgid":"Share pins","msgstr":["Delite oznake"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Ustvarite zgodbo iz oznak"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Izvoz oznake v računalnik"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Uvoz oznak iz datoteke"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Izbriši vseh oznake"]},"Story":{"msgid":"Story","msgstr":["Zgodba"]},"Export":{"msgid":"Export","msgstr":["Izvozi"]},"Import":{"msgid":"Import","msgstr":["Uvozi"]},"Clear":{"msgid":"Clear","msgstr":["Počisti"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Delite povezavo do oznak"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Posodobitev zbirke oznak."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Pri posodabljanju zbirke oznak je prišlo do težave: ${ updatingPinsError }."]},"Opacity":{"msgid":"Opacity","msgstr":["Neprozornost"]},"Split position":{"msgid":"Split position","msgstr":["Razdeljeni položaj"]},"split":{"msgid":"split","msgstr":["razdelitev"]},"opacity":{"msgid":"opacity","msgstr":["neprozornost"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["Ni slojev za primerjavo."]},"Remove all":{"msgid":"Remove all","msgstr":["Odstrani vse"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Dodaj vse oznake"]},"Split":{"msgid":"Split","msgstr":["Deljenje"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Pri prenosu vaših primerov je prišlo do težave"]},"Download":{"msgid":"Download","msgstr":["Prenesi"]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Vizualizacija terena v 3D"]},"Go to Place":{"msgid":"Go to Place","msgstr":["Pojdi na kraj"]},"Labels":{"msgid":"Labels","msgstr":["Oznake"]},"Borders":{"msgid":"Borders","msgstr":["Meje"]},"Roads":{"msgid":"Roads","msgstr":["Ceste"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Povečaj"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Pomanjšaj"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["O EO Browserju"]},"Contact us":{"msgid":"Contact us","msgstr":["Obrnite se na nas"]},"Get data":{"msgid":"Get data","msgstr":["Prenos podatkov"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Za uporabo te funkcije se morate prijaviti."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Izberi sloj."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["Prenos slike v načinu za primerjavo ni mogoč."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["Ta vir podatkov ni podprt."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":["Statistične informacije / Feature Info Service chart"]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Statistične informacije / Feature Info Service chart - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["izberi sloj"]},"not available for ":{"msgid":"not available for ","msgstr":["ni na voljo za "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["ni na voljo za \"${ props.presetLayerName }\" (layer with value is not set up)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Najprej poiščite podatke."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Ustvarjanje animacije s časovnim zaporedjem"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Označite zanimivo točko"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Središče karte na element"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Odstranite geometrijo"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Območje interesa"]},"Select mode":{"msgid":"Select mode","msgstr":["Izberi način"]},"Mode:":{"msgid":"Mode:","msgstr":["Način:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Odstranitev meritev"]},"km":{"msgid":"km","msgstr":["km"]},"m":{"msgid":"m","msgstr":["m"]},"Gain":{"msgid":"Gain","msgstr":["Jakost"]},"Gamma":{"msgid":"Gamma","msgstr":["Gamma"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Min. kakovost podatkov"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Povečanje vzorčenja"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Zmanjšanje vzorčenja"]},"Reset all":{"msgid":"Reset all","msgstr":["Ponastavi vse"]},"filter by months":{"msgid":"filter by months","msgstr":["filtriranje po mesecih"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Kopiraj geometrijo v odložišče"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Prekliči urejanje."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Narišite interesno področje"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Najmanjša pokritost z oblaki"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Uporaba dodatnih zbirk podatkov (napredno)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Vrstni red mozaičenja"]},"Most recent":{"msgid":"Most recent","msgstr":["Najnovejši"]},"Least recent":{"msgid":"Least recent","msgstr":["Najmanj nedavno"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Prilagodite časovni razpon"]},"Back":{"msgid":"Back","msgstr":["Nazaj"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Napaka pri nalaganju skripte. Preverite svoj URL."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Če želite urediti kodo, odstranite potrditev možnosti Naloži skripto iz naslova URL"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Nalaganje skripte iz naslova URL"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["Vnesite URL za vašo skripto"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Skript je naložen."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Dovoljene so samo domene HTTPS."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Nalaganje skripte v urejevalnik kode"]},"Refresh":{"msgid":"Refresh","msgstr":["Osveži"]},"orbit":{"msgid":"orbit","msgstr":["orbita"]},"day":{"msgid":"day","msgstr":["dan"]},"week":{"msgid":"week","msgstr":["teden"]},"month":{"msgid":"month","msgstr":["mesec"]},"year":{"msgid":"year","msgstr":["leto"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Izberite 1 sliko na:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Časovno zaporedje"]},"Select All":{"msgid":"Select All","msgstr":["Izberi vse"]},"Speed:":{"msgid":"Speed:","msgstr":["Hitrost:"]},"frames / s":{"msgid":"frames / s","msgstr":["okvirjev / s"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Priprava..."]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Datoteke ni bilo mogoče prenesti:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Ne morem prenesti prek okvirja"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["Datoteke ZIP ni bilo mogoče shraniti:"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Pri prenosu posnetka je prišlo do težave"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Napaka pri pridobivanju posnetka: url je prazen!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Napaka pri pridobivanju posnetka:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["Ni bilo mogoče naložiti posnetka iz bloba"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Povlecite kanale na polja RGB."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Povlecite kanale v enačbo indeksa"]},"Index ":{"msgid":"Index ","msgstr":["Indeks "]},"Threshold":{"msgid":"Threshold","msgstr":["Prag"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Odstranite izbirnik barv"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Dodajanje izbirnika barv"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Kliknite za postavitev oznake"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Kliknite za postavitev prvega verteksa"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Kliknite za nadaljevanje risanja"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Za dokončanje kliknite prvi označevalec"]},"Show captions":{"msgid":"Show captions","msgstr":["Prikaži napise"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Prikaži naslov diapozitiva"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Dodaj pregledno karto"]},"Show legend":{"msgid":"Show legend","msgstr":["Prikaži legendo"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["V trenutnem vidnem polju ni bila najdena nobena oznaka."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Nekatere oznake (${ N_PINS_OUTSIDE_BOUNDS }) so prezrte, ker niso znotraj izbranega območja."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["Če želite ustvariti zgodbo iz oznak, se pomaknite do želenega položaja na karti.\n\nZa ustvarjanje zgodbe bodo uporabljene vse oznake v trenutnem vidnem polju, ostale ne bodo upoštevane."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["Datoteka bo imela vključen logotip."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["Kanal dataMask bo vključen v surove kanale kot drugi kanal."]},"Show logo":{"msgid":"Show logo","msgstr":["Prikaži logotip"]},"Image format":{"msgid":"Image format","msgstr":["Format posnetka"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Ločljivost posnetka"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Koordinatni sistem"]},"Layers":{"msgid":"Layers","msgstr":["Sloji"]},"Visualized":{"msgid":"Visualized","msgstr":["Vizualizirano"]},"Raw":{"msgid":"Raw","msgstr":["Neobdelan"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["Sliki bodo dodani prekrivni sloji (oznake krajev, ulice in politične meje)."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Izvožene slike bodo vključevale vir podatkov in datum, merilo povečave in lastnika"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Izvoženi sliki dodajte kratek opis"]},"Description":{"msgid":"Description","msgstr":["Opis"]},"Image format:":{"msgid":"Image format:","msgstr":["Format posnetka:"]},"Basic":{"msgid":"Basic","msgstr":["Osnovni"]},"Analytical":{"msgid":"Analytical","msgstr":["Analitični"]},"High-res print":{"msgid":"High-res print","msgstr":["Tiskanje v visoki ločljivosti"]},"Download image":{"msgid":"Download image","msgstr":["Prenos posnetka"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["Med dodajanjem nekaterih posnetkov je prišlo do napake:"]},"min/px":{"msgid":"min/px","msgstr":["min/px"]},"sec/px":{"msgid":"sec/px","msgstr":["sec/px"]},"Resolution":{"msgid":"Resolution","msgstr":["Razrešitev"]},"lat.":{"msgid":"lat.","msgstr":["lat."]},"deg/px":{"msgid":"deg/px","msgstr":["deg/px"]},"long.":{"msgid":"long.","msgstr":["long."]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Predvidena ločljivost: ${ formattedResolution } m/px"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Napaka: KMZ/JPG in KMZ/PNG nista podprta pri fuziji podatkov."]},"Image download":{"msgid":"Image download","msgstr":["Prenos posnetka"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Širina slike [palcev]:"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Višina slike [palcev]:"]},"DPI:":{"msgid":"DPI:","msgstr":["DPI:"]},"5 years":{"msgid":"5 years","msgstr":["5 let"]},"2 years":{"msgid":"2 years","msgstr":["2 leti"]},"1 year":{"msgid":"1 year","msgstr":["1 leto"]},"6 months":{"msgid":"6 months","msgstr":["6 mesecev"]},"3 months":{"msgid":"3 months","msgstr":["3 mesece"]},"1 month":{"msgid":"1 month","msgstr":["1 mesec"]},"Retry":{"msgid":"Retry","msgstr":["Ponovi"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Nalaganje, počakajte"]},"mean":{"msgid":"mean","msgstr":["povprečje"]},"median":{"msgid":"median","msgstr":["mediana"]},"st. dev.":{"msgid":"st. dev.","msgstr":["st. dev."]},"min / max":{"msgid":"min / max","msgstr":["min / maks"]},"Export CSV":{"msgid":"Export CSV","msgstr":["Izvozi CSV"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Časovni razpon:"]},"Date:":{"msgid":"Date:","msgstr":["Datum:"]},"Single date":{"msgid":"Single date","msgstr":["Enkratni datum"]},"Timespan":{"msgid":"Timespan","msgstr":["Časovni razpon"]},"hh":{"msgid":"hh","msgstr":["hh"]},"mm":{"msgid":"mm","msgstr":["mm"]},"From:":{"msgid":"From:","msgstr":["Od:"]},"Until:":{"msgid":"Until:","msgstr":["Do:"]},"Apply":{"msgid":"Apply","msgstr":["Uporabi"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Deli na Facebooku"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Delite na Twitterju"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Oglejte si to "]},"Logout":{"msgid":"Logout","msgstr":["Odjava"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["S prijavo lahko odklenete napredne funkcije, kot so časovno zaporedje, prenos analitičnih podatkov, lastne konfiguracije in še več."]},"Login":{"msgid":"Login","msgstr":["Prijava"]},"Default":{"msgid":"Default","msgstr":["Privzeto"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Spremljanje Zemlje iz vesolja"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Kmetijstvo"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Atmosfera in onesnaževanje zraka"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Zaznavanje sprememb skozi čas"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Poplave in suše"]},"Geology":{"msgid":"Geology","msgstr":["Geologija"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Oceani in vodna telesa"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Sneg in ledeniki"]},"Urban":{"msgid":"Urban","msgstr":["Urbano"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Rastlinstvo in gozdarstvo"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Vulkani"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Požari v naravi"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Kanal 1 - modra - 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Kanal 2 - zelena - 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Kanal 3 - rdeča - 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Kanal 4 - NIR - 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Kanal 5 - SWIR-1 - 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Kanal 7 - SWIR-2 - 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Kanal 8 - pankromatski - 520-900 nm"]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":["Kanal 1 - Obala/Aerosol - 433-453 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Kanal 2 - modra - 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Kanal 3 - zelena - 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Kanal 4 - rdeča - 630-680 nm"]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":["Kanal 5 - NIR - 845-885 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Kanal 6 - SWIR-1 - 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Kanal 7 - SWIR-2 - 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Kanal 8 - pankromatsko - 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Kanal 9 - Cirrus - 1360-1390 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Odsevnost"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Temperatura svetlosti"]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":["Ustvarite časovno zaporedje tega območja"]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":["Opozorilo: Naslednje plasti uporabljajo dataProducts, zato želena vrsta podatkov morda ne bo nastavljena:"]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":["Opozorilo: Evalscript ni v tipičnem formatu V3 in želenega tipa podatkov ni bilo mogoče nastaviti:"]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":["To pomeni, da je parameter \"sampleType\" verjetno nastavljen na privzeto vrednost (AUTO). To lahko popravite tako, da uredite svoj evalscript. Več o parametru \"sampleType\" je v dokumentaciji"]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":["Napaka: Vizualizacijo z učinki lahko prenesete samo v formatih JPEG ali PNG."]},"Measure":{"msgid":"Measure","msgstr":["Izmeri"]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":["Storitve Sentinel-1 so na voljo v oblaku EOCloud in AWS. Zmogljivosti vsake od njih se\nse razlikujejo. Več informacij najdete na"]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":["Format Tagged Image File Format (TIFF) lahko vsebuje veliko število kanalov, vendar številni običajni pregledovalniki slik (npr. Windows Photo Viewer) ne morejo prikazati slik TIFF z več kot 3 kanali.\nČe je ta možnost omogočena, bodo v sliko vključeni samo prvi trije kanali.\nČe je ta možnost onemogočena, bodo v sliko vključeni vsi kanali, vendar boste morali za prikaz slike TIFF uporabiti program, ki podpira več kot 3 kanale (npr. QGIS)."]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":["Dodajanje maske dataMask v surove sloje"]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":["Obreži dodatne sloje"]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":["EPSG:3857 ni na voljo, če je podan AOI."]},"Creating link...":{"msgid":"Creating link...","msgstr":["Ustvarjanje povezave..."]},"OK":{"msgid":"OK","msgstr":["Vredu"]},"Hello,":{"msgid":"Hello,","msgstr":["Pozdravljeni,"]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":["Ta oznaka trenutno nima opisa."]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":["Vaš spletni brskalnik ne podpira zmogljivosti 3D, ki so potrebne za prikaz te vsebine."]},"More information":{"msgid":"More information","msgstr":["Več informacij"]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":["Ne morete se povezati s storitvijo 3D! Ponoven poskus?"]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":["Slika je prevelika za to napravo!\nVelikost slike: {0}x{1}, max: {2}"]},"Home":{"msgid":"Home","msgstr":["Domov"]},"Shading":{"msgid":"Shading","msgstr":["Senčenje"]},"Sphere mode":{"msgid":"Sphere mode","msgstr":["Način krogle"]},"Eye height":{"msgid":"Eye height","msgstr":["Višina oči"]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":["Posnetka ni mogoče naložiti"]},"Geometries":{"msgid":"Geometries","msgstr":["Geometrije"]},"Now":{"msgid":"Now","msgstr":["Zdaj"]},"Terrain":{"msgid":"Terrain","msgstr":["Teren"]},"Time":{"msgid":"Time","msgstr":["Ura"]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":["Napredni učinki RGB"]},"Left button":{"msgid":"Left button","msgstr":["Levi gumb"]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":["Kliknite in povlecite z levim miškinim gumbom, da se premikate po karti na stalni višini. Za vrtenje uporabite SHIFT + levi gumb."]},"Right button":{"msgid":"Right button","msgstr":["Desni gumb"]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":["Z desnim klikom in vlečenjem navzgor/navzdol spremenite višino kamere. Kliknite z desno tipko miške in\nin povlecite levo/desno, da obrnete pogled kamere."]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":["Srednji gumb/kolesce"]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":["S kolescem za pomikanje lahko spremenite višino kamere (enako kot z desnim klikom + vlečenjem\nnavzgor/navzdol). S klikom in vlečenjem kolesca spremenite kot kamere."]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":["Navigacija s tipkovnico"]},"Arrow keys":{"msgid":"Arrow keys","msgstr":["Tipke s puščicami"]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":["S smernimi tipkami se premikajte po karti na stalni višini."]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":["SHIFT + smerne tipke"]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":["Če želite spremeniti pogled kamere, držite tipko SHIFT in hkrati pritiskajte smerne tipke."]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":["Stran navzgor/stran navzdol"]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":["S tipkama PG UP ali PG DN spremenite višino kamere."]},"Map navigation":{"msgid":"Map navigation","msgstr":["Navigacija po karti"]},"Pan console":{"msgid":"Pan console","msgstr":["Konzola za premikanje"]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":["Konzola za pomikanje omogoča premikanje po karti na določeni višini. Kliknite in povlecite za premikanje\nneprekinjeno. Bolj ko se vlečete od središča, hitreje se boste premikali."]},"Camera console":{"msgid":"Camera console","msgstr":["Konzola za kamero"]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":["Konzola za kamero premika samo pogled kamere. Če želite spremeniti pogled kamere, kliknite in povlecite.\nBolj ko vlečete od središča, hitreje boste spremenili pogled."]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":["Gumbi za povečavo"]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":["S klikom nanje spremenite višino kamere. Z gumbom plus boste premaknili kamero\nbližje Zemlji, z gumbom minus pa se bo kamera oddaljila od nje."]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["**Copernicus DEM** predstavlja površino Zemlje, vključno s stavbami, infrastrukturo in vegetacijo. Podobno kot Mapzen DEM temelji na kombinaciji različnih modelov (osnova [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Gre za statično zbirko (neodvisno od datuma) z globalno pokritostjo.\n\n**Prostorska ločljivost:** 90 m\n\nVir: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":["**DMV** (digitalni model višin, digital elevation mode, DEM) je digitalni prikaz terena (običajno zemeljske površine). Dobimo ga tako, da celoten svet razdelimo na celice, v vsaki pa je ustrezna vrednost nadmorske višine v metrih. Glede na velikost mrežne celice je lahko bolj (visoka ločljivost) ali manj podroben (nizka ločljivost). Zbirke podatkov Sentinel Hub DEM (Mapzen in Copernicus) so statične (neodvisne od datuma) in na voljo po vsem svetu.\n\n**Običajna uporaba:** Modeliranje vodnih tokov, ortorektifikacija posnetkov Sentinel-1 in inženirstvo."]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["**Copernicus DEM** predstavlja površino Zemlje, vključno s stavbami, infrastrukturo in vegetacijo. Podobno kot Mapzen DEM temelji na kombinaciji različnih modelov (osnova [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Gre za statično zbirko (neodvisno od datuma) z globalno pokritostjo.\n\n**Prostorska ločljivost:** 30 m, zapolnjeno z 90 m (kjer 30-metrski deli niso objavljene).\n\nVir: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":["Osnovni nabor podatkov:"]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":["Psevdonim vira podatkov:"]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":["Dodatne zbirke podatkov:"]},"Cancel":{"msgid":"Cancel","msgstr":["Prekliči"]},"Error":{"msgid":"Error","msgstr":["Napaka"]},"Help":{"msgid":"Help","msgstr":["Pomoč"]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":["Položaj 3D kamere na podlagi 2D karte"]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":["Navigacija z miško"]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":["Vaših uporabniških primerov ni bilo mogoče naložiti, ker vaš račun Sentinel Hub ni bil nastavljen ali je potekel. Še vedno lahko uporabljate brskalnik EO Browser, vendar ne boste mogli uporabljati uporabniških primerov. Da bi lahko nastavili osebne primere, lahko zaprosite za 30-dnevni brezplačni preizkus ali razmislite o eni od naročnin: "]},"User Instances":{"msgid":"User Instances","msgstr":["Uporabniške konfiguracije"]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":["To so deli teme, ki vsebujejo vire podatkov, ki niso na voljo:"]},"Disabled":{"msgid":"Disabled","msgstr":["Onemogočeno"]},"Yes":{"msgid":"Yes","msgstr":["Da"]},"Orthorectification":{"msgid":"Orthorectification","msgstr":["Ortorektifikacija"]},"Zoom to location":{"msgid":"Zoom to location","msgstr":["Povečanje na lokacijo"]},"Remove layer":{"msgid":"Remove layer","msgstr":["Odstranite sloj"]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":["Kanal 10 - termična infrardeča svetloba (TIRS) - 10895 nm"]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":["Kanal 11 - toplotna infrardeča svetloba (TIRS) - 12005 nm"]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":["Glavna diskretna klasifikacija pokrovnosti tal v skladu s shemo FAO LCCS"]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":["Verjetnost razvrstitve, kazalnik kakovosti za diskretno razvrščanje"]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":["Vrsta gozda za vse piksle, v katerih je delež dreves večji od 1 %"]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":["Delež pokritosti (%) za razred gole in redke vegetacije"]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":["Delni pokrov (%) za razred obdelovalnih površin"]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":["Delež pokritosti (%) za razred zeljne vegetacije"]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":["Delni pokrov (%) za razred mahov in lišajev"]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":["Delež pokritosti (%) za razred grmičevja"]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":["Delna pokritost (%) za razred sneg in led"]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":["Delni pokrov (%) za razred gozd"]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":["Delna pokritost (%) za razred pozidanih površin"]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":["Delna pokritost (%) za razred stalnih celinskih vodnih teles"]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":["Delež pokritosti (%) za razred sezonskih celinskih vodnih teles"]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":["Kazalnik gostote podatkov, ki kaže kakovost vhodnih podatkov EO (0 = slabi, 100 = popolni podatki)"]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":["Sloj kakovosti glede zaznavanja sprememb v tekočem kartiranem letu v primerjavi s prejšnjim kartiranim letom. Gre za 3-stopenjsko masko zaupanja za vse karte CONSO in NRT z opredelitvami vrednosti:\n0 = brez spremembe\n1 - nizka zanesljivost\n2 - srednja zanesljivost\n3 = visoka zanesljivost\nOPOMBA: Vrednosti maske Change_Confidence_layer v podatkih za leto 2015 niso prikazane pravilno, zato te maske v podatkih za leto 2015 ne uporabljajte."]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":["Povlecite razrede na polja RGB."]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":["Zbirka **CORINE Land Cover (CLC)** je vektorski podatkovni niz, sestavljen iz 44 razredov pokrovnosti in rabe tal, ki so bili pridobljeni iz podatkov več satelitskih misij. V večini evropskih držav je CLC izdelan z vizualno interpretacijo satelitskih posnetkov visoke ločljivosti. V nekaj državah se uporabljajo polavtomatske rešitve z uporabo nacionalnih podatkov in-situ, obdelavo satelitskih posnetkov, integracijo in posploševanjem GIS. Več informacij je [tukaj](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover)\n\n**Pokritost**: Večji del Evrope.\n\n**Dostopnost podatkov**:\nPodatki CLC se posodabljajo vsakih šest let. V brskalniku EO Browser so podatki na voljo na naslednje datume:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Običajna uporaba**:\nSpremljanje, analiza in napovedovanje sprememb rabe in pokrovnosti tal za različne namene, vključno z okoljem, kmetijstvom, prometom in prostorskim načrtovanjem."]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":["*Izdelki *Global Land Cover** zagotavljajo diskretno karto klasifikacije pokrovnosti tal v skladu s sistemom klasifikacije pokrovnosti tal UN-FAO. Dodatni neprekinjeni frakcijski sloji za vse osnovne razrede pokrovnosti tal so vključeni kot kanali, ki zagotavljajo podrobnejše informacije o vsakem razredu pokrovnosti tal. Več informacij je [tukaj](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover)\n\n**Pokritost**: Globalno.\n\n**Dostopnost podatkov**:\nVsako leto se posodobi. V brskalniku EO Browser so podatki na voljo na naslednje datume:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Običajna uporaba**\nSpremljanje rabe tal in pokrovnosti tal, ki se uporablja za pomoč pri političnih odločitvah o različnih vprašanjih, vključno s kmetijstvom in prehransko varnostjo, biotsko raznovrstnostjo, podnebnimi spremembami, gozdnimi in vodnimi viri, degradacijo in dezertifikacijo tal ter razvojem podeželja."]},"File upload":{"msgid":"File upload","msgstr":["Nalaganje datoteke"]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Naložite datoteko KML/KMZ, GPX ali GEOJSON/JSON in ustvarite interesno območje. Območje bo uporabljeno za izrezovanje pri izvozu slike."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":["Spustite datoteko KML/KMZ, GPX, GEOJSON/JSON ali preiščite svoj računalnik"]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":["Sloj za zaznavanje glavnih vodnih teles, ki prikazuje vodne in nevodne piksle\n0 = morje\n70 = voda\n251 = ni podatkov\n255 = ni vode"]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":["Sloj kakovosti, ki vsebuje informacije o pojavljanju vodnih teles\n0 = morje\n71 = zelo majhna pojavnost\n72 = nizka pojavnost\n73 = srednja pojavnost\n74 = visoka pojavnost\n75 = zelo visoka pojavnost\n76 = stalna pojavnost\n251 = ni podatkov\n252 = oblak\n255 = ni voda"]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":["Izdelek **Vodna telesa** na svetovni ravni prikazuje površino, ki jo stalno, sezonsko ali občasno pokrivajo celinske vode. Vsebuje glavni sloj zaznavanja vodnih teles (Water Body, WB) in sloj kakovosti (Quality, QUAL), ki zagotavlja informacije o sezonski dinamiki zaznanih vodnih teles. Več informacij je [tukaj](https://collections.sentinel-hub.com/water-bodies/)\n\n**Pokritost**:\nGlobalna pokritost od zemljepisne dolžine -180°E do +180°W in zemljepisne širine +80°S do -60°S. Odvisno od meseca satelit Sentinel-2 ne pokriva nekaterih območij na visokih zemljepisnih širinah.\n\n**Dostopnost podatkov**:\nOd oktobra 2020, posodablja se mesečno\n\n**Običajna uporaba**\nSpremljanje vodnih teles, suš, poplav in podnebnih sprememb."]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":["Ultra modra (443 nm)"]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":["Modra (482 nm)"]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":["Zelena (561,5 nm)"]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":["Rdeča (654,5 nm)"]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":["Bližnja infrardeča svetloba (NIR) (865 nm)"]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":["Kratkovalovna infrardeča svetloba (SWIR) 1 (1608,5 nm)"]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":["Kratkovalovna infrardeča svetloba (SWIR) 2 (2200,5 nm)"]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":["Termična infrardeča svetloba (TIRS) 1 (10895 nm)"]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":["*Podatki *Level-1** (iz zbirke **Landsat Collection 2**) zagotavljajo globalne podatke o odbojnosti in temperaturi nad atmosfero. \n\nPodatki so bili obdelani v več korakih, vključno z geometrijskimi in radiometričnimi izboljšavami\n\nVeč informacij o podatkih Level-1 je [tukaj](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) in [tukaj](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)"]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":["Podatki **Level-2** (iz zbirke **Landsat Collection 2**) zagotavljajo globalne izdelke o odbojnosti površja in temperaturi površja znanstvene kakovosti (CEOS Analysis Ready Data, podatki pripravljeni za analizo)\n\nPodatkovni izdelki so ustvarjeni na podlagi vhodnih podatkov Level-1, ki izpolnjujejo omejitev <76 stopinj Sončevega zenitnega kota in vključujejo zahtevane pomožne vhodne podatke za ustvarjanje znanstveno izvedljivega izdelka\n\nVeč o podatkih Level-2 je na voljo [tukaj](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) in [tukaj](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/)."]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["**Landsat 8** je najnovejši izstreljeni satelit Landsat (zagotovila ga je NASA/USGS), ki nosi instrumenta Operational Land Imager (OLI) in Thermal Infrared Sensor (TIRS) z 9 optičnimi in 2 termičnima kanaloma. Ta dva senzorja zagotavljata sezonsko pokritost svetovnega kopnega.\n\n**Prostorska ločljivost:** 15 m za pankromatski kanal in 30 m za ostale (termični kanali so ponovno vzorčeni s 100 m).\n\n**čas ponovnega ogleda:** 16 dni\n\n**dostopnost podatkov:** od februarja 2013\n\n**Običajna uporaba:** spremljanje vegetacije, raba tal, karte pokrovnosti tal, spremljanje sprememb itd."]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":["Spremembe v pojavljanju vode med dvema obdobjema, pri čemer prvo obdobje zajema obdobje od leta 1984 do 1999, drugo pa obdobje od leta 2000 do 2019."]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":["Največji obseg površinskih vodnih teles v časovnem obdobju 36 let."]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":["Medletna in letna pogostost prisotnosti površinskih voda v časovnem obdobju med letoma 1984 in 2019."]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":["Medletna spremenljivost prisotnosti površinskih voda v določenem vodnem obdobju v celotnem časovnem razponu od leta 1984 do leta 2019."]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":["Medletna porazdelitev površinskih voda v letu 2019."]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":["Prikazuje spremembe v treh razredih površinskih voda (1) nevoda, (2) sezonska voda in (3) stalna voda med prvim in zadnjim letom v 36-letnem časovnem obdobju."]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":["# Dobrodošli v EO Browserju!\n\nCeloten arhiv satelitov Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA\narhiv satelitov Landsat 5, 7 in 8, globalni arhiv satelitov Landsat 8, Envisat Meris,\nMODIS, Proba-V in GIBS na enem mestu.\n\n[Predstavitvena stran brskalnika EO Browser](https://www.sentinel-hub.com/explore/eobrowser/) \n[Uporabniški priročnik za brskalnik EO Browser](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":["#### Kratek pregled funkcij brskalnika EO\n\nEO Browser na enem mestu združuje celoten arhiv satelitov Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, arhiv ESA Landsat 5, 7 in 8, globalni arhiv Landsat 8, Envisat Meris, MODIS, Proba-V in GIBS ter omogoča pregledovanje in primerjavo slik polne ločljivosti iz teh virov. Preprosto se odpravite na območje, ki vas zanima, izberete vire podatkov, časovno območje in pokritost z oblaki ter preglejte dobljene podatke.\n\nVodič lahko nadaljujete s klikom na gumb \"Naprej\" ali pa ga zaprete. S klikom na ikono info v zgornjem desnem kotu lahko vedno nadaljujete z vodičem, če ste ga zaprli po pomoti ali ker ste želeli nekaj preizkusiti."]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":["**Prijavljeni uporabniki** lahko uporabljajo teme po meri, shranjujejo in nalagajo oznake, z oznakami ustvarijo zgodbo, merijo razdalje, ustvarijo\nčasovno zaporedje in uporabljajo napredni prenos posnetkov.\n\nČe želite ustvariti brezplačen račun, kliknite [tukaj]\nali v aplikaciji kliknite na **Prijava** in nato \"Prijavite se\"."]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":["V zavihku **Odkrij** lahko:\n\n- Izberete **temo.**\n- **Iščete** podatke.\n- Raziščete **Poudarke** za izbrano temo.\n\nV spustnem oknu **Teme** so na voljo različne vnaprej konfigurirane teme in lastne nastavljene konfiguracije, če ste prijavljeni. Če želite ustvariti lastno konfiguracijo, kliknite na\nikono za nastavitve in se prijavite z uporabniškim računom, ki ga uporabljate za EO Browser.\n\nV razdelku **Poišči** lahko nastavite kriterije za iskanje:\n - Izberite, s katerih satelitov želite prejemati podatke, tako da izberete potrditvena polja.\n - Po potrebi izberite dodatne možnosti, na primer z drsnikom pokritost z oblaki.\n - Izberite časovno območje tako, da vpišete datum ali ga izberete s koledarja.\n\nOpise satelitov in podatkovnih virov lahko preberete s klikom na ikono\n poleg imena vira podatkov.\n\nKo pritisnete gumb Išči, se prikaže seznam rezultatov. Vsak rezultat je predstavljen \ns predogledno sliko in ustreznimi podatki, značilnimi za podatkovni vir. Pri nekaterih podatkovnih virih je za vsak rezultat vidna tudi ikona povezave .\nS klikom nanjo se prikažejo neposredne povezave do neobdelane slike rezultata v EO Cloud ali SciHub. S klikom na gumb Prikaži se odpre zavihek **Prikaži** za izbrani rezultat.\n\nV razdelku **Poudarki** so na voljo vnaprej izbrane zanimive lokacije, povezane z izbrano temo."]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":["V zavihku Prikaži lahko izberete različne vnaprej pripravljene kombinacije spektralnih kanalov ali kombinacije po meri za vizualizacijo podatkov za izbrani rezultat.\n\nNekatere pogoste možnosti:\n- **Naravne barve** - vizualna interpretacija pokritosti tal.\n- **Umetne barve** - vizualna interpretacija vegetacije.\n- **NDVI** - normiran diferencialni indeks vegetacije.\n- **Indeks vlažnosti** - indeks vlažnosti\n- **SWIR** - kratkovalovni infrardeči spekter\n- **NDWI** - Normalizirani diferencialni vodni indeks\n- **NDSI** - Normalizirani diferencialni snežni indeks\n\nVečina vizualizacij je opremljena z opisom in legendo, ki si ju lahko ogledate s klikom na razširitev\n .\n \nZa večino podatkovnih virov je na voljo možnost skripte **Po meri**. Kliknite jo, če želite izbrati poljuben\nkombinacije kanalov, kombinacije indeksov ali napišite svojo lastno skripto za vizualizacijo podatkov. Prav tako lahko\nuporabite skripte po meri, ki so shranjene drugje, bodisi na Google Drive, v GitHub ali v našem [skladišču skript po meri](https://custom-scripts.sentinel-hub.com/) \nURL naslov skripte prilepite v besedilno polje na plošči za napredno urejanje skript in kliknite Osveži.\n \nDatum lahko spremenite neposredno v zavihku Prikaži, ne da bi se vrnili v zavihek **Odkrij**. Vnesite ga ali izberite s koledarja .\n\nNad vizualizacijami imate na voljo vrsto dodatnih orodij. Upoštevajte, da je njihova uporabnost odvisna od vira podatkov.\n- **Pripnite sloj**, da ga shranite v aplikacijo za prihodnjo uporabo - s klikom na ikono priponke .\n- Izberite **napredne možnosti**, kot je metoda vzorčenja, ali uporabite različne **učinke**, kot sta kontrast (ojačenje) in svetilnost (gama) - s klikom na ikono drsnikov učinkov .\n- Dodajte plast v zavihek **Primerjaj** za poznejšo primerjavo - s klikom na ikono za primerjavo .\n- **Povečajte** na sredino ploščice - s klikom na križec .\n- Preklopite **vidnost plasti** - s klikom na ikono vidnosti .\n- Vizualizacijo **delite** v družabnih medijih - s klikom na ikono za deljenje ."]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":["V zavihku **Primerjaj** boste našli vse vizualizacije, ki ste jih prek spletne strani dodali v **Primerjaj** \n\nNa voljo sta dva načina:\n - **Prosojnost** (potegnite drsnik za prosojnost v levo ali desno, da se slike med seboj prelivajo)\n - **Delitev** (narišite drsnik za razdelitev v levo ali desno, da določite mejo med primerjanimi slikami)\n\nNa primerjalno ploščo lahko s spletno stranjo dodate **vse oznake** ali odstranite vse vizualizacije\nz zavihka **Primerjaj** z gumbom **Odstrani vse**."]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":["V zavihku **Oznake** so shranjeni označeni (priljubljeni/shranjeni) elementi. Elementi vsebujejo informacije\no lokaciji, viru podatkov in uporabljeni vizualizaciji, stopnji povečave in času.\n\nZa vsako oznako je na voljo več možnosti :\n\n- Spremenite **red** - s klikom na ikono premikanja\n\n \n \n \nv zgornjem levem kotu oznake in jo povlecite po seznamu navzgor ali navzdol.\n- **Preimenovanje** - s klikom na ikono svinčnika poleg imena priponke.\n- Dodajanje na zavihek **Primerjaj** - s klikom na ikono za primerjavo \n- Vnesite **opis** - s klikom na ikono za razširitev .\n- **Odstrani** - s klikom na ikono za odstranitev .\n- **Zoom** na lokacijo oznake - s klikom na povezavo Lat/Lon.\n\nV vrstici nad oznakami so na voljo različne možnosti, ki veljajo za vse oznake:\n- Ustvarite zgodbo iz oznak - s klikom na **Zgodba**.\n- Delite svoje oznake z drugimi prek povezave - s klikom na **Deli**\n- Izvozite oznake kot datoteko JSON - s klikom na **Izvozi**.\n- Uvozi oznake iz datoteke JSON - s klikom na **Uvozi**.\n- Izbrišite vse oznake - s klikom na **Počisti**."]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":["Lokacijo poiščite tako, da zemljevid premikate z miško ali vnesete lokacijo v iskalnik in izberete najbolj ustrezen zadetek."]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":["Tu lahko izberete, kateri osnovni sloj in prekrivni sloji (ceste, meje, oznake) bodo prikazani na zemljevidu."]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":["Tu lahko preklapljate med **normalnim** in **izobraževalnim** načinom. Način **izobraževanja** ponuja nekoliko poenostavljeno različico aplikacije.\nDo nje lahko dostopate tudi neposredno prek [namenskega URL] (https://apps.sentinel-hub.com/eo-browser-education/)."]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":["Vodič si lahko kadar koli ogledate s klikom na to informacijsko ikono\n\n\n."]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":["To orodje omogoča risanje poligona na karti in prikaz velikosti poligona.\n\nVsi sloji, ki vračajo eno samo vrednost (kot so NDVI, indeks vlage, NDWI, ...), podpirajo prikaz\nindeksa za izbrano območje skozi čas. S klikom na ikono diagrama boste\nprikazali grafikon. Poligon lahko odstranite s klikom na ikono za odstranitev .\n\nPrav tako lahko naložite datoteko KML/KMZ, GPX ali GEOJSON/JSON z geometrijo poligona.\n\nZ ikono dveh listov lahko kopirate koordinate poligona kot datoteko GEOJSON, s križcem \nusmeri karto na narisani poligon.\n\nIzvožene slike bodo pri analitičnih prenosih obrezane na območje zanimanja."]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":["S tem orodjem lahko označite točko na karti.\n\nS klikom na ikono grafikona si lahko ogledate tudi statistične podatke za nekatere sloje.\n\nOznako lahko odstranite s klikom na ikono za odstranitev .\n"]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":["S tem orodjem lahko merite razdalje in območja na karti.\n\nVsak klik miške ustvari novo točko na poti. Če želite ustaviti dodajanje točk, pritisnite tipko Esc
\nali dvakrat kliknite na karto. \nMeritev lahko odstranite s klikom na ikono za odstranitev ."]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":["S tem orodjem lahko prenesete sliko vizualiziranih podatkov za prikazano lokacijo. Izberete lahko\nprikaz podnapisov in lahko dodate svoj opis.\nČe omogočite analitični način, lahko izbirate med različnimi oblikami slik, ločljivostmi slik in\nkoordinatnimi sistemi. Izberete lahko tudi več slojev in jih prenesete kot datoteko .zip.
\n\nKliknite gumb za prenos\n Prenos\nin slika(-e) se bo(-jo) začela(-e) prenašati. Postopek lahko traja nekaj sekund, odvisno od izbrane\nločljivosti in števila izbranih slojev.\n\nPred prenosom lahko določite interesno območje (AOI) s klikom na orodje za izbiro območja\nikono . Vaši podatki bodo obrezani tako, da bodo ustrezali temu območju."]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":["S tem orodjem lahko ustvarite časovno animacijo vizualiziranega sloja in prikazane lokacije.\n\nNajprej izberite časovno obdobje. Rezultate iskanja lahko dodatno opredelite tako, da jih filtrirate po mesecih\n(potrditveno polje Filtriraj po mesecih) in/ali izbiro enega posnetka na določeno obdobje (orbito, dan, teden, mesec,\nleto).\n\nNato pritisnite Search (Iskanje) in izberite svoje slike.\nIzberete lahko vse, tako da označite potrditveno polje, ali filtrirate posnetke glede na pokritost z oblaki s premikanjem drsnika. Lahko pa izberete posnetke\ntako, da se pomikate po seznamu in jih izberete. S potrditvenim poljem **Robovi** lahko omogočite/izključite robove na sliki.\n\nČasovno zaporedje si lahko ogledate s pritiskom na gumb za predvajanje na dnu slike. Nastavite lahko tudi hitrost\n(število sličic na sekundo).\n\nKo ste zadovoljni z rezultatom, kliknite gumb za prenos in časovno zaporedje bo\nprenešeno kot datoteka .gif
."]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":["Dosegli ste konec vadnice. Če imate še kakšno vprašanje, ga lahko zastavite na [forumu](https://forum.sentinel-hub.com/)\nali se z nami povežite [prek e-pošte](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nČe si boste še kdaj želeli ogledati ta vodič, ga lahko vedno prikličete s \nklikom na ikono \n\n\n v zgornjem desnem kotu."]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":["#### Kratek pregled funkcij EO Broserje\n\nČe imate majhen zaslon, si [tukaj] (https://www.sentinel-hub.com/explore/eobrowser/user-guide/) oglejte uporabniški priročnik.\n\nTe informacije si lahko vedno znova ogledate s klikom na ikono info\n\n\n\nv zgornjem desnem kotu.\n\n#### Drugi viri\n- [Predstavitvena stran EO Browserja](https://www.sentinel-hub.com/explore/eobrowser/)\n- [Posodobitve EO Browserja poletje 2018 - videoposnetek] (https://www.youtube.com/embed/m3pron0C0kE)"]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":["Kaj je EO Browser?"]},"User Account":{"msgid":"User Account","msgstr":["Uporabniški račun"]},"Discover Tab":{"msgid":"Discover Tab","msgstr":["Zavihek Odkrij"]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":["Zavihek Prikaži"]},"Compare Tab":{"msgid":"Compare Tab","msgstr":["Zavihek Primerjaj"]},"Pins Tab":{"msgid":"Pins Tab","msgstr":["Zavihek Oznake"]},"Search Places":{"msgid":"Search Places","msgstr":["Iskanje krajev"]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":["Sloji in prekrivanja"]},"Education Mode":{"msgid":"Education Mode","msgstr":["Način izobraževanja"]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":["Informacije in vodič"]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":["Narišite območje zanimanja"]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":["Označite točko zanimanja"]},"Measure Distances":{"msgid":"Measure Distances","msgstr":["Merjenje razdalj"]},"Download Image":{"msgid":"Download Image","msgstr":["Prenos posnetka"]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":["Ustvarjanje animacije s časovnim zaporedjem"]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":["Srečno brskanje!"]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":["Dobrodošli v EO Browserju!"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":["Kanal 1 - rumena snov in detritični pigmenti - 412,5 nm"]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":["Kanal 2 - absorpcijski maksimum klorofila - 442 nm"]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":["Kanal 3 - klorofil in drugi pigmenti - 490 nm"]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":["Kanal 4 - suspendirane usedline, rdeče plime - 510 nm"]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":["Kanal 5 - absorpcijski minimum klorofila - 560 nm"]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":["Kanal 6 - suspendirana usedlina - 620 nm"]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":["Kanal 7 - absorpcija klorofila in fluo. referenca - 665 nm"]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":["Kanal 8 - vrh fluorescence klorofila - 681 nm"]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":["Kanal 9 - referenčni fluo, popravki za atmosfero - 709 nm"]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":["Kanal 10 - vegetacija, oblaki - 753 nm"]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":["Kanal 11 - absorpcijski pas R-veje O2 - 761 nm"]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":["Kanal 12 - korekcije atmosfere - 779 nm"]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":["Kanal 13 - vegetacija, vodna para referenca - 865 nm"]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":["Kanal 14 - korekcije atmosfere - 885 nm"]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":["Kanal 15 - vodna para, kopno - 900 nm"]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":["Landsat 5 (arhiv ESA)"]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":["Landsat 7 (arhiv ESA)"]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":["Landsat 8 (arhiv ESA)"]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":["Landsat 8 (arhiv USGS)"]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":["Landsat 8 L1"]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":["Landsat 8 L2"]},"Red band":{"msgid":"Red band","msgstr":["Rdeči kanal"]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":["841-876 nm (NIR)"]},"Blue band":{"msgid":"Blue band","msgstr":["Modri kanal"]},"Green band":{"msgid":"Green band","msgstr":["Zeleni kanal"]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":["1230 - 1250 nm"]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":["1628 - 1652 nm"]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":["2105 - 2155 nm"]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":["Kanal 1 - obalni aerosol - 443 nm"]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":["Kanal 2 - modra barva - 490 nm"]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":["Kanal 3 - zelena - 560 nm"]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":["Kanal 4 - rdeča barva - 665 nm"]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":["Kanal 5 - vegetacija Rdeči rob - 705 nm"]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":["Kanal 6 - vegetacija Rdeči rob - 740 nm"]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":["Kanal 7 - rdeči rob vegetacije - 783 nm"]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":["Kanal 8 - NIR - 842 nm"]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":["Kanal 9 - vodna para - 945 nm"]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":["Kanal 10 - SWIR - Cirrus - 1375 nm"]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":["Kanal 11 - SWIR - 1610 nm"]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":["Kanal 12 - SWIR - 2190 nm"]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":["Kanal 8A - rdeči rob vegetacije - 865 nm"]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (atmosfersko popravjeno)"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":["Kanal 1 - korekcija aerosolov, izboljšano pridobivanje vodnih sestavin - 400 nm"]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":["Kanal 2 - rumena snov in detritični pigmenti (motnost)-412 nm"]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":["Kanal 3 - največja absorpcija Chl, biogeokemija, vegetacija - 442,5 nm"]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":["Kanal 4 - visoka vsebnost Chl, drugi pigmenti - 490 nm"]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":["Kanal 5 - Chl, sediment, motnost, rdeči plima - 510 nm"]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":["Kanal 6 - referenčni klorofil (Chl minimum) - 560 nm"]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":["Kanal 7 - obremenitev s sedimenti - 620 nm"]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":["Kanal 8 - Chl (2. abs. max. Chl), sediment, rumena snov/rastlinstvo - 665 nm"]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":["Kanal 9 - za boljše pridobivanje fluorescence, boljše upoštevanje skupaj s pasovi 8 (665 nm) in 10 (681.25 nm) - 673,75 nm "]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":["Kanal 10 - vrh fluorescence Chl, rdeči rob - 681,25 nm"]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":["Kanal 11 - osnovna fluorescenca Chl, prehod rdečega roba - 708,75 nm"]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":["Kanal 12 - absorpcija O2/oblaki, vegetacija - 753,75 nm"]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":["Kanal 13 - absorpcijski pas O2/aerosol corr. - 761,25 nm"]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":["Kanal 14 - atmosferski popravek - 764,375 nm"]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":["Kanal 15 - O2A se uporablja za merjenje pritiska nad oblaki, fluorescenca nad kopnim - 767,5 nm"]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":["Kanal 16 - Atmos. kor./aerosol kor. - 778,75 nm"]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":["Kanal 17 - korektura ozračja/korektura aerosolov, oblaki, koregistracija pikslov - 865 nm"]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":["Kanal 18 - referenčni pas absorpcije vodne pare. Skupni referenčni pas z instrumentom SLSTR. Spremljanje vegetacije - 885 nm"]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":["Kanal 19 - absorpcija vodne pare/nadzor vegetacije (največja odbojnost) - 900 nm"]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":["Kanal 20 - absorpcija vodne pare, atmos./aerosol kor. - 940 nm"]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":["Kanal 21 - Atmos. kor./aerosol kor. - 1020 nm"]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":["Kanal F1 - toplotna emisija IR ognja - Aktivni ogenj - 3742,00 nm"]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":["Kanal F2 - toplotna emisija IR ognja - Aktivni ogenj - 10854,00 nm"]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":["Kanal S1 - VNIR - pregledovanje oblakov, spremljanje vegetacije, aerosol - 554,27 nm"]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":["Kanal S2 - VNIR - NDVI, spremljanje vegetacije, aerosol - 659,47 nm"]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":["Kanal S3 - VNIR - NDVI, označevanje oblakov, koregistracija pikslov - 868,00 nm"]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":["Kanal S4 - SWIR - Zaznavanje cirrusa nad kopnim - 1374,80 nm"]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":["Kanal S5 - SWIR - čiščenje oblakov, led, sneg, spremljanje vegetacije - 1613,40 nm"]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":["Kanal S6 - SWIR - stanje vegetacije in razkrajanje oblakov - 2255,70 nm"]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":["Kanal S7 - termični IR ambient - SST, LST, aktivni požar - 3742,00 nm"]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":["Kanal S8 - termični IR ambient - SST, LST, aktivni požar - 10854,00 nm"]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":["Kanal S9 - termični IR-območje - SST, LST - 12022,50 nm"]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":["Na podlagi kombinacije kanalov 4, 3, 2"]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":["Na podlagi kombinacije kanalov (B04-B03)/(B04+B03)"]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":["Na podlagi kombinacije kanalov 5, 4, 3"]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":["Na podlagi barvnih kanalov 4, 3, 2 in pankromatskega kanala 8"]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":["Na podlagi kombinacije kanalov (B05-B04)/(B05+B04)"]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":["VV - linearno gama0 - ortorektificirano"]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":["VV - linearni gama0 - neortorektificiran"]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":["VH - linearni gama0 - ortorektificirano"]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":["Na podlagi kombinacije kanalov 3, 2, 1"]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":["VH - linearna gama 0 - neortorektificirano"]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":["Barvna slika s preslikavo vhodnih pasov. Vrednost [RGB] = [VV, 2 VH, VV / VH / 100,0] - linearno gama0 - ortorektificirano"]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":["VV - decibel gamma0 [-20,0] - ortorektificirano"]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":["VH - decibel gama0 [-20,0] - ortorektificirano"]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":["Vrne kompozit (VH, VV, VH-VV)"]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":["HH - linearno gama0 - ortorektificirano"]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":["HV - linearna gama0 - ortorektificirana"]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":["Barvna slika s preslikavo vhodnih pasov. Vrednost [RGB] = [HH, 2 HV, HH / HV / 100,0] - linearna gama0 - ortorektificirana"]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":["HH - decibel gamma0 [-20,0] - ortorektificirano"]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":["HV - decibel gamma0 [-20,0] - ortorektificirano"]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":["HH - linearni gama0 - neortorektificiran"]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":["Na podlagi kanalov 4,3,2"]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":["Na podlagi kanalov 8,4,3"]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":["Na podlagi kanalov 12,11,4"]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Na podlagi kombinacije kanalov (B8 - B4)/(B8 + B4)"]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":["Na podlagi kombinacije kanalov (B8A - B11)/(B8A + B11)"]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":["Na podlagi kanalov 12,8A,4"]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":["Na podlagi kombinacije kanalov (B3 - B8)/(B3 + B8)"]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":["Na podlagi kombinacije kanalov (B3 - B11)/(B3 + B11); vrednosti nad 0,42 veljajo za zasnežene"]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":["Klasifikacija podatkov Sentinel-2 kot rezultat algoritma ESA za razvrščanje scen."]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":["Indeks UV aerosolov od 380 in 340 nm"]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":["Na podlagi kombinacije kanalov (B3 - B11)/(B3 + B11)"]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":["OLCI Indeks kopenskega klorofila, ki temelji na kombinaciji kanalov (B12 - B11)/(B11 - B10)"]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":["Indeks UV aerosolov od 388 in 354 nm"]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":["Srednje razmerje mešanja metana v suhem zraku v stolpcu"]},"Cloud base height":{"msgid":"Cloud base height","msgstr":["Višina dna oblakov"]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":["Tlak na dnu oblakov"]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":["Efektivni radiometrični delež oblakov"]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":["Optična debelina oblaka"]},"Cloud top height":{"msgid":"Cloud top height","msgstr":["Višina vrha oblakov"]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":["Tlak na vrhu oblakov"]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":["Skupni stolpec ogljikovega monoksida"]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":["Formaldehidni troposferski vertikalni stolpec"]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":["Troposferski stolpec dušikovega dioksida"]},"Ozone total column":{"msgid":"Ozone total column","msgstr":["Skupni stolpec ozona"]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":["Skupni stolpec žveplovega dioksida"]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":["Na podlagi kanalov 2, 1, 4"]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":["Na podlagi kombinacije kanalov (B02 - B01)/(B02 + B01)"]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":["Na podlagi kombinacije kanalov (B02 - B05)/(B02 + B05)"]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":["Na podlagi kombinacije kanalov (B06 - B07)/(B06 + B07)"]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":["Na podlagi kanalov 1, 4, 3"]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":["Na podlagi kombinacije kanalov 7, 5, 3"]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":["(B09 - B08)/(B09 + B08)"]},"B09 / B08":{"msgid":"B09 / B08","msgstr":["B09 / B08"]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":["Na podlagi kombinacije kanalov 13, 5, 2"]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":["Na podlagi kanalov 13, 4, 1"]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":["Na podlagi kanalov 13, 5, 2"]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":["Na podlagi kombinacije kanalov (B13-B07) / (B13+B07)"]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":["Indeks kopenskega klorofila"]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V 10-dnevna sinteza\nVrh krošnje (z atmosferskim popravkom)\nČasovna ločljivost: 10 dni na dan\nLočljivost: 333 m (velikost pikslov)"]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V dnevna sinteza\nVrh atmosfere\nČasovna ločljivost: dnevno\nLočljivost: dnevna: 333 m (velikost piksla)"]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-dnevno sinteza\nVrh atmosfere\nČasovna ločljivost: 5-dnevna resolucija\nLočljivost: 100 m (velikost piksla)"]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V dnevna sinteza\nVrh krošnje (atmosfersko popravljen)\nČasovna ločljivost: dnevno\nLočljivost: dnevna ločljivost: 333 m (velikost piksla)"]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":["OrbitTracks_Aqua_Descending"]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-dnevna sinteza\nVrh krošnje (atmosfersko korigirano)\nČasovna ločljivost: 5-dnevno\nLočljivost: 100 m (velikost pikslov)"]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":["OrbitTracks_Aqua_Ascending"]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":["OrbitTracks_Aura_Ascending"]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":["OrbitTracks_Aura_Descending"]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":["OrbitTracks_CloudSat_Ascending"]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":["OrbitTracks_Calipso_Ascending"]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":["OrbitTracks_Calipso_Descending"]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":["OrbitTracks_CloudSat_Descending"]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":["OrbitTracks_CYGNSS_Ascending"]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":["OrbitTracks_CYGNSS_Descending"]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":["OrbitTracks_GCOM-C_Ascending"]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":["OrbitTracks_GCOM-C_Descending"]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":["OrbitTracks_GCOM-W1_Ascending"]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":["OrbitTracks_GCOM-W1_Descending"]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":["OrbitTracks_GOSAT-2_Ascending"]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":["OrbitTracks_GOSAT-2_Descending"]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":["OrbitTracks_GOSAT_Ascending"]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":["OrbitTracks_GOSAT_Descending"]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":["OrbitTracks_GPM_Ascending"]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":["OrbitTracks_GPM_Descending"]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":["OrbitTracks_ICESAT-2_Ascending"]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":["OrbitTracks_ICESAT-2_Descending"]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":["OrbitTracks_ISS_Ascending"]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":["OrbitTracks_ISS_Descending"]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":["OrbitTracks_Landsat-7_Ascending"]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":["OrbitTracks_Landsat-7_Descending"]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":["OrbitTracks_Landsat-8_Ascending"]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":["OrbitTracks_METOP-A_Ascending"]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":["OrbitTracks_METOP-B_Descending"]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":["OrbitTracks_Landsat-8_Descending"]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":["OrbitTracks_METOP-C_Ascending"]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":["OrbitTracks_METOP-C_Descending"]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":["OrbitTracks_NOAA-20_Ascending"]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":["OrbitTracks_NOAA-20_Descending"]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":["OrbitTracks_METOP-B_Ascending"]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":["OrbitTracks_OCO-2_Ascending"]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":["OrbitTracks_OCO-2_Descending"]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":["OrbitTracks_SAOCOM1-A_Ascending"]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":["OrbitTracks_SAOCOM1-A_Descending"]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":["OrbitTracks_Sentinel-1A_Ascending"]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":["OrbitTracks_Sentinel-1B_Ascending"]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":["OrbitTracks_Sentinel-1A_Descending"]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":["OrbitTracks_METOP-A_Descending"]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":["OrbitTracks_Sentinel-1B_Descending"]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":["OrbitTracks_Sentinel-2A_Ascending"]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":["OrbitTracks_Sentinel-2A_Descending"]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":["OrbitTracks_Sentinel-2B_Ascending"]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":["OrbitTracks_Sentinel-2B_Descending"]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":["OrbitTracks_Sentinel-5P_Ascending"]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":["OrbitTracks_Sentinel-5P_Descending"]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":["OrbitTracks_SMAP_Ascending"]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":["OrbitTracks_SMAP_Descending"]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":["OrbitTracks_Suomi_NPP_Descending"]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":["OrbitTracks_Suomi_NPP_Ascending"]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":["OrbitTracks_Terra_Descending"]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":["OrbitTracks_Terra_Ascending"]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":["Na podlagi kanalov 4, 3, 2, ki so okrepljeni s pasovoma 12 in 11."]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":["Na podlagi kanalov B07, B06, B04"]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Na podlagi kombinacije kanalov (B8 - B4)/(B8 + B4)"]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":["Na podlagi toplotnega kanala 10"]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":["Na podlagi kanalov B12, B11, B8A"]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":["Na podlagi kombinacije kanalov (B08 - B12)/(B08 + B12)"]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":["Izboljšana vizualizacija naravnih barv"]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":["Na podlagi kombinacije kanalov 8, 6, 4"]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":["Izboljšani vegetacijski indeks"]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":["Na podlagi kombinacije: BSI, B08, B11"]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":["Klasifikacija NDMI za namakanje"]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":["Na podlagi kanalov B11, B08, B02"]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":["Lažne barve 13, 5, 2"]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":["Na podlagi kombinacije kanalov (B13 - B07) / (B13 + B07)"]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":["Na podlagi kanalov 12,8,2"]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":["Na podlagi kanalov 4, 3, 2"]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":["Na podlagi kanalov 8, 4, 3"]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":["Na podlagi kanalov 12, 8, 2"]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":["Na podlagi kanalov 8, 11, 12"]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":["Sedimentacija vode in vsebnost klorofila"]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":["Na podlagi kanalov 12, 8A, 4"]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":["Na podlagi NDSI"]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":["Na podlagi kombinacije kanalov 11, 8, 2"]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":["Na podlagi kanalov B12, B11, B04"]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":["Na podlagi kanalov 4, 3 in 2"]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":["Atmosfersko odporen vegetacijski indeks"]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":["Vegetacijski indeks, prilagojen tlom"]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":["Modificiran indeks odbojnosti antocianina"]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":["# Kanali termičnih IR emisij\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) ima dva posebna kanala (F1 in F2) za zaznavanje temperature zemeljskega površja (LST). Kanal F2 z osrednjo valovno dolžino 10854 nm meri v termalni infrardeči svetlobi ali TIR. Zelo uporaben je za spremljanje požarov in dogodkov z visoko temperaturo pri ločljivosti 1 km.\n\n\n\nVeč informacij je [tukaj](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)"]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":["# Metan (CH4)\n\n\n\nMetan je za ogljikovim dioksidom najpomembnejši povzročitelj za antropogeno (zaradi človekove dejavnosti) povečan učinek tople grede. Meritve so navedene v delcih na milijardo (ppb) s prostorsko ločljivostjo 7 km x 3,5 km.\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/methane)"]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":["# Formaldehid (HCHO)\n\n\n\nDolgoročna satelitska opazovanja troposferskega formaldehida (HCHO) so bistvenega pomena za podporo študijam kakovosti zraka in kemijsko-podnebnim študijam od regionalnega do globalnega obsega. Sezonska in medletna nihanja porazdelitve formaldehida so v glavnem povezana s temperaturnimi spremembami in požari, pa tudi s spremembami antropogenih dejavnosti (ki jih je povzročil človek). Koncentracije HCHO, katerih življenjska doba je nekaj ur, v mejnem sloju atmosfere so lahko neposredno povezane s sproščanjem kratkoživih ogljikovodikov, ki jih večinoma ni mogoče neposredno opazovati iz vesolja. Meritve so v mol na kvadratni meter (mol/ m^2).\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/formaldehyde)"]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":["# Žveplov dioksid (SO2)\n\n\n\nŽveplov dioksid vstopa v zemeljsko ozračje z naravnimi in antropogenimi (človeškimi) procesi. Ima vlogo v kemiji na lokalni in svetovni ravni, njegov vpliv pa sega od kratkoročnega onesnaževanja do učinkov na podnebje. Le približno 30 % odstotkov izpustov SO2 prihaja iz naravnih virov, večina je antropogenega izvora. Instrument Sentinel-5P/TROPOMI snema Zemljino površje s časom ponovnega obiska en dan in prostorsko ločljivostjo 3,5 x 7 km, kar omogoča ločljivost drobnih podrobnosti, vključno z zaznavanjem manjših tokov SO2. Meritve so v mol na kvadratni meter (mol/m^2).\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/sulphur-dioxide)"]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":["# Ozon (O3)\n\n\n\nOzon je ključnega pomena za ravnovesje zemeljskega ozračja. V stratosferi ozonska plast ščiti biosfero pred nevarnim sončnim ultravijoličnim sevanjem. V troposferi deluje kot učinkovito čistilno sredstvo, vendar pri visoki koncentraciji postane tudi škodljiv za zdravje ljudi, živali in rastlinstva. Ozon je tudi pomemben povzročitelj toplogrednih plinov, ki prispevajo k stalnim podnebnim spremembam. Od odkritja ozonske luknje na Antarktiki v osemdesetih letih prejšnjega stoletja in poznejšega sprejetja Montrealskega protokola, ki ureja proizvodnjo snovi, ki tanjšajo ozonski plašč in vsebujejo klor, se ozon redno spremlja s tal in iz vesolja. Meritve so v mol na kvadratni meter (mol/m^2)\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/total-ozone-column)"]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":["# Dušikov dioksid (NO2)\n\n\n\nDušikov dioksid (NO2) in dušikov oksid (NO) skupaj običajno imenujemo dušikovi oksidi. Sta pomembna plina v Zemljinem ozračju, ki sta prisotna v sledovih tako v troposferi kot v stratosferi. V ozračje vstopata zaradi antropogenih dejavnosti (zlasti zgorevanja fosilnih goriv in biomase) in naravnih procesov (kot so mikrobiološki procesi v tleh, gozdni požari in strele). Meritve so v mol na kvadratni meter (mol/ m^2).\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/nitrogen-dioxide)"]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":["# Ogljikov monoksid (CO)\n\n\n\nOgljikov monoksid (CO) je pomemben atmosferski plin. Na nekaterih mestnih območjih je glavno onesnaževalo ozračja. Glavni viri CO so zgorevanje fosilnih goriv, izgorevanje biomase ter oksidacija metana in drugih ogljikovodikov v ozračju. Skupni stolpec ogljikovega monoksida se meri v mol na kvadratni meter (mol/ m^2).\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/carbon-monoxide)"]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":["# Indeks aerosolov\n\nIndeks aerosolov (AI) je kvalitativni indeks, ki označuje prisotnost povišanih plasti aerosolov v ozračju. Uporablja se lahko za ugotavljanje prisotnosti aerosolov kot sta puščavski prah in vulkanski pepel, ki absorbirajo UV žarke. Pozitivne vrednosti (od svetlo modre do rdeče) kažejo na prisotnost aerosolov, ki absorbirajo UV-žarke. Ta indeks se izračuna za dva para valovnih dolžin: 340/380 nm in 354/388 nm.\n\nVeč informacij je [tukaj](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)"]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":["# Spodnja višina oblakov\n\nVišina spodnjega dela oblakov, izmerjena v metrih (m)."]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":["# Tlak na dnu oblakov\n\nTlak, izmerjen na spodnji strani oblakov, v pascalih (Pa)."]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":["# Učinkoviti radiometrični delež oblakov\n\nEfektivni radiometrični delež oblakov predstavlja delež Zemljine površine, ki ga pokrivajo oblaki, deljen s celotno površino. Oblaki vplivajo na pridobivanje slednih plinov zaradi ščitenja, albeda in absorpcije v oblakih. Efektivni radiometrični delež oblakov je pomemben parameter za popravljanje teh učinkov."]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":["# Optična debelina oblaka\n\nDebelina oblakov je ključni parameter za opredelitev optičnih lastnosti oblakov. Je merilo, koliko sončne svetlobe preide skozi oblake in doseže Zemljino površje. Večja kot je optična debelina oblakov, več sončne svetlobe oblak razprši in odbije. Temno modra barva prikazuje, kje so nizke vrednosti optične debeline oblakov, rdeča pa večje vrednosti optične debeline oblakov."]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":["# Višina vrha oblakov\n\nVišina vrha oblakov, izmerjena v metrih (m)."]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":["# Tlak na vrhu oblakov\n\nTlak, izmerjen na vrhu oblakov, v pascalih (Pa)."]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Termalni kanal 10\n\nTa toplotna vizualizacija temelji na kanalu 10 (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih). Pri osrednji valovni dolžini 10895 nm meri v termalni infrardeči svetlobi ali TIR. Namesto merjenja temperature zraka, kot to počnejo vremenske postaje, kanal 10 zajema podatke o površju, ki je pogosto veliko bolj vroča. Termični kanal 10 je uporaben za določanje temperature površja in se zbira z ločljivostjo 100 metrov.\n\n\n\nVeč informacij je [tukaj](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) in [tukaj](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":["# Normalizirani diferencialni vegetacijski indeks (NDVI)\n\nNormalizirani diferencialni vegetacijski indeks je preprost, vendar učinkovit indeks za kvantifikacijo zelene vegetacije. Gre za merilo zdravstvenega stanja vegetacije, ki temelji na tem, kako rastline odbijajo svetlobo pri določenih valovnih dolžinah. Razpon vrednosti indeksa NDVI je od -1 do 1. Negativne vrednosti NDVI (vrednosti, ki se približujejo -1) ustrezajo vodi. Vrednosti blizu nič (-0,1 do 0,1) običajno ustrezajo golim območjem kamenja, peska ali snega. Nizke pozitivne vrednosti pomenijo grmičevje in travnike (približno 0,2 do 0,4), visoke vrednosti pa zmerne in tropske deževne gozdove (vrednosti, ki se približujejo 1).\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) in [tukaj](https://eos.com/ndvi/)"]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":["# Izboljšani indeks vegetacije (EVI)\n\nIzboljšani indeks vegetacije (Enhanced Vegetation Index, EVI) je \"optimiziran\" indeks vegetacije, saj popravlja ozadje tal in atmosferske vplive. Zelo uporaben je na območjih z gosto pokritostjo s krošnjami. Razpon vrednosti za EVI je od -1 do 1, pri čemer je zdrava vegetacija običajno med 0,20 do 0,80.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) in [tukaj](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)"]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Indeks atmosfersko odporne vegetacije (ARVI)\n\nAtmospherically Resistant Vegetation Index (ARVI) je vegetacijski indeks, ki zmanjšuje učinke atmosferskega sipanja. Najbolj uporaben je za območja z visoko vsebnostjo atmosferskega aerosola (megla, prah, dim, onesnaženost zraka). Razpon indeksa ARVI je od -1 do 1, pri čemer se zelena vegetacija običajno nahaja med vrednostmi od 0,20 do 0,80.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) in [tukaj](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Vegetacijski indeks prilagojen zaradi prsti (SAVI)\n\nVegetacijski indeks, prilagojen zaradi prsti, je podoben normiranemu diferencialnemu vegetacijskemu indeksu (NDVI), vendar se uporablja na območjih, kjer je vegetacijski pokrov nizek (< 40 %). Indeks je tehnika transformacije, ki zmanjšuje vplive osvetljenosti tal na spektralne vegetacijske indekse, ki vključujejo rdeče in bližnje infrardeče (NIR) valovne dolžine. Indeks je koristen pri analizi mladih poljščin, sušnih območij z redko vegetacijo in izpostavljenih površin tal.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) in [tukaj](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":["# Modificiran indeks odbojnosti antocianina (mARI/ARI2)\n\nAntocianini so pigmenti, ki so pogosti v višjih rastlinah in povzročajo njihovo rdečo, modro in vijolično obarvanost. Zagotavljajo dragocene informacije o fiziološkem stanju rastlin, saj veljajo za indikatorje različnih vrst stresa rastlin. Odsevnost antocianina je največja okoli 550 nm. Vendar iste valovne dolžine odbija tudi klorofil. Za izolacijo antocianinov se odšteje spektralni pas 700 nm, ki odbija samo klorofil in ne antocianinov.\n\nZa korekcijo gostote in debeline listov se osnovnemu indeksu ARI doda bližnji infrardeči spektralni pas (v priporočenih valovnih dolžinah 760-800 nm), ki je povezan z razpršitvijo listov. Novi indeks se imenuje modificirani ARI ali mARI (tudi ARI2).\n\nvrednosti mARI za pregledana drevesa v [tem izvirnem članku](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) so se gibale med 0 in 8.\n\n\n\n\n\nVeč informacij je [tukaj](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)"]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":["# Skripta Zeleno mesto\n\nNamen skripte Zeleno mesto je povečati ozaveščenost o zelenih površinah v mestih po vsem svetu. Skripta upošteva normirani indeks razlike vegetacije (NDVI) in prave barvne valovne dolžine; ločuje pozidana območja od vegetacijskih, zato je uporabna za odkrivanje mestnih območij. Pozidana območja so prikazana v sivi barvi, vegetacija pa v zeleni.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":["# Skripta za klasifikacijo urbanih površin\n\nNamen skripte je zaznati pozidana območja tako, da jih loči od neplodnih tal, vegetacije in vode. Območja z visoko vsebnostjo vlage so prikazana z modro barvo; območja, ki označujejo pozidana območja, so prikazana z belo barvo; območja z vegetacijo so prikazana z zeleno barvo; vse ostalo označuje neplodna tla in je prikazano z rjavo barvo.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)"]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":["# Skripta urbane infrardeče vizualizacije\n\nTa skripta, ki jo je izdelal Leo Tolari, združuje pravo barvno vizualizacijo z bližnjo infrardečo (NIR) in kratkovalovno infrardečo (SWIR) valovno dolžino. Skripta poudarja urbana območja bolje kot prava barva, hkrati pa je še vedno videti naravno.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)"]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":["# NDMI za vlažnostni stres\n\nNormalizirani indeks razlike vlage (Normalized Difference Moisture Index, NDMI) za stres zaradi vlage se lahko uporablja za odkrivanje namakanja. Pri vseh vrednostih indeksa nad 0 je ob poznavanju rabe tal in pokrovnosti tal mogoče ugotoviti, ali je bilo izvedeno namakanje. Ob poznavanju vrste pridelkov (npr. agrumi) je mogoče ugotoviti, ali je namakanje v ključni rastni poletni sezoni učinkovito ali ne, ter ugotoviti, ali so nekateri deli kmetije premalo ali preveč namočeni.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)"]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":["# Normalizirani diferencialni indeks vlage (NDMI)\n\nNormalizirani diferencialni indeks vlage (Normalized Difference Moisture Index, NDMI) se uporablja za določanje vsebnosti vode v vegetaciji in spremljanje suše. Razpon vrednosti NDMI je od -1 do 1. Negativne vrednosti NDMI (vrednosti, ki se približujejo -1) pomenijo neplodna tla. Vrednosti okoli nič (-0,2 do 0,4) na splošno ustrezajo vodnemu stresu. Visoke, pozitivne vrednosti pomenijo visoko krošnjo brez vodnega stresa (približno od 0,4 do 1).\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":["# Normalizirani diferencialni vodni indeks (NDWI)\n\nZa kartiranje vodnih teles je najprimernejši normalizirani diferencialni vodni indeks (Normalized Difference Water Index, NDWI). Vrednosti vodnih teles so večje od 0,5. Vegetacija ima manjše vrednosti. Pozidani elementi imajo pozitivne vrednosti med nič in 0,2.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":["# Normalizirani diferencialni vodni indeks (NDWI)\n\nZa kartiranje vodnih teles je najprimernejši normalizirani diferencialni vodni indeks (Normalized Difference Water Index, NDWI). Vrednosti vodnih teles so večje od 0,5. Vegetacija ima manjše vrednosti. Pozidani elementi imajo pozitivne vrednosti med nič in 0,2."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Lažno barvni kompozit\n\nLažno barvni kompozit uporablja vsaj eno očem nevidno valovno dolžino za slikanje Zemlje. Lažni barvni kompozit, ki uporablja bližnje infrardeče, rdeče in zelene pasove, je zelo priljubljen (pas je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo prikaže v različnih pasovih). Lažni barvni kompozit se najpogosteje uporablja za ocenjevanje gostote in zdravja rastlin, saj rastline odbijajo bližnjo infrardečo in zeleno svetlobo, medtem ko rdečo absorbirajo. Mesta in izpostavljena tla so siva ali rjava, voda pa je videti modra ali črna.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) in [tukaj](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Lažno barvni kompozit\n\nLažno barvni kompozit uporablja vsaj eno očem nevidno valovno dolžino za slikanje Zemlje. Lažni barvni kompozit, ki uporablja bližnje infrardeče, rdeče in zelene pasove, je zelo priljubljen (pas je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo prikaže v različnih pasovih). Lažni barvni kompozit se najpogosteje uporablja za ocenjevanje gostote in zdravja rastlin, saj rastline odbijajo bližnjo infrardečo in zeleno svetlobo, medtem ko rdečo absorbirajo. Mesta in izpostavljena tla so siva ali rjava, voda pa je videti modra ali črna.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) in [tukaj](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":["# Lažno barvni kompozit\n\nLažno barvni kompozit uporablja vsaj eno očem nevidno valovno dolžino za slikanje Zemlje. Lažno barvni kompozit, ki uporablja bližnje infrardeče, rdeče in zelene pasove, je zelo priljubljen (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo zajema v različnih kanalih). Lažni barvni kompozit se najpogosteje uporablja za ocenjevanje gostote in zdravja rastlin, saj rastline odbijajo bližnjo infrardečo in zeleno svetlobo, medtem ko rdečo absorbirajo. Mesta in gola tla so siva ali rjava, voda pa je videti modra ali črna.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) in [tukaj](https://earthobservatory.nasa.gov/features/FalseColor)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Lažno barvni kompozit\n\nLažno barvni kompozit uporablja vsaj eno očem nevidno valovno dolžino za slikanje Zemlje. Lažni barvni kompozit, ki uporablja bližnje infrardeče, rdeče in zelene pasove, je zelo priljubljen (pas je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo prikaže v različnih pasovih). Lažni barvni kompozit se najpogosteje uporablja za ocenjevanje gostote in zdravja rastlin, saj rastline odbijajo bližnjo infrardečo in zeleno svetlobo, medtem ko rdečo absorbirajo. Mesta in izpostavljena tla so siva ali rjava, voda pa je videti modra ali črna.\n\n\n\nVeč informacij je [tukaj](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) in [tukaj](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy)."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) in [tukaj](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) in [tukaj](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) in [tukaj](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj] (https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)"]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":["# Izboljšana vizualizacija pravih barv\n\nTa skripta uporablja optimizacijo osvetlitve, da se izogne prežganim pikam in izravna osvetlitev. Zaradi nje so oblaki videti naravno in ohranijo čim več vizualnih informacij. Deli Sentinel-3 OLCI pokrivajo velika območja, kar omogoča opazovanje velikih oblačnih tvorb, kot so orkani.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)"]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Izostreni naravno barvni kompozit\n\nIzostreni (pansharpened) naravno barvni kompozit je narejen z uporabo podatkov v naravnih barvah (rdeča, zelena in modra; RGB) in njihovo izboljšavo z uporabo pankromatskega kanala 8 (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih). posnetek iz pan kanala je podoben črno-belemu filmu: združuje svetlobo iz rdečega, zelenega in modrega dela spektra v eno samo sliko bolše ločljivosti. Izostrene slike imajo štirikrat večjo ločljivost kot običajne kompozitne slike v pravih barvah, kar močno poveča uporabnost posnetkov Landsat.\n\n\n\nVeč informacij je [tukaj](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) in [tukaj](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":["# Lažno barvni kompozit urbano\n\nTa kompozit se uporablja za jasnejšo vizualizacijo urbanih območij. Vegetacija je vidna v zelenih odtenkih, urbanizirana območja pa so prikazana z belo, sivo ali vijolično barvo. Tla, pesek in minerali so prikazani v različnih barvah. Sneg in led sta prikazana v temno modri barvi, voda pa v črni ali modri. Poplavljena območja so zelo temno modra in skoraj črna. Sestavljeni posnetek je uporaben za odkrivanje požarov v naravi in kalder vulkanov, saj so prikazani v rdečih in rumenih odtenkih.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) in [tukaj](https://eos.com/false-color/)"]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Lažno barvni kompozit urbano\n\nTa kompozit uporablja kombinacijo kanalov v vidnem in kratkovalovnem infrardečem spektru (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih). Prikazuje vegetacijo v zelenih odtenkih. Temnejši odtenki zelene barve označujejo gostejšo vegetacijo, redka vegetacija pa ima svetlejše odtenke. Mestna območja so modra, tla pa imajo različne odtenke rjave barve.\n\n\n\nVeč informacij je [tukaj](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":["# Kmetijski kompozit\n\nTa kompozit uporablja kratkovalovne infrardeče, bližnje infrardeče in modre kanale za spremljanje zdravja pridelkov (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih). Kratkovalovni in bližnji infrardeči pasovi še posebej dobro poudarijo gosto vegetacijo, ki je na kompozitu videti temno zelena. Posevki so videti živo zeleni, gola zemlja pa magenta.\n\n\n\nVeč informacij je [tukaj](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) in [tukaj](https://gisgeography.com/sentinel-2-bands-combinations/)"]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":["# Klasifikator snega\n\nCilj algoritma je zaznati sneg z razvrščanjem pikslov na podlagi različnih pragov svetlosti in normaliziranega diferencialnega snežnega indeksa (NDSI). Vrednosti, ki so razvrščene kot sneg, se vrnejo v svetlo živahni modri barvi. Skripta lahko precenjuje območja s snegom nad oblaki.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":["# Ulyssys Water Quality Viewer (UWQV)\n\nSkripta je namenjena dinamični vizualizaciji stanja klorofila in sedimentov v vodnih telesih, ki so glavni kazalniki kakovosti vode. Vsebnost klorofila se spreminja v barvah od temno modre (nizka vsebnost klorofila) prek zelene do rdeče (visoka vsebnost klorofila). Koncentracija sedimenta je obarvana rjavo; nepregledna rjava barva pomeni visoko vsebnost sedimenta.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)"]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":["# Kratkovalovni infrardeči kompozit (SWIR)\n\nZ meritvami kratkovalovnega infrardečega spektra (SWIR) lahko znanstveniki ocenijo, koliko vode je prisotne v rastlinah in tleh, saj voda absorbira valovne dolžine SWIR. Kratkovalovni infrardeči kanali (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih) so uporabni tudi za razlikovanje med vrstami oblakov (vodni in ledeni oblaki), snegom in ledom, ki so v vidni svetlobi videti beli. Na tem sestavljenem posnetku je vegetacija prikazana v zelenih odtenkih, tla in pozidana območja so v različnih odtenkih rjave barve, voda pa je videti črna. Novo požgana zemljišča močno odsevajo v pasovih SWIR, zaradi česar so dragoceni za kartiranje požarne škode. Vsaka vrsta kamnin različno odbija kratkovalovno infrardečo svetlobo, zato je s primerjavo odbite svetlobe SWIR mogoče kartirati geologijo.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/composites/)"]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":["# Normalizirani diferencialni snežni indeks (NDSI)\n\nNormalizirani diferencialni snežni indeks (Normalised Difference Snow Index, NDSI) Sentinel-2 se lahko uporablja za razlikovanje med oblakom in snežno odejo, saj sneg absorbira kratkovalovno infrardečo svetlobo, vendar odbija vidno svetlobo, medtem ko je oblak običajno odbijajoč v obeh valovnih dolžinah. Snežna odeja je prikazana s svetlo živo modro barvo.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/)."]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":["# Poudarjene optimizirane naravne barve\n\nNamen te skripte je prikazati Zemljo v čudovitih naravnih barvah. Uporablja optimizacijo osvetlitve, da se izogne prežganim pikam in izravna osvetlitev.\n\n\n\nVeč informacij je [tukaj](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)"]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":["# Geologija kompozit 12, 8, 2\n\nTa kompozit uporablja kratkovalovni infrardeči (SWIR) pas 12 za razlikovanje med različnimi vrstami kamnin (pas je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo slika v različnih pasovih). Vsaka vrsta kamnin in mineralov različno odbija kratkovalovno infrardečo svetlobo, zato je s primerjavo odbite svetlobe SWIR mogoče kartirati geologijo. Bližnji infrardeči (NIR) pas 8 poudarja vegetacijo, pas 2 pa zaznava vlago, oba pa prispevata k razlikovanju zemeljskih materialov. Kompozit je uporaben za iskanje geoloških formacij in značilnosti (npr. prelomov, lomov), litologije (npr. granita, bazalta itd.) in za uporabo v rudarstvu.\n\n\n\nVeč informacij je [tukaj](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)"]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":["# Geologija kompozit 8, 11, 12 \n\nTa kompozit uporablja oba kratkovalovna infrardeča (SWIR) pasova 11 in 12 za razlikovanje med različnimi vrstami kamnin (pas je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo slika v različnih pasovih). Vsaka vrsta kamnin in mineralov različno odbija kratkovalovno infrardečo svetlobo, zato je s primerjavo odbite svetlobe SWIR mogoče kartirati geologijo. 8. pas bližnje infrardeče svetlobe (NIR) osvetljuje vegetacijo, kar prispeva k razlikovanju zemeljskih materialov. Vegetacija je na kompozitu videti rdeča. Kompozit je uporaben za razlikovanje vegetacije in zemljišč, zlasti geoloških značilnosti, ki so lahko koristne za rudarjenje in raziskovanje mineralov.\n\n\n\nVeč informacij je [tukaj](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) in [tukaj](http://murphygeological.com/new---sentinel-2.html#)"]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":["# Požari v naravi\n\nTa skripta, ki jo je ustvaril Pierre Markuse, vizualizira požare v naravi na podlagi podatkov Sentinel-2. Združuje naravno barvno ozadje z nekaterimi podatki NIR/SWIR za prodiranje dima in več podrobnosti, hkrati pa dodaja poudarke iz B11 in B12, da prikaže požare v rdeči in oranžni barvi.\n\n\n\nVeč informacij je [tukaj](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)"]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":["# Izboljšana prava barva\n\nTa skripta, ki jo je ustvaril Pierre Markuse, uporablja več kanalov (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo slika v različnih kanalih) ter nasičenost in svetlost za izboljšanje vizualizacije pravih barv.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)"]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":["# Indeks pogorelosti območja\n\nIndeks pogorelosti (Burned Area Index) izkorišča širši spekter vidnih, rdečih, NIR in SWIR pasov.\n\nOpis vrednosti:()=> Razpon vrednosti indeksa je `-1` do `1` za območja po požaru in `1` do `6` za aktivne požare. Različna intenzivnost požarov lahko povzroči različne pragove; trenutne vrednosti so bile po prvotnem avtorju umerjene na večinoma sredozemskih regijah.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)"]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":["# Normalizirano razmerje pogorelosti (NBR)\n\nNormalizirano razmerje pogorelosti (Normalized Burn Ratio) se pogosto uporablja za oceno pogorišč. Uporablja bližnje infrardeče (NIR) in kratkovalovne infrardeče (SWIR) valovne dolžine. Zdrava vegetacija ima visoko odbojnost v bližnjem infrardečem delu spektra in nizko kratkovalovno infrardečo odbojnost. Po drugi strani pa imajo požgana območja visoko kratkovalovno infrardečo odbojnost, vendar nizko odbojnost v bližnjem infrardečem delu spektra. Temnejši piksli označujejo požgana območja.\n\n\n\nVeč informacij je [tukaj](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) in [tukaj](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":["# Prodiranje v ozračje\n\nTa kompozit uporablja različne kanale (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih) v nevidnem delu elektromagnetnega spektra, da zmanjša vpliv atmosfere na posnetek. Kratkovalovna infrardeča pasova 11 in 12 se močno odbijata od segretih območij, zato sta uporabna za kartiranje požarov in požganih območij. Kratkovalovni infrardeči pas 8 se nasprotno močno odbija od vegetacije, kar pomeni odsotnost požara. Vegetacija se prikaže v modri barvi, kar prikazuje podrobnosti, povezane z močjo vegetacije. Zdrava vegetacija je prikazana v svetlo modri barvi, medtem ko je stresna, redka ali/in suha vegetacija prikazana v motni modri barvi. Urbane značilnosti so bele, sive, cian ali vijolične barve.\n\n\n\nVeč informacij je [tukaj](https://eos.com/atmospheric-penetration/)"]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":["# Vizualizacija golih tal\n\nVizualizacija golih tal (prsti) je lahko uporabna za kartiranje prsti, raziskovanje lokacije zemeljskih plazov ali obsega erozije na območjih brez vegetacije. Ta vizualizacija prikazuje vso vegetacijo v zeleni barvi, nerodovitna tla pa v rdeči. Voda je prikazana v črni barvi.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) in [tukaj](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)"]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":["# Naravno barvni kompozit z IR poudarki\n\nTa kompozit izboljša pravo barvno vizualizacijo z dodajanjem kratkovalovnih infrardečih valovnih dolžin, da okrepi podrobnosti. Pregreta območja prikaže v rdeči/oranžni barvi.\n\n\n\nVeč informacij je [tukaj](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)"]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":["# Odkrivanje požganih območij\n\nTa skripta se uporablja za odkrivanje nedavno požganih območij velikega obsega. Z rdečo barvo obarvani piksli označujejo požgana območja, vsi drugi piksli pa so vrnjeni v pravi barvi. Skripta včasih precenjuje požgana območja nad vodo in oblaki.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":["# Klasifikacija scene\n\n\n\nKlasifikacija je bila razvita za razlikovanje med oblačnimi piksli, jasnimi piksli in vodnimi piksli podatkov Sentinel-2 in je rezultat algoritma ESA za klasifikacijo. Na voljo je dvanajst različnih klasifikacij, vključno z razredi oblakov, vegetacije, tal/puščave, vode in snega. Ne gre za karto klasifikacije pokrovnosti tal v ožjem smislu.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/)."]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":["# Indeks kopenskega klorofila (OTCI)\n\n\n\nIndeks kopenskega klorofila (OTCI) je ocenjen na podlagi vsebnosti klorofila v kopenskem rastlinju in se lahko uporablja za spremljanje stanja in zdravja rastlinja. Nizke vrednosti OTCI običajno pomenijo vodo, pesek ali sneg. Izjemno visoke vrednosti, ki so prikazane z belo barvo, običajno kažejo tudi na odsotnost klorofila. Običajno predstavljajo gola tla, skale ali oblake. Vrednosti klorofila, ki so vmes v razponu od rdeče (nizke vrednosti klorofila) do temno zelene (visoke vrednosti klorofila), se lahko uporabijo za določanje zdravja vegetacije.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":["# Normaliziran diferencialni indeks slanosti\n\nIndeks prikazuje količino soli v tleh. Zasoljevanje tal je eden najpogostejših procesov degradacije tal, zlasti na sušnih in polsušnih območjih, kjer količina padavin presega izhlapevanje\n\nVišje vrednosti kažejo na večjo slanost, nizke vrednosti pa na manjšo slanost.\n\nPreberite več [tukaj,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [tukaj](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) in [tukaj](https://www.indexdatabase.de/db/i-single.php?id=57)"]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":["# Karta klasifikacije\n\n\n\nTa sloj vizualizira globalni zemljevid klasifikacije pokrovnosti tal s 23 razredi, opredeljenimi s sistemom klasifikacije pokrovnosti tal UN-FAO (LCCS), in barvno shemo, opredeljeno v uporabniškem priročniku izdelka. Karta je [tukaj](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)"]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":["# Tipi gozdov\n\n\n\nVizualizirani tipi gozdov na podlagi 6 razredov, kot so opredeljeni v klasifikacijskem sistemu UN-FAO Land Cover Classification System (LCCS). Več je [tukaj](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)."]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC)\n\n\n\nV sloju Corine Land Cover je prikazanih vseh 44 razredov CLC. Preberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Grajene površine\n\n\n\nV tem sloju Corine Land Cover je prikazanih samo 11 razredov grajenih površin na podlagi klasifikacije [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Preberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto z vsemi razredi [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Kmetijska območja\n\n\n\nV tem sloju Corine Land Cover je prikazanih samo 11 kmetijskih razredov na podlagi klasifikacije [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Preberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto z vsemi razredi [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Gozd in polnaravna območja\n\n\n\nV tem sloju Corine Land Cover je prikazanih samo 12 razredov gozdov in polnaravnih območij na podlagi klasifikacije [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Preberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto z vsemi razredi [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - mokrišča\n\n\n\nV tem sloju Corine Land Cover je prikazanih samo 5 razredov mokrišč na podlagi klasifikacije [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html)\nPreberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto z vsemi razredi [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Vodna telesa\n\n\n\nV tem sloju Corine Land Cover je prikazanih samo 6 razredov vodnih teles na podlagi klasifikacije [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Preberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto z vsemi razredi [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":["# Vodna telesa - pojavljanje\n\n\n\nTa sloj prikazuje 6 ravni pojavljanja sloja kakovosti (QUAL) in zagotavlja informacije o sezonski dinamiki zaznanih vodnih teles. QUAL se ustvari iz statističnih podatkov o pojavljanju vodnih teles, izračunanih iz mesečnih izdelkov Vodna telesa. Statistični podatki o pojavljanju so razvrščeni od majhnega pojavljanja do stalnega pojavljanja. Več informacij je [tukaj](https://collections.sentinel-hub.com/water-bodies/readme.html) in [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#)."]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":["# Vodna telesa\n\n\n\nTa sloj vizualizira sloj zaznavanja vodnih teles (Water Bodies, WB), ki prikazuje vodna telesa, zaznana z uporabo modificiranega normiranega diferencialnega vodega indeksa (MNDWI), pridobljenega iz podatkov Sentinel-2 Level 1C. Več informacij je [tukaj](https://collections.sentinel-hub.com/water-bodies/readme.html) in [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/)."]},"Level 1":{"msgid":"Level 1","msgstr":["Level 1"]},"Level 2":{"msgid":"Level 2","msgstr":["Level 2"]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":["Zbirka **Landsat 4-5 TM** vsebuje posnetke narejene s \"Thematic Mapper (TM)\" senzorjem, ki je bil vgrajen v Landsat 4 in 5 satelite. Na voljo je 6 optičnih in en termalni infrardeči pas, vsi v 30 metrski ločljivosti. Podatki so arhivirani, z globalno pokritostjo kopnega, na voljo za leta med 1982 in 2012. Na izbiro sta produkta: Level-1 nad atmosfero in pa Level-2 s površinsko odbojnostjo.\n\n**Prostorska ločljivost**: 30 metrov\n\n**Čas ponovnega obiska** 16 dni\n\n**Razpoložljivost podatkov**: globalni, Level-1 od avgusta 1982 do maja 2012, Level-2 od julija 1984 do maja 2012. \n\n**Skupna uporaba**: spremljanje vegetacije, vodnih in ledenih virov, zaznavanje sprememb in izdelava zemljevidov uporabe tal."]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["Produkt **Landsat 4-5 TM Level-1** daje na voljo posnetke odbojnosti nad atmosfero (TOA). Podatki Level-1 so ustvarjeni z obdelavo Landsat TM podatkov s standardnimi parametri, kot so kubična konvolucija in popravek terena. Izvedite več [tukaj](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) in [tukaj](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects)."]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["Produkt **Landsat 4-5 TM Level-2** je ustvarjen s predelavo podatkov Level-1 na površinsko odbojnost, torej približek površinske spektralne odbojnosti na višini tal, v odsotnosti atmosferskega sipanja in absorpcije. Izvedite več [tukaj](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) in [tukaj](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects)."]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":["Modra (450-520 nm)"]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":["Zelena (520-600 nm)"]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":["Rdeča (630-690 nm)"]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":["Bližnja infrardeča (NIR) (760-900 nm)"]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":["Kratkovalovna infrardeča (SWIR) 1 (1550-1750 nm)"]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":["Termalna infrardeča (10400-12500 nm)"]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":["Kratkovalovna infrardeča (SWIR) 2 (2080-2350 nm)"]},"Please select a layer":{"msgid":"Please select a layer","msgstr":["Prosimo izberite sloj"]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":["Histogram je lahko prikazan le tekom vizualizacije"]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":["Histogram ni na voljo za "]},"Recalculate":{"msgid":"Recalculate","msgstr":["Ponovno preračunaj"]},"Histogram":{"msgid":"Histogram","msgstr":["Histogram"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":["# Kompozit naravnih barv\n\nSenzorji na satelitih lahko vidijo Zemljo v različnih pasovih elektromagnetnega spektra. Vsak tak pas se imenuje kanal. Landsat 4-5 TM sateliti imajo 7 kanalov. Kompozit naravnih barv uporablja pasove vidne svetlobe, torej rdeči, zelen in moder kanal, kar ima za posledico naravno izgledajočo sliko, ki je dobra predstavitev Zemlje, kakor jo sicer z očmi vidijo ljudje.\n\n\n\nVeč informacij [tukaj](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites)."]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":["# Termalni kanal 6\n\nTa termalni prikaz bazira na kanalu 6 (kanal je pas elektromagnetnega spektra, senzor na satelitih namreč lahko vidi Zemljo v različnih spektralnih pasovih). Pri srednji valovni dolžini 11040 nm zaznava termalno infrardeče valovanje, imenovano TIR. Namesto merjenja temperature zraka, kakor to počnejo vremenske postaje, kanal 6 zaznava vrednosti na površini tal, ki so običajno precej toplejša. Termalni kanal 6 je uporaben za pridobivanje površinske temperature. Zajeman je v 120-metrski in prevzorčen v 30-metrsko ločljivost.\n\n\n\nVeč informacij [tukaj](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites)."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Kompozit umetnih barv\n\nKompozit umetnih barv uporablja vsaj en pas nevidne svetlobe za prikaz Zemlje. Zelo popularen je kompozit, ki uporablja bližnji infrardeči, rdeči in zelen kanal (kanal je pas elektromagnetnega spektra, senzor na satelitih namreč lahko vidi Zemljo v različnih spektralnih pasovih). Kompozit umetnih barv je najpogosteje uporabljen za oceno gostote in zdravja rastlin, saj rastline odbijajo bližnjo infrardečo svetlobo in absorbirajo rdečo. Mesta in gola površina so prikazani v sivi ali kožni barvi, medtem ko je voda prikazana kot modra ali črna.\n\n\n\nVeč informacij [tukaj](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":["# Globalne površinske vode - pojavnost\n\n\n\nSloj prikazuje (medletne in celoletne) variacije površinskih voda v časovnem razponu med marcem 1984 in decembrom 2019. Področja stalnih voda s 100% pojavnostjo preko 36 let so prikazana v modri barvi, medtem ko nianse svetlejše roza in vijolične barve predstavljajo nižje stopnje prisotnosti vode. Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/)."]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":["# Globalne površinske vode - intenzivnost sprememb pojavnosti\n\n\n\nSloj prikazuje spremembe pojavnosti voda med dvema različnima časovnima obdobjema: prvi med marcem 1984 in decembrom 1999 ter drugi med januarjem 2000 in decembrom 2019. Področja, kjer se je povečala pojavnost vode, so prikazana v niansah zelene barve, področja brez spremembe v črni in področja z zmanjšano pojavnostjo so prikazana v niansah rdeče barve. Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/)."]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":["# Globalne površinske vode - sezonskost\n\n\n\nSloj sezonskosti daje na voljo podatek o razporeditvi površinskih voda v letu 2019. Stalne vodne površine (kjer je bila voda prisotna 12 mesecev) so prikazana v temnomodri barvi, medtem ko so sezonske vode (kjer je bila voda prisotna manj kot 12 mesecev) prikazane v vedno svetlejši modri barvi. V najsvetlejši modri barvi so prikazana področja, kjer je bila voda prisotna le en mesec. Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#)."]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":["# Globalne površinske vode - ponovna pojavnost\n\n\n\nSloj ponovne pojavnosti prikazuje kako pogosto se vode vračajo na določeno območje med leti 1984 in 2019. Oranžna barva predstavlja nizko stopnjo vračanja (voda se redko vrača), medtem ko svetlomodra barva predstavlja visoko stopnjo vračanja (voda se vrača zelo pogosto). Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/)."]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":["# Globalne površinske vode - prehodi\n\n\n\nSloj prehodov je izpeljan iz primerjave med prvim in zadnjim letom tekom 36-letnega časovnega obdobja. Prikazuje pretvorbe med sezonskimi in stalnimi vodami. Kot primer: \"presahla sezonska voda\" pomeni, da se je sezonska voda izsušila in se je površina spremenila v trdna tla, \"nova sezonska voda\" pomeni, da se je področje trdnih tal zalilo z vodo, in tako dalje. Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) in izvedite kaj posamezni razred predstavlja [tukaj](https://global-surface-water.appspot.com/faq)."]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":["# Globalne površinske vode - obseg\n\n\n\nTa sloj prikazuje vodo v modri barvi. Kombinira vse preostale sloje in prikazuje lokacije, kjer je bila voda prisotna kadarkoli v 36-letnem obdobju. Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/)."]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":["Zbirka **Globalne površinske vode** je izpeljana iz posnetkov sistemov Landsat 5, 7 in 8 in prikazuje različne vidike prostorske in časovne razporeditve površinskih voda med leti 1984 in 2020 (revizija vsako leto), na globalnem nivoju v šestih različnih slojih. Pod pojmom \"površinska voda\" se smatra vsak iz vesolja viden in odkrit vodnat predel (sladkovodni in morski) večji kot 30m², vključno z naravnimi in umetnimi vodnimi telesi. Več informacij [tukaj](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Pokritost**: Globalna pokritost med geografsko dolžino 170°E in 180°W ter med geografsko širino 80°N in 50°S.\n\n**Razpoložljivost podatkov**: 1984 - 2019, 1984 - 2020 2019.\n\n**Prostorska ločljivost**: 30 metrov.\n\n**Skupna uporaba**: Spremljanje vodnih teles za področja oskrbe z vodo, klimatskega modeliranja, ohranitve biodiverzitete in varnosti hrane."]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":["**Mapzen DEM** temelji na SRTM30 (Shuttle Radar Topography Mission) in [drugih virih]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). Batimetrični podatki so vzeti iz [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). Gre za statično zbirko (neodvisno od datuma) z globalno pokritostjo.\n\n**Prostorska ločljivost:**Večinoma 90 m, na nekaterih območjih do 10 m.delu ZDA.\n\nVir: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)"]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":["Komercialni Podatki"]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Maksimalna oblačnost:"]},"Order name":{"msgid":"Order name","msgstr":["Ime naročila"]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":["Naročeni produkti bodo izrezani v skladu z označeno površino"]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":["Izberi"]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":["Parametri procesiranja"]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":["Možnosti iskanja"]},"Order options":{"msgid":"Order options","msgstr":["Možnosti naročil"]},"My orders":{"msgid":"My orders","msgstr":["Moja naročila"]},"My quotas":{"msgid":"My quotas","msgstr":["Moje kvote"]},"Use current display area":{"msgid":"Use current display area","msgstr":["Uporabi trenutno prikazano območje"]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":["Pokritost z oblaki"]},"Constellation":{"msgid":"Constellation","msgstr":["Ozvezdje"]},"Processing level":{"msgid":"Processing level","msgstr":["Nivo procesiranja"]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":["Pokritost"]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":["Ločjivost v pikslih"]},"Hide details":{"msgid":"Hide details","msgstr":["Skrij podrobnosti"]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":["Pripravi naročilo"]},"From":{"msgid":"From","msgstr":["Od"]},"To":{"msgid":"To","msgstr":["Do"]},"Provider":{"msgid":"Provider","msgstr":["Ponudnik"]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":["Napredne možnosti"]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":["Senzor"]},"Processing Level":{"msgid":"Processing Level","msgstr":["Nivo procesiranja"]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":["Pokritost s snegom"]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":["dodaj"]},"remove":{"msgid":"remove","msgstr":["odstrani"]},"Show results on map":{"msgid":"Show results on map","msgstr":["Prikaži rezultate na zemljevidu"]},"Order type":{"msgid":"Order type","msgstr":["Tip naročila"]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":["Ustvari naročilo"]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":["Zaključena naročila"]},"Created at":{"msgid":"Created at","msgstr":["Ustvarjeno ob"]},"Confirmed at":{"msgid":"Confirmed at","msgstr":["Potrjeno ob"]},"Size":{"msgid":"Size","msgstr":["Velikost"]},"Status":{"msgid":"Status","msgstr":["Status"]},"All input parameters":{"msgid":"All input parameters","msgstr":["Vsi vhodni parametri"]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":["Potrdi"]},"Delete":{"msgid":"Delete","msgstr":["Izbriši"]},"Show coverage":{"msgid":"Show coverage","msgstr":["Prikaži pokritost"]},"Show data":{"msgid":"Show data","msgstr":["Prikaži podatke"]},"No orders found":{"msgid":"No orders found","msgstr":["Ni naročil"]},"Error confirming order":{"msgid":"Error confirming order","msgstr":["Napaka pri potrjevanju naročila"]},"Error deleting order":{"msgid":"Error deleting order","msgstr":["Napaka pri brisanju naročila"]},"Confirm order":{"msgid":"Confirm order","msgstr":["Potrdi naročilo"]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":["Ali ste prepričani, da želite potrditi to naročilo?"]},"Delete order":{"msgid":"Delete order","msgstr":["Izbriši naročilo"]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":["Ali ste prepričani, da želite izbrisati to naročilo?"]},"Refresh orders":{"msgid":"Refresh orders","msgstr":["Osveži naročila"]},"Creating order":{"msgid":"Creating order","msgstr":["Ustvarjam naročilo"]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":["Ročni vnos"]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Prenos podatkov"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":["Nastavitve"]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":["Nebo / atmosfera"]},"Sun":{"msgid":"Sun","msgstr":["Sonce"]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":["Čas sonca (UTC)"]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":["Sončne projicirane sence"]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":["Parametri senčenja"]},"Ambient factor":{"msgid":"Ambient factor","msgstr":["Faktor ambienta"]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":["Faktor difuzije"]},"Specular factor":{"msgid":"Specular factor","msgstr":["Faktor zrcalnosti"]},"Specular power":{"msgid":"Specular power","msgstr":["Stopnja zrcalnosti"]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":["Vidnost sence"]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":["Razdalja risanja senc"]},"Shadow map size":{"msgid":"Shadow map size","msgstr":["Velikost slike senčenja"]},"Parameters":{"msgid":"Parameters","msgstr":["Parametri"]},"Local time on computer":{"msgid":"Local time on computer","msgstr":["Lokalni čas na računalniku"]},"Edit":{"msgid":"Edit","msgstr":["Uredi"]},"Reset values":{"msgid":"Reset values","msgstr":["Ponastavi vrednosti"]},"Current time":{"msgid":"Current time","msgstr":["Trenutni čas"]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":["Dvoklik z levim gumbom miške na konzoli za kamero ponastavi pogled kamere, tako da gleda proti severu in navzdol."]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":["Vertikalno skaliranje terena"]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":["Odsev leč kamere (pri soncu)"]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":["primerjati morate vsaj 2 sloja"]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":["Rotacija okoli kliknjene točke"]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":["Kadar je ta opcija vklopljena, premikanje miške, medtem ko je srednji gumb pritisnjen, premika svet okoli kliknjene točke. V nasprotnem primeru kamera rotira okoli lastne osi. \nČe je pred začetkom rotiranja pritisnjena tipka Alt, se obnašanje invertira."]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":["Brskaj, prikaži in analiziraj podatke zelo visoke ločljivosti (VHR) kar v EO Browserju, in dostopaj do globalnih arhivov Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) in [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) ter [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nOpazuj planet v ločljivosti vse od 3 metrov do kar 0.5 metra, za ceno od 0.9 EUR na km².\n\n![Primer slike v visoki ločljivosti.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, vsebuje podatke iz Pleiades procesirane s pomočjo Sentinel Hub\n\nKaj potrebuješ: \n- Aktivno Sentinel Hub naročnino za iskanje metapodatkov. Če računa še nimaš: [Prijava](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Vnaprej zakupljeno kvoto za katero koli od konstelacij. Obišči [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) za vzpostavitev naročnine in nakup paketa komercialnih podatkov."]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]}}}}
\ No newline at end of file
+{"charset":"utf-8","headers":{"content-type":"text/plain; charset=utf-8","plural-forms":"nplurals=4; plural=(n%100==1 ? 0 : n%100==2 ? 1 : n%100>=3 && n%100<=4 ? 2 : 3);","mime-version":"1.0","content-transfer-encoding":"8bit","project-id-version":"","pot-creation-date":"","po-revision-date":"","language-team":"","x-generator":"Poedit 3.0","last-translator":"","language":"sl_SI"},"translations":{"":{"":{"msgid":"","msgstr":["Content-Type: text/plain; charset=utf-8\nPlural-Forms: nplurals=4; plural=(n%100==1 ? 0 : n%100==2 ? 1 : n%100>=3 && n%100<=4 ? 2 : 3);\nmime-version: 1.0\nContent-Transfer-Encoding: 8bit\nProject-Id-Version: \nPOT-Creation-Date: \nPO-Revision-Date: \nLanguage-Team: \nx-generator: Poedit 3.0\nLast-Translator: \nLanguage: sl_SI\n"]},"Education":{"msgid":"Education","msgstr":["Izobraževanje"]},"Normal":{"msgid":"Normal","msgstr":["Običajno"]},"Close":{"msgid":"Close","msgstr":["Zapri"]},"Close and don't show again":{"msgid":"Close and don't show again","msgstr":["Zapri in ne pokaži več"]},"Previous":{"msgid":"Previous","msgstr":["Nazaj"]},"End tutorial":{"msgid":"End tutorial","msgstr":["Konec"]},"Next":{"msgid":"Next","msgstr":["Naprej"]},"Continue with tutorial":{"msgid":"Continue with tutorial","msgstr":["Nadaljujte z vodičem"]},"Don't show again":{"msgid":"Don't show again","msgstr":["Ne pokaži več"]},"Show info":{"msgid":"Show info","msgstr":["Pokaži informacije"]},"Discover":{"msgid":"Discover","msgstr":["Odkrij"]},"Visualize":{"msgid":"Visualize","msgstr":["Prikaži"]},"Compare":{"msgid":"Compare","msgstr":["Primerjaj"]},"Pins":{"msgid":"Pins","msgstr":["Oznake"]},"An error has occurred while fetching images:":{"msgid":"An error has occurred while fetching images:","msgstr":["Med dodajanjem posnetkov je prišlo do napake:"]},"No tile found":{"msgid":"No tile found","msgstr":["Noben del ni bil najden"]},"Dataset":{"msgid":"Dataset","msgstr":["Zbirka podatkov"]},"Show":{"msgid":"Show","msgstr":["Prikaži"]},"Show effects and advanced options":{"msgid":"Show effects and advanced options","msgstr":["Pokaži učinke in napredne možnosti"]},"Show visualization":{"msgid":"Show visualization","msgstr":["Pokaži prikaz"]},"Add to Pins":{"msgid":"Add to Pins","msgstr":["Dodaj med oznake"]},"Add to compare":{"msgid":"Add to compare","msgstr":["Dodaj za primerjavo"]},"Zoom to tile":{"msgid":"Zoom to tile","msgstr":["Povečanje na del"]},"Hide layer":{"msgid":"Hide layer","msgstr":["Skrij plast"]},"Show layer":{"msgid":"Show layer","msgstr":["Pokaži plast"]},"Share":{"msgid":"Share","msgstr":["Deli"]},"Custom":{"msgid":"Custom","msgstr":["Po meri"]},"Create custom visualization":{"msgid":"Create custom visualization","msgstr":["Ustvari prikaz po meri"]},"Zoom in to view data":{"msgid":"Zoom in to view data","msgstr":["Povečaj, da boš videl podatke"]},"Free sign up":{"msgid":"Free sign up","msgstr":["Brezplačna registracija"]},"for all features":{"msgid":"for all features","msgstr":["za vse funkcije"]},"Powered by":{"msgid":"Powered by","msgstr":["Poganja ga"]},"with contributions by":{"msgid":"with contributions by","msgstr":["s prispevki"]},"Please select data source(s)!":{"msgid":"Please select data source(s)!","msgstr":["Izberi vir(e) podatkov!"]},"Invalid time range!":{"msgid":"Invalid time range!","msgstr":["Neveljaven časovni obseg!"]},"No results found":{"msgid":"No results found","msgstr":["Ni zadetkov"]},"Theme":{"msgid":"Theme","msgstr":["Tema"]},"Manage configuration instances":{"msgid":"Manage configuration instances","msgstr":["Upravljanje konfiguracij"]},"Login to use custom configuration instances.":{"msgid":"Login to use custom configuration instances.","msgstr":["Prijava za uporabo konfiguracij po meri."]},"Error retrieving additional data!":{"msgid":"Error retrieving additional data!","msgstr":["Napaka pri pridobivanju dodatnih podatkov!"]},"Search":{"msgid":"Search","msgstr":["Poišči"]},"Highlights":{"msgid":"Highlights","msgstr":["Poudarki"]},"Data sources":{"msgid":"Data sources","msgstr":["Viri podatkov"]},"Please select a theme":{"msgid":"Please select a theme","msgstr":["Izberi temo"]},"Time range [UTC]":{"msgid":"Time range [UTC]","msgstr":["Časovni razpon [UTC]"]},"Date":{"msgid":"Date","msgstr":["Datum"]},"Hide description":{"msgid":"Hide description","msgstr":["Skrij opis"]},"Show description":{"msgid":"Show description","msgstr":["Prikaži opis"]},"This theme has no highlights":{"msgid":"This theme has no highlights","msgstr":["Tema nima poudarkov"]},"Based on: ":{"msgid":"Based on: ","msgstr":["Na podlagi: "]},"1 day (S1)":{"msgid":"1 day (S1)","msgstr":["1 dan (S1)"]},"5 day (S5)":{"msgid":"5 day (S5)","msgstr":["5 dni (S5)"]},"10 day (S10)":{"msgid":"10 day (S10)","msgstr":["10 dni (S10)"]},"O3 (Ozone)":{"msgid":"O3 (Ozone)","msgstr":["O3 (Ozon)"]},"NO2 (Nitrogen dioxide)":{"msgid":"NO2 (Nitrogen dioxide)","msgstr":["NO2 (dušikov dioksid)"]},"SO2 (Sulfur dioxide)":{"msgid":"SO2 (Sulfur dioxide)","msgstr":["SO2 (žveplov dioksid)"]},"CO (Carbon monoxide)":{"msgid":"CO (Carbon monoxide)","msgstr":["CO (ogljikov monoksid)"]},"HCHO (Formaldehyde)":{"msgid":"HCHO (Formaldehyde)","msgstr":["HCHO (formaldehid)"]},"CH4 (Methane)":{"msgid":"CH4 (Methane)","msgstr":["CH4 (metan)"]},"AER AI (Aerosol Index)":{"msgid":"AER AI (Aerosol Index)","msgstr":["AER AI (indeks aerosolov)"]},"Cloud":{"msgid":"Cloud","msgstr":["Oblačnost"]},"Other":{"msgid":"Other","msgstr":["Drugo"]},"Max. cloud coverage":{"msgid":"Max. cloud coverage","msgstr":["Maksimalna oblačnost"]},"Advanced search":{"msgid":"Advanced search","msgstr":["Napredno iskanje"]},"Data location":{"msgid":"Data location","msgstr":["Viri podatkov"]},"Please select at least one location!":{"msgid":"Please select at least one location!","msgstr":["Izberi vsaj eno lokacijo!"]},"Acquisition mode":{"msgid":"Acquisition mode","msgstr":["Način zajema"]},"Polarization":{"msgid":"Polarization","msgstr":["Polarizacija"]},"Please select at least one data acquisition mode!":{"msgid":"Please select at least one data acquisition mode!","msgstr":["Izberi vsaj en način pridobivanja podatkov!"]},"Please select at least one polarization!":{"msgid":"Please select at least one polarization!","msgstr":["Izberi vsaj eno polarizacijo!"]},"Orbit direction":{"msgid":"Orbit direction","msgstr":["Smer orbite"]},"Please select at least one orbit direction!":{"msgid":"Please select at least one orbit direction!","msgstr":["Izberi vsaj eno smer orbite!"]},"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).":{"msgid":"**MERIS** (Medium-resolution spectrometer) was a sensor on board the [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) satellite with the primary mission to observe land and ocean colour and the atmosphere. It is no longer active and has been succeeded by Sentinel-3.\n\n**Spatial resolution:** Full resolution land & coast: 260m x 290m (that is only details bigger than 260m x 290m can be seen).\n\n**Revisit time:** maximum 3 days to revisit the same area.\n\n**Data availability:** From June 2002 to April 2012.\n\n**Common usage:** Ocean monitoring (phytoplankton, suspended matter), atmosphere (water vapour, CO2, clouds, aerosols), and land (vegetation index, global coverage, moisture).","msgstr":["**MERIS** (Medium-resolution spectrometer) je bil senzor na krovu satelita [ENVISAT](https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat) s primarno misijo opazovanja barve kopnega in oceana ter atmosfere. Ni več aktiven in ga je naselil Sentinel-3.\n\n**Prostorska ločljivost:** Polna ločljivost kopno in obala: 260 m x 290 m (videti je mogoče le podrobnosti večje od 260 m x 290 m).\n\n**Čas ponovnega obiska:** največ 3 dni za ponoven obisk istega območja.\n\n**Razpoložljivost podatkov:** Od junija 2002 do aprila 2012.\n\n**Običajna raba:** Spremljanje oceana (fitoplankton, suspendirana snov), ozračja (vodna para, CO2, oblaki, aerosoli) in kopno (vegetacijski indeks, globalna pokritost, vlaga)."]},"Credits:":{"msgid":"Credits:","msgstr":["Zahvala:"]},"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.":{"msgid":"**GIBS** (Global Imagery Browse Services) provides quick access to over 600 satellite imagery\nproducts, covering every part of the world. Most imagery is available within a few hours after\nsatellite overpass, some products span almost 30 years.","msgstr":["**GIBS** (Global Imagery Browse Services) omogoča hiter dostop do več kot 600 satelitskih \nizdelkov, ki pokrivajo vse dele sveta. Večina posnetkov je na voljo v nekaj urah po\npreletu satelita, nekateri izdelki zajemajo skoraj 30 let."]},"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"The series of **Landsat** satellites of NASA/ U.S. Geological Survey are similar to Sentinel-2 (they capture visible and infrared wavelengths)\nand additionally can capture thermal infrared (Landsat 8). The Landsat series has a long history of imagery spanning nearly five decades.\n This platform gives you access to imagery acquired by Landsat 5, 7 and 8.\n\n**Spatial resolution:** 15m, 30m, and 100m resampled to 30m, depending on the wavelength (that is, only details bigger than 10m and 30m, can be seen). More info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Revisit time:** Maximum 8 days to revisit the same area using the two operational satellites Landsat 7 and Landsat 8.\n\n**Data availability:** Europe and North Africa from 1984 - 2011 (Landsat 5), 1999 - 2003 (Landsat 7), 2013 until present (Landsat 8) from the ESA archive. The global U.S. Geological Survey (USGS) archive since April 2013 until today (Landsat 8 only) .\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["Serija satelitov **Landsat** agencije NASA/ Geološkega zavoda ZDA je podobna satelitu Sentinel-2 (zajemajo vidne in infrardeče valovne dolžine)\ndodatno pa lahko zajamejo toplotno infrardečo svetlobo (Landsat 8). Serija satelitov Landsat ima dolgo zgodovino slikanja, ki zajema skoraj pet desetletij.\n Platforma omogoča dostop do posnetkov, pridobljenih s sateliti Landsat 5, 7 in 8.\n\n**Prostorska ločljivost:** 15 m, 30 m in 100 m, prevzorčeno na 30 m, odvisno od valovne dolžine (to pomeni, da so vidne le podrobnosti, večje od 10 m in 30 m). Več informacij je [tukaj](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products).\n\n**Določen čas ponovnega obiska:** Največ 8 dni za ponovni obisk istega območja z uporabo dveh operativnih satelitov Landsat 7 in Landsat 8.\n\n**Dostopnost podatkov:** Evropa in severna Afrika od leta 1984 do 2011 (Landsat 5), od leta 1999 do 2003 (Landsat 7), od leta 2013 do danes (Landsat 8) iz arhiva ESA. Globalni arhiv U.S. Geological Survey (USGS) od aprila 2013 do danes (samo Landsat 8) .\n\n**Pogosta uporaba:** Spremljanje vegetacije, raba tal, karte pokrovnosti tal, spremljanje sprememb itd."]},"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.":{"msgid":"Nasa's **MODIS** – (Moderate Resolution Imaging Spectroradiometer) acquires data with the objective\nto improve our understanding of global processes occurring on land. EO browser provides data for\nobservation of land (bands 1-7).\n\n**Spatial resolution:** 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36).\n\n**Revisit time:** Global coverage in 1 – 2 days with both Aqua and Terra satellites.\n\n**Data availability:** Since January 2013.\n\n**Common usage:** Monitoring of land, clouds, ocean colour at a global scale.","msgstr":["NASA **MODIS** (spektroradiometer za slikanje z zmerno ločljivostjo) pridobiva podatke, da bi\nizboljšali razumevanje globalnih procesov, ki se odvijajo na kopnem. Pregledovalnik EO Browser zagotavlja \npodatke za opazovanje kopnega (kanali 1-7).\n\n**Prostorska ločljivost:** 250 m (kanali 1-2), 500 m (kanali 3-7), 1000 m (kanali 8-36).\n\n**Čas ponovnega obiska:** globalna pokritost v 1 do 2 dneh s satelitoma Aqua in Terra.\n\n** Razpoložljivost podatkov:** od januarja 2013.\n\n**Običajna uporaba:** Spremljanje barve kopnega, oblakov in oceanov na svetovni ravni."]},"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.":{"msgid":"The **Proba-V** satellite is a small satellite designed to map land cover and vegetation growth\nacross the entire globe every two days. EO Browser provides derived products which minimize cloud\ncover by combining cloud-free measurement within a 1 day (S1), 5 day (S5) and 10 day (S10) period.\n\n**Spatial resolution:** 100m for S1 and S5, 333m for S1 and S10, 1000m for S1 and S10.\n\n**Revisit time:** 1 day for latitudes 35-75°N and 35-56°S, 2 days for latitudes between 35°N\nand 35°S.\n\n**Data availability:** Since October 2013.\n\n**Common usage:** MThe observation of land cover, vegetation growth, climate impact assessment,\nwater resource management, agricultural monitoring and food security estimates, inland water\nresource monitoring and tracking the steady spread of deserts and deforestation.","msgstr":["Satelit **Proba-V** je majhen satelit, namenjen kartiranju pokrovnosti tal in rasti vegetacije\npo vsem svetu vsaka dva dni. EO Browser zagotavlja izpeljane izdelke, ki čim bolj zmanjšujejo količino oblakov\ntako, da združujejo meritve brez oblakov v obdobju enega dneva (S1), petih dni (S5) in desetih dni (S10).\n\n**Prostorska ločljivost:** 100 m za S1 in S5, 333 m za S1 in S10, 1000 m za S1 in S10.\n\n**Čas ponovnega obiska:** 1 dan za zemljepisne širine 35-75° S in 35-56° J, 2 dni za zemljepisne širine med 35° S\nin 35° j. š.\n\n**Dostopnost podatkov:** od oktobra 2013.\n\n**Običajna uporaba:** opazovanje pokrovnosti tal, rasti vegetacije, ocena vpliva podnebja,\nupravljanje vodnih virov, spremljanje kmetijstva in ocene prehranske varnosti, celinske vode\nvirov ter spremljanje stalnega širjenja puščav in krčenja gozdov."]},"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.":{"msgid":"**Sentinel-1** provides all-weather, day and night radar imagery for land and ocean services. EO\nBrowser provides data acquired in Interferometric Wide Swath (IW) and Extra Wide Swath (EW) modes\nprocessed to Level-1 Ground Range Detected (GRD).\n\n**Pixel spacing:** 10m (IW), 40m (EW).\n\n**Revisit time:** <= 5 days using both satellites.\n\n**Revisit time** (for asc/desc and overlap using both satellites): <= 3 days, see [observation scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Data availability:** Since October 2014.\n\n**Common usage:** Maritime and land monitoring, emergency response, climate change.","msgstr":["**Sentinel-1** zagotavlja dnevne in nočne radarske posnetke v vsakem vremenu za kopenske in oceanske storitve. EO\nBrowser zagotavlja podatke, pridobljene v interferometričnih načinih Wide Swath (IW) in Extra Wide Swath (EW)\nobdelani do ravni 1 Ground Range Detected (GRD).\n\n** Velikost piksla:** 10 m (IW), 40 m (EW).\n\n**Čas ponovnega obiska:** <= 5 dni z uporabo obeh satelitov.\n\n**Čas ponovnega obiska** (za asc/desc in prekrivanje z uporabo obeh satelitov): <= 3 dni, glej [scenarij opazovanja](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)\n\n**Dostopnost podatkov:** od oktobra 2014.\n\n**Običajna uporaba:** spremljanje morja in kopnega, odzivanje na izredne razmere, podnebne spremembe."]},"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.":{"msgid":"**Sentinel-2** provides high-resolution images in the visible and infrared wavelengths, to monitor vegetation, soil and water cover, inland waterways and coastal areas. .\n\n**Spatial resolution:** 10m, 20m, and 60m, depending on the wavelength (that is, only details bigger than 10m, 20m, and 60m can be seen). More info [here](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). \n\n**Revisit time:** maximum 5 days to revisit the same area, using both satellites.\n\n**Data availability:** Since June 2015. Full global coverage since March 2017.\n\n**Common usage:** Land-cover maps, land-change detection maps, vegetation monitoring, monitoring of burnt areas.","msgstr":["**Sentinel-2** zagotavlja posnetke visoke ločljivosti v vidni in infrardeči valovni dolžini za spremljanje vegetacije, tal in vodnih površin, celinskih vodnih poti in obalnih območij. .\n\n**Prostorska ločljivost:** 10 m, 20 m in 60 m, odvisno od valovne dolžine (to pomeni, da so vidne le podrobnosti, večje od 10 m, 20 m in 60 m). Več informacij je [tukaj](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial)\n\n**Čas ponovnega obiska:** največ 5 dni za ponovni obisk istega območja z uporabo obeh satelitov.\n\n** Razpoložljivost podatkov:** od junija 2015. Celotna globalna pokritost od marca 2017.\n\n**Običajna uporaba:** karte pokrovnosti tal, zaznavanja sprememb tal, spremljanje vegetacije, spremljanje požganih območij."]},"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).":{"msgid":"Level 2A data are high quality data where the effects of the atmosphere on the light being reflected off of the surface of the Earth and reaching the sensor are excluded. Data are available globally since March 2017.\n\nMore info about atmospheric correction [here](http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction).","msgstr":["Podatki stopnje 2A so visokokakovostni podatki, pri katerih so odstranjeni učinki atmosfere na svetlobo, ki se odbije od zemeljske površine in doseže senzor. Podatki so na voljo po vsem svetu od marca 2017.\n\nVeč informacij o atmosferskih popravkih [tukaj] (http://fis.uni-bonn.de/en/recherchetools/infobox/professionals/preprocessing/atmospheric-correction)."]},"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.":{"msgid":"Level 1C data are data of sufficient quality for most investigations, where all image corrections were done except for the atmospheric correction. Data are available globally since June 2015 onwards.","msgstr":["Podatki stopnje 1C so podatki zadostne kakovosti za večino raziskav. Opravljeni so vsi popravki, razen atmosferskih. Podatki so na voljo po vsem svetu od junija 2015 dalje."]},"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.":{"msgid":"**Sentinel-3** mission main objective is to measure sea surface topography, sea and land surface temperature, ocean and land surface colour. Sentinel-3 has four different instruments on board. Data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Instrument (SLSTR) are available in this platform.\n\n**Data availability:** Since May 2016 onwards.","msgstr":["Glavni cilj misije **Sentinel-3** je merjenje topografije morske površine, temperature morske in kopenske površine ter barve oceana in kopenske površine. Sentinel-3 ima na krovu štiri različne instrumente. Na tej platformi so na voljo podatki, pridobljeni s senzorjema Ocean and Land Colour Instrument (OLCI) in Sea and Land Surface Temperature Instrument (SLSTR).\n\n** Razpoložljivost podatkov:** od maja 2016 dalje."]},"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.":{"msgid":"The **Sea and Land Surface Temperature (SLSTR)** instrument on board Sentinel-3 measures the global and regional sea and land surface \ntemperature. The SLSTR covers the visible, shortwave infrared, and thermal infrared wavelengths of the electromagnetic spectrum. \n\n**Spatial resolution:** 500m for visible, near- and shortwave infrared wavelengths and 1km for thermal infrared (that is, only details \nbigger than 500m and 1km can be seen, respectively).\n\n**Revisit time:** Maximum 1 day to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Climate change monitoring, vegetation monitoring, active fire detection, land and sea surface temperature monitoring.","msgstr":["Instrument **Sea and Land Surface Temperature (SLSTR)** na krovu Sentinel-3 meri globalno in regionalno temperaturo morja in površja kopnega\ntemperaturo tal in kopnega. SLSTR pokriva vidno, kratkovalovno infrardeče in toplotno infrardeče valovne dolžine elektromagnetnega spektra\n\n**Prostorska ločljivost:** 500 m za vidne, bližnje in kratkovalovne infrardeče valovne dolžine ter 1 km za toplotne infrardeče valovne dolžine (to pomeni le podrobnosti\nvečje od 500 m oziroma 1 km).\n\n**Čas ponovnega obiska:** največ 1 dan za ponovni ogled istega območja z uporabo obeh satelitov.\n\n**Dostopnost podatkov:** od maja 2016 dalje.\n\n**Običajna uporaba:** spremljanje podnebnih sprememb, spremljanje vegetacije, odkrivanje aktivnih požarov, spremljanje temperature zemeljske in morske površine."]},"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.":{"msgid":"The **Ocean and Land Colour Instrument (OLCI)** on board Sentinel-3 is a spectrometer that \nmeasures the solar radiation reflected by Earth, and it monitors the ocean, the environment, \nand climate. It provides more frequent visible imagery than Sentinel-2 but at a lower resolution\nand with more wavelengths covered. The Sentinel-3 OLCI instrument continues the measurements previously performed by the MERIS instrument on board Envisat, whose mission concluded.\n\n**Spatial resolution:** 300m (that is, only details bigger than 300m can be seen).\n\n**Revisit time:** Maximum 2 days to revisit the same area, using both satellites.\n\n**Data availability:** Since May 2016 onwards.\n\n**Common usage:** Surface topography, ocean and land surface colour observations and monitoring.","msgstr":["**Ocean and Land Colour Instrument (OLCI)** na krovu Sentinel-3 je spektrometer, ki\nmeri sončno sevanje, ki se odbija od Zemlje, in spremlja oceane, okolje,\nin podnebje. Zagotavlja pogostejše vidne posnetke kot Sentinel-2, vendar z nižjo ločljivostjo\nin z več zajetimi valovnimi dolžinami. Instrument Sentinel-3 OLCI nadaljuje meritve, ki jih je prej opravljal instrument MERIS na krovu satelita Envisat, katerega misija se je končala.\n\n**Prostorska ločljivost:** 300 m (to pomeni, da so vidne le podrobnosti, večje od 300 m).\n\n**Čas ponovnega obiska:** največ 2 dni za ponovni obisk istega območja z uporabo obeh satelitov.\n\n** Razpoložljivost podatkov:** od maja 2016 dalje.\n\n**Običajna uporaba:** opazovanje in spremljanje topografije površja, barve oceanov in kopnega."]},"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).":{"msgid":"**Sentinel-5P** is a satellite that provides atmospheric measurements to be used for air quality, ozone monitoring, UV radiation,\nand climate monitoring and forecasting.\n\n**Spatial resolution:** 7 x 3.5km (that is, only details bigger than 7 x 3.5km can be seen).\n\n**Revisit time:** Maximum 1 day to revisit the same area.\n\n**Data availability:** Since April 2018 onwards.\n\n**Common usage:** Monitoring the concentration of carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (Cloud).","msgstr":["**Sentinel-5P** je satelit, ki zagotavlja meritve ozračja za spremljanje kakovosti zraka, koncentracij ozona in jakost UV-sevanja,\nter spremljanje in napovedovanje podnebja.\n\n**Prostorska ločljivost:** 7 x 3,5 km (to pomeni, da so vidne le podrobnosti, večje od 7 x 3,5 km).\n\n**Čas ponovnega obiska: ** največ en dan za ponovni ogled istega območja.\n\n** Razpoložljivost podatkov:** od aprila 2018 dalje.\n\n**Običajna uporaba:** spremljanje koncentracije ogljikovega monoksida (CO), dušikovega dioksida (NO2) in ozona (O3) v zraku. Spremljanje aerosolnega indeksa UV (AER_AI) in različnih geofizikalnih parametrov oblakov (Cloud)."]},"Copied":{"msgid":"Copied","msgstr":["Kopirano"]},"Copy to clipboard":{"msgid":"Copy to clipboard","msgstr":["Kopiraj v odložišče"]},"Data source name":{"msgid":"Data source name","msgstr":["Ime vira podatkov"]},"Sensing time":{"msgid":"Sensing time","msgstr":["Čas zajema"]},"Cloud coverage":{"msgid":"Cloud coverage","msgstr":["Pokritost z oblaki"]},"Sun elevation":{"msgid":"Sun elevation","msgstr":["Višina Sonca"]},"MGRS location":{"msgid":"MGRS location","msgstr":["Lokacija MGRS"]},"AWS path":{"msgid":"AWS path","msgstr":["Pot AWS"]},"EO Cloud path":{"msgid":"EO Cloud path","msgstr":["Pot EO Cloud"]},"CreoDIAS path":{"msgid":"CreoDIAS path","msgstr":["Pot CreoDIAS"]},"SciHub link":{"msgid":"SciHub link","msgstr":["Povezava SciHub"]},"Back to search":{"msgid":"Back to search","msgstr":["Nazaj na iskanje"]},"Showing ${ this.state.results.length } result":{"msgid":"Showing ${ this.state.results.length } result","msgid_plural":"Showing ${ this.state.results.length } results","msgstr":["Prikaz ${ this.state.results.length } rezultatov","Prikaz ${ this.state.results.length } rezultatov","Prikaz ${ this.state.results.length } rezultatov","Prikaz ${ this.state.results.length } rezultatov"]},"Load more":{"msgid":"Load more","msgstr":["Naloži več"]},"Loading more results ...":{"msgid":"Loading more results ...","msgstr":["Nalaganje več rezultatov …"]},"Results":{"msgid":"Results","msgstr":["Rezultati"]},"Showing ${ this.state.selectedTiles.length } result.":{"msgid":"Showing ${ this.state.selectedTiles.length } result.","msgid_plural":"Showing ${ this.state.selectedTiles.length } results.","msgstr":["Prikaz ${ this.state.selectedTiles.length } rezultatov.","Prikaz ${ this.state.selectedTiles.length } rezultatov.","Prikaz ${ this.state.selectedTiles.length } rezultatov.","Prikaz ${ this.state.selectedTiles.length } rezultatov."]},"Edit pin description":{"msgid":"Edit pin description","msgstr":["Urejanje opisa oznake"]},"Reject changes":{"msgid":"Reject changes","msgstr":["Zavrni spremembe"]},"Confirm changes":{"msgid":"Confirm changes","msgstr":["Potrdi spremembe"]},"Rename pin":{"msgid":"Rename pin","msgstr":["Preimenuj oznako"]},"Remove pin":{"msgid":"Remove pin","msgstr":["Odstrani oznako"]},"Zoom to pinned location":{"msgid":"Zoom to pinned location","msgstr":["Povečanje na označeno lokacijo"]},"Lat/Lon":{"msgid":"Lat/Lon","msgstr":["Lat/Lon"]},"Zoom":{"msgid":"Zoom","msgstr":["Povečava"]},"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?":{"msgid":"You are about to add ${ N_PINS } pin(s) to your pin collection. Do you want to proceed?","msgstr":["V svojo zbirko boš dodal ${ N_PINS } oznak(-o). Želiš nadaljevati?"]},"WARNING: You're about to delete a pin. Do you wish to continue?":{"msgid":"WARNING: You're about to delete a pin. Do you wish to continue?","msgstr":["OPOZORILO: izbrisali boste oznako. Ali želiš nadaljevati?"]},"WARNING: You're about to delete all pins. Do you wish to continue?":{"msgid":"WARNING: You're about to delete all pins. Do you wish to continue?","msgstr":["OPOZORILO: izbrisali boste vse oznake. Ali želiš nadaljevati?"]},"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.":{"msgid":"No pins. Go to the Visualize tab to save a pin or upload a JSON file with saved pins.","msgstr":["Brez oznak. Pojdite na zavihek Prikaži in shranite oznako ali naložite datoteko JSON s shranjenimi oznakami."]},"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.":{"msgid":"Note that the pins will be saved only if you log in. Otherwise, the pins will be lost once the application is closed.","msgstr":["Upoštevajte, da bodo oznake shranjene le, če se prijavite. V nasprotnem primeru se bodo po zaprtju aplikacije izgubile."]},"Deselect all":{"msgid":"Deselect all","msgstr":["Odznači vse"]},"Select all":{"msgid":"Select all","msgstr":["Izberi vse"]},"No pins.":{"msgid":"No pins.","msgstr":["Brez oznak."]},"Create link (${ selectedPins.length } pin selected)":{"msgid":"Create link (${ selectedPins.length } pin selected)","msgid_plural":"Create link (${ selectedPins.length } pins selected)","msgstr":["Ustvari povezavo (${ selectedPins.length } izbranih oznak)","Ustvari povezavo (${ selectedPins.length } izbrana oznaka)","Ustvari povezavo (${ selectedPins.length } izbrani oznaki)","Ustvari povezavo (${ selectedPins.length } izbrane oznake)"]},"File type not supported":{"msgid":"File type not supported","msgstr":["Vrsta datoteke ni podprta"]},"not supported":{"msgid":"not supported","msgstr":["ni podprto"]},"No pins were found.":{"msgid":"No pins were found.","msgstr":["Niso bilo najdenih oznak."]},"Error parsing file:":{"msgid":"Error parsing file:","msgstr":["Napaka pri razčlenjevanju datoteke:"]},"Upload a JSON file with saved pins.":{"msgid":"Upload a JSON file with saved pins.","msgstr":["Prenesite datoteko JSON s shranjenimi oznakami."]},"Drop JSON file or search your computer":{"msgid":"Drop JSON file or search your computer","msgstr":["Spustite datoteko JSON ali poiščite v računalniku"]},"Keep existing pins":{"msgid":"Keep existing pins","msgstr":["Ohrani obstoječe oznake"]},"Share pins":{"msgid":"Share pins","msgstr":["Delite oznake"]},"Create a story from pins":{"msgid":"Create a story from pins","msgstr":["Ustvarite zgodbo iz oznak"]},"Export pins to the computer":{"msgid":"Export pins to the computer","msgstr":["Izvoz oznake v računalnik"]},"Import pins from a saved file":{"msgid":"Import pins from a saved file","msgstr":["Uvoz oznak iz datoteke"]},"Delete all pins":{"msgid":"Delete all pins","msgstr":["Izbriši vseh oznake"]},"Story":{"msgid":"Story","msgstr":["Zgodba"]},"Export":{"msgid":"Export","msgstr":["Izvozi"]},"Import":{"msgid":"Import","msgstr":["Uvozi"]},"Clear":{"msgid":"Clear","msgstr":["Počisti"]},"Share pins link":{"msgid":"Share pins link","msgstr":["Delite povezavo do oznak"]},"Updating pin collection.":{"msgid":"Updating pin collection.","msgstr":["Posodobitev zbirke oznak."]},"There was a problem permanently updating the pin collection: ${ updatingPinsError }.":{"msgid":"There was a problem permanently updating the pin collection: ${ updatingPinsError }.","msgstr":["Pri posodabljanju zbirke oznak je prišlo do težave: ${ updatingPinsError }."]},"Opacity":{"msgid":"Opacity","msgstr":["Neprozornost"]},"Split position":{"msgid":"Split position","msgstr":["Razdeljeni položaj"]},"split":{"msgid":"split","msgstr":["razdelitev"]},"opacity":{"msgid":"opacity","msgstr":["neprozornost"]},"No layers to compare.":{"msgid":"No layers to compare.","msgstr":["Ni slojev za primerjavo."]},"Remove all":{"msgid":"Remove all","msgstr":["Odstrani vse"]},"Add all pins":{"msgid":"Add all pins","msgstr":["Dodaj vse oznake"]},"Split":{"msgid":"Split","msgstr":["Deljenje"]},"There was a problem downloading your instances":{"msgid":"There was a problem downloading your instances","msgstr":["Pri prenosu vaših primerov je prišlo do težave"]},"Download":{"msgid":"Download","msgstr":["Prenesi"]},"Visualize terrain in 3D":{"msgid":"Visualize terrain in 3D","msgstr":["Vizualizacija terena v 3D"]},"Go to Place":{"msgid":"Go to Place","msgstr":["Pojdi na kraj"]},"Labels":{"msgid":"Labels","msgstr":["Oznake"]},"Borders":{"msgid":"Borders","msgstr":["Meje"]},"Roads":{"msgid":"Roads","msgstr":["Ceste"]},"Zoom in":{"msgid":"Zoom in","msgstr":["Povečaj"]},"Zoom out":{"msgid":"Zoom out","msgstr":["Pomanjšaj"]},"About EO Browser":{"msgid":"About EO Browser","msgstr":["O EO Browserju"]},"Contact us":{"msgid":"Contact us","msgstr":["Obrnite se na nas"]},"Get data":{"msgid":"Get data","msgstr":["Prenos podatkov"]},"You need to log in to use this function.":{"msgid":"You need to log in to use this function.","msgstr":["Za uporabo te funkcije se morate prijaviti."]},"Please select a layer.":{"msgid":"Please select a layer.","msgstr":["Izberi sloj."]},"Downloading image in compare mode is not possible.":{"msgid":"Downloading image in compare mode is not possible.","msgstr":["Prenos slike v načinu za primerjavo ni mogoč."]},"This datasource is not supported.":{"msgid":"This datasource is not supported.","msgstr":["Ta vir podatkov ni podprt."]},"Statistical Info / Feature Info Service chart":{"msgid":"Statistical Info / Feature Info Service chart","msgstr":["Statistične informacije / Feature Info Service chart"]},"Statistical Info / Feature Info Service chart - ":{"msgid":"Statistical Info / Feature Info Service chart - ","msgstr":["Statistične informacije / Feature Info Service chart - "]},"please select a layer":{"msgid":"please select a layer","msgstr":["izberi sloj"]},"not available for ":{"msgid":"not available for ","msgstr":["ni na voljo za "]},"not available for \"${ props.presetLayerName }\" (layer with value is not set up)":{"msgid":"not available for \"${ props.presetLayerName }\" (layer with value is not set up)","msgstr":["ni na voljo za \"${ props.presetLayerName }\" (layer with value is not set up)"]},"Search for data first.":{"msgid":"Search for data first.","msgstr":["Najprej poiščite podatke."]},"Create timelapse animation":{"msgid":"Create timelapse animation","msgstr":["Ustvarjanje animacije s časovnim zaporedjem"]},"Mark point of interest":{"msgid":"Mark point of interest","msgstr":["Označite zanimivo točko"]},"Center map on feature":{"msgid":"Center map on feature","msgstr":["Središče karte na element"]},"Remove geometry":{"msgid":"Remove geometry","msgstr":["Odstranite geometrijo"]},"Area of interest":{"msgid":"Area of interest","msgstr":["Območje interesa"]},"Select mode":{"msgid":"Select mode","msgstr":["Izberi način"]},"Mode:":{"msgid":"Mode:","msgstr":["Način:"]},"Remove measurement":{"msgid":"Remove measurement","msgstr":["Odstranitev meritev"]},"km":{"msgid":"km","msgstr":["km"]},"m":{"msgid":"m","msgstr":["m"]},"Gain":{"msgid":"Gain","msgstr":["Jakost"]},"Gamma":{"msgid":"Gamma","msgstr":["Gamma"]},"R":{"msgid":"R","msgstr":["R"]},"G":{"msgid":"G","msgstr":["G"]},"B":{"msgid":"B","msgstr":["B"]},"Min. data quality":{"msgid":"Min. data quality","msgstr":["Min. kakovost podatkov"]},"Upsampling":{"msgid":"Upsampling","msgstr":["Povečanje vzorčenja"]},"Downsampling":{"msgid":"Downsampling","msgstr":["Zmanjšanje vzorčenja"]},"Reset all":{"msgid":"Reset all","msgstr":["Ponastavi vse"]},"filter by months":{"msgid":"filter by months","msgstr":["filtriranje po mesecih"]},"Copy geometry to clipboard":{"msgid":"Copy geometry to clipboard","msgstr":["Kopiraj geometrijo v odložišče"]},"Cancel edit.":{"msgid":"Cancel edit.","msgstr":["Prekliči urejanje."]},"Draw area of interest":{"msgid":"Draw area of interest","msgstr":["Narišite interesno področje"]},"Least cloud coverage":{"msgid":"Least cloud coverage","msgstr":["Najmanjša pokritost z oblaki"]},"Use additional datasets (advanced)":{"msgid":"Use additional datasets (advanced)","msgstr":["Uporaba dodatnih zbirk podatkov (napredno)"]},"Mosaicking order":{"msgid":"Mosaicking order","msgstr":["Vrstni red mozaičenja"]},"Most recent":{"msgid":"Most recent","msgstr":["Najnovejši"]},"Least recent":{"msgid":"Least recent","msgstr":["Najmanj nedavno"]},"Customize timespan":{"msgid":"Customize timespan","msgstr":["Prilagodite časovni razpon"]},"Back":{"msgid":"Back","msgstr":["Nazaj"]},"Error loading script. Check your URL.":{"msgid":"Error loading script. Check your URL.","msgstr":["Napaka pri nalaganju skripte. Preverite svoj URL."]},"Uncheck Load script from URL to edit the code":{"msgid":"Uncheck Load script from URL to edit the code","msgstr":["Če želite urediti kodo, odstranite potrditev možnosti Naloži skripto iz naslova URL"]},"Load script from URL":{"msgid":"Load script from URL","msgstr":["Nalaganje skripte iz naslova URL"]},"Enter URL to your script":{"msgid":"Enter URL to your script","msgstr":["Vnesite URL za vašo skripto"]},"Script loaded.":{"msgid":"Script loaded.","msgstr":["Skript je naložen."]},"Only HTTPS domains are allowed.":{"msgid":"Only HTTPS domains are allowed.","msgstr":["Dovoljene so samo domene HTTPS."]},"Load script into code editor":{"msgid":"Load script into code editor","msgstr":["Nalaganje skripte v urejevalnik kode"]},"Refresh":{"msgid":"Refresh","msgstr":["Osveži"]},"orbit":{"msgid":"orbit","msgstr":["orbita"]},"day":{"msgid":"day","msgstr":["dan"]},"week":{"msgid":"week","msgstr":["teden"]},"month":{"msgid":"month","msgstr":["mesec"]},"year":{"msgid":"year","msgstr":["leto"]},"Select 1 image per:":{"msgid":"Select 1 image per:","msgstr":["Izberite 1 sliko na:"]},"Timelapse":{"msgid":"Timelapse","msgstr":["Časovno zaporedje"]},"Select All":{"msgid":"Select All","msgstr":["Izberi vse"]},"Speed:":{"msgid":"Speed:","msgstr":["Hitrost:"]},"frames / s":{"msgid":"frames / s","msgstr":["okvirjev / s"]},"Preparing...":{"msgid":"Preparing...","msgstr":["Priprava..."]},"Could not download files:":{"msgid":"Could not download files:","msgstr":["Datoteke ni bilo mogoče prenesti:"]},"Can't download via canvas":{"msgid":"Can't download via canvas","msgstr":["Ne morem prenesti prek okvirja"]},"Could not ZIP files:":{"msgid":"Could not ZIP files:","msgstr":["Datoteke ZIP ni bilo mogoče shraniti:"]},"There was a problem downloading image":{"msgid":"There was a problem downloading image","msgstr":["Pri prenosu posnetka je prišlo do težave"]},"Error fetching image: url is empty!":{"msgid":"Error fetching image: url is empty!","msgstr":["Napaka pri pridobivanju posnetka: url je prazen!"]},"Error fetching image:":{"msgid":"Error fetching image:","msgstr":["Napaka pri pridobivanju posnetka:"]},"Could not load image from blob":{"msgid":"Could not load image from blob","msgstr":["Ni bilo mogoče naložiti posnetka iz bloba"]},"Drag bands onto RGB fields.":{"msgid":"Drag bands onto RGB fields.","msgstr":["Povlecite kanale na polja RGB."]},"Drag bands into the index equation":{"msgid":"Drag bands into the index equation","msgstr":["Povlecite kanale v enačbo indeksa"]},"Index ":{"msgid":"Index ","msgstr":["Indeks "]},"Threshold":{"msgid":"Threshold","msgstr":["Prag"]},"Remove color picker":{"msgid":"Remove color picker","msgstr":["Odstranite izbirnik barv"]},"Add color picker":{"msgid":"Add color picker","msgstr":["Dodajanje izbirnika barv"]},"Click to place marker":{"msgid":"Click to place marker","msgstr":["Kliknite za postavitev oznake"]},"Click to place first vertex":{"msgid":"Click to place first vertex","msgstr":["Kliknite za postavitev prvega verteksa"]},"Click to continue drawing":{"msgid":"Click to continue drawing","msgstr":["Kliknite za nadaljevanje risanja"]},"Click first marker to finish":{"msgid":"Click first marker to finish","msgstr":["Za dokončanje kliknite prvi označevalec"]},"Show captions":{"msgid":"Show captions","msgstr":["Prikaži napise"]},"Show slide title":{"msgid":"Show slide title","msgstr":["Prikaži naslov diapozitiva"]},"Add map overlays":{"msgid":"Add map overlays","msgstr":["Dodaj pregledno karto"]},"Show legend":{"msgid":"Show legend","msgstr":["Prikaži legendo"]},"No pins were found within the current field of view.":{"msgid":"No pins were found within the current field of view.","msgstr":["V trenutnem vidnem polju ni bila najdena nobena oznaka."]},"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.":{"msgid":"Some pins (${ N_PINS_OUTSIDE_BOUNDS }) are ignored because they are not within the selected area.","msgstr":["Nekatere oznake (${ N_PINS_OUTSIDE_BOUNDS }) so prezrte, ker niso znotraj izbranega območja."]},"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.":{"msgid":"To create a pin story, navigate to the desired position on the map.\n\nAll pins within the current field of view will be used to create the story, the rest will be ignored.","msgstr":["Če želite ustvariti zgodbo iz oznak, se pomaknite do želenega položaja na karti.\n\nZa ustvarjanje zgodbe bodo uporabljene vse oznake v trenutnem vidnem polju, ostale ne bodo upoštevane."]},"File will have logo attached.":{"msgid":"File will have logo attached.","msgstr":["Datoteka bo imela vključen logotip."]},"A dataMask-band will be included in the downloaded raw bands as second band.":{"msgid":"A dataMask-band will be included in the downloaded raw bands as second band.","msgstr":["Kanal dataMask bo vključen v surove kanale kot drugi kanal."]},"Show logo":{"msgid":"Show logo","msgstr":["Prikaži logotip"]},"Image format":{"msgid":"Image format","msgstr":["Format posnetka"]},"Image resolution":{"msgid":"Image resolution","msgstr":["Ločljivost posnetka"]},"Coordinate system":{"msgid":"Coordinate system","msgstr":["Koordinatni sistem"]},"Layers":{"msgid":"Layers","msgstr":["Sloji"]},"Visualized":{"msgid":"Visualized","msgstr":["Vizualizirano"]},"Raw":{"msgid":"Raw","msgstr":["Neobdelan"]},"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.":{"msgid":"The map's overlay layers (place labels, streets and political boundaries) will be added to the image.","msgstr":["Sliki bodo dodani prekrivni sloji (oznake krajev, ulice in politične meje)."]},"Exported image(s) will include datasource and date, zoom scale and branding":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding","msgstr":["Izvožene slike bodo vključevale vir podatkov in datum, merilo povečave in lastnika"]},"Add a short description to the exported image":{"msgid":"Add a short description to the exported image","msgstr":["Izvoženi sliki dodajte kratek opis"]},"Description":{"msgid":"Description","msgstr":["Opis"]},"Image format:":{"msgid":"Image format:","msgstr":["Format posnetka:"]},"Basic":{"msgid":"Basic","msgstr":["Osnovni"]},"Analytical":{"msgid":"Analytical","msgstr":["Analitični"]},"High-res print":{"msgid":"High-res print","msgstr":["Tiskanje v visoki ločljivosti"]},"Download image":{"msgid":"Download image","msgstr":["Prenos posnetka"]},"An error has occurred while fetching some of the images:":{"msgid":"An error has occurred while fetching some of the images:","msgstr":["Med dodajanjem nekaterih posnetkov je prišlo do napake:"]},"min/px":{"msgid":"min/px","msgstr":["min/px"]},"sec/px":{"msgid":"sec/px","msgstr":["sec/px"]},"Resolution":{"msgid":"Resolution","msgstr":["Razrešitev"]},"lat.":{"msgid":"lat.","msgstr":["lat."]},"deg/px":{"msgid":"deg/px","msgstr":["deg/px"]},"long.":{"msgid":"long.","msgstr":["long."]},"Projected resolution: ${ formattedResolution } m/px":{"msgid":"Projected resolution: ${ formattedResolution } m/px","msgstr":["Predvidena ločljivost: ${ formattedResolution } m/px"]},"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.":{"msgid":"Error: Data fusion does not support KMZ/JPG and KMZ/PNG formats.","msgstr":["Napaka: KMZ/JPG in KMZ/PNG nista podprta pri fuziji podatkov."]},"Image download":{"msgid":"Image download","msgstr":["Prenos posnetka"]},"Image width [inches]:":{"msgid":"Image width [inches]:","msgstr":["Širina slike [palcev]:"]},"Image height [inches]:":{"msgid":"Image height [inches]:","msgstr":["Višina slike [palcev]:"]},"DPI:":{"msgid":"DPI:","msgstr":["DPI:"]},"5 years":{"msgid":"5 years","msgstr":["5 let"]},"2 years":{"msgid":"2 years","msgstr":["2 leti"]},"1 year":{"msgid":"1 year","msgstr":["1 leto"]},"6 months":{"msgid":"6 months","msgstr":["6 mesecev"]},"3 months":{"msgid":"3 months","msgstr":["3 mesece"]},"1 month":{"msgid":"1 month","msgstr":["1 mesec"]},"Retry":{"msgid":"Retry","msgstr":["Ponovi"]},"Loading, please wait":{"msgid":"Loading, please wait","msgstr":["Nalaganje, počakajte"]},"mean":{"msgid":"mean","msgstr":["povprečje"]},"median":{"msgid":"median","msgstr":["mediana"]},"st. dev.":{"msgid":"st. dev.","msgstr":["st. dev."]},"min / max":{"msgid":"min / max","msgstr":["min / maks"]},"Export CSV":{"msgid":"Export CSV","msgstr":["Izvozi CSV"]},"Timespan:":{"msgid":"Timespan:","msgstr":["Časovni razpon:"]},"Date:":{"msgid":"Date:","msgstr":["Datum:"]},"Single date":{"msgid":"Single date","msgstr":["Enkratni datum"]},"Timespan":{"msgid":"Timespan","msgstr":["Časovni razpon"]},"hh":{"msgid":"hh","msgstr":["hh"]},"mm":{"msgid":"mm","msgstr":["mm"]},"From:":{"msgid":"From:","msgstr":["Od:"]},"Until:":{"msgid":"Until:","msgstr":["Do:"]},"Apply":{"msgid":"Apply","msgstr":["Uporabi"]},"Share on Facebook":{"msgid":"Share on Facebook","msgstr":["Deli na Facebooku"]},"Share on Twitter":{"msgid":"Share on Twitter","msgstr":["Delite na Twitterju"]},"Check this out ":{"msgid":"Check this out ","msgstr":["Oglejte si to "]},"Logout":{"msgid":"Logout","msgstr":["Odjava"]},"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.":{"msgid":"Login to unlock advanced features such as timelapse, analytical download, own configurations and more.","msgstr":["S prijavo lahko odklenete napredne funkcije, kot so časovno zaporedje, prenos analitičnih podatkov, lastne konfiguracije in še več."]},"Login":{"msgid":"Login","msgstr":["Prijava"]},"Default":{"msgid":"Default","msgstr":["Privzeto"]},"Monitoring Earth from Space":{"msgid":"Monitoring Earth from Space","msgstr":["Spremljanje Zemlje iz vesolja"]},"Agriculture":{"msgid":"Agriculture","msgstr":["Kmetijstvo"]},"Atmosphere and Air Pollution":{"msgid":"Atmosphere and Air Pollution","msgstr":["Atmosfera in onesnaževanje zraka"]},"Change Detection through Time":{"msgid":"Change Detection through Time","msgstr":["Zaznavanje sprememb skozi čas"]},"Floods and Droughts":{"msgid":"Floods and Droughts","msgstr":["Poplave in suše"]},"Geology":{"msgid":"Geology","msgstr":["Geologija"]},"Ocean and Water Bodies":{"msgid":"Ocean and Water Bodies","msgstr":["Oceani in vodna telesa"]},"Snow and Glaciers":{"msgid":"Snow and Glaciers","msgstr":["Sneg in ledeniki"]},"Urban":{"msgid":"Urban","msgstr":["Urbano"]},"Vegetation and Forestry":{"msgid":"Vegetation and Forestry","msgstr":["Rastlinstvo in gozdarstvo"]},"Volcanoes":{"msgid":"Volcanoes","msgstr":["Vulkani"]},"Wildfires":{"msgid":"Wildfires","msgstr":["Požari v naravi"]},"Band 1 - Blue - 450-515 nm":{"msgid":"Band 1 - Blue - 450-515 nm","msgstr":["Kanal 1 - modra - 450-515 nm"]},"Band 2 - Green - 525-605 nm":{"msgid":"Band 2 - Green - 525-605 nm","msgstr":["Kanal 2 - zelena - 525-605 nm"]},"Band 3 - Red - 630-690 nm":{"msgid":"Band 3 - Red - 630-690 nm","msgstr":["Kanal 3 - rdeča - 630-690 nm"]},"Band 4 - NIR - 750-900 nm":{"msgid":"Band 4 - NIR - 750-900 nm","msgstr":["Kanal 4 - NIR - 750-900 nm"]},"Band 5 - SWIR-1 - 1550-1750 nm":{"msgid":"Band 5 - SWIR-1 - 1550-1750 nm","msgstr":["Kanal 5 - SWIR-1 - 1550-1750 nm"]},"Band 7 - SWIR-2 - 2090-2350 nm":{"msgid":"Band 7 - SWIR-2 - 2090-2350 nm","msgstr":["Kanal 7 - SWIR-2 - 2090-2350 nm"]},"Band 8 - Panchromatic - 520-900 nm":{"msgid":"Band 8 - Panchromatic - 520-900 nm","msgstr":["Kanal 8 - pankromatski - 520-900 nm"]},"Band 1 - Coastal/Aerosol - 433-453 nm":{"msgid":"Band 1 - Coastal/Aerosol - 433-453 nm","msgstr":["Kanal 1 - Obala/Aerosol - 433-453 nm"]},"Band 2 - Blue - 450-515 nm":{"msgid":"Band 2 - Blue - 450-515 nm","msgstr":["Kanal 2 - modra - 450-515 nm"]},"Band 3 - Green - 525-600 nm":{"msgid":"Band 3 - Green - 525-600 nm","msgstr":["Kanal 3 - zelena - 525-600 nm"]},"Band 4 - Red - 630-680 nm":{"msgid":"Band 4 - Red - 630-680 nm","msgstr":["Kanal 4 - rdeča - 630-680 nm"]},"Band 5 - NIR - 845-885 nm":{"msgid":"Band 5 - NIR - 845-885 nm","msgstr":["Kanal 5 - NIR - 845-885 nm"]},"Band 6 - SWIR-1 - 1560-1660 nm":{"msgid":"Band 6 - SWIR-1 - 1560-1660 nm","msgstr":["Kanal 6 - SWIR-1 - 1560-1660 nm"]},"Band 7 - SWIR-2 - 2100-2300 nm":{"msgid":"Band 7 - SWIR-2 - 2100-2300 nm","msgstr":["Kanal 7 - SWIR-2 - 2100-2300 nm"]},"Band 8 - Panchromatic - 500-680 nm":{"msgid":"Band 8 - Panchromatic - 500-680 nm","msgstr":["Kanal 8 - pankromatsko - 500-680 nm"]},"Band 9 - Cirrus - 1360-1390 nm":{"msgid":"Band 9 - Cirrus - 1360-1390 nm","msgstr":["Kanal 9 - Cirrus - 1360-1390 nm"]},"Reflectance":{"msgid":"Reflectance","msgstr":["Odsevnost"]},"Brightness temperature":{"msgid":"Brightness temperature","msgstr":["Temperatura svetlosti"]},"Create a timelapse of this area":{"msgid":"Create a timelapse of this area","msgstr":["Ustvarite časovno zaporedje tega območja"]},"Warning: Following layers use dataProducts, so the desired data type might not be set:":{"msgid":"Warning: Following layers use dataProducts, so the desired data type might not be set:","msgstr":["Opozorilo: Naslednje plasti uporabljajo dataProducts, zato želena vrsta podatkov morda ne bo nastavljena:"]},"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:":{"msgid":"Warning: Evalscript is not in a typical V3 format and the desired data type could not be set for:","msgstr":["Opozorilo: Evalscript ni v tipičnem formatu V3 in želenega tipa podatkov ni bilo mogoče nastaviti:"]},"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation":{"msgid":"This means \"sampleType\" parameter is likely set to default (AUTO). You can fix this by editing your evalscript. Learn more about \"sampleType\" in the documentation","msgstr":["To pomeni, da je parameter \"sampleType\" verjetno nastavljen na privzeto vrednost (AUTO). To lahko popravite tako, da uredite svoj evalscript. Več o parametru \"sampleType\" je v dokumentaciji"]},"Error: You can only download visualization with effects in JPEG or PNG formats.":{"msgid":"Error: You can only download visualization with effects in JPEG or PNG formats.","msgstr":["Napaka: Vizualizacijo z učinki lahko prenesete samo v formatih JPEG ali PNG."]},"Measure":{"msgid":"Measure","msgstr":["Izmeri"]},"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at":{"msgid":"Sentinel-1 services are available both on EOCloud and AWS. The capabilities of each\nservice differ. More infos at","msgstr":["Storitve Sentinel-1 so na voljo v oblaku EOCloud in AWS. Zmogljivosti vsake od njih se\nse razlikujejo. Več informacij najdete na"]},"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.":{"msgid":"The Tagged Image File Format (TIFF) can hold a large number of bands, however many common image viewers (e.g. Windows Photo Viewer) can't display TIFF images with more than 3 bands.\nIf this option is enabled, only the first 3 bands will be included in the image.\nIf this option is disabled, all bands will be included in the image, but you will have to use an application which supports more than 3 bands (e.g. QGIS) to display the TIFF image.","msgstr":["Format Tagged Image File Format (TIFF) lahko vsebuje veliko število kanalov, vendar številni običajni pregledovalniki slik (npr. Windows Photo Viewer) ne morejo prikazati slik TIFF z več kot 3 kanali.\nČe je ta možnost omogočena, bodo v sliko vključeni samo prvi trije kanali.\nČe je ta možnost onemogočena, bodo v sliko vključeni vsi kanali, vendar boste morali za prikaz slike TIFF uporabiti program, ki podpira več kot 3 kanale (npr. QGIS)."]},"Add dataMask band to raw layers":{"msgid":"Add dataMask band to raw layers","msgstr":["Dodajanje maske dataMask v surove sloje"]},"Clip extra bands":{"msgid":"Clip extra bands","msgstr":["Obreži dodatne sloje"]},"EPSG:3857 is not available when an AOI is specified.":{"msgid":"EPSG:3857 is not available when an AOI is specified.","msgstr":["EPSG:3857 ni na voljo, če je podan AOI."]},"Creating link...":{"msgid":"Creating link...","msgstr":["Ustvarjanje povezave..."]},"OK":{"msgid":"OK","msgstr":["Vredu"]},"Hello,":{"msgid":"Hello,","msgstr":["Pozdravljeni,"]},"This pin currently has no description.":{"msgid":"This pin currently has no description.","msgstr":["Ta oznaka trenutno nima opisa."]},"Your web browser doesn't support 3D capabilities, that are needed to display this content.":{"msgid":"Your web browser doesn't support 3D capabilities, that are needed to display this content.","msgstr":["Vaš spletni brskalnik ne podpira zmogljivosti 3D, ki so potrebne za prikaz te vsebine."]},"More information":{"msgid":"More information","msgstr":["Več informacij"]},"Cannot connect to the 3D service! Retry?":{"msgid":"Cannot connect to the 3D service! Retry?","msgstr":["Ne morete se povezati s storitvijo 3D! Ponoven poskus?"]},"The image is too big for this device!\nImage size: {0}x{1}, max: {2}":{"msgid":"The image is too big for this device!\nImage size: {0}x{1}, max: {2}","msgstr":["Slika je prevelika za to napravo!\nVelikost slike: {0}x{1}, max: {2}"]},"Home":{"msgid":"Home","msgstr":["Domov"]},"Shading":{"msgid":"Shading","msgstr":["Senčenje"]},"Sphere mode":{"msgid":"Sphere mode","msgstr":["Način krogle"]},"Eye height":{"msgid":"Eye height","msgstr":["Višina oči"]},"Cannot load the image":{"msgid":"Cannot load the image","msgstr":["Posnetka ni mogoče naložiti"]},"Geometries":{"msgid":"Geometries","msgstr":["Geometrije"]},"Now":{"msgid":"Now","msgstr":["Zdaj"]},"Terrain":{"msgid":"Terrain","msgstr":["Teren"]},"Time":{"msgid":"Time","msgstr":["Ura"]},"Advanced RGB effects":{"msgid":"Advanced RGB effects","msgstr":["Napredni učinki RGB"]},"Left button":{"msgid":"Left button","msgstr":["Levi gumb"]},"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.":{"msgid":"Click and drag using the left mouse button to move across the map at a fixed height. Use SHIFT + left button to rotate.","msgstr":["Kliknite in povlecite z levim miškinim gumbom, da se premikate po karti na stalni višini. Za vrtenje uporabite SHIFT + levi gumb."]},"Right button":{"msgid":"Right button","msgstr":["Desni gumb"]},"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.":{"msgid":"Right click and drag up/down to change the elevation of the camera. Right click and\ndrag left/right to rotate the camera's view.","msgstr":["Z desnim klikom in vlečenjem navzgor/navzdol spremenite višino kamere. Kliknite z desno tipko miške in\nin povlecite levo/desno, da obrnete pogled kamere."]},"Middle button/wheel":{"msgid":"Middle button/wheel","msgstr":["Srednji gumb/kolesce"]},"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.":{"msgid":"Use the scroll wheel to change the elevation of the camera (same as right click + drag\nup/down). Click and drag the wheel button to change the angle of the camera.","msgstr":["S kolescem za pomikanje lahko spremenite višino kamere (enako kot z desnim klikom + vlečenjem\nnavzgor/navzdol). S klikom in vlečenjem kolesca spremenite kot kamere."]},"Keyboard navigation":{"msgid":"Keyboard navigation","msgstr":["Navigacija s tipkovnico"]},"Arrow keys":{"msgid":"Arrow keys","msgstr":["Tipke s puščicami"]},"Use the arrow keys to move across the map at a fixed height.":{"msgid":"Use the arrow keys to move across the map at a fixed height.","msgstr":["S smernimi tipkami se premikajte po karti na stalni višini."]},"SHIFT + arrow keys":{"msgid":"SHIFT + arrow keys","msgstr":["SHIFT + smerne tipke"]},"Hold the SHIFT key while pressing the arrow keys to change the camera's view.":{"msgid":"Hold the SHIFT key while pressing the arrow keys to change the camera's view.","msgstr":["Če želite spremeniti pogled kamere, držite tipko SHIFT in hkrati pritiskajte smerne tipke."]},"Page up/Page down":{"msgid":"Page up/Page down","msgstr":["Stran navzgor/stran navzdol"]},"Use the PG UP or PG DN keys to change the elevation of the camera.":{"msgid":"Use the PG UP or PG DN keys to change the elevation of the camera.","msgstr":["S tipkama PG UP ali PG DN spremenite višino kamere."]},"Map navigation":{"msgid":"Map navigation","msgstr":["Navigacija po karti"]},"Pan console":{"msgid":"Pan console","msgstr":["Konzola za premikanje"]},"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.":{"msgid":"The pan console allows you to move across the map at a fixed height. Click and drag to move\ncontinuously. The farther you drag from the center, the faster you will move.","msgstr":["Konzola za pomikanje omogoča premikanje po karti na določeni višini. Kliknite in povlecite za premikanje\nneprekinjeno. Bolj ko se vlečete od središča, hitreje se boste premikali."]},"Camera console":{"msgid":"Camera console","msgstr":["Konzola za kamero"]},"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.":{"msgid":"The camera console moves the camera's view only. Click and drag to change the camera's view.\nThe farther you drag from the center, the faster you will change the view.","msgstr":["Konzola za kamero premika samo pogled kamere. Če želite spremeniti pogled kamere, kliknite in povlecite.\nBolj ko vlečete od središča, hitreje boste spremenili pogled."]},"Zoom buttons":{"msgid":"Zoom buttons","msgstr":["Gumbi za povečavo"]},"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.":{"msgid":"Clicking them will change the elevation of the camera. The plus button will move the camera\ncloser to the earth, the minus button will move the camera further away.","msgstr":["S klikom nanje spremenite višino kamere. Z gumbom plus boste premaknili kamero\nbližje Zemlji, z gumbom minus pa se bo kamera oddaljila od nje."]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 90 m\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["**Copernicus DEM** predstavlja površino Zemlje, vključno s stavbami, infrastrukturo in vegetacijo. Podobno kot Mapzen DEM temelji na kombinaciji različnih modelov (osnova [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Gre za statično zbirko (neodvisno od datuma) z globalno pokritostjo.\n\n**Prostorska ločljivost:** 90 m\n\nVir: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.":{"msgid":"A **DEM** (Digital Elevation Model) is a digital representation of a terrain (usually Earth's surface). It is obtained by dividing the whole globe into grid cells, each holding a corresponding altitude value in meters. Depending on the gride cell size, a DEM can be more detailed (high resolution) or less detailed (low resolution). Sentinel Hub DEM data collections (Mapzen and Copernicus) are static (independent of date) and globally available.\n\n**Common usage:** Modelling water flows, orthorectification of Sentinel-1 imagery and engineering.","msgstr":["**DMV** (digitalni model višin, digital elevation mode, DEM) je digitalni prikaz terena (običajno zemeljske površine). Dobimo ga tako, da celoten svet razdelimo na celice, v vsaki pa je ustrezna vrednost nadmorske višine v metrih. Glede na velikost mrežne celice je lahko bolj (visoka ločljivost) ali manj podroben (nizka ločljivost). Zbirke podatkov Sentinel Hub DEM (Mapzen in Copernicus) so statične (neodvisne od datuma) in na voljo po vsem svetu.\n\n**Običajna uporaba:** Modeliranje vodnih tokov, ortorektifikacija posnetkov Sentinel-1 in inženirstvo."]},"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)":{"msgid":"The **Copernicus DEM** represents the surface of the Earth including buildings, infrastructure and vegetation. Similar to the Mapzen DEM, it is based on a combination of different DEMs (basis [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** 30 m infilled with 90 m (where 30 m tiles are not released).\n\nCredits: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)","msgstr":["**Copernicus DEM** predstavlja površino Zemlje, vključno s stavbami, infrastrukturo in vegetacijo. Podobno kot Mapzen DEM temelji na kombinaciji različnih modelov (osnova [WorldDEMTM](https://www.geospatialworld.net/article/worlddemtm-new-standard-of-global-elevation-models/)). Gre za statično zbirko (neodvisno od datuma) z globalno pokritostjo.\n\n**Prostorska ločljivost:** 30 m, zapolnjeno z 90 m (kjer 30-metrski deli niso objavljene).\n\nVir: [ESA](https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198)"]},"Primary dataset:":{"msgid":"Primary dataset:","msgstr":["Osnovni nabor podatkov:"]},"Datasource alias:":{"msgid":"Datasource alias:","msgstr":["Psevdonim vira podatkov:"]},"Additional datasets:":{"msgid":"Additional datasets:","msgstr":["Dodatne zbirke podatkov:"]},"Cancel":{"msgid":"Cancel","msgstr":["Prekliči"]},"Error":{"msgid":"Error","msgstr":["Napaka"]},"Help":{"msgid":"Help","msgstr":["Pomoč"]},"Position 3D camera based on 2D map":{"msgid":"Position 3D camera based on 2D map","msgstr":["Položaj 3D kamere na podlagi 2D karte"]},"Mouse navigation":{"msgid":"Mouse navigation","msgstr":["Navigacija z miško"]},"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ":{"msgid":"Your user instances could not be loaded as your Sentinel Hub account was not set up/expired. You can still use EO Browser but you will not be able to use personal user instances. To be able to set up personal user instances you can apply for a 30-days free trial or consider subscribing to one of the plans: ","msgstr":["Vaših uporabniških primerov ni bilo mogoče naložiti, ker vaš račun Sentinel Hub ni bil nastavljen ali je potekel. Še vedno lahko uporabljate brskalnik EO Browser, vendar ne boste mogli uporabljati uporabniških primerov. Da bi lahko nastavili osebne primere, lahko zaprosite za 30-dnevni brezplačni preizkus ali razmislite o eni od naročnin: "]},"User Instances":{"msgid":"User Instances","msgstr":["Uporabniške konfiguracije"]},"These are theme parts which contain unavailable data sources:":{"msgid":"These are theme parts which contain unavailable data sources:","msgstr":["To so deli teme, ki vsebujejo vire podatkov, ki niso na voljo:"]},"Disabled":{"msgid":"Disabled","msgstr":["Onemogočeno"]},"Yes":{"msgid":"Yes","msgstr":["Da"]},"Orthorectification":{"msgid":"Orthorectification","msgstr":["Ortorektifikacija"]},"Zoom to location":{"msgid":"Zoom to location","msgstr":["Povečanje na lokacijo"]},"Remove layer":{"msgid":"Remove layer","msgstr":["Odstranite sloj"]},"Band 10 - Thermal Infrared (TIRS) - 10895 nm":{"msgid":"Band 10 - Thermal Infrared (TIRS) - 10895 nm","msgstr":["Kanal 10 - termična infrardeča svetloba (TIRS) - 10895 nm"]},"Band 11 - Thermal Infrared (TIRS) - 12005 nm":{"msgid":"Band 11 - Thermal Infrared (TIRS) - 12005 nm","msgstr":["Kanal 11 - toplotna infrardeča svetloba (TIRS) - 12005 nm"]},"Main discrete land cover classification according to FAO LCCS scheme":{"msgid":"Main discrete land cover classification according to FAO LCCS scheme","msgstr":["Glavna diskretna klasifikacija pokrovnosti tal v skladu s shemo FAO LCCS"]},"Classification probability, a quality indicator for the discrete classification":{"msgid":"Classification probability, a quality indicator for the discrete classification","msgstr":["Verjetnost razvrstitve, kazalnik kakovosti za diskretno razvrščanje"]},"Forest type for all pixels where tree cover fraction is bigger than 1 %":{"msgid":"Forest type for all pixels where tree cover fraction is bigger than 1 %","msgstr":["Vrsta gozda za vse piksle, v katerih je delež dreves večji od 1 %"]},"Fractional cover (%) for the bare and sparse vegetation class":{"msgid":"Fractional cover (%) for the bare and sparse vegetation class","msgstr":["Delež pokritosti (%) za razred gole in redke vegetacije"]},"Fractional cover (%) for the cropland class":{"msgid":"Fractional cover (%) for the cropland class","msgstr":["Delni pokrov (%) za razred obdelovalnih površin"]},"Fractional cover (%) for the herbaceous vegetation class":{"msgid":"Fractional cover (%) for the herbaceous vegetation class","msgstr":["Delež pokritosti (%) za razred zeljne vegetacije"]},"Fractional cover (%) for the moss & lichen class":{"msgid":"Fractional cover (%) for the moss & lichen class","msgstr":["Delni pokrov (%) za razred mahov in lišajev"]},"Fractional cover (%) for the shrubland class":{"msgid":"Fractional cover (%) for the shrubland class","msgstr":["Delež pokritosti (%) za razred grmičevja"]},"Fractional cover (%) for the snow & ice class":{"msgid":"Fractional cover (%) for the snow & ice class","msgstr":["Delna pokritost (%) za razred sneg in led"]},"Fractional cover (%) for the forest class":{"msgid":"Fractional cover (%) for the forest class","msgstr":["Delni pokrov (%) za razred gozd"]},"Fractional cover (%) for the built-up class":{"msgid":"Fractional cover (%) for the built-up class","msgstr":["Delna pokritost (%) za razred pozidanih površin"]},"Fractional cover (%) for the permanent inland water bodies class":{"msgid":"Fractional cover (%) for the permanent inland water bodies class","msgstr":["Delna pokritost (%) za razred stalnih celinskih vodnih teles"]},"Fractional cover (%) for the seasonal inland water bodies class":{"msgid":"Fractional cover (%) for the seasonal inland water bodies class","msgstr":["Delež pokritosti (%) za razred sezonskih celinskih vodnih teles"]},"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)":{"msgid":"Data density indicator showing quality of the EO input data (0 = bad, 100 = perfect data)","msgstr":["Kazalnik gostote podatkov, ki kaže kakovost vhodnih podatkov EO (0 = slabi, 100 = popolni podatki)"]},"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.":{"msgid":"Quality layer regarding the change detection of the current mapped year to the previous mapped year. It is a 3 level confidence mask for all CONSO and NRT maps with value definitions as:\n0 = No change.\n1 - Potential confidence.\n2 - Medium confidence.\n3 = High confidence.\nNOTE: The values of Change_Confidence_layer band in 2015 data are not shown correctly, therefore this band in 2015 data should not be used.","msgstr":["Sloj kakovosti glede zaznavanja sprememb v tekočem kartiranem letu v primerjavi s prejšnjim kartiranim letom. Gre za 3-stopenjsko masko zaupanja za vse karte CONSO in NRT z opredelitvami vrednosti:\n0 = brez spremembe\n1 - nizka zanesljivost\n2 - srednja zanesljivost\n3 = visoka zanesljivost\nOPOMBA: Vrednosti maske Change_Confidence_layer v podatkih za leto 2015 niso prikazane pravilno, zato te maske v podatkih za leto 2015 ne uporabljajte."]},"Drag classes onto RGB fields.":{"msgid":"Drag classes onto RGB fields.","msgstr":["Povlecite razrede na polja RGB."]},"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"**CORINE Land Cover (CLC)** inventory is a vector-based dataset that consists of 44 land cover and land use classes, derived from a series of satellite missions. In the majority of European countries, CLC is produced using visual interpretation of high resolution satellite imagery. In a few countries semi-automatic solutions are applied, using national in-situ data, satellite image processing, GIS integration and generalisation. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover). \n\n**Coverage**: Most of Europe.\n\n**Data availability**:\nCLC data is updated every 6 years. In EO Browser, data is available on the following dates:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Common Usage**:\nLand use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":["Zbirka **CORINE Land Cover (CLC)** je vektorski podatkovni niz, sestavljen iz 44 razredov pokrovnosti in rabe tal, ki so bili pridobljeni iz podatkov več satelitskih misij. V večini evropskih držav je CLC izdelan z vizualno interpretacijo satelitskih posnetkov visoke ločljivosti. V nekaj državah se uporabljajo polavtomatske rešitve z uporabo nacionalnih podatkov in-situ, obdelavo satelitskih posnetkov, integracijo in posploševanjem GIS. Več informacij je [tukaj](https://github.com/sentinel-hub/public-collections/tree/main/collections/corine-land-cover)\n\n**Pokritost**: Večji del Evrope.\n\n**Dostopnost podatkov**:\nPodatki CLC se posodabljajo vsakih šest let. V brskalniku EO Browser so podatki na voljo na naslednje datume:\n01-01-1990, 01-01-2000, 01-01-2006, 01-01-2012, 01-01-2018.\n\n**Običajna uporaba**:\nSpremljanje, analiza in napovedovanje sprememb rabe in pokrovnosti tal za različne namene, vključno z okoljem, kmetijstvom, prometom in prostorskim načrtovanjem."]},"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.":{"msgid":"**Global Land Cover** products provide a discrete land cover classification map according to UN-FAO Land Cover Classification System. Additional continuous fractional layers for all basic land cover classes are included as bands, to provide more detailed information on each land cover class. More information [here](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover). \n\n**Coverage**: Global.\n\n**Data availability**:\nUpdated on a yearly basis. In EO Browser, data is available on the following dates:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Common Usage**: \nLand use and land cover monitoring, used to aid policy decisions on various issues, including agriculture and food security, biodiversity, climate change, forest and water resources, land degradation & desertification and rural development.","msgstr":["*Izdelki *Global Land Cover** zagotavljajo diskretno karto klasifikacije pokrovnosti tal v skladu s sistemom klasifikacije pokrovnosti tal UN-FAO. Dodatni neprekinjeni frakcijski sloji za vse osnovne razrede pokrovnosti tal so vključeni kot kanali, ki zagotavljajo podrobnejše informacije o vsakem razredu pokrovnosti tal. Več informacij je [tukaj](https://github.com/sentinel-hub/public-collections/tree/main/collections/global-land-cover)\n\n**Pokritost**: Globalno.\n\n**Dostopnost podatkov**:\nVsako leto se posodobi. V brskalniku EO Browser so podatki na voljo na naslednje datume:\n01-01-2015, 01-01-2016, 01-01-2017, 01-01-2018, 01-01-2019.\n\n**Običajna uporaba**\nSpremljanje rabe tal in pokrovnosti tal, ki se uporablja za pomoč pri političnih odločitvah o različnih vprašanjih, vključno s kmetijstvom in prehransko varnostjo, biotsko raznovrstnostjo, podnebnimi spremembami, gozdnimi in vodnimi viri, degradacijo in dezertifikacijo tal ter razvojem podeželja."]},"File upload":{"msgid":"File upload","msgstr":["Nalaganje datoteke"]},"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.":{"msgid":"Upload a KML/KMZ, GPX or GEOJSON/JSON file to create area of interest. Area will be used for clipping when exporting an image.","msgstr":["Naložite datoteko KML/KMZ, GPX ali GEOJSON/JSON in ustvarite interesno območje. Območje bo uporabljeno za izrezovanje pri izvozu slike."]},"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer":{"msgid":"Drop KML/KMZ, GPX, GEOJSON/JSON file or search your computer","msgstr":["Spustite datoteko KML/KMZ, GPX, GEOJSON/JSON ali preiščite svoj računalnik"]},"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water":{"msgid":"Main Water Bodies detection layer showing water pixels and non-water pixels\n0 = Sea\n70 = Water\n251 = No data\n255 = No water","msgstr":["Sloj za zaznavanje glavnih vodnih teles, ki prikazuje vodne in nevodne piksle\n0 = morje\n70 = voda\n251 = ni podatkov\n255 = ni vode"]},"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water":{"msgid":"Quality layer which gives information on water bodies occurrence\n0 = Sea\n71 = Very low occurence\n72 = Low occurence\n73 = Medium occurence\n74 = High occurence\n75 = Very high occurence\n76 = Permanent occurence\n251 = No data\n252 = Cloud\n255 = Not water","msgstr":["Sloj kakovosti, ki vsebuje informacije o pojavljanju vodnih teles\n0 = morje\n71 = zelo majhna pojavnost\n72 = nizka pojavnost\n73 = srednja pojavnost\n74 = visoka pojavnost\n75 = zelo visoka pojavnost\n76 = stalna pojavnost\n251 = ni podatkov\n252 = oblak\n255 = ni voda"]},"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.":{"msgid":"The **Water Bodies** product shows the surface extent covered by inland water on a permanent, seasonal or occasional basis on a global scale. It contains one main Water Body detection layer (WB) and one Quality layer (QUAL), that provides information on the seasonal dynamics of the detected water bodies. More information [here](https://collections.sentinel-hub.com/water-bodies/). \n\n**Coverage**:\nGlobal coverage from longitude -180°E to +180°W and latitude +80°N to -60°S. Depending on the month, some high latitude areas are not covered by Sentinel-2 satellites.\n\n**Data Availability**:\nSince October 2020, updated monthly. \n\n**Common Usage**\nMonitoring of water bodies, droughts, floods and climate change.","msgstr":["Izdelek **Vodna telesa** na svetovni ravni prikazuje površino, ki jo stalno, sezonsko ali občasno pokrivajo celinske vode. Vsebuje glavni sloj zaznavanja vodnih teles (Water Body, WB) in sloj kakovosti (Quality, QUAL), ki zagotavlja informacije o sezonski dinamiki zaznanih vodnih teles. Več informacij je [tukaj](https://collections.sentinel-hub.com/water-bodies/)\n\n**Pokritost**:\nGlobalna pokritost od zemljepisne dolžine -180°E do +180°W in zemljepisne širine +80°S do -60°S. Odvisno od meseca satelit Sentinel-2 ne pokriva nekaterih območij na visokih zemljepisnih širinah.\n\n**Dostopnost podatkov**:\nOd oktobra 2020, posodablja se mesečno\n\n**Običajna uporaba**\nSpremljanje vodnih teles, suš, poplav in podnebnih sprememb."]},"Ultra Blue (443 nm)":{"msgid":"Ultra Blue (443 nm)","msgstr":["Ultra modra (443 nm)"]},"Blue (482 nm)":{"msgid":"Blue (482 nm)","msgstr":["Modra (482 nm)"]},"Green (561.5 nm)":{"msgid":"Green (561.5 nm)","msgstr":["Zelena (561,5 nm)"]},"Red (654.5 nm)":{"msgid":"Red (654.5 nm)","msgstr":["Rdeča (654,5 nm)"]},"Near Infrared (NIR) (865 nm)":{"msgid":"Near Infrared (NIR) (865 nm)","msgstr":["Bližnja infrardeča svetloba (NIR) (865 nm)"]},"Shortwave Infrared (SWIR) 1 (1608.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1608.5 nm)","msgstr":["Kratkovalovna infrardeča svetloba (SWIR) 1 (1608,5 nm)"]},"Shortwave Infrared (SWIR) 2 (2200.5 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2200.5 nm)","msgstr":["Kratkovalovna infrardeča svetloba (SWIR) 2 (2200,5 nm)"]},"Thermal Infrared (TIRS) 1(10895 nm)":{"msgid":"Thermal Infrared (TIRS) 1(10895 nm)","msgstr":["Termična infrardeča svetloba (TIRS) 1 (10895 nm)"]},"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)":{"msgid":"**Level-1** data (from **Landsat Collection 2**) provides global top of the atmosphere reflectance and top of the atmosphere brightness temperature products. \n\nThe data underwent several processing steps including geometric and radiometric improvements. \n\nMore info about Level-1 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)","msgstr":["*Podatki *Level-1** (iz zbirke **Landsat Collection 2**) zagotavljajo globalne podatke o odbojnosti in temperaturi nad atmosfero. \n\nPodatki so bili obdelani v več korakih, vključno z geometrijskimi in radiometričnimi izboljšavami\n\nVeč informacij o podatkih Level-1 je [tukaj](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-1-data?qt-science_support_page_related_con=1#qt-science_support_page_related_con) in [tukaj](https://docs.sentinel-hub.com/api/latest/data/landsat-8/)"]},"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).":{"msgid":"**Level-2** data (from **Landsat Collection 2**) provides global surface reflectance and surface temperature science products (CEOS Analysis Ready Data). \n\nThe data products are generated from Collection 2 Level-1 inputs that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. \n\nLearn more about Level-2 data [here](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) and [here](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/).","msgstr":["Podatki **Level-2** (iz zbirke **Landsat Collection 2**) zagotavljajo globalne izdelke o odbojnosti površja in temperaturi površja znanstvene kakovosti (CEOS Analysis Ready Data, podatki pripravljeni za analizo)\n\nPodatkovni izdelki so ustvarjeni na podlagi vhodnih podatkov Level-1, ki izpolnjujejo omejitev <76 stopinj Sončevega zenitnega kota in vključujejo zahtevane pomožne vhodne podatke za ustvarjanje znanstveno izvedljivega izdelka\n\nVeč o podatkih Level-2 je na voljo [tukaj](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products) in [tukaj](https://docs.sentinel-hub.com/api/latest/data/landsat-8-l2/)."]},"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.":{"msgid":"**Landsat 8** is the most recently launched Landsat satellite (provided by NASA/USGS) and carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass.\n\n**Spatial resolution:** 15 m for the panchromatic band and 30 m for the rest (the thermal bands is re-sampled from 100 m).\n\n**Revisit time:** 16 days\n\n**Data availability:** Since February 2013\n\n**Common usage:** Vegetation monitoring, land use, land cover maps, change monitoring, etc.","msgstr":["**Landsat 8** je najnovejši izstreljeni satelit Landsat (zagotovila ga je NASA/USGS), ki nosi instrumenta Operational Land Imager (OLI) in Thermal Infrared Sensor (TIRS) z 9 optičnimi in 2 termičnima kanaloma. Ta dva senzorja zagotavljata sezonsko pokritost svetovnega kopnega.\n\n**Prostorska ločljivost:** 15 m za pankromatski kanal in 30 m za ostale (termični kanali so ponovno vzorčeni s 100 m).\n\n**čas ponovnega ogleda:** 16 dni\n\n**dostopnost podatkov:** od februarja 2013\n\n**Običajna uporaba:** spremljanje vegetacije, raba tal, karte pokrovnosti tal, spremljanje sprememb itd."]},"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.":{"msgid":"Changes in water occurrence between two epochs, the first ranging from 1984 to 1999 and the second covering 2000 to 2019.","msgstr":["Spremembe v pojavljanju vode med dvema obdobjema, pri čemer prvo obdobje zajema obdobje od leta 1984 do 1999, drugo pa obdobje od leta 2000 do 2019."]},"Maximum extent of surface water bodies in the 36-year time range.":{"msgid":"Maximum extent of surface water bodies in the 36-year time range.","msgstr":["Največji obseg površinskih vodnih teles v časovnem obdobju 36 let."]},"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.":{"msgid":"Intra- and inter-annual frequency of surface water presence in the time range between 1984 and 2019.","msgstr":["Medletna in letna pogostost prisotnosti površinskih voda v časovnem obdobju med letoma 1984 in 2019."]},"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.":{"msgid":"Inter-annual variability of surface water presence in a defined water period within the entire time range from 1984 to 2019.","msgstr":["Medletna spremenljivost prisotnosti površinskih voda v določenem vodnem obdobju v celotnem časovnem razponu od leta 1984 do leta 2019."]},"Intra-annual distribution of surface water in 2019.":{"msgid":"Intra-annual distribution of surface water in 2019.","msgstr":["Medletna porazdelitev površinskih voda v letu 2019."]},"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.":{"msgid":"Visualises changes in the three surface water classes (1) not water, (2) seasonal water, and (3) permanent water between the first and last year in the 36-year time period.","msgstr":["Prikazuje spremembe v treh razredih površinskih voda (1) nevoda, (2) sezonska voda in (3) stalna voda med prvim in zadnjim letom v 36-letnem časovnem obdobju."]},"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)":{"msgid":"# Welcome To EO Browser!\n\nA complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s \narchive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, \nMODIS, Proba-V and GIBS products in one place.\n\n[EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/) \n[EO Browser user guide](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)","msgstr":["# Dobrodošli v EO Browserju!\n\nCeloten arhiv satelitov Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA\narhiv satelitov Landsat 5, 7 in 8, globalni arhiv satelitov Landsat 8, Envisat Meris,\nMODIS, Proba-V in GIBS na enem mestu.\n\n[Predstavitvena stran brskalnika EO Browser](https://www.sentinel-hub.com/explore/eobrowser/) \n[Uporabniški priročnik za brskalnik EO Browser](https://www.sentinel-hub.com/explore/eobrowser/user-guide/)"]},"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.":{"msgid":"#### Quick overview of EO Browser features\n\nEO Browser combines a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, MODIS, Proba-V and GIBS products in one place and makes it possible to browse and compare full resolution images from those sources. You simply go to your area of interest, select data sources, time range and cloud coverage, and inspect the resulting data.\n\nYou can continue the tutorial by clicking on the \"Next\" button or you can close it. By clicking the info icon in the top right corner you can always resume the tutorial in case you closed it by mistake or because you wanted to try things.","msgstr":["#### Kratek pregled funkcij brskalnika EO\n\nEO Browser na enem mestu združuje celoten arhiv satelitov Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, arhiv ESA Landsat 5, 7 in 8, globalni arhiv Landsat 8, Envisat Meris, MODIS, Proba-V in GIBS ter omogoča pregledovanje in primerjavo slik polne ločljivosti iz teh virov. Preprosto se odpravite na območje, ki vas zanima, izberete vire podatkov, časovno območje in pokritost z oblaki ter preglejte dobljene podatke.\n\nVodič lahko nadaljujete s klikom na gumb \"Naprej\" ali pa ga zaprete. S klikom na ikono info v zgornjem desnem kotu lahko vedno nadaljujete z vodičem, če ste ga zaprli po pomoti ali ker ste želeli nekaj preizkusiti."]},"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".":{"msgid":"**Logged-in users** can use their custom themes, save and load pins, create a pin story, measure distances, create a\ntimelapse and use the advanced image download.\n\nTo create a free account simply click [here]\nor within the app on **Login** and then \"Sign Up\".","msgstr":["**Prijavljeni uporabniki** lahko uporabljajo teme po meri, shranjujejo in nalagajo oznake, z oznakami ustvarijo zgodbo, merijo razdalje, ustvarijo\nčasovno zaporedje in uporabljajo napredni prenos posnetkov.\n\nČe želite ustvariti brezplačen račun, kliknite [tukaj]\nali v aplikaciji kliknite na **Prijava** in nato \"Prijavite se\"."]},"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.":{"msgid":"In the **Discover** tab you can:\n\n- Select a **Theme.**\n- **Search** for data.\n- View theme **Highlights.**\n\nThe **Theme** dropdown offers you different preconfigured themes as well as your own custom configured instances if you are Logged-in. To create an instance, click on\nthe settings icon and log in with the same credentials as you used for EO Browser.\n\nUnder **Search** you can set search criteria:\n - Choose from which satellites you want to receive the data by selecting checkboxes.\n - Select additional options where applicable, for example, cloud coverage with the slider.\n - Select the time range by either typing the date or select the date from the calendar.\n\nYou can read explanations of satellites by clicking on the question icon\n next to the data source name.\n\nOnce you hit Search you get a list of results. Each result is presented \nwith a preview image, and relevant data specific to the datasource. For some data sources, the link icon is also visible for each result.\nClicking on it reveals direct links to the raw image of the result on EO Cloud or SciHub. Clicking on the Visualize button will open the **Visualize** tab for the selected result.\n\nUnder **Highlight**, you find preselected interesting locations connected to the selected theme.","msgstr":["V zavihku **Odkrij** lahko:\n\n- Izberete **temo.**\n- **Iščete** podatke.\n- Raziščete **Poudarke** za izbrano temo.\n\nV spustnem oknu **Teme** so na voljo različne vnaprej konfigurirane teme in lastne nastavljene konfiguracije, če ste prijavljeni. Če želite ustvariti lastno konfiguracijo, kliknite na\nikono za nastavitve in se prijavite z uporabniškim računom, ki ga uporabljate za EO Browser.\n\nV razdelku **Poišči** lahko nastavite kriterije za iskanje:\n - Izberite, s katerih satelitov želite prejemati podatke, tako da izberete potrditvena polja.\n - Po potrebi izberite dodatne možnosti, na primer z drsnikom pokritost z oblaki.\n - Izberite časovno območje tako, da vpišete datum ali ga izberete s koledarja.\n\nOpise satelitov in podatkovnih virov lahko preberete s klikom na ikono\n poleg imena vira podatkov.\n\nKo pritisnete gumb Išči, se prikaže seznam rezultatov. Vsak rezultat je predstavljen \ns predogledno sliko in ustreznimi podatki, značilnimi za podatkovni vir. Pri nekaterih podatkovnih virih je za vsak rezultat vidna tudi ikona povezave .\nS klikom nanjo se prikažejo neposredne povezave do neobdelane slike rezultata v EO Cloud ali SciHub. S klikom na gumb Prikaži se odpre zavihek **Prikaži** za izbrani rezultat.\n\nV razdelku **Poudarki** so na voljo vnaprej izbrane zanimive lokacije, povezane z izbrano temo."]},"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .":{"msgid":"In the Visualize tab you can select different pre-installed or custom spectral band combinations to visualise data for the selected result.\n\nSome of the common options:\n- **True Color** - Visual interpretation of land cover.\n- **False Color** - Visual interpretation of vegetation.\n- **NDVI** - Vegetation index.\n- **Moisture index** - Moisture index\n- **SWIR** - Shortwave-infrared index.\n- **NDWI** - Normalized Difference Water Index.\n- **NDSI** - Normalized Difference Snow Index.\n\nMost visualizations are given a description and a legend, which you can view by clicking on the expand\nicon .\n \nFor most data sources the **Custom Script** option is available. Click on it to select custom\nband combinations, index combinations or write your own classification script for the visualisation of data. You can also\nuse custom scripts, which are stored elsewhere, either on Google drive, GitHub or in our [Custom script repository](https://custom-scripts.sentinel-hub.com/). \nPaste the URL of the script into a text box in the advanced script editing panel and click Refresh.\n \nYou can change the date directly in the Visualize tab, without going back to the **Discover** tab. Type in or select it from the calendar .\n\nAbove the visualizations you have on line of additional tools. Note that their avalibilty depends on the data source.\n- **Pin layer** to save it in the application for future use - by clicking on the pin icon .\n- Select **advanced options** like the sampling method or apply different **effects** such as contrast (gain) and luminance (gamma) - by clicking on the effect sliders icon .\n- Add a layer to the **Compare** tab for later comparison - by clicking on the compare icon .\n- **Zoom** to the centre of the tile - by clicking on the crosshair .\n- Toggle **layer visibility** - by clicking on the visibility icon .\n- **Share** your visualization on social media - by clicking on the share icon .","msgstr":["V zavihku Prikaži lahko izberete različne vnaprej pripravljene kombinacije spektralnih kanalov ali kombinacije po meri za vizualizacijo podatkov za izbrani rezultat.\n\nNekatere pogoste možnosti:\n- **Naravne barve** - vizualna interpretacija pokritosti tal.\n- **Umetne barve** - vizualna interpretacija vegetacije.\n- **NDVI** - normiran diferencialni indeks vegetacije.\n- **Indeks vlažnosti** - indeks vlažnosti\n- **SWIR** - kratkovalovni infrardeči spekter\n- **NDWI** - Normalizirani diferencialni vodni indeks\n- **NDSI** - Normalizirani diferencialni snežni indeks\n\nVečina vizualizacij je opremljena z opisom in legendo, ki si ju lahko ogledate s klikom na razširitev\n .\n \nZa večino podatkovnih virov je na voljo možnost skripte **Po meri**. Kliknite jo, če želite izbrati poljuben\nkombinacije kanalov, kombinacije indeksov ali napišite svojo lastno skripto za vizualizacijo podatkov. Prav tako lahko\nuporabite skripte po meri, ki so shranjene drugje, bodisi na Google Drive, v GitHub ali v našem [skladišču skript po meri](https://custom-scripts.sentinel-hub.com/) \nURL naslov skripte prilepite v besedilno polje na plošči za napredno urejanje skript in kliknite Osveži.\n \nDatum lahko spremenite neposredno v zavihku Prikaži, ne da bi se vrnili v zavihek **Odkrij**. Vnesite ga ali izberite s koledarja .\n\nNad vizualizacijami imate na voljo vrsto dodatnih orodij. Upoštevajte, da je njihova uporabnost odvisna od vira podatkov.\n- **Pripnite sloj**, da ga shranite v aplikacijo za prihodnjo uporabo - s klikom na ikono priponke .\n- Izberite **napredne možnosti**, kot je metoda vzorčenja, ali uporabite različne **učinke**, kot sta kontrast (ojačenje) in svetilnost (gama) - s klikom na ikono drsnikov učinkov .\n- Dodajte plast v zavihek **Primerjaj** za poznejšo primerjavo - s klikom na ikono za primerjavo .\n- **Povečajte** na sredino ploščice - s klikom na križec .\n- Preklopite **vidnost plasti** - s klikom na ikono vidnosti .\n- Vizualizacijo **delite** v družabnih medijih - s klikom na ikono za deljenje ."]},"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.":{"msgid":"In the **Compare** tab you will find all visualizations that you added via to **Compare**. \n\nThere are two modes:\n - **Opacity** (Draw opacity slider left or right to fade between compared images)\n - **Split** (Draw split slider left or right to set the boundary between compared images)\n\nYou can add all pins to the compare panel using **Add all pins** or remove all visualizations\nfrom the **Compare** tab with the **Remove all** button.","msgstr":["V zavihku **Primerjaj** boste našli vse vizualizacije, ki ste jih prek spletne strani dodali v **Primerjaj** \n\nNa voljo sta dva načina:\n - **Prosojnost** (potegnite drsnik za prosojnost v levo ali desno, da se slike med seboj prelivajo)\n - **Delitev** (narišite drsnik za razdelitev v levo ali desno, da določite mejo med primerjanimi slikami)\n\nNa primerjalno ploščo lahko s spletno stranjo dodate **vse oznake** ali odstranite vse vizualizacije\nz zavihka **Primerjaj** z gumbom **Odstrani vse**."]},"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.":{"msgid":"The **Pins** tab contains your pinned (favourite/saved) items. Pinned items contain information\nabout location, data source and its specific layer, zoom level and time.\n\nFor each pin you have several options on how to interact with a single pin:\n\n- Change **order** - by clicking on the move icon\n\n \n \n \nin the top left corner of the pin and dragging the pin up or down the list.\n- **Rename** - by clicking on the pencil icon next to the pin's name.\n- Add to the **Compare** tab - by clicking on the compare icon \n- Enter a **description** - by clicking on the expand icon .\n- **Remove** - by clicking the remove icon .\n- **Zoom** to the pin's location - by clicking on the Lat/Lon.\n\nIn the line above all pins you have different options that apply for all pins:\n- Create your own story from pins - by clicking on **Story**.\n- Share your pins with others via a link - by clicking on **Share**.\n- Export pins as a JSON file - by clicking on **Export**.\n- Import pins from a JSON file - by clicking on **Import**.\n- Delete all pins - by clicking on **Clear**.","msgstr":["V zavihku **Oznake** so shranjeni označeni (priljubljeni/shranjeni) elementi. Elementi vsebujejo informacije\no lokaciji, viru podatkov in uporabljeni vizualizaciji, stopnji povečave in času.\n\nZa vsako oznako je na voljo več možnosti :\n\n- Spremenite **red** - s klikom na ikono premikanja\n\n \n \n \nv zgornjem levem kotu oznake in jo povlecite po seznamu navzgor ali navzdol.\n- **Preimenovanje** - s klikom na ikono svinčnika poleg imena priponke.\n- Dodajanje na zavihek **Primerjaj** - s klikom na ikono za primerjavo \n- Vnesite **opis** - s klikom na ikono za razširitev .\n- **Odstrani** - s klikom na ikono za odstranitev .\n- **Zoom** na lokacijo oznake - s klikom na povezavo Lat/Lon.\n\nV vrstici nad oznakami so na voljo različne možnosti, ki veljajo za vse oznake:\n- Ustvarite zgodbo iz oznak - s klikom na **Zgodba**.\n- Delite svoje oznake z drugimi prek povezave - s klikom na **Deli**\n- Izvozite oznake kot datoteko JSON - s klikom na **Izvozi**.\n- Uvozi oznake iz datoteke JSON - s klikom na **Uvozi**.\n- Izbrišite vse oznake - s klikom na **Počisti**."]},"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.":{"msgid":"Search for a location either by scrolling the map with a mouse or enter the location in the search\nfield.","msgstr":["Lokacijo poiščite tako, da zemljevid premikate z miško ali vnesete lokacijo v iskalnik in izberete najbolj ustrezen zadetek."]},"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.":{"msgid":"Here you can select which base layer and overlays (roads, borders, labels) are shown on the map.","msgstr":["Tu lahko izberete, kateri osnovni sloj in prekrivni sloji (ceste, meje, oznake) bodo prikazani na zemljevidu."]},"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).":{"msgid":"Here you can switch between the **normal** and the **education** mode. The **education** mode offers you a slightly simplified version of the app.\nIt can also be accessed directly via its [dedicated URL](https://apps.sentinel-hub.com/eo-browser-education/).","msgstr":["Tu lahko preklapljate med **normalnim** in **izobraževalnim** načinom. Način **izobraževanja** ponuja nekoliko poenostavljeno različico aplikacije.\nDo nje lahko dostopate tudi neposredno prek [namenskega URL] (https://apps.sentinel-hub.com/eo-browser-education/)."]},"You can view the tutorial anytime by clicking on this info icon\n\n\n.":{"msgid":"You can view the tutorial anytime by clicking on this info icon\n\n\n.","msgstr":["Vodič si lahko kadar koli ogledate s klikom na to informacijsko ikono\n\n\n."]},"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.":{"msgid":"This tool allows you to draw a polygon on the map and display the polygon's size.\n\nAll layers that return a single value (such as NDVI, Moisture index, NDWI,…) support viewing the\nindex for the selected area over time. Clicking the chart icon will\ndisplay the charts. You can remove the polygon by clicking the remove icon .\n\nYou can also upload a KML/KMZ, GPX or GEOJSON/JSON file with a polygon geometry.\n\nThe two sheets icon lets you copy the polygon coordinates as a GEOJSON, the crosshair \ncentres the map to the drawn polygon.\n\nExported images will be cropped to the area of interest in analytical downloads.","msgstr":["To orodje omogoča risanje poligona na karti in prikaz velikosti poligona.\n\nVsi sloji, ki vračajo eno samo vrednost (kot so NDVI, indeks vlage, NDWI, ...), podpirajo prikaz\nindeksa za izbrano območje skozi čas. S klikom na ikono diagrama boste\nprikazali grafikon. Poligon lahko odstranite s klikom na ikono za odstranitev .\n\nPrav tako lahko naložite datoteko KML/KMZ, GPX ali GEOJSON/JSON z geometrijo poligona.\n\nZ ikono dveh listov lahko kopirate koordinate poligona kot datoteko GEOJSON, s križcem \nusmeri karto na narisani poligon.\n\nIzvožene slike bodo pri analitičnih prenosih obrezane na območje zanimanja."]},"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n":{"msgid":"With this tool, you can mark a point on the map.\n\nYou can also view statistical data for some layers by clicking on the chart icon\n. \nYou can remove the mark by clicking the remove icon .\n","msgstr":["S tem orodjem lahko označite točko na karti.\n\nS klikom na ikono grafikona si lahko ogledate tudi statistične podatke za nekatere sloje.\n\nOznako lahko odstranite s klikom na ikono za odstranitev .\n"]},"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .":{"msgid":"With this tool, you can measure distances and areas on the map.\n\nEvery mouse click creates a new point on the path. To stop adding points, press Esc
key\nor double click on the map. \nYou can remove the measurement by clicking the remove icon .","msgstr":["S tem orodjem lahko merite razdalje in območja na karti.\n\nVsak klik miške ustvari novo točko na poti. Če želite ustaviti dodajanje točk, pritisnite tipko Esc
\nali dvakrat kliknite na karto. \nMeritev lahko odstranite s klikom na ikono za odstranitev ."]},"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.":{"msgid":"With this tool, you can download an image of visualized data for the displayed location. You can choose\nto show captions and you can add your own description.\nBy enabling Analytical mode, you can choose between various image formats, image resolutions and\ncoordinate systems. You can also select multiple layers and download them as a .zip
file.\n\nClick the download button\nDownload\nand your image(s) will begin to download. The process can take a few seconds, depending on the selected\nresolution and the number of selected layers.\n\nBefore downloading, you can define an area of interest (AOI) by clicking on the Area selection tool\nicon. Your data will be clipped to match this area.","msgstr":["S tem orodjem lahko prenesete sliko vizualiziranih podatkov za prikazano lokacijo. Izberete lahko\nprikaz podnapisov in lahko dodate svoj opis.\nČe omogočite analitični način, lahko izbirate med različnimi oblikami slik, ločljivostmi slik in\nkoordinatnimi sistemi. Izberete lahko tudi več slojev in jih prenesete kot datoteko .zip.
\n\nKliknite gumb za prenos\n Prenos\nin slika(-e) se bo(-jo) začela(-e) prenašati. Postopek lahko traja nekaj sekund, odvisno od izbrane\nločljivosti in števila izbranih slojev.\n\nPred prenosom lahko določite interesno območje (AOI) s klikom na orodje za izbiro območja\nikono . Vaši podatki bodo obrezani tako, da bodo ustrezali temu območju."]},"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.":{"msgid":"With this tool, you can create a timelapse animation of the visualised layer and displayed location.\n\nFirst, choose a time range. You can refine your search results further by filtering them by months\n(filter by months checkbox) and/or selecting one image per defined period (orbit, day, week, month,\nyear).\n\nThen press Search and select your images.\nYou can select all by checking the checkbox or filter the images by cloud coverage by moving the slider. Or you can pick images one by\none by scrolling through the list and selecting them. Via the **Borders** checkbox, you can enable/disable the borders on your image.\n\nYou can preview the timelapse by pressing the play button on the bottom. You can also set the speed\n(frames per second).\n\nWhen you are satisfied with the result, click the download button and the timelapse will be\ndownloaded as a .gif
file.","msgstr":["S tem orodjem lahko ustvarite časovno animacijo vizualiziranega sloja in prikazane lokacije.\n\nNajprej izberite časovno obdobje. Rezultate iskanja lahko dodatno opredelite tako, da jih filtrirate po mesecih\n(potrditveno polje Filtriraj po mesecih) in/ali izbiro enega posnetka na določeno obdobje (orbito, dan, teden, mesec,\nleto).\n\nNato pritisnite Search (Iskanje) in izberite svoje slike.\nIzberete lahko vse, tako da označite potrditveno polje, ali filtrirate posnetke glede na pokritost z oblaki s premikanjem drsnika. Lahko pa izberete posnetke\ntako, da se pomikate po seznamu in jih izberete. S potrditvenim poljem **Robovi** lahko omogočite/izključite robove na sliki.\n\nČasovno zaporedje si lahko ogledate s pritiskom na gumb za predvajanje na dnu slike. Nastavite lahko tudi hitrost\n(število sličic na sekundo).\n\nKo ste zadovoljni z rezultatom, kliknite gumb za prenos in časovno zaporedje bo\nprenešeno kot datoteka .gif
."]},"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.":{"msgid":"You have reached the end of the tutorial. If you have any other questions, feel free to ask us on [the forum](https://forum.sentinel-hub.com/)\nor contact us [via email](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nIf you want to view the tutorial in the future you can always view it by clicking the info icon\n\n\n\nin the top right corner.","msgstr":["Dosegli ste konec vadnice. Če imate še kakšno vprašanje, ga lahko zastavite na [forumu](https://forum.sentinel-hub.com/)\nali se z nami povežite [prek e-pošte](mailto:info@sentinel-hub.com?Subject=EO%20Browser%20Feedback).\n\n\nČe si boste še kdaj želeli ogledati ta vodič, ga lahko vedno prikličete s \nklikom na ikono \n\n\n v zgornjem desnem kotu."]},"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)":{"msgid":"#### Quick overview of EO Browser features\n\nIf you have a small screen, please go [here](https://www.sentinel-hub.com/explore/eobrowser/user-guide/) to view our user guide.\n\nYou can always view this info again by clicking the info icon\n\n\n\nin the top right corner.\n\n#### Other resources\n- [EO Browser presentation page](https://www.sentinel-hub.com/explore/eobrowser/)\n- [EO Browser Summer 2018 updates - video](https://www.youtube.com/embed/m3pron0C0kE)","msgstr":["#### Kratek pregled funkcij EO Broserje\n\nČe imate majhen zaslon, si [tukaj] (https://www.sentinel-hub.com/explore/eobrowser/user-guide/) oglejte uporabniški priročnik.\n\nTe informacije si lahko vedno znova ogledate s klikom na ikono info\n\n\n\nv zgornjem desnem kotu.\n\n#### Drugi viri\n- [Predstavitvena stran EO Browserja](https://www.sentinel-hub.com/explore/eobrowser/)\n- [Posodobitve EO Browserja poletje 2018 - videoposnetek] (https://www.youtube.com/embed/m3pron0C0kE)"]},"What Is EO Browser?":{"msgid":"What Is EO Browser?","msgstr":["Kaj je EO Browser?"]},"User Account":{"msgid":"User Account","msgstr":["Uporabniški račun"]},"Discover Tab":{"msgid":"Discover Tab","msgstr":["Zavihek Odkrij"]},"Visualize Tab":{"msgid":"Visualize Tab","msgstr":["Zavihek Prikaži"]},"Compare Tab":{"msgid":"Compare Tab","msgstr":["Zavihek Primerjaj"]},"Pins Tab":{"msgid":"Pins Tab","msgstr":["Zavihek Oznake"]},"Search Places":{"msgid":"Search Places","msgstr":["Iskanje krajev"]},"Layers And Overlays":{"msgid":"Layers And Overlays","msgstr":["Sloji in prekrivanja"]},"Education Mode":{"msgid":"Education Mode","msgstr":["Način izobraževanja"]},"Information And Tutorial":{"msgid":"Information And Tutorial","msgstr":["Informacije in vodič"]},"Draw Area Of Interest":{"msgid":"Draw Area Of Interest","msgstr":["Narišite območje zanimanja"]},"Mark Point Of Interest":{"msgid":"Mark Point Of Interest","msgstr":["Označite točko zanimanja"]},"Measure Distances":{"msgid":"Measure Distances","msgstr":["Merjenje razdalj"]},"Download Image":{"msgid":"Download Image","msgstr":["Prenos posnetka"]},"Create Timelapse Animation":{"msgid":"Create Timelapse Animation","msgstr":["Ustvarjanje animacije s časovnim zaporedjem"]},"Happy Browsing!":{"msgid":"Happy Browsing!","msgstr":["Srečno brskanje!"]},"Welcome To EO Browser!":{"msgid":"Welcome To EO Browser!","msgstr":["Dobrodošli v EO Browserju!"]},"Band 1 - Yellow substance and detrital pigments - 412.5 nm":{"msgid":"Band 1 - Yellow substance and detrital pigments - 412.5 nm","msgstr":["Kanal 1 - rumena snov in detritični pigmenti - 412,5 nm"]},"Band 2 - Chlorophyll absorption maximum - 442 nm":{"msgid":"Band 2 - Chlorophyll absorption maximum - 442 nm","msgstr":["Kanal 2 - absorpcijski maksimum klorofila - 442 nm"]},"Band 3 - Chlorophyll and other pigments - 490 nm":{"msgid":"Band 3 - Chlorophyll and other pigments - 490 nm","msgstr":["Kanal 3 - klorofil in drugi pigmenti - 490 nm"]},"Band 4 - Suspended sediment, red tides - 510 nm":{"msgid":"Band 4 - Suspended sediment, red tides - 510 nm","msgstr":["Kanal 4 - suspendirane usedline, rdeče plime - 510 nm"]},"Band 5 - Chlorophyll absorption minimum - 560 nm":{"msgid":"Band 5 - Chlorophyll absorption minimum - 560 nm","msgstr":["Kanal 5 - absorpcijski minimum klorofila - 560 nm"]},"Band 6 - Suspended sediment - 620 nm":{"msgid":"Band 6 - Suspended sediment - 620 nm","msgstr":["Kanal 6 - suspendirana usedlina - 620 nm"]},"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm":{"msgid":"Band 7 - Chlorophyll absorption & fluo. reference - 665 nm","msgstr":["Kanal 7 - absorpcija klorofila in fluo. referenca - 665 nm"]},"Band 8 - Chlorophyll fluorescence peak - 681 nm":{"msgid":"Band 8 - Chlorophyll fluorescence peak - 681 nm","msgstr":["Kanal 8 - vrh fluorescence klorofila - 681 nm"]},"Band 9 - Fluo. reference, atmosphere corrections - 709 nm":{"msgid":"Band 9 - Fluo. reference, atmosphere corrections - 709 nm","msgstr":["Kanal 9 - referenčni fluo, popravki za atmosfero - 709 nm"]},"Band 10 - Vegetation, cloud - 753 nm":{"msgid":"Band 10 - Vegetation, cloud - 753 nm","msgstr":["Kanal 10 - vegetacija, oblaki - 753 nm"]},"Band 11 - O2 R- branch absorption band - 761 nm":{"msgid":"Band 11 - O2 R- branch absorption band - 761 nm","msgstr":["Kanal 11 - absorpcijski pas R-veje O2 - 761 nm"]},"Band 12 - Atmosphere corrections - 779 nm":{"msgid":"Band 12 - Atmosphere corrections - 779 nm","msgstr":["Kanal 12 - korekcije atmosfere - 779 nm"]},"Band 13 - Vegetation, water vapour reference - 865 nm":{"msgid":"Band 13 - Vegetation, water vapour reference - 865 nm","msgstr":["Kanal 13 - vegetacija, vodna para referenca - 865 nm"]},"Band 14 - Atmosphere corrections - 885 nm":{"msgid":"Band 14 - Atmosphere corrections - 885 nm","msgstr":["Kanal 14 - korekcije atmosfere - 885 nm"]},"Band 15 - Water vapour, land - 900 nm":{"msgid":"Band 15 - Water vapour, land - 900 nm","msgstr":["Kanal 15 - vodna para, kopno - 900 nm"]},"Landsat 5 (ESA archive)":{"msgid":"Landsat 5 (ESA archive)","msgstr":["Landsat 5 (arhiv ESA)"]},"Landsat 7 (ESA archive)":{"msgid":"Landsat 7 (ESA archive)","msgstr":["Landsat 7 (arhiv ESA)"]},"Landsat 8 (ESA archive)":{"msgid":"Landsat 8 (ESA archive)","msgstr":["Landsat 8 (arhiv ESA)"]},"Landsat 8 (USGS archive)":{"msgid":"Landsat 8 (USGS archive)","msgstr":["Landsat 8 (arhiv USGS)"]},"Landsat 8 L1":{"msgid":"Landsat 8 L1","msgstr":["Landsat 8 L1"]},"Landsat 8 L2":{"msgid":"Landsat 8 L2","msgstr":["Landsat 8 L2"]},"Red band":{"msgid":"Red band","msgstr":["Rdeči kanal"]},"841 - 876 nm (NIR)":{"msgid":"841 - 876 nm (NIR)","msgstr":["841-876 nm (NIR)"]},"Blue band":{"msgid":"Blue band","msgstr":["Modri kanal"]},"Green band":{"msgid":"Green band","msgstr":["Zeleni kanal"]},"1230 - 1250 nm":{"msgid":"1230 - 1250 nm","msgstr":["1230 - 1250 nm"]},"1628 - 1652 nm":{"msgid":"1628 - 1652 nm","msgstr":["1628 - 1652 nm"]},"2105 - 2155 nm":{"msgid":"2105 - 2155 nm","msgstr":["2105 - 2155 nm"]},"Band 1 - Coastal aerosol - 443 nm":{"msgid":"Band 1 - Coastal aerosol - 443 nm","msgstr":["Kanal 1 - obalni aerosol - 443 nm"]},"Band 2 - Blue - 490 nm":{"msgid":"Band 2 - Blue - 490 nm","msgstr":["Kanal 2 - modra barva - 490 nm"]},"Band 3 - Green - 560 nm":{"msgid":"Band 3 - Green - 560 nm","msgstr":["Kanal 3 - zelena - 560 nm"]},"Band 4 - Red - 665 nm":{"msgid":"Band 4 - Red - 665 nm","msgstr":["Kanal 4 - rdeča barva - 665 nm"]},"Band 5 - Vegetation Red Edge - 705 nm":{"msgid":"Band 5 - Vegetation Red Edge - 705 nm","msgstr":["Kanal 5 - vegetacija Rdeči rob - 705 nm"]},"Band 6 - Vegetation Red Edge - 740 nm":{"msgid":"Band 6 - Vegetation Red Edge - 740 nm","msgstr":["Kanal 6 - vegetacija Rdeči rob - 740 nm"]},"Band 7 - Vegetation Red Edge - 783 nm":{"msgid":"Band 7 - Vegetation Red Edge - 783 nm","msgstr":["Kanal 7 - rdeči rob vegetacije - 783 nm"]},"Band 8 - NIR - 842 nm":{"msgid":"Band 8 - NIR - 842 nm","msgstr":["Kanal 8 - NIR - 842 nm"]},"Band 9 - Water vapour - 945 nm":{"msgid":"Band 9 - Water vapour - 945 nm","msgstr":["Kanal 9 - vodna para - 945 nm"]},"Band 10 - SWIR - Cirrus - 1375 nm":{"msgid":"Band 10 - SWIR - Cirrus - 1375 nm","msgstr":["Kanal 10 - SWIR - Cirrus - 1375 nm"]},"Band 11 - SWIR - 1610 nm":{"msgid":"Band 11 - SWIR - 1610 nm","msgstr":["Kanal 11 - SWIR - 1610 nm"]},"Band 12 - SWIR - 2190 nm":{"msgid":"Band 12 - SWIR - 2190 nm","msgstr":["Kanal 12 - SWIR - 2190 nm"]},"Band 8A - Vegetation Red Edge - 865 nm":{"msgid":"Band 8A - Vegetation Red Edge - 865 nm","msgstr":["Kanal 8A - rdeči rob vegetacije - 865 nm"]},"L2A (atmospherically corrected)":{"msgid":"L2A (atmospherically corrected)","msgstr":["L2A (atmosfersko popravjeno)"]},"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm":{"msgid":"Band 1 - Aerosol correction, improved water constituent retrieval - 400 nm","msgstr":["Kanal 1 - korekcija aerosolov, izboljšano pridobivanje vodnih sestavin - 400 nm"]},"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm":{"msgid":"Band 2 - Yellow substance and detrital pigments (turbidity)-412 nm","msgstr":["Kanal 2 - rumena snov in detritični pigmenti (motnost)-412 nm"]},"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm":{"msgid":"Band 3 - Chl absorption max., biogeochemistry, vegetation - 442.5 nm","msgstr":["Kanal 3 - največja absorpcija Chl, biogeokemija, vegetacija - 442,5 nm"]},"Band 4 - High Chl, other pigments - 490 nm":{"msgid":"Band 4 - High Chl, other pigments - 490 nm","msgstr":["Kanal 4 - visoka vsebnost Chl, drugi pigmenti - 490 nm"]},"Band 5 - Chl, sediment, turbidity, red tide - 510 nm":{"msgid":"Band 5 - Chl, sediment, turbidity, red tide - 510 nm","msgstr":["Kanal 5 - Chl, sediment, motnost, rdeči plima - 510 nm"]},"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm":{"msgid":"Band 6 - Chlorophyll reference (Chl minimum) - 560 nm","msgstr":["Kanal 6 - referenčni klorofil (Chl minimum) - 560 nm"]},"Band 7 - Sediment loading - 620 nm":{"msgid":"Band 7 - Sediment loading - 620 nm","msgstr":["Kanal 7 - obremenitev s sedimenti - 620 nm"]},"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm":{"msgid":"Band 8 - Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation - 665 nm","msgstr":["Kanal 8 - Chl (2. abs. max. Chl), sediment, rumena snov/rastlinstvo - 665 nm"]},"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm":{"msgid":"Band 9 - For improved fluorescence retrieval and to better account for spectral smile together with the band 8 (665 nm) and band 10 (681.25 nm) - 673.75 nm","msgstr":["Kanal 9 - za boljše pridobivanje fluorescence, boljše upoštevanje skupaj s pasovi 8 (665 nm) in 10 (681.25 nm) - 673,75 nm "]},"Band 10 - Chl fluorescence peak, red edge - 681.25 nm":{"msgid":"Band 10 - Chl fluorescence peak, red edge - 681.25 nm","msgstr":["Kanal 10 - vrh fluorescence Chl, rdeči rob - 681,25 nm"]},"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm":{"msgid":"Band 11 - Chl fluorescence baseline, red edge transition - 708.75 nm","msgstr":["Kanal 11 - osnovna fluorescenca Chl, prehod rdečega roba - 708,75 nm"]},"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm":{"msgid":"Band 12 - O2 absorption/clouds, vegetation - 753.75 nm","msgstr":["Kanal 12 - absorpcija O2/oblaki, vegetacija - 753,75 nm"]},"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm":{"msgid":"Band 13 - O2 absorption band/aerosol corr. - 761.25 nm","msgstr":["Kanal 13 - absorpcijski pas O2/aerosol corr. - 761,25 nm"]},"Band 14 - Atmospheric correction - 764.375 nm":{"msgid":"Band 14 - Atmospheric correction - 764.375 nm","msgstr":["Kanal 14 - atmosferski popravek - 764,375 nm"]},"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm":{"msgid":"Band 15 - O2A used for cloud top pressure, fluorescence over land - 767.5 nm","msgstr":["Kanal 15 - O2A se uporablja za merjenje pritiska nad oblaki, fluorescenca nad kopnim - 767,5 nm"]},"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm":{"msgid":"Band 16 - Atmos. corr./aerosol corr. - 778.75 nm","msgstr":["Kanal 16 - Atmos. kor./aerosol kor. - 778,75 nm"]},"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm":{"msgid":"Band 17 - Atmos. corr./aerosol corr., clouds, pixel co-registration - 865 nm","msgstr":["Kanal 17 - korektura ozračja/korektura aerosolov, oblaki, koregistracija pikslov - 865 nm"]},"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm":{"msgid":"Band 18 - Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring - 885 nm","msgstr":["Kanal 18 - referenčni pas absorpcije vodne pare. Skupni referenčni pas z instrumentom SLSTR. Spremljanje vegetacije - 885 nm"]},"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm":{"msgid":"Band 19 - Water vapour absorption/vegetation monitoring (max. reflectance) - 900 nm","msgstr":["Kanal 19 - absorpcija vodne pare/nadzor vegetacije (največja odbojnost) - 900 nm"]},"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm":{"msgid":"Band 20 - Water vapour absorption, atmos./aerosol corr. - 940 nm","msgstr":["Kanal 20 - absorpcija vodne pare, atmos./aerosol kor. - 940 nm"]},"Band 21 - Atmos./aerosol corr. - 1020 nm":{"msgid":"Band 21 - Atmos./aerosol corr. - 1020 nm","msgstr":["Kanal 21 - Atmos. kor./aerosol kor. - 1020 nm"]},"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm":{"msgid":"Band F1 - Thermal IR fire emission - Active fire - 3742.00 nm","msgstr":["Kanal F1 - toplotna emisija IR ognja - Aktivni ogenj - 3742,00 nm"]},"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm":{"msgid":"Band F2 - Thermal IR fire emission - Active fire - 10854.00 nm","msgstr":["Kanal F2 - toplotna emisija IR ognja - Aktivni ogenj - 10854,00 nm"]},"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm":{"msgid":"Band S1 - VNIR - Cloud screening, vegetation monitoring, aerosol - 554.27 nm","msgstr":["Kanal S1 - VNIR - pregledovanje oblakov, spremljanje vegetacije, aerosol - 554,27 nm"]},"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm":{"msgid":"Band S2 - VNIR - NDVI, vegetation monitoring, aerosol - 659.47 nm","msgstr":["Kanal S2 - VNIR - NDVI, spremljanje vegetacije, aerosol - 659,47 nm"]},"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm":{"msgid":"Band S3 - VNIR - NDVI, cloud flagging, pixel co-registration - 868.00 nm","msgstr":["Kanal S3 - VNIR - NDVI, označevanje oblakov, koregistracija pikslov - 868,00 nm"]},"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm":{"msgid":"Band S4 - SWIR - Cirrus detection over land - 1374.80 nm","msgstr":["Kanal S4 - SWIR - Zaznavanje cirrusa nad kopnim - 1374,80 nm"]},"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm":{"msgid":"Band S5 - SWIR - Cloud clearing, ice, snow, vegetation monitoring - 1613.40 nm","msgstr":["Kanal S5 - SWIR - čiščenje oblakov, led, sneg, spremljanje vegetacije - 1613,40 nm"]},"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm":{"msgid":"Band S6 - SWIR - Vegetation state and cloud clearing - 2255.70 nm","msgstr":["Kanal S6 - SWIR - stanje vegetacije in razkrajanje oblakov - 2255,70 nm"]},"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm":{"msgid":"Band S7 - Thermal IR Ambient - SST, LST, active fire - 3742.00 nm","msgstr":["Kanal S7 - termični IR ambient - SST, LST, aktivni požar - 3742,00 nm"]},"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm":{"msgid":"Band S8 - Thermal IR Ambient - SST, LST, active fire - 10854.00 nm","msgstr":["Kanal S8 - termični IR ambient - SST, LST, aktivni požar - 10854,00 nm"]},"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm":{"msgid":"Band S9 - Thermal IR Ambient - SST, LST - 12022.50 nm","msgstr":["Kanal S9 - termični IR-območje - SST, LST - 12022,50 nm"]},"Based on the combination of bands 4, 3, 2":{"msgid":"Based on the combination of bands 4, 3, 2","msgstr":["Na podlagi kombinacije kanalov 4, 3, 2"]},"Based on the combination of bands (B04-B03)/(B04+B03)":{"msgid":"Based on the combination of bands (B04-B03)/(B04+B03)","msgstr":["Na podlagi kombinacije kanalov (B04-B03)/(B04+B03)"]},"Based on the combination of bands 5, 4, 3":{"msgid":"Based on the combination of bands 5, 4, 3","msgstr":["Na podlagi kombinacije kanalov 5, 4, 3"]},"Based on true color bands 4, 3, 2 and a pan band 8":{"msgid":"Based on true color bands 4, 3, 2 and a pan band 8","msgstr":["Na podlagi barvnih kanalov 4, 3, 2 in pankromatskega kanala 8"]},"Based on the combination of bands (B05-B04)/(B05+B04)":{"msgid":"Based on the combination of bands (B05-B04)/(B05+B04)","msgstr":["Na podlagi kombinacije kanalov (B05-B04)/(B05+B04)"]},"VV - linear gamma0 - orthorectified":{"msgid":"VV - linear gamma0 - orthorectified","msgstr":["VV - linearno gama0 - ortorektificirano"]},"VV - linear gamma0 - non-orthorectified":{"msgid":"VV - linear gamma0 - non-orthorectified","msgstr":["VV - linearni gama0 - neortorektificiran"]},"VH - linear gamma0 - orthorectified":{"msgid":"VH - linear gamma0 - orthorectified","msgstr":["VH - linearni gama0 - ortorektificirano"]},"Based on the combination of bands 3, 2, 1":{"msgid":"Based on the combination of bands 3, 2, 1","msgstr":["Na podlagi kombinacije kanalov 3, 2, 1"]},"VH - linear gamma 0 - non-orthorectified":{"msgid":"VH - linear gamma 0 - non-orthorectified","msgstr":["VH - linearna gama 0 - neortorektificirano"]},"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [VV, 2 VH, VV / VH / 100.0] - linear gamma0 - orthorectified","msgstr":["Barvna slika s preslikavo vhodnih pasov. Vrednost [RGB] = [VV, 2 VH, VV / VH / 100,0] - linearno gama0 - ortorektificirano"]},"VV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VV - decibel gamma0 [-20,0] - orthorectified","msgstr":["VV - decibel gamma0 [-20,0] - ortorektificirano"]},"VH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"VH - decibel gamma0 [-20,0] - orthorectified","msgstr":["VH - decibel gama0 [-20,0] - ortorektificirano"]},"Returns a composite of (VH, VV, VH-VV)":{"msgid":"Returns a composite of (VH, VV, VH-VV)","msgstr":["Vrne kompozit (VH, VV, VH-VV)"]},"HH - linear gamma0 - orthorectified":{"msgid":"HH - linear gamma0 - orthorectified","msgstr":["HH - linearno gama0 - ortorektificirano"]},"HV - linear gamma0 - orthorectified":{"msgid":"HV - linear gamma0 - orthorectified","msgstr":["HV - linearna gama0 - ortorektificirana"]},"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified":{"msgid":"Color image by mapping the input bands. Value [RGB] = [HH, 2 HV, HH / HV / 100.0] - linear gamma0 - orthorectified","msgstr":["Barvna slika s preslikavo vhodnih pasov. Vrednost [RGB] = [HH, 2 HV, HH / HV / 100,0] - linearna gama0 - ortorektificirana"]},"HH - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HH - decibel gamma0 [-20,0] - orthorectified","msgstr":["HH - decibel gamma0 [-20,0] - ortorektificirano"]},"HV - decibel gamma0 [-20,0] - orthorectified":{"msgid":"HV - decibel gamma0 [-20,0] - orthorectified","msgstr":["HV - decibel gamma0 [-20,0] - ortorektificirano"]},"HH - linear gamma0 - non-orthorectified":{"msgid":"HH - linear gamma0 - non-orthorectified","msgstr":["HH - linearni gama0 - neortorektificiran"]},"Based on bands 4,3,2":{"msgid":"Based on bands 4,3,2","msgstr":["Na podlagi kanalov 4,3,2"]},"Based on bands 8,4,3":{"msgid":"Based on bands 8,4,3","msgstr":["Na podlagi kanalov 8,4,3"]},"Based on bands 12,11,4":{"msgid":"Based on bands 12,11,4","msgstr":["Na podlagi kanalov 12,11,4"]},"Based on combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Na podlagi kombinacije kanalov (B8 - B4)/(B8 + B4)"]},"Based on combination of bands (B8A - B11)/(B8A + B11)":{"msgid":"Based on combination of bands (B8A - B11)/(B8A + B11)","msgstr":["Na podlagi kombinacije kanalov (B8A - B11)/(B8A + B11)"]},"Based on bands 12,8A,4":{"msgid":"Based on bands 12,8A,4","msgstr":["Na podlagi kanalov 12,8A,4"]},"Based on combination of bands (B3 - B8)/(B3 + B8)":{"msgid":"Based on combination of bands (B3 - B8)/(B3 + B8)","msgstr":["Na podlagi kombinacije kanalov (B3 - B8)/(B3 + B8)"]},"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11); values above 0.42 are regarded as snowy","msgstr":["Na podlagi kombinacije kanalov (B3 - B11)/(B3 + B11); vrednosti nad 0,42 veljajo za zasnežene"]},"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.":{"msgid":"Classification of Sentinel2 data as result of ESA's Scene classificaiton algorithm.","msgstr":["Klasifikacija podatkov Sentinel-2 kot rezultat algoritma ESA za razvrščanje scen."]},"UV Aerosol Index from 380 and 340 nm":{"msgid":"UV Aerosol Index from 380 and 340 nm","msgstr":["Indeks UV aerosolov od 380 in 340 nm"]},"Based on combination of bands (B3 - B11)/(B3 + B11)":{"msgid":"Based on combination of bands (B3 - B11)/(B3 + B11)","msgstr":["Na podlagi kombinacije kanalov (B3 - B11)/(B3 + B11)"]},"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)":{"msgid":"OLCI Terrestrial Chlorophyll Index, Based on combination of bands (B12 - B11)/(B11 - B10)","msgstr":["OLCI Indeks kopenskega klorofila, ki temelji na kombinaciji kanalov (B12 - B11)/(B11 - B10)"]},"UV Aerosol Index from 388 and 354 nm":{"msgid":"UV Aerosol Index from 388 and 354 nm","msgstr":["Indeks UV aerosolov od 388 in 354 nm"]},"Column averaged dry air mixing ratio of methane":{"msgid":"Column averaged dry air mixing ratio of methane","msgstr":["Srednje razmerje mešanja metana v suhem zraku v stolpcu"]},"Cloud base height":{"msgid":"Cloud base height","msgstr":["Višina dna oblakov"]},"Cloud base pressure":{"msgid":"Cloud base pressure","msgstr":["Tlak na dnu oblakov"]},"Effective radiometric cloud fraction":{"msgid":"Effective radiometric cloud fraction","msgstr":["Efektivni radiometrični delež oblakov"]},"Cloud optical thickness":{"msgid":"Cloud optical thickness","msgstr":["Optična debelina oblaka"]},"Cloud top height":{"msgid":"Cloud top height","msgstr":["Višina vrha oblakov"]},"Cloud top pressure":{"msgid":"Cloud top pressure","msgstr":["Tlak na vrhu oblakov"]},"Carbon Monoxide total column":{"msgid":"Carbon Monoxide total column","msgstr":["Skupni stolpec ogljikovega monoksida"]},"Formaldehyde troposheric vertical column":{"msgid":"Formaldehyde troposheric vertical column","msgstr":["Formaldehidni troposferski vertikalni stolpec"]},"Nitrogen Dioxide tropospheric column":{"msgid":"Nitrogen Dioxide tropospheric column","msgstr":["Troposferski stolpec dušikovega dioksida"]},"Ozone total column":{"msgid":"Ozone total column","msgstr":["Skupni stolpec ozona"]},"Sulfur Dioxide total column":{"msgid":"Sulfur Dioxide total column","msgstr":["Skupni stolpec žveplovega dioksida"]},"Based on bands 2, 1, 4":{"msgid":"Based on bands 2, 1, 4","msgstr":["Na podlagi kanalov 2, 1, 4"]},"Based on combination of bands (B02 - B01)/(B02 + B01)":{"msgid":"Based on combination of bands (B02 - B01)/(B02 + B01)","msgstr":["Na podlagi kombinacije kanalov (B02 - B01)/(B02 + B01)"]},"Based on combination of bands (B02 - B05)/(B02 + B05)":{"msgid":"Based on combination of bands (B02 - B05)/(B02 + B05)","msgstr":["Na podlagi kombinacije kanalov (B02 - B05)/(B02 + B05)"]},"Based on combination of bands (B06 - B07)/(B06 + B07)":{"msgid":"Based on combination of bands (B06 - B07)/(B06 + B07)","msgstr":["Na podlagi kombinacije kanalov (B06 - B07)/(B06 + B07)"]},"Based on bands 1, 4, 3":{"msgid":"Based on bands 1, 4, 3","msgstr":["Na podlagi kanalov 1, 4, 3"]},"Based on the combination of bands 7, 5, 3":{"msgid":"Based on the combination of bands 7, 5, 3","msgstr":["Na podlagi kombinacije kanalov 7, 5, 3"]},"(B09 - B08)/(B09 + B08)":{"msgid":"(B09 - B08)/(B09 + B08)","msgstr":["(B09 - B08)/(B09 + B08)"]},"B09 / B08":{"msgid":"B09 / B08","msgstr":["B09 / B08"]},"Based on the combination of bands 13, 5, 2":{"msgid":"Based on the combination of bands 13, 5, 2","msgstr":["Na podlagi kombinacije kanalov 13, 5, 2"]},"Based on bands 13, 4, 1":{"msgid":"Based on bands 13, 4, 1","msgstr":["Na podlagi kanalov 13, 4, 1"]},"Based on bands 13, 5, 2":{"msgid":"Based on bands 13, 5, 2","msgstr":["Na podlagi kanalov 13, 5, 2"]},"Based on the combination of bands (B13-B07) / (B13+B07)":{"msgid":"Based on the combination of bands (B13-B07) / (B13+B07)","msgstr":["Na podlagi kombinacije kanalov (B13-B07) / (B13+B07)"]},"Terrestrial Chlorophyl Index":{"msgid":"Terrestrial Chlorophyl Index","msgstr":["Indeks kopenskega klorofila"]},"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V 10-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 10-daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V 10-dnevna sinteza\nVrh krošnje (z atmosferskim popravkom)\nČasovna ločljivost: 10 dni na dan\nLočljivost: 333 m (velikost pikslov)"]},"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Atmosphere\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V dnevna sinteza\nVrh atmosfere\nČasovna ločljivost: dnevno\nLočljivost: dnevna: 333 m (velikost piksla)"]},"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Atmosphere\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-dnevno sinteza\nVrh atmosfere\nČasovna ločljivost: 5-dnevna resolucija\nLočljivost: 100 m (velikost piksla)"]},"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)":{"msgid":"PROBA-V daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: daily\nResolution: 333M (pixel size)","msgstr":["PROBA-V dnevna sinteza\nVrh krošnje (atmosfersko popravljen)\nČasovna ločljivost: dnevno\nLočljivost: dnevna ločljivost: 333 m (velikost piksla)"]},"OrbitTracks_Aqua_Descending":{"msgid":"OrbitTracks_Aqua_Descending","msgstr":["OrbitTracks_Aqua_Descending"]},"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)":{"msgid":"PROBA-V 5-daily Synthesis\nTop of Canopy (Atmospherically corrected)\ntemporal resolution: 5-daily\nResolution: 100M (pixel size)","msgstr":["PROBA-V 5-dnevna sinteza\nVrh krošnje (atmosfersko korigirano)\nČasovna ločljivost: 5-dnevno\nLočljivost: 100 m (velikost pikslov)"]},"OrbitTracks_Aqua_Ascending":{"msgid":"OrbitTracks_Aqua_Ascending","msgstr":["OrbitTracks_Aqua_Ascending"]},"OrbitTracks_Aura_Ascending":{"msgid":"OrbitTracks_Aura_Ascending","msgstr":["OrbitTracks_Aura_Ascending"]},"OrbitTracks_Aura_Descending":{"msgid":"OrbitTracks_Aura_Descending","msgstr":["OrbitTracks_Aura_Descending"]},"OrbitTracks_CloudSat_Ascending":{"msgid":"OrbitTracks_CloudSat_Ascending","msgstr":["OrbitTracks_CloudSat_Ascending"]},"OrbitTracks_Calipso_Ascending":{"msgid":"OrbitTracks_Calipso_Ascending","msgstr":["OrbitTracks_Calipso_Ascending"]},"OrbitTracks_Calipso_Descending":{"msgid":"OrbitTracks_Calipso_Descending","msgstr":["OrbitTracks_Calipso_Descending"]},"OrbitTracks_CloudSat_Descending":{"msgid":"OrbitTracks_CloudSat_Descending","msgstr":["OrbitTracks_CloudSat_Descending"]},"OrbitTracks_CYGNSS_Ascending":{"msgid":"OrbitTracks_CYGNSS_Ascending","msgstr":["OrbitTracks_CYGNSS_Ascending"]},"OrbitTracks_CYGNSS_Descending":{"msgid":"OrbitTracks_CYGNSS_Descending","msgstr":["OrbitTracks_CYGNSS_Descending"]},"OrbitTracks_GCOM-C_Ascending":{"msgid":"OrbitTracks_GCOM-C_Ascending","msgstr":["OrbitTracks_GCOM-C_Ascending"]},"OrbitTracks_GCOM-C_Descending":{"msgid":"OrbitTracks_GCOM-C_Descending","msgstr":["OrbitTracks_GCOM-C_Descending"]},"OrbitTracks_GCOM-W1_Ascending":{"msgid":"OrbitTracks_GCOM-W1_Ascending","msgstr":["OrbitTracks_GCOM-W1_Ascending"]},"OrbitTracks_GCOM-W1_Descending":{"msgid":"OrbitTracks_GCOM-W1_Descending","msgstr":["OrbitTracks_GCOM-W1_Descending"]},"OrbitTracks_GOSAT-2_Ascending":{"msgid":"OrbitTracks_GOSAT-2_Ascending","msgstr":["OrbitTracks_GOSAT-2_Ascending"]},"OrbitTracks_GOSAT-2_Descending":{"msgid":"OrbitTracks_GOSAT-2_Descending","msgstr":["OrbitTracks_GOSAT-2_Descending"]},"OrbitTracks_GOSAT_Ascending":{"msgid":"OrbitTracks_GOSAT_Ascending","msgstr":["OrbitTracks_GOSAT_Ascending"]},"OrbitTracks_GOSAT_Descending":{"msgid":"OrbitTracks_GOSAT_Descending","msgstr":["OrbitTracks_GOSAT_Descending"]},"OrbitTracks_GPM_Ascending":{"msgid":"OrbitTracks_GPM_Ascending","msgstr":["OrbitTracks_GPM_Ascending"]},"OrbitTracks_GPM_Descending":{"msgid":"OrbitTracks_GPM_Descending","msgstr":["OrbitTracks_GPM_Descending"]},"OrbitTracks_ICESAT-2_Ascending":{"msgid":"OrbitTracks_ICESAT-2_Ascending","msgstr":["OrbitTracks_ICESAT-2_Ascending"]},"OrbitTracks_ICESAT-2_Descending":{"msgid":"OrbitTracks_ICESAT-2_Descending","msgstr":["OrbitTracks_ICESAT-2_Descending"]},"OrbitTracks_ISS_Ascending":{"msgid":"OrbitTracks_ISS_Ascending","msgstr":["OrbitTracks_ISS_Ascending"]},"OrbitTracks_ISS_Descending":{"msgid":"OrbitTracks_ISS_Descending","msgstr":["OrbitTracks_ISS_Descending"]},"OrbitTracks_Landsat-7_Ascending":{"msgid":"OrbitTracks_Landsat-7_Ascending","msgstr":["OrbitTracks_Landsat-7_Ascending"]},"OrbitTracks_Landsat-7_Descending":{"msgid":"OrbitTracks_Landsat-7_Descending","msgstr":["OrbitTracks_Landsat-7_Descending"]},"OrbitTracks_Landsat-8_Ascending":{"msgid":"OrbitTracks_Landsat-8_Ascending","msgstr":["OrbitTracks_Landsat-8_Ascending"]},"OrbitTracks_METOP-A_Ascending":{"msgid":"OrbitTracks_METOP-A_Ascending","msgstr":["OrbitTracks_METOP-A_Ascending"]},"OrbitTracks_METOP-B_Descending":{"msgid":"OrbitTracks_METOP-B_Descending","msgstr":["OrbitTracks_METOP-B_Descending"]},"OrbitTracks_Landsat-8_Descending":{"msgid":"OrbitTracks_Landsat-8_Descending","msgstr":["OrbitTracks_Landsat-8_Descending"]},"OrbitTracks_METOP-C_Ascending":{"msgid":"OrbitTracks_METOP-C_Ascending","msgstr":["OrbitTracks_METOP-C_Ascending"]},"OrbitTracks_METOP-C_Descending":{"msgid":"OrbitTracks_METOP-C_Descending","msgstr":["OrbitTracks_METOP-C_Descending"]},"OrbitTracks_NOAA-20_Ascending":{"msgid":"OrbitTracks_NOAA-20_Ascending","msgstr":["OrbitTracks_NOAA-20_Ascending"]},"OrbitTracks_NOAA-20_Descending":{"msgid":"OrbitTracks_NOAA-20_Descending","msgstr":["OrbitTracks_NOAA-20_Descending"]},"OrbitTracks_METOP-B_Ascending":{"msgid":"OrbitTracks_METOP-B_Ascending","msgstr":["OrbitTracks_METOP-B_Ascending"]},"OrbitTracks_OCO-2_Ascending":{"msgid":"OrbitTracks_OCO-2_Ascending","msgstr":["OrbitTracks_OCO-2_Ascending"]},"OrbitTracks_OCO-2_Descending":{"msgid":"OrbitTracks_OCO-2_Descending","msgstr":["OrbitTracks_OCO-2_Descending"]},"OrbitTracks_SAOCOM1-A_Ascending":{"msgid":"OrbitTracks_SAOCOM1-A_Ascending","msgstr":["OrbitTracks_SAOCOM1-A_Ascending"]},"OrbitTracks_SAOCOM1-A_Descending":{"msgid":"OrbitTracks_SAOCOM1-A_Descending","msgstr":["OrbitTracks_SAOCOM1-A_Descending"]},"OrbitTracks_Sentinel-1A_Ascending":{"msgid":"OrbitTracks_Sentinel-1A_Ascending","msgstr":["OrbitTracks_Sentinel-1A_Ascending"]},"OrbitTracks_Sentinel-1B_Ascending":{"msgid":"OrbitTracks_Sentinel-1B_Ascending","msgstr":["OrbitTracks_Sentinel-1B_Ascending"]},"OrbitTracks_Sentinel-1A_Descending":{"msgid":"OrbitTracks_Sentinel-1A_Descending","msgstr":["OrbitTracks_Sentinel-1A_Descending"]},"OrbitTracks_METOP-A_Descending":{"msgid":"OrbitTracks_METOP-A_Descending","msgstr":["OrbitTracks_METOP-A_Descending"]},"OrbitTracks_Sentinel-1B_Descending":{"msgid":"OrbitTracks_Sentinel-1B_Descending","msgstr":["OrbitTracks_Sentinel-1B_Descending"]},"OrbitTracks_Sentinel-2A_Ascending":{"msgid":"OrbitTracks_Sentinel-2A_Ascending","msgstr":["OrbitTracks_Sentinel-2A_Ascending"]},"OrbitTracks_Sentinel-2A_Descending":{"msgid":"OrbitTracks_Sentinel-2A_Descending","msgstr":["OrbitTracks_Sentinel-2A_Descending"]},"OrbitTracks_Sentinel-2B_Ascending":{"msgid":"OrbitTracks_Sentinel-2B_Ascending","msgstr":["OrbitTracks_Sentinel-2B_Ascending"]},"OrbitTracks_Sentinel-2B_Descending":{"msgid":"OrbitTracks_Sentinel-2B_Descending","msgstr":["OrbitTracks_Sentinel-2B_Descending"]},"OrbitTracks_Sentinel-5P_Ascending":{"msgid":"OrbitTracks_Sentinel-5P_Ascending","msgstr":["OrbitTracks_Sentinel-5P_Ascending"]},"OrbitTracks_Sentinel-5P_Descending":{"msgid":"OrbitTracks_Sentinel-5P_Descending","msgstr":["OrbitTracks_Sentinel-5P_Descending"]},"OrbitTracks_SMAP_Ascending":{"msgid":"OrbitTracks_SMAP_Ascending","msgstr":["OrbitTracks_SMAP_Ascending"]},"OrbitTracks_SMAP_Descending":{"msgid":"OrbitTracks_SMAP_Descending","msgstr":["OrbitTracks_SMAP_Descending"]},"OrbitTracks_Suomi_NPP_Descending":{"msgid":"OrbitTracks_Suomi_NPP_Descending","msgstr":["OrbitTracks_Suomi_NPP_Descending"]},"OrbitTracks_Suomi_NPP_Ascending":{"msgid":"OrbitTracks_Suomi_NPP_Ascending","msgstr":["OrbitTracks_Suomi_NPP_Ascending"]},"OrbitTracks_Terra_Descending":{"msgid":"OrbitTracks_Terra_Descending","msgstr":["OrbitTracks_Terra_Descending"]},"OrbitTracks_Terra_Ascending":{"msgid":"OrbitTracks_Terra_Ascending","msgstr":["OrbitTracks_Terra_Ascending"]},"Based on bands 4, 3, 2 enhanced by bands 12 and 11.":{"msgid":"Based on bands 4, 3, 2 enhanced by bands 12 and 11.","msgstr":["Na podlagi kanalov 4, 3, 2, ki so okrepljeni s pasovoma 12 in 11."]},"Based on bands B07, B06, B04":{"msgid":"Based on bands B07, B06, B04","msgstr":["Na podlagi kanalov B07, B06, B04"]},"Based on the combination of bands (B8 - B4)/(B8 + B4)":{"msgid":"Based on the combination of bands (B8 - B4)/(B8 + B4)","msgstr":["Na podlagi kombinacije kanalov (B8 - B4)/(B8 + B4)"]},"Based on thermal band 10":{"msgid":"Based on thermal band 10","msgstr":["Na podlagi toplotnega kanala 10"]},"Based on bands B12, B11, B8A":{"msgid":"Based on bands B12, B11, B8A","msgstr":["Na podlagi kanalov B12, B11, B8A"]},"Based on the combination of bands (B08 - B12)/(B08 + B12)":{"msgid":"Based on the combination of bands (B08 - B12)/(B08 + B12)","msgstr":["Na podlagi kombinacije kanalov (B08 - B12)/(B08 + B12)"]},"Enhanced natural color visualization":{"msgid":"Enhanced natural color visualization","msgstr":["Izboljšana vizualizacija naravnih barv"]},"Based on the combination of bands 8, 6, 4":{"msgid":"Based on the combination of bands 8, 6, 4","msgstr":["Na podlagi kombinacije kanalov 8, 6, 4"]},"Enhanced Vegetation Index":{"msgid":"Enhanced Vegetation Index","msgstr":["Izboljšani vegetacijski indeks"]},"Based on the combination: BSI, B08, B11":{"msgid":"Based on the combination: BSI, B08, B11","msgstr":["Na podlagi kombinacije: BSI, B08, B11"]},"Classified NDMI for irrigation":{"msgid":"Classified NDMI for irrigation","msgstr":["Klasifikacija NDMI za namakanje"]},"Based on bands B11, B08, B02":{"msgid":"Based on bands B11, B08, B02","msgstr":["Na podlagi kanalov B11, B08, B02"]},"False Color 13, 5, 2":{"msgid":"False Color 13, 5, 2","msgstr":["Lažne barve 13, 5, 2"]},"Based on the combination of bands (B13 - B07) / (B13 + B07)":{"msgid":"Based on the combination of bands (B13 - B07) / (B13 + B07)","msgstr":["Na podlagi kombinacije kanalov (B13 - B07) / (B13 + B07)"]},"Based on bands 12,8,2":{"msgid":"Based on bands 12,8,2","msgstr":["Na podlagi kanalov 12,8,2"]},"Based on bands 4, 3, 2":{"msgid":"Based on bands 4, 3, 2","msgstr":["Na podlagi kanalov 4, 3, 2"]},"Based on bands 8, 4, 3":{"msgid":"Based on bands 8, 4, 3","msgstr":["Na podlagi kanalov 8, 4, 3"]},"Based on bands 12, 8, 2":{"msgid":"Based on bands 12, 8, 2","msgstr":["Na podlagi kanalov 12, 8, 2"]},"Based on bands 8, 11, 12":{"msgid":"Based on bands 8, 11, 12","msgstr":["Na podlagi kanalov 8, 11, 12"]},"Water sedimentation and chlorophyll content":{"msgid":"Water sedimentation and chlorophyll content","msgstr":["Sedimentacija vode in vsebnost klorofila"]},"Based on bands 12, 8A, 4":{"msgid":"Based on bands 12, 8A, 4","msgstr":["Na podlagi kanalov 12, 8A, 4"]},"Based on NDSI":{"msgid":"Based on NDSI","msgstr":["Na podlagi NDSI"]},"Based on the combination of bands 11, 8, 2":{"msgid":"Based on the combination of bands 11, 8, 2","msgstr":["Na podlagi kombinacije kanalov 11, 8, 2"]},"Based on bands B12, B11, B04":{"msgid":"Based on bands B12, B11, B04","msgstr":["Na podlagi kanalov B12, B11, B04"]},"Based on bands 4, 3 ,2":{"msgid":"Based on bands 4, 3 ,2","msgstr":["Na podlagi kanalov 4, 3 in 2"]},"Atmospherically Resistant Vegetation Index":{"msgid":"Atmospherically Resistant Vegetation Index","msgstr":["Atmosfersko odporen vegetacijski indeks"]},"Soil Adjusted Vegetation Index":{"msgid":"Soil Adjusted Vegetation Index","msgstr":["Vegetacijski indeks, prilagojen tlom"]},"Modified Anthocyanin Reflectance Index":{"msgid":"Modified Anthocyanin Reflectance Index","msgstr":["Modificiran indeks odbojnosti antocianina"]},"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)":{"msgid":"# Thermal IR fire emission bands\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)","msgstr":["# Kanali termičnih IR emisij\n\nSentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) ima dva posebna kanala (F1 in F2) za zaznavanje temperature zemeljskega površja (LST). Kanal F2 z osrednjo valovno dolžino 10854 nm meri v termalni infrardeči svetlobi ali TIR. Zelo uporaben je za spremljanje požarov in dogodkov z visoko temperaturo pri ločljivosti 1 km.\n\n\n\nVeč informacij je [tukaj](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature)"]},"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)":{"msgid":"# Methane (CH4)\n\n\n\nMethane is, after carbon dioxide, the most important contributor to the anthropogenically (caused by human activity) enhanced greenhouse effect. Measurements are provided in parts per billion (ppb) with a spatial resolution of 7 km x 3.5 km.\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/methane)","msgstr":["# Metan (CH4)\n\n\n\nMetan je za ogljikovim dioksidom najpomembnejši povzročitelj za antropogeno (zaradi človekove dejavnosti) povečan učinek tople grede. Meritve so navedene v delcih na milijardo (ppb) s prostorsko ločljivostjo 7 km x 3,5 km.\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/methane)"]},"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)":{"msgid":"# Formaldehyde (HCHO)\n\n\n\nLong term satellite observations of tropospheric formaldehyde (HCHO) are essential to support air quality and chemistry-climate related studies from the regional to the global scale. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic (human-made) activities. Its lifetime being of the order of a few hours, HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/formaldehyde)","msgstr":["# Formaldehid (HCHO)\n\n\n\nDolgoročna satelitska opazovanja troposferskega formaldehida (HCHO) so bistvenega pomena za podporo študijam kakovosti zraka in kemijsko-podnebnim študijam od regionalnega do globalnega obsega. Sezonska in medletna nihanja porazdelitve formaldehida so v glavnem povezana s temperaturnimi spremembami in požari, pa tudi s spremembami antropogenih dejavnosti (ki jih je povzročil človek). Koncentracije HCHO, katerih življenjska doba je nekaj ur, v mejnem sloju atmosfere so lahko neposredno povezane s sproščanjem kratkoživih ogljikovodikov, ki jih večinoma ni mogoče neposredno opazovati iz vesolja. Meritve so v mol na kvadratni meter (mol/ m^2).\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/formaldehyde)"]},"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)":{"msgid":"# Sulfur Dioxide (SO2)\n\n\n\nSulphur dioxide enters the Earth’s atmosphere through both natural and anthropogenic (human made) processes. It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. Sentinel-5P/TROPOMI instrument samples the Earth’s surface with a revisit time of one day with a spatial resolution of 3.5 x 7 km which allows the resolution of fine details including the detection of smaller SO2 plumes. Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/sulphur-dioxide)","msgstr":["# Žveplov dioksid (SO2)\n\n\n\nŽveplov dioksid vstopa v zemeljsko ozračje z naravnimi in antropogenimi (človeškimi) procesi. Ima vlogo v kemiji na lokalni in svetovni ravni, njegov vpliv pa sega od kratkoročnega onesnaževanja do učinkov na podnebje. Le približno 30 % odstotkov izpustov SO2 prihaja iz naravnih virov, večina je antropogenega izvora. Instrument Sentinel-5P/TROPOMI snema Zemljino površje s časom ponovnega obiska en dan in prostorsko ločljivostjo 3,5 x 7 km, kar omogoča ločljivost drobnih podrobnosti, vključno z zaznavanjem manjših tokov SO2. Meritve so v mol na kvadratni meter (mol/m^2).\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/sulphur-dioxide)"]},"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)":{"msgid":"# Ozone (O3)\n\n\n\nOzone is of crucial importance for the equilibrium of the Earth atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. Measurements are in mol per square meter (mol/ m^2)\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/total-ozone-column)","msgstr":["# Ozon (O3)\n\n\n\nOzon je ključnega pomena za ravnovesje zemeljskega ozračja. V stratosferi ozonska plast ščiti biosfero pred nevarnim sončnim ultravijoličnim sevanjem. V troposferi deluje kot učinkovito čistilno sredstvo, vendar pri visoki koncentraciji postane tudi škodljiv za zdravje ljudi, živali in rastlinstva. Ozon je tudi pomemben povzročitelj toplogrednih plinov, ki prispevajo k stalnim podnebnim spremembam. Od odkritja ozonske luknje na Antarktiki v osemdesetih letih prejšnjega stoletja in poznejšega sprejetja Montrealskega protokola, ki ureja proizvodnjo snovi, ki tanjšajo ozonski plašč in vsebujejo klor, se ozon redno spremlja s tal in iz vesolja. Meritve so v mol na kvadratni meter (mol/m^2)\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/total-ozone-column)"]},"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)":{"msgid":"# Nitrogen Dioxide (NO2)\n\n\n\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides. They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (particularly fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). Measurements are in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/nitrogen-dioxide)","msgstr":["# Dušikov dioksid (NO2)\n\n\n\nDušikov dioksid (NO2) in dušikov oksid (NO) skupaj običajno imenujemo dušikovi oksidi. Sta pomembna plina v Zemljinem ozračju, ki sta prisotna v sledovih tako v troposferi kot v stratosferi. V ozračje vstopata zaradi antropogenih dejavnosti (zlasti zgorevanja fosilnih goriv in biomase) in naravnih procesov (kot so mikrobiološki procesi v tleh, gozdni požari in strele). Meritve so v mol na kvadratni meter (mol/ m^2).\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/nitrogen-dioxide)"]},"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)":{"msgid":"# Carbon Monoxide (CO)\n\n\n\nCarbon monoxide (CO) is an important atmospheric trace gas. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. The carbon monoxide total column is measured in mol per square meter (mol/ m^2).\n\n\n\nMore info [here.](http://www.tropomi.eu/data-products/carbon-monoxide)","msgstr":["# Ogljikov monoksid (CO)\n\n\n\nOgljikov monoksid (CO) je pomemben atmosferski plin. Na nekaterih mestnih območjih je glavno onesnaževalo ozračja. Glavni viri CO so zgorevanje fosilnih goriv, izgorevanje biomase ter oksidacija metana in drugih ogljikovodikov v ozračju. Skupni stolpec ogljikovega monoksida se meri v mol na kvadratni meter (mol/ m^2).\n\n\n\nVeč informacij je [tukaj](http://www.tropomi.eu/data-products/carbon-monoxide)"]},"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)":{"msgid":"# Aerosol Index\n\nThe Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols in the atmosphere. It can be used to detect the presence of UV absorbing aerosols such as desert dust and volcanic ash plumes. Positive values (from light blue to red) indicate the presence of UV-absorbing aerosol. This index is calculated for two pairs of wavelengths: 340/380 nm and 354/388 nm.\n\nMore info [here.](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)","msgstr":["# Indeks aerosolov\n\nIndeks aerosolov (AI) je kvalitativni indeks, ki označuje prisotnost povišanih plasti aerosolov v ozračju. Uporablja se lahko za ugotavljanje prisotnosti aerosolov kot sta puščavski prah in vulkanski pepel, ki absorbirajo UV žarke. Pozitivne vrednosti (od svetlo modre do rdeče) kažejo na prisotnost aerosolov, ki absorbirajo UV-žarke. Ta indeks se izračuna za dva para valovnih dolžin: 340/380 nm in 354/388 nm.\n\nVeč informacij je [tukaj](https://earth.esa.int/web/sentinel/technical-guides/sentinel-5p/level-2/aerosol-index)"]},"# Cloud base height\n\nHeight of cloud base measured in meters (m).":{"msgid":"# Cloud base height\n\nHeight of cloud base measured in meters (m).","msgstr":["# Spodnja višina oblakov\n\nVišina spodnjega dela oblakov, izmerjena v metrih (m)."]},"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).":{"msgid":"# Cloud base pressure\n\nPressure measured at cloud base in Pascal (Pa).","msgstr":["# Tlak na dnu oblakov\n\nTlak, izmerjen na spodnji strani oblakov, v pascalih (Pa)."]},"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.":{"msgid":"# Effective radiometric cloud fraction\n\nEffective radiometric cloud fraction represents the portion of the Earth's surface covered by clouds, divided by the total surface. Clouds have shielding, albedo, and in-cloud absorption effects on trace gas retrieval. The effective radiometric cloud fraction is an important parameter to correct these effects.","msgstr":["# Učinkoviti radiometrični delež oblakov\n\nEfektivni radiometrični delež oblakov predstavlja delež Zemljine površine, ki ga pokrivajo oblaki, deljen s celotno površino. Oblaki vplivajo na pridobivanje slednih plinov zaradi ščitenja, albeda in absorpcije v oblakih. Efektivni radiometrični delež oblakov je pomemben parameter za popravljanje teh učinkov."]},"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.":{"msgid":"# Cloud optical thickness\n\nThe cloud thickness is a key parameter to characterise optical properties of clouds. It is a measure of how much sunlight passes through the cloud to reach Earth's surface. The higher a cloud's optical thickness, the more sunlight the cloud is scattering and reflecting. Dark blue shows where there are low cloud optical thickness values and red shows larger cloud optical thickness.","msgstr":["# Optična debelina oblaka\n\nDebelina oblakov je ključni parameter za opredelitev optičnih lastnosti oblakov. Je merilo, koliko sončne svetlobe preide skozi oblake in doseže Zemljino površje. Večja kot je optična debelina oblakov, več sončne svetlobe oblak razprši in odbije. Temno modra barva prikazuje, kje so nizke vrednosti optične debeline oblakov, rdeča pa večje vrednosti optične debeline oblakov."]},"# Cloud top height\n\nHeight of cloud top measured in meters (m).":{"msgid":"# Cloud top height\n\nHeight of cloud top measured in meters (m).","msgstr":["# Višina vrha oblakov\n\nVišina vrha oblakov, izmerjena v metrih (m)."]},"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).":{"msgid":"# Cloud top pressure\n\nPressure measured at cloud top in Pascal (Pa).","msgstr":["# Tlak na vrhu oblakov\n\nTlak, izmerjen na vrhu oblakov, v pascalih (Pa)."]},"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Thermal band 10\n\nThis thermal visualization is based on band 10 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10895 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 10 reports on the ground itself, which is often much hotter. Thermal band 10 is useful in providing surface temperatures and is collected with a 100-meter resolution.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Termalni kanal 10\n\nTa toplotna vizualizacija temelji na kanalu 10 (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih). Pri osrednji valovni dolžini 10895 nm meri v termalni infrardeči svetlobi ali TIR. Namesto merjenja temperature zraka, kot to počnejo vremenske postaje, kanal 10 zajema podatke o površju, ki je pogosto veliko bolj vroča. Termični kanal 10 je uporaben za določanje temperature površja in se zbira z ločljivostjo 100 metrov.\n\n\n\nVeč informacij je [tukaj](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products) in [tukaj](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)":{"msgid":"# Normalized Difference Vegetation Index (NDVI)\n\nThe normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) and [here.](https://eos.com/ndvi/)","msgstr":["# Normalizirani diferencialni vegetacijski indeks (NDVI)\n\nNormalizirani diferencialni vegetacijski indeks je preprost, vendar učinkovit indeks za kvantifikacijo zelene vegetacije. Gre za merilo zdravstvenega stanja vegetacije, ki temelji na tem, kako rastline odbijajo svetlobo pri določenih valovnih dolžinah. Razpon vrednosti indeksa NDVI je od -1 do 1. Negativne vrednosti NDVI (vrednosti, ki se približujejo -1) ustrezajo vodi. Vrednosti blizu nič (-0,1 do 0,1) običajno ustrezajo golim območjem kamenja, peska ali snega. Nizke pozitivne vrednosti pomenijo grmičevje in travnike (približno 0,2 do 0,4), visoke vrednosti pa zmerne in tropske deževne gozdove (vrednosti, ki se približujejo 1).\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ndvi/) in [tukaj](https://eos.com/ndvi/)"]},"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)":{"msgid":"# Enhanced Vegetation Index (EVI)\n\nThe enhanced vegetation index (EVI) is an 'optimized' vegetation index as it corrects for soil background signals and atmospheric influences. It is very useful in areas of dense canopy cover. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\n\n\n\n\n\nMore infos [here](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) and [here.](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)","msgstr":["# Izboljšani indeks vegetacije (EVI)\n\nIzboljšani indeks vegetacije (Enhanced Vegetation Index, EVI) je \"optimiziran\" indeks vegetacije, saj popravlja ozadje tal in atmosferske vplive. Zelo uporaben je na območjih z gosto pokritostjo s krošnjami. Razpon vrednosti za EVI je od -1 do 1, pri čemer je zdrava vegetacija običajno med 0,20 do 0,80.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/evi/) in [tukaj](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_4.php)"]},"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Atmospherically Resistant Vegetation Index (ARVI)\n\nThe Atmospherically Resistant Vegetation Index (ARVI) is a vegetation index that minimizes the effects of atmospheric scattering. It is most useful for regions with high content of atmospheric aerosol (fog, dust, smoke, air pollution). The range for an ARVI is -1 to 1 where green vegetation generally falls between values of 0.20 to 0.80.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Indeks atmosfersko odporne vegetacije (ARVI)\n\nAtmospherically Resistant Vegetation Index (ARVI) je vegetacijski indeks, ki zmanjšuje učinke atmosferskega sipanja. Najbolj uporaben je za območja z visoko vsebnostjo atmosferskega aerosola (megla, prah, dim, onesnaženost zraka). Razpon indeksa ARVI je od -1 do 1, pri čemer se zelena vegetacija običajno nahaja med vrednostmi od 0,20 do 0,80.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/arvi/) in [tukaj](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)":{"msgid":"# Soil Adjusted Vegetation Index (SAVI)\n\nThe Soil Adjusted Vegetation Index is similar to Normalized Difference Vegetation Index (NDVI) but is used in areas where vegetative cover is low (< 40%). The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The index is helpful when analysing young crops, arid regions with sparse vegetation and exposed soil surfaces.\n\n\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) and [here.](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)","msgstr":["# Vegetacijski indeks prilagojen zaradi prsti (SAVI)\n\nVegetacijski indeks, prilagojen zaradi prsti, je podoben normiranemu diferencialnemu vegetacijskemu indeksu (NDVI), vendar se uporablja na območjih, kjer je vegetacijski pokrov nizek (< 40 %). Indeks je tehnika transformacije, ki zmanjšuje vplive osvetljenosti tal na spektralne vegetacijske indekse, ki vključujejo rdeče in bližnje infrardeče (NIR) valovne dolžine. Indeks je koristen pri analizi mladih poljščin, sušnih območij z redko vegetacijo in izpostavljenih površin tal.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/savi/) in [tukaj](https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/)"]},"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)":{"msgid":"# Modified Anthocyanin Reflectance Index (mARI/ARI2)\n\nAnthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses. The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.\n\nTo correct for leaf density and thickness, the near infrared spectral band (in the recommended wavelengths of 760-800nm), which is related to leaf scattering, is added to the basic ARI index. The new index is called modified ARI or mARI (also ARI2).\n\nmARI values for the examined trees in [this original article](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) ranged in values from 0 to 8.\n\n\n\n\n\nMore info [here.](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)","msgstr":["# Modificiran indeks odbojnosti antocianina (mARI/ARI2)\n\nAntocianini so pigmenti, ki so pogosti v višjih rastlinah in povzročajo njihovo rdečo, modro in vijolično obarvanost. Zagotavljajo dragocene informacije o fiziološkem stanju rastlin, saj veljajo za indikatorje različnih vrst stresa rastlin. Odsevnost antocianina je največja okoli 550 nm. Vendar iste valovne dolžine odbija tudi klorofil. Za izolacijo antocianinov se odšteje spektralni pas 700 nm, ki odbija samo klorofil in ne antocianinov.\n\nZa korekcijo gostote in debeline listov se osnovnemu indeksu ARI doda bližnji infrardeči spektralni pas (v priporočenih valovnih dolžinah 760-800 nm), ki je povezan z razpršitvijo listov. Novi indeks se imenuje modificirani ARI ali mARI (tudi ARI2).\n\nvrednosti mARI za pregledana drevesa v [tem izvirnem članku](https://custom-scripts.sentinel-hub.com/sentinel-2/mari/) so se gibale med 0 in 8.\n\n\n\n\n\nVeč informacij je [tukaj](https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1227&context=natrespapers)"]},"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)":{"msgid":"# Green City Script\n\nThe Green city script aims to raise awareness of green areas in cities around the world. The script takes into account the Normalized Difference Vegetation Index (NDVI) and true color wavelengths; it separates built up areas from vegetated ones, making it useful for detecting urban areas. Built up areas are displayed in grey and vegetation is displayed in green.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)","msgstr":["# Skripta Zeleno mesto\n\nNamen skripte Zeleno mesto je povečati ozaveščenost o zelenih površinah v mestih po vsem svetu. Skripta upošteva normirani indeks razlike vegetacije (NDVI) in prave barvne valovne dolžine; ločuje pozidana območja od vegetacijskih, zato je uporabna za odkrivanje mestnih območij. Pozidana območja so prikazana v sivi barvi, vegetacija pa v zeleni.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/green_city/)"]},"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)":{"msgid":"# Urban Classified Script\n\nThe Urban Classified script aims to detect built up areas by separating them from barren ground, vegetation and water. Areas with a high moisture content are returned in blue; areas indicating built up areas are returned in white; vegetated areas are returned in green; everything else indicates barren ground and is displayed in brown colors.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)","msgstr":["# Skripta za klasifikacijo urbanih površin\n\nNamen skripte je zaznati pozidana območja tako, da jih loči od neplodnih tal, vegetacije in vode. Območja z visoko vsebnostjo vlage so prikazana z modro barvo; območja, ki označujejo pozidana območja, so prikazana z belo barvo; območja z vegetacijo so prikazana z zeleno barvo; vse ostalo označuje neplodna tla in je prikazano z rjavo barvo.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_classified/)"]},"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)":{"msgid":"# Urban Land Infrared Color Script\n\nThis script, made by Leo Tolari, combines true color visualization with near infrared (NIR) and shortwave infrared (SWIR) wavelengths. The script highlights urban areas better than true color, while still looking natural.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)","msgstr":["# Skripta urbane infrardeče vizualizacije\n\nTa skripta, ki jo je izdelal Leo Tolari, združuje pravo barvno vizualizacijo z bližnjo infrardečo (NIR) in kratkovalovno infrardečo (SWIR) valovno dolžino. Skripta poudarja urbana območja bolje kot prava barva, hkrati pa je še vedno videti naravno.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/urban_land_infrared/)"]},"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)":{"msgid":"# NDMI for Moisture Stress\n\nThe Normalized Difference Moisture Index (NDMI) for moisture stress can be used to detect irrigation. For all the index values above 0, knowing the land use and land cover, it is possible to determine whether irrigation has taken place. Knowing the type of crop grown (e.g. citrus crops), it is possible to identify whether irrigation is being effective or not during the crucial growing summer season, as well as find out if some parts of the farm are being under or over-irrigated.\n\n\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)","msgstr":["# NDMI za vlažnostni stres\n\nNormalizirani indeks razlike vlage (Normalized Difference Moisture Index, NDMI) za stres zaradi vlage se lahko uporablja za odkrivanje namakanja. Pri vseh vrednostih indeksa nad 0 je ob poznavanju rabe tal in pokrovnosti tal mogoče ugotoviti, ali je bilo izvedeno namakanje. Ob poznavanju vrste pridelkov (npr. agrumi) je mogoče ugotoviti, ali je namakanje v ključni rastni poletni sezoni učinkovito ali ne, ter ugotoviti, ali so nekateri deli kmetije premalo ali preveč namočeni.\n\n\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi_special/#)"]},"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)":{"msgid":"# Normalized Difference Moisture Index (NDMI)\n\nThe normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)","msgstr":["# Normalizirani diferencialni indeks vlage (NDMI)\n\nNormalizirani diferencialni indeks vlage (Normalized Difference Moisture Index, NDMI) se uporablja za določanje vsebnosti vode v vegetaciji in spremljanje suše. Razpon vrednosti NDMI je od -1 do 1. Negativne vrednosti NDMI (vrednosti, ki se približujejo -1) pomenijo neplodna tla. Vrednosti okoli nič (-0,2 do 0,4) na splošno ustrezajo vodnemu stresu. Visoke, pozitivne vrednosti pomenijo visoko krošnjo brez vodnega stresa (približno od 0,4 do 1).\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ndmi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)","msgstr":["# Normalizirani diferencialni vodni indeks (NDWI)\n\nZa kartiranje vodnih teles je najprimernejši normalizirani diferencialni vodni indeks (Normalized Difference Water Index, NDWI). Vrednosti vodnih teles so večje od 0,5. Vegetacija ima manjše vrednosti. Pozidani elementi imajo pozitivne vrednosti med nič in 0,2.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ndwi/)"]},"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.":{"msgid":"# Normalized Difference Water Index (NDWI)\n\nThe normalized difference water index is most appropriate for water body mapping. Values of water bodies are larger than 0.5. Vegetation has smaller values. Built-up features have positive values between zero and 0.2.","msgstr":["# Normalizirani diferencialni vodni indeks (NDWI)\n\nZa kartiranje vodnih teles je najprimernejši normalizirani diferencialni vodni indeks (Normalized Difference Water Index, NDWI). Vrednosti vodnih teles so večje od 0,5. Vegetacija ima manjše vrednosti. Pozidani elementi imajo pozitivne vrednosti med nič in 0,2."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Lažno barvni kompozit\n\nLažno barvni kompozit uporablja vsaj eno očem nevidno valovno dolžino za slikanje Zemlje. Lažni barvni kompozit, ki uporablja bližnje infrardeče, rdeče in zelene pasove, je zelo priljubljen (pas je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo prikaže v različnih pasovih). Lažni barvni kompozit se najpogosteje uporablja za ocenjevanje gostote in zdravja rastlin, saj rastline odbijajo bližnjo infrardečo in zeleno svetlobo, medtem ko rdečo absorbirajo. Mesta in izpostavljena tla so siva ali rjava, voda pa je videti modra ali črna.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) in [tukaj](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) and [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Lažno barvni kompozit\n\nLažno barvni kompozit uporablja vsaj eno očem nevidno valovno dolžino za slikanje Zemlje. Lažni barvni kompozit, ki uporablja bližnje infrardeče, rdeče in zelene pasove, je zelo priljubljen (pas je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo prikaže v različnih pasovih). Lažni barvni kompozit se najpogosteje uporablja za ocenjevanje gostote in zdravja rastlin, saj rastline odbijajo bližnjo infrardečo in zeleno svetlobo, medtem ko rdečo absorbirajo. Mesta in izpostavljena tla so siva ali rjava, voda pa je videti modra ali črna.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false_color_infrared/) in [tukaj](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://earthobservatory.nasa.gov/features/FalseColor)","msgstr":["# Lažno barvni kompozit\n\nLažno barvni kompozit uporablja vsaj eno očem nevidno valovno dolžino za slikanje Zemlje. Lažno barvni kompozit, ki uporablja bližnje infrardeče, rdeče in zelene pasove, je zelo priljubljen (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo zajema v različnih kanalih). Lažni barvni kompozit se najpogosteje uporablja za ocenjevanje gostote in zdravja rastlin, saj rastline odbijajo bližnjo infrardečo in zeleno svetlobo, medtem ko rdečo absorbirajo. Mesta in gola tla so siva ali rjava, voda pa je videti modra ali črna.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) in [tukaj](https://earthobservatory.nasa.gov/features/FalseColor)"]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Lažno barvni kompozit\n\nLažno barvni kompozit uporablja vsaj eno očem nevidno valovno dolžino za slikanje Zemlje. Lažni barvni kompozit, ki uporablja bližnje infrardeče, rdeče in zelene pasove, je zelo priljubljen (pas je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo prikaže v različnih pasovih). Lažni barvni kompozit se najpogosteje uporablja za ocenjevanje gostote in zdravja rastlin, saj rastline odbijajo bližnjo infrardečo in zeleno svetlobo, medtem ko rdečo absorbirajo. Mesta in izpostavljena tla so siva ali rjava, voda pa je videti modra ali črna.\n\n\n\nVeč informacij je [tukaj](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) and [here.](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy).","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/true_color/) in [tukaj](http://www.fis.uni-bonn.de/en/recherchetools/infobox/professionals/remote-sensing-systems/spectroscopy)."]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 5 has 7 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) in [tukaj](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 7 has 8 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) and [here.](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/Landsat-57/composites/) in [tukaj](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 8 has 11 bands. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/landsat-8/composites/) in [tukaj](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/meris/)"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum . Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)","msgstr":["# Naravno barvni kompozit\n\nSenzorji, ki jih nosijo sateliti, lahko Zemljo snemajo v različnih območjih elektromagnetnega spektra. Vsako področje v spektru se imenuje kanal. Pravi barvni kompozit uporablja kanale vidne svetlobe rdeče, zelene in modre barve v ustreznih rdečih, zelenih in modrih barvnih kanalih, rezultat pa je naravno obarvan izdelek, ki je dober prikaz Zemlje, kot bi jo ljudje videli v naravi.\n\n\n\nVeč informacij je [tukaj] (https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/overview/heritage)"]},"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)":{"msgid":"# Enhanced True Color Visualization\n\nThis script uses highlight optimization to avoid burnt out pixels and to even out the exposure. It makes clouds look natural and keep as much visual information as possible. Sentinel-3 OLCI tiles cover large areas, making it possible to observe large cloud formations, such as hurricanes.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)","msgstr":["# Izboljšana vizualizacija pravih barv\n\nTa skripta uporablja optimizacijo osvetlitve, da se izogne prežganim pikam in izravna osvetlitev. Zaradi nje so oblaki videti naravno in ohranijo čim več vizualnih informacij. Deli Sentinel-3 OLCI pokrivajo velika območja, kar omogoča opazovanje velikih oblačnih tvorb, kot so orkani.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-3/true_color_highlight_optimized/)"]},"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)":{"msgid":"# Pansharpened True Color\n\nThe pansharpened true color composite is done by using the usual true color data (red, green and blue (RGB)) and enhancing them by using the panchromatic band 8, or pan band (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). An image from the pan band is similar to black-and-white film: it combines light from the red, green, and blue parts of the spectrum into a single measure of overall visible reflectance. Pansharpened images have 4x the resolution of the usual true color composite, greatly enhancing the usefulness of Landsat imagery.\n\n\n\nMore info [here](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) and [here.](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)","msgstr":["# Izostreni naravno barvni kompozit\n\nIzostreni (pansharpened) naravno barvni kompozit je narejen z uporabo podatkov v naravnih barvah (rdeča, zelena in modra; RGB) in njihovo izboljšavo z uporabo pankromatskega kanala 8 (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih). posnetek iz pan kanala je podoben črno-belemu filmu: združuje svetlobo iz rdečega, zelenega in modrega dela spektra v eno samo sliko bolše ločljivosti. Izostrene slike imajo štirikrat večjo ločljivost kot običajne kompozitne slike v pravih barvah, kar močno poveča uporabnost posnetkov Landsat.\n\n\n\nVeč informacij je [tukaj](https://blog.mapbox.com/pansharpening-for-higher-resolution-in-landsat-live-e4717cd7c356) in [tukaj](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/)"]},"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)":{"msgid":"# False Color Urban composite\n\nThis composite is used to visualize urbanized areas more clearly. Vegetation is visible in shades of green, while urbanized areas are represented by white, grey, or purple. Soils, sand, and minerals are shown in a variety of colors. Snow and ice appear as dark blue, and water as black or blue. Flooded areas are very dark blue and almost black. The composite is useful for detecting wildfires and calderas of volcanoes, as they are displayed in shades of red and yellow.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) and [here.](https://eos.com/false-color/)","msgstr":["# Lažno barvni kompozit urbano\n\nTa kompozit se uporablja za jasnejšo vizualizacijo urbanih območij. Vegetacija je vidna v zelenih odtenkih, urbanizirana območja pa so prikazana z belo, sivo ali vijolično barvo. Tla, pesek in minerali so prikazani v različnih barvah. Sneg in led sta prikazana v temno modri barvi, voda pa v črni ali modri. Poplavljena območja so zelo temno modra in skoraj črna. Sestavljeni posnetek je uporaben za odkrivanje požarov v naravi in kalder vulkanov, saj so prikazani v rdečih in rumenih odtenkih.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/false-color-urban-rgb/) in [tukaj](https://eos.com/false-color/)"]},"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)":{"msgid":"# False Color Urban composite\n\nThis composite uses a combination of bands in visible and in short wave infrared (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). It displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation have lighter shades. Urban areas are blue and soils have various shades of brown.\n\n\n\nMore info [here.](https://gisgeography.com/landsat-8-bands-combinations/)","msgstr":["# Lažno barvni kompozit urbano\n\nTa kompozit uporablja kombinacijo kanalov v vidnem in kratkovalovnem infrardečem spektru (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih). Prikazuje vegetacijo v zelenih odtenkih. Temnejši odtenki zelene barve označujejo gostejšo vegetacijo, redka vegetacija pa ima svetlejše odtenke. Mestna območja so modra, tla pa imajo različne odtenke rjave barve.\n\n\n\nVeč informacij je [tukaj](https://gisgeography.com/landsat-8-bands-combinations/)"]},"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)":{"msgid":"# Agriculture composite\n\nThis composite uses short-wave infrared, near-infrared and blue bands to monitor crop health (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Both short-wave and near infrared bands are particularly good at highlighting dense vegetation, which appears dark green in the composite. Crops appear in a vibrant green and bare earth appears magenta.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](https://gisgeography.com/sentinel-2-bands-combinations/)","msgstr":["# Kmetijski kompozit\n\nTa kompozit uporablja kratkovalovne infrardeče, bližnje infrardeče in modre kanale za spremljanje zdravja pridelkov (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih). Kratkovalovni in bližnji infrardeči pasovi še posebej dobro poudarijo gosto vegetacijo, ki je na kompozitu videti temno zelena. Posevki so videti živo zeleni, gola zemlja pa magenta.\n\n\n\nVeč informacij je [tukaj](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) in [tukaj](https://gisgeography.com/sentinel-2-bands-combinations/)"]},"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)":{"msgid":"# Snow Classifier\n\nThe Snow Classifier algorithm aims to detect snow by classifying pixels based on different brightness and Normalized Difference Snow Index (NDSI) thresholds. Values classified as snow are returned in bright vivid blue. The script can overestimate snow areas over clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)","msgstr":["# Klasifikator snega\n\nCilj algoritma je zaznati sneg z razvrščanjem pikslov na podlagi različnih pragov svetlosti in normaliziranega diferencialnega snežnega indeksa (NDSI). Vrednosti, ki so razvrščene kot sneg, se vrnejo v svetlo živahni modri barvi. Skripta lahko precenjuje območja s snegom nad oblaki.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/snow_classifier/)"]},"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)":{"msgid":"# Ulyssys Water Quality Viewer (UWQV)\n\nThe script aims to dynamically visualize the chlorophyll and sediment conditions of water bodies, which are primary indicators of water quality. The chlorophyll content ranges in colors from dark blue (low chlorophyll content) through green to red (high chlorophyll content). Sediment concentrations are colored brown; opaque brown indicates high sediment content.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)","msgstr":["# Ulyssys Water Quality Viewer (UWQV)\n\nSkripta je namenjena dinamični vizualizaciji stanja klorofila in sedimentov v vodnih telesih, ki so glavni kazalniki kakovosti vode. Vsebnost klorofila se spreminja v barvah od temno modre (nizka vsebnost klorofila) prek zelene do rdeče (visoka vsebnost klorofila). Koncentracija sedimenta je obarvana rjavo; nepregledna rjava barva pomeni visoko vsebnost sedimenta.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ulyssys_water_quality_viewer/)"]},"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)":{"msgid":"# Short wave infrared composite (SWIR)\n\nShort wave infrared (SWIR) measurements can help scientists estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light. In this composite vegetation appears in shades of green, soils and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damages. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/)","msgstr":["# Kratkovalovni infrardeči kompozit (SWIR)\n\nZ meritvami kratkovalovnega infrardečega spektra (SWIR) lahko znanstveniki ocenijo, koliko vode je prisotne v rastlinah in tleh, saj voda absorbira valovne dolžine SWIR. Kratkovalovni infrardeči kanali (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih) so uporabni tudi za razlikovanje med vrstami oblakov (vodni in ledeni oblaki), snegom in ledom, ki so v vidni svetlobi videti beli. Na tem sestavljenem posnetku je vegetacija prikazana v zelenih odtenkih, tla in pozidana območja so v različnih odtenkih rjave barve, voda pa je videti črna. Novo požgana zemljišča močno odsevajo v pasovih SWIR, zaradi česar so dragoceni za kartiranje požarne škode. Vsaka vrsta kamnin različno odbija kratkovalovno infrardečo svetlobo, zato je s primerjavo odbite svetlobe SWIR mogoče kartirati geologijo.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/composites/)"]},"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).":{"msgid":"# Normalised Difference Snow Index (NDSI)\n\nThe Sentinel-2 normalised difference snow index can be used to differentiate between cloud and snow cover as snow absorbs in the short-wave infrared light, but reflects the visible light, whereas cloud is generally reflective in both wavelengths. Snow cover is represented in bright vivid blue.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/).","msgstr":["# Normalizirani diferencialni snežni indeks (NDSI)\n\nNormalizirani diferencialni snežni indeks (Normalised Difference Snow Index, NDSI) Sentinel-2 se lahko uporablja za razlikovanje med oblakom in snežno odejo, saj sneg absorbira kratkovalovno infrardečo svetlobo, vendar odbija vidno svetlobo, medtem ko je oblak običajno odbijajoč v obeh valovnih dolžinah. Snežna odeja je prikazana s svetlo živo modro barvo.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/ndsi-visualized/)."]},"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)":{"msgid":"# Highlight Optimized Natural Color\n\nThis script aims to display the Earth in beautiful natural color images. It uses highlight optimization to avoid burnt out pixels and to even out the exposure.\n\n\n\nMore info [here.](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)","msgstr":["# Poudarjene optimizirane naravne barve\n\nNamen te skripte je prikazati Zemljo v čudovitih naravnih barvah. Uporablja optimizacijo osvetlitve, da se izogne prežganim pikam in izravna osvetlitev.\n\n\n\nVeč informacij je [tukaj](https://sentinel-hub.github.io/custom-scripts/sentinel-2/highlight_optimized_natural_color/#)"]},"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)":{"msgid":"# Geology 12, 8, 2 composite\n\nThis composite uses short-wave infrared (SWIR) band 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects short-wave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near infrared (NIR) band 8 highlights vegetation and band 2 detects moisture, both contributing to differentiation of ground materials. The composite is useful for finding geological formations and features (e.g. faults, fractures), lithology (e.g. granite, basalt, etc.) and mining applications.\n\n\n\nMore info [here.](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)","msgstr":["# Geologija kompozit 12, 8, 2\n\nTa kompozit uporablja kratkovalovni infrardeči (SWIR) pas 12 za razlikovanje med različnimi vrstami kamnin (pas je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo slika v različnih pasovih). Vsaka vrsta kamnin in mineralov različno odbija kratkovalovno infrardečo svetlobo, zato je s primerjavo odbite svetlobe SWIR mogoče kartirati geologijo. Bližnji infrardeči (NIR) pas 8 poudarja vegetacijo, pas 2 pa zaznava vlago, oba pa prispevata k razlikovanju zemeljskih materialov. Kompozit je uporaben za iskanje geoloških formacij in značilnosti (npr. prelomov, lomov), litologije (npr. granita, bazalta itd.) in za uporabo v rudarstvu.\n\n\n\nVeč informacij je [tukaj](https://www.euspaceimaging.com/wp-content/uploads/2018/06/EUSI-SWIR.pdf)"]},"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)":{"msgid":"# Geology 8, 11, 12 composite\n\nThis composite uses both short-wave infrared (SWIR) bands 11 and 12 to differentiate among different rock types (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). Each rock and mineral type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light. Near Infrared (NIR) band 8 highlights vegetation, contributing to differentiation of ground materials. Vegetation in the composite appears red. The composite is useful for differentiating vegetation, and land especially geologic features that can be useful for mining and mineral exploration.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) and [here.](http://murphygeological.com/new---sentinel-2.html#)","msgstr":["# Geologija kompozit 8, 11, 12 \n\nTa kompozit uporablja oba kratkovalovna infrardeča (SWIR) pasova 11 in 12 za razlikovanje med različnimi vrstami kamnin (pas je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo slika v različnih pasovih). Vsaka vrsta kamnin in mineralov različno odbija kratkovalovno infrardečo svetlobo, zato je s primerjavo odbite svetlobe SWIR mogoče kartirati geologijo. 8. pas bližnje infrardeče svetlobe (NIR) osvetljuje vegetacijo, kar prispeva k razlikovanju zemeljskih materialov. Vegetacija je na kompozitu videti rdeča. Kompozit je uporaben za razlikovanje vegetacije in zemljišč, zlasti geoloških značilnosti, ki so lahko koristne za rudarjenje in raziskovanje mineralov.\n\n\n\nVeč informacij je [tukaj](https://earthobservatory.nasa.gov/features/FalseColor/page5.php) in [tukaj](http://murphygeological.com/new---sentinel-2.html#)"]},"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)":{"msgid":"# Wildfires\n\nThis script, created by Pierre Markuse, visualizes wildfires using Sentinel-2 data. It combines natural color background with some NIR/SWIR data for smoke penetration and more detail, while adding highlights from B11 and B12 to show fires in red and orange colors.\n\n\n\nMore info [here.](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)","msgstr":["# Požari v naravi\n\nTa skripta, ki jo je ustvaril Pierre Markuse, vizualizira požare v naravi na podlagi podatkov Sentinel-2. Združuje naravno barvno ozadje z nekaterimi podatki NIR/SWIR za prodiranje dima in več podrobnosti, hkrati pa dodaja poudarke iz B11 in B12, da prikaže požare v rdeči in oranžni barvi.\n\n\n\nVeč informacij je [tukaj](https://pierre-markuse.net/2017/08/07/visualizing-wildfires-sentinel-2-imagery-eo-browser/)"]},"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)":{"msgid":"# Enhanced True Color\n\nThis script, created by Pierre Markuse, uses multiple bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) and saturation and brightness control to enhance the true color visualization.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)","msgstr":["# Izboljšana prava barva\n\nTa skripta, ki jo je ustvaril Pierre Markuse, uporablja več kanalov (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo slika v različnih kanalih) ter nasičenost in svetlost za izboljšanje vizualizacije pravih barv.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-3/enhanced_true_color-2/#)"]},"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)":{"msgid":"# Burned Area Index\n\nBurned Area Index takes advantage of the wider spectrum of Visible, Red-Edge, NIR and SWIR bands.\n\nValues description:()=> The range of values for the index is `-1` to `1` for burn scars, and `1` - `6` for active fires. Different fire intensities may result in different thresholds; the current values were calibrated, as per original author, on mostly Mediterranen regions.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)","msgstr":["# Indeks pogorelosti območja\n\nIndeks pogorelosti (Burned Area Index) izkorišča širši spekter vidnih, rdečih, NIR in SWIR pasov.\n\nOpis vrednosti:()=> Razpon vrednosti indeksa je `-1` do `1` za območja po požaru in `1` do `6` za aktivne požare. Različna intenzivnost požarov lahko povzroči različne pragove; trenutne vrednosti so bile po prvotnem avtorju umerjene na večinoma sredozemskih regijah.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/bais2/)"]},"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)":{"msgid":"# Normalized Burn Ratio (NBR)\n\nNormalized Burn Ratio is frequently used to estimate burn severity. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. Healthy vegetation has a high reflectance in the near-infrared portion of the spectrum, and a low short-wave infrared reflectance. On the other hand, burned areas have a high shortwave infrared reflectance but low reflectance in the near infrared Darker pixels indicate burned areas.\n\n\n\nMore info [here](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) and [here.](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)","msgstr":["# Normalizirano razmerje pogorelosti (NBR)\n\nNormalizirano razmerje pogorelosti (Normalized Burn Ratio) se pogosto uporablja za oceno pogorišč. Uporablja bližnje infrardeče (NIR) in kratkovalovne infrardeče (SWIR) valovne dolžine. Zdrava vegetacija ima visoko odbojnost v bližnjem infrardečem delu spektra in nizko kratkovalovno infrardečo odbojnost. Po drugi strani pa imajo požgana območja visoko kratkovalovno infrardečo odbojnost, vendar nizko odbojnost v bližnjem infrardečem delu spektra. Temnejši piksli označujejo požgana območja.\n\n\n\nVeč informacij je [tukaj](http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html) in [tukaj](https://mybinder.org/v2/gh/sentinel-hub/education/master?filepath=wildfires%2FWildfires%20from%20Satellite%20Images.ipynb)"]},"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)":{"msgid":"# Atmospheric penetration\n\nThis composite uses different bands (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands) in the non-visible part of the electromagnetic spectrum to reduce the influence of the atmosphere in the image. Short wave infrared bands 11 and 12 are highly reflected by the heated areas, making them useful for fire and burned area mapping. Short wave infrared band 8, is on contrary, highly reflected by vegetation, which signifies absence of fire. Vegetation appears blue, displaying details related to the vegetation vigor. Healthy vegetation is shown in light blue while the stressed, sparse or/and arid vegetation appears in dull blue. Urban features are white, grey, cyan or purple.\n\n\n\nMore info [here.](https://eos.com/atmospheric-penetration/)","msgstr":["# Prodiranje v ozračje\n\nTa kompozit uporablja različne kanale (kanal je območje elektromagnetnega spektra; satelitski senzor lahko Zemljo snema v različnih kanalih) v nevidnem delu elektromagnetnega spektra, da zmanjša vpliv atmosfere na posnetek. Kratkovalovna infrardeča pasova 11 in 12 se močno odbijata od segretih območij, zato sta uporabna za kartiranje požarov in požganih območij. Kratkovalovni infrardeči pas 8 se nasprotno močno odbija od vegetacije, kar pomeni odsotnost požara. Vegetacija se prikaže v modri barvi, kar prikazuje podrobnosti, povezane z močjo vegetacije. Zdrava vegetacija je prikazana v svetlo modri barvi, medtem ko je stresna, redka ali/in suha vegetacija prikazana v motni modri barvi. Urbane značilnosti so bele, sive, cian ali vijolične barve.\n\n\n\nVeč informacij je [tukaj](https://eos.com/atmospheric-penetration/)"]},"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)":{"msgid":"# Barren Soil Visualization\n\nThe Barren Soil Visualization can be useful for soil mapping, to investigate the location of landslides or the extent of erosion in non-vegetated areas. This visualization shows all vegetation in green and the barren ground in red. Water appears black.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) and [here.](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)","msgstr":["# Vizualizacija golih tal\n\nVizualizacija golih tal (prsti) je lahko uporabna za kartiranje prsti, raziskovanje lokacije zemeljskih plazov ali obsega erozije na območjih brez vegetacije. Ta vizualizacija prikazuje vso vegetacijo v zeleni barvi, nerodovitna tla pa v rdeči. Voda je prikazana v črni barvi.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/barren_soil/) in [tukaj](https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6)"]},"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)":{"msgid":"# True Color with IR Highlights composite\n\nThis composite enhances the true color visualization by adding the shortwave infrared wavelengths to amplify details. It displays heated areas in red/orange.\n\n\n\nMore info [here.](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)","msgstr":["# Naravno barvni kompozit z IR poudarki\n\nTa kompozit izboljša pravo barvno vizualizacijo z dodajanjem kratkovalovnih infrardečih valovnih dolžin, da okrepi podrobnosti. Pregreta območja prikaže v rdeči/oranžni barvi.\n\n\n\nVeč informacij je [tukaj](https://medium.com/sentinel-hub/active-volcanoes-as-seen-from-space-9d1de0133733)"]},"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)":{"msgid":"# Detection of Burned Areas\n\nThis script is used to detect large scale recently burned areas. Pixels colored red highlight burned areas, and all other pixels are returned in true color. The script sometimes overestimates burned areas over water and clouds.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)","msgstr":["# Odkrivanje požganih območij\n\nTa skripta se uporablja za odkrivanje nedavno požganih območij velikega obsega. Z rdečo barvo obarvani piksli označujejo požgana območja, vsi drugi piksli pa so vrnjeni v pravi barvi. Skripta včasih precenjuje požgana območja nad vodo in oblaki.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/burned_area_ms/)"]},"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).":{"msgid":"# Scene classification\n\n\n\nScene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.\n\n\n\nMore info [here](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/).","msgstr":["# Klasifikacija scene\n\n\n\nKlasifikacija je bila razvita za razlikovanje med oblačnimi piksli, jasnimi piksli in vodnimi piksli podatkov Sentinel-2 in je rezultat algoritma ESA za klasifikacijo. Na voljo je dvanajst različnih klasifikacij, vključno z razredi oblakov, vegetacije, tal/puščave, vode in snega. Ne gre za karto klasifikacije pokrovnosti tal v ožjem smislu.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-2/scene-classification/)."]},"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)":{"msgid":"# Terrestrial Chlorophyll Index (OTCI)\n\n\n\nThe Terrestrial Chlorophyll Index (OTCI) is estimated based on the chlorophyll content in terrestrial vegetation and can be used to monitor vegetation condition and health. Low OTCI values usually signify water, sand or snow. Extremely high values, displayed with white, usually suggest the absence of chlorophyll as well. They generally represent either bare ground, rock or clouds. The chlorophyll values in between range from red (low chlorophyll values) to dark green (high chlorophyll values) can be used to determine vegetation health.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)","msgstr":["# Indeks kopenskega klorofila (OTCI)\n\n\n\nIndeks kopenskega klorofila (OTCI) je ocenjen na podlagi vsebnosti klorofila v kopenskem rastlinju in se lahko uporablja za spremljanje stanja in zdravja rastlinja. Nizke vrednosti OTCI običajno pomenijo vodo, pesek ali sneg. Izjemno visoke vrednosti, ki so prikazane z belo barvo, običajno kažejo tudi na odsotnost klorofila. Običajno predstavljajo gola tla, skale ali oblake. Vrednosti klorofila, ki so vmes v razponu od rdeče (nizke vrednosti klorofila) do temno zelene (visoke vrednosti klorofila), se lahko uporabijo za določanje zdravja vegetacije.\n\n\n\nVeč informacij je [tukaj](https://custom-scripts.sentinel-hub.com/sentinel-3/otci/)"]},"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)":{"msgid":"# Normalized Difference Salinity Index\n\nThe index visualizes the amount of salt present in soils. Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. \n\nHigher values indicate higher salinity and low values indicate lower salinity.\n\nRead more [here,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [here](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) and [here.](https://www.indexdatabase.de/db/i-single.php?id=57)","msgstr":["# Normaliziran diferencialni indeks slanosti\n\nIndeks prikazuje količino soli v tleh. Zasoljevanje tal je eden najpogostejših procesov degradacije tal, zlasti na sušnih in polsušnih območjih, kjer količina padavin presega izhlapevanje\n\nVišje vrednosti kažejo na večjo slanost, nizke vrednosti pa na manjšo slanost.\n\nPreberite več [tukaj,](https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/wrem/khaier.pdf) [tukaj](https://modis.gsfc.nasa.gov/sci_team/pubs/abstract_new.php?id=29271) in [tukaj](https://www.indexdatabase.de/db/i-single.php?id=57)"]},"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)":{"msgid":"# Discrete Classification Map\n\n\n\nThis layer visualises Global Land Cover discrete classification map with 23 classes defined using the UN-FAO Land Cover Classification System (LCCS) and with color scheme defined in the Product User Manual. Map [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)","msgstr":["# Karta klasifikacije\n\n\n\nTa sloj vizualizira globalni zemljevid klasifikacije pokrovnosti tal s 23 razredi, opredeljenimi s sistemom klasifikacije pokrovnosti tal UN-FAO (LCCS), in barvno shemo, opredeljeno v uporabniškem priročniku izdelka. Karta je [tukaj](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)"]},"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).":{"msgid":"# Forest Types\n\n\n\nVisualized forest types based on 6 classes, as defined in the UN-FAO Land Cover Classification System (LCCS). More [here.](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf).","msgstr":["# Tipi gozdov\n\n\n\nVizualizirani tipi gozdov na podlagi 6 razredov, kot so opredeljeni v klasifikacijskem sistemu UN-FAO Land Cover Classification System (LCCS). Več je [tukaj](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.3.pdf)."]},"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC)\n\n\n\nIn this Corine Land Cover layer, all 44 classes are shown. Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC)\n\n\n\nV sloju Corine Land Cover je prikazanih vseh 44 razredov CLC. Preberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Artificial Surfaces\n\n\n\nIn this Corine Land Cover layer, only the 11 artificial surface classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Grajene površine\n\n\n\nV tem sloju Corine Land Cover je prikazanih samo 11 razredov grajenih površin na podlagi klasifikacije [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Preberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto z vsemi razredi [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Agricultural Areas\n\n\n\nIn this Corine Land Cover layer, only the 11 agricultural classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Kmetijska območja\n\n\n\nV tem sloju Corine Land Cover je prikazanih samo 11 kmetijskih razredov na podlagi klasifikacije [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Preberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto z vsemi razredi [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Forest and Seminatural Areas\n\n\n\nIn this Corine Land Cover layer, only the 12 Forest and Seminatural Area classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Gozd in polnaravna območja\n\n\n\nV tem sloju Corine Land Cover je prikazanih samo 12 razredov gozdov in polnaravnih območij na podlagi klasifikacije [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Preberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto z vsemi razredi [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Wetlands\n\n\n\nIn this Corine Land Cover layer, only the 5 Wetland classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). \nLearn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - mokrišča\n\n\n\nV tem sloju Corine Land Cover je prikazanih samo 5 razredov mokrišč na podlagi klasifikacije [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html)\nPreberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto z vsemi razredi [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).":{"msgid":"# Corine Land Cover (CLC) - Water Bodies\n\n\n\nIn this Corine Land Cover layer, only the 6 Water body classes are shown, based on the classification [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Learn about each class [here](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) and see the evalscript with all the classes [here](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/).","msgstr":["# Corine Land Cover (CLC) - Vodna telesa\n\n\n\nV tem sloju Corine Land Cover je prikazanih samo 6 razredov vodnih teles na podlagi klasifikacije [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html). Preberite več o vsakem razredu [tukaj](https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf) in si oglejte skripto z vsemi razredi [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover/)."]},"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).":{"msgid":"# Water Bodies - Occurrence\n\n\n\nThis layer displays the 6 occurrence levels of the Quality layer (QUAL), providing information on the seasonal dynamics of the detected water bodies. QUAL is generated from water body occurrence statistics computed from previous monthly Water Bodies products. The occurrence statistics is ranked from low occurrence to permanent occurrence. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#).","msgstr":["# Vodna telesa - pojavljanje\n\n\n\nTa sloj prikazuje 6 ravni pojavljanja sloja kakovosti (QUAL) in zagotavlja informacije o sezonski dinamiki zaznanih vodnih teles. QUAL se ustvari iz statističnih podatkov o pojavljanju vodnih teles, izračunanih iz mesečnih izdelkov Vodna telesa. Statistični podatki o pojavljanju so razvrščeni od majhnega pojavljanja do stalnega pojavljanja. Več informacij je [tukaj](https://collections.sentinel-hub.com/water-bodies/readme.html) in [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies-occurence/#)."]},"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).":{"msgid":"# Water Bodies\n\n\n\nThis layer visualizes the Water Bodies detection layer (WB), which shows water bodies detected using the Modified Normalized Difference Water Index (MNDWI) derived from Sentinel-2 Level 1C data. More information [here](https://collections.sentinel-hub.com/water-bodies/readme.html), and [here](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/).","msgstr":["# Vodna telesa\n\n\n\nTa sloj vizualizira sloj zaznavanja vodnih teles (Water Bodies, WB), ki prikazuje vodna telesa, zaznana z uporabo modificiranega normiranega diferencialnega vodega indeksa (MNDWI), pridobljenega iz podatkov Sentinel-2 Level 1C. Več informacij je [tukaj](https://collections.sentinel-hub.com/water-bodies/readme.html) in [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/water-bodies/)."]},"Level 1":{"msgid":"Level 1","msgstr":["Level 1"]},"Level 2":{"msgid":"Level 2","msgstr":["Level 2"]},"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"The **Landsat 4-5 TM** collection includes imagery produced with the Thematic Mapper (TM) sensor, which was carried onboard Landsat 4 and 5 satellites. There are 6 optical and one thermal infrared band available, all in 30 meter resolution. Data is archived, with global coverage over land, available from 1982 to 2012. Top of the atmosphere level-1, and surface reflectance level-2 products are provided.\n\n**Spatial resolution**: 30 meter\n\n**Revisit time** 16 days\n\n**Data availability**: global, Level-1 from August 1982 to May 2012, Level-2 from July 1984 to May 2012. \n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":["Zbirka **Landsat 4-5 TM** vsebuje posnetke narejene s \"Thematic Mapper (TM)\" senzorjem, ki je bil vgrajen v Landsat 4 in 5 satelite. Na voljo je 6 optičnih in en termalni infrardeči pas, vsi v 30 metrski ločljivosti. Podatki so arhivirani, z globalno pokritostjo kopnega, na voljo za leta med 1982 in 2012. Na izbiro sta produkta: Level-1 nad atmosfero in pa Level-2 s površinsko odbojnostjo.\n\n**Prostorska ločljivost**: 30 metrov\n\n**Čas ponovnega obiska** 16 dni\n\n**Razpoložljivost podatkov**: globalni, Level-1 od avgusta 1982 do maja 2012, Level-2 od julija 1984 do maja 2012. \n\n**Skupna uporaba**: spremljanje vegetacije, vodnih in ledenih virov, zaznavanje sprememb in izdelava zemljevidov uporabe tal."]},"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-1** product provides top of the atmosphere (TOA) reflectance imagery. Level-1 data is produced by processing Landsat TM data with standard processing parameters, such as cubic convolution and terrain correction. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["Produkt **Landsat 4-5 TM Level-1** daje na voljo posnetke odbojnosti nad atmosfero (TOA). Podatki Level-1 so ustvarjeni z obdelavo Landsat TM podatkov s standardnimi parametri, kot so kubična konvolucija in popravek terena. Izvedite več [tukaj](https://collections.sentinel-hub.com/landsat-4-5-tm-l1/) in [tukaj](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-collection-2?qt-science_center_objects=0#qt-science_center_objects)."]},"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).":{"msgid":"**Landsat 4-5 TM Level-2** product is produced by processing Level-1 data to surface reflectance - an estimate of the surface spectral reflectance at ground level in the absence of atmospheric scattering and absorption. Learn more [here](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) and [here](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects).","msgstr":["Produkt **Landsat 4-5 TM Level-2** je ustvarjen s predelavo podatkov Level-1 na površinsko odbojnost, torej približek površinske spektralne odbojnosti na višini tal, v odsotnosti atmosferskega sipanja in absorpcije. Izvedite več [tukaj](https://collections.sentinel-hub.com/landsat-4-5-tm-l2/) in [tukaj](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-tm-collection-2-level-2-science?qt-science_center_objects=0#qt-science_center_objects)."]},"Blue (450-520 nm)":{"msgid":"Blue (450-520 nm)","msgstr":["Modra (450-520 nm)"]},"Green (520-600 nm)":{"msgid":"Green (520-600 nm)","msgstr":["Zelena (520-600 nm)"]},"Red (630-690 nm)":{"msgid":"Red (630-690 nm)","msgstr":["Rdeča (630-690 nm)"]},"Near Infrared (NIR) (760-900 nm)":{"msgid":"Near Infrared (NIR) (760-900 nm)","msgstr":["Bližnja infrardeča (NIR) (760-900 nm)"]},"Shortwave Infrared (SWIR) 1 (1550-1750 nm)":{"msgid":"Shortwave Infrared (SWIR) 1 (1550-1750 nm)","msgstr":["Kratkovalovna infrardeča (SWIR) 1 (1550-1750 nm)"]},"Thermal Infrared (10400-12500 nm)":{"msgid":"Thermal Infrared (10400-12500 nm)","msgstr":["Termalna infrardeča (10400-12500 nm)"]},"Shortwave Infrared (SWIR) 2 (2080-2350 nm)":{"msgid":"Shortwave Infrared (SWIR) 2 (2080-2350 nm)","msgstr":["Kratkovalovna infrardeča (SWIR) 2 (2080-2350 nm)"]},"Please select a layer":{"msgid":"Please select a layer","msgstr":["Prosimo izberite sloj"]},"Histogram can be displayed only while visualizing":{"msgid":"Histogram can be displayed only while visualizing","msgstr":["Histogram je lahko prikazan le tekom vizualizacije"]},"Histogram not available for ":{"msgid":"Histogram not available for ","msgstr":["Histogram ni na voljo za "]},"Recalculate":{"msgid":"Recalculate","msgstr":["Ponovno preračunaj"]},"Histogram":{"msgid":"Histogram","msgstr":["Histogram"]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Landsat 4-5 TM has 7 bands. The true color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":["# Kompozit naravnih barv\n\nSenzorji na satelitih lahko vidijo Zemljo v različnih pasovih elektromagnetnega spektra. Vsak tak pas se imenuje kanal. Landsat 4-5 TM sateliti imajo 7 kanalov. Kompozit naravnih barv uporablja pasove vidne svetlobe, torej rdeči, zelen in moder kanal, kar ima za posledico naravno izgledajočo sliko, ki je dobra predstavitev Zemlje, kakor jo sicer z očmi vidijo ljudje.\n\n\n\nVeč informacij [tukaj](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites)."]},"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).":{"msgid":"# Thermal band 6\n\nThis thermal visualization is based on band 6 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 11040 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band 6 reports on the ground itself, which is often much hotter. Thermal band 6 is useful in providing surface temperatures and is collected with a 120-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites).","msgstr":["# Termalni kanal 6\n\nTa termalni prikaz bazira na kanalu 6 (kanal je pas elektromagnetnega spektra, senzor na satelitih namreč lahko vidi Zemljo v različnih spektralnih pasovih). Pri srednji valovni dolžini 11040 nm zaznava termalno infrardeče valovanje, imenovano TIR. Namesto merjenja temperature zraka, kakor to počnejo vremenske postaje, kanal 6 zaznava vrednosti na površini tal, ki so običajno precej toplejša. Termalni kanal 6 je uporaben za pridobivanje površinske temperature. Zajeman je v 120-metrski in prevzorčen v 30-metrsko ločljivost.\n\n\n\nVeč informacij [tukaj](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites)."]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black.\n\n\n\nMore info [here](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)","msgstr":["# Kompozit umetnih barv\n\nKompozit umetnih barv uporablja vsaj en pas nevidne svetlobe za prikaz Zemlje. Zelo popularen je kompozit, ki uporablja bližnji infrardeči, rdeči in zelen kanal (kanal je pas elektromagnetnega spektra, senzor na satelitih namreč lahko vidi Zemljo v različnih spektralnih pasovih). Kompozit umetnih barv je najpogosteje uporabljen za oceno gostote in zdravja rastlin, saj rastline odbijajo bližnjo infrardečo svetlobo in absorbirajo rdečo. Mesta in gola površina so prikazani v sivi ali kožni barvi, medtem ko je voda prikazana kot modra ali črna.\n\n\n\nVeč informacij [tukaj](https://earthobservatory.nasa.gov/features/FalseColor/page6.php)"]},"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).":{"msgid":"# Global Surface Water - Occurrence\n\n\n\nThe layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2019. Permanent water areas with 100% occurrence over the 36 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/).","msgstr":["# Globalne površinske vode - pojavnost\n\n\n\nSloj prikazuje (medletne in celoletne) variacije površinskih voda v časovnem razponu med marcem 1984 in decembrom 2019. Področja stalnih voda s 100% pojavnostjo preko 36 let so prikazana v modri barvi, medtem ko nianse svetlejše roza in vijolične barve predstavljajo nižje stopnje prisotnosti vode. Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_occurrence/)."]},"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).":{"msgid":"# Global Surface Water - Occurrence Change Intensity\n\n\n\nThe layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2019. Areas with increase in water occurrence are visualized in different shades of green, areas with no change are colored black and areas with decrease are shown in shades of red. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/).","msgstr":["# Globalne površinske vode - intenzivnost sprememb pojavnosti\n\n\n\nSloj prikazuje spremembe pojavnosti voda med dvema različnima časovnima obdobjema: prvi med marcem 1984 in decembrom 1999 ter drugi med januarjem 2000 in decembrom 2019. Področja, kjer se je povečala pojavnost vode, so prikazana v niansah zelene barve, področja brez spremembe v črni in področja z zmanjšano pojavnostjo so prikazana v niansah rdeče barve. Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_change/)."]},"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).":{"msgid":"# Global Surface Water - Seasonality\n\n\n\nThe Seasonality layer provides information on the distribution of surface water in 2019. Permanent water bodies (water was present for 12 months) are colored in dark blue and seasonal water (water was present for less than 12 months) in gradually lighter shades of blue, with the lightest blue showing areas where water was present for only 1 month. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#).","msgstr":["# Globalne površinske vode - sezonskost\n\n\n\nSloj sezonskosti daje na voljo podatek o razporeditvi površinskih voda v letu 2019. Stalne vodne površine (kjer je bila voda prisotna 12 mesecev) so prikazana v temnomodri barvi, medtem ko so sezonske vode (kjer je bila voda prisotna manj kot 12 mesecev) prikazane v vedno svetlejši modri barvi. V najsvetlejši modri barvi so prikazana področja, kjer je bila voda prisotna le en mesec. Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_seasonality/#)."]},"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).":{"msgid":"# Global Surface Water - Recurrence\n\n\n\nThe Recurrence layer shows how frequently water returned to a particular location in a defined water period between 1984 and 2019. Orange color indicates low recurrence (water returned to the area infrequently), and light blue color indicates high recurrence (water returned to the area frequently). Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/).","msgstr":["# Globalne površinske vode - ponovna pojavnost\n\n\n\nSloj ponovne pojavnosti prikazuje kako pogosto se vode vračajo na določeno območje med leti 1984 in 2019. Oranžna barva predstavlja nizko stopnjo vračanja (voda se redko vrača), medtem ko svetlomodra barva predstavlja visoko stopnjo vračanja (voda se vrača zelo pogosto). Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_recurrence/)."]},"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).":{"msgid":"# Global Surface Water - Transitions\n\n\n\nThe Transitions layer is derived from a comparison between the first and last year in the 36-year time period. It visualises conversions between seasonal and permanent water. For example, \"lost seasonal\" means, that previously seasonal water was converted to land, \"new seasonal\" means that land has been converted to seasonal waters and so on. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) and learn what each class means [here](https://global-surface-water.appspot.com/faq).","msgstr":["# Globalne površinske vode - prehodi\n\n\n\nSloj prehodov je izpeljan iz primerjave med prvim in zadnjim letom tekom 36-letnega časovnega obdobja. Prikazuje pretvorbe med sezonskimi in stalnimi vodami. Kot primer: \"presahla sezonska voda\" pomeni, da se je sezonska voda izsušila in se je površina spremenila v trdna tla, \"nova sezonska voda\" pomeni, da se je področje trdnih tal zalilo z vodo, in tako dalje. Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_transitions/) in izvedite kaj posamezni razred predstavlja [tukaj](https://global-surface-water.appspot.com/faq)."]},"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).":{"msgid":"# Global Surface Water - Extent\n\n\n\nThis layer visualizes water in blue. It combines all the other layers and visualizes all the locations for which water presence has ever been detected over the 36-year period. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/).","msgstr":["# Globalne površinske vode - obseg\n\n\n\nTa sloj prikazuje vodo v modri barvi. Kombinira vse preostale sloje in prikazuje lokacije, kjer je bila voda prisotna kadarkoli v 36-letnem obdobju. Izvedite več [tukaj](https://custom-scripts.sentinel-hub.com/copernicus_services/global_surface_water_extent/)."]},"The setup function in the evalscript does not contain the correct output. The output needs to include:":{"msgid":"The setup function in the evalscript does not contain the correct output. The output needs to include:","msgstr":[""]},"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.":{"msgid":"You are visualising a layer that doesn't represent an index. The histogram feature currently only works for index layers (e.g. NDVI).\n\nPlease select an index layer to use this feature.","msgstr":[""]},"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.":{"msgid":"The **Global Surface Water** collection is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at a global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies. More information [here](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Coverage**: Global coverage from longitude 170°E to 180°W and latitude 80°N to 50°S.\n\n**Data Availability**: 1984 - 2019, 1984 - 2020.\n\n**Spatial resolution**: 30 meters.\n\n**Common Usage**: Monitoring of water bodies for water resource management, climate modelling, biodiversity conservation and food security.","msgstr":["Zbirka **Globalne površinske vode** je izpeljana iz posnetkov sistemov Landsat 5, 7 in 8 in prikazuje različne vidike prostorske in časovne razporeditve površinskih voda med leti 1984 in 2020 (revizija vsako leto), na globalnem nivoju v šestih različnih slojih. Pod pojmom \"površinska voda\" se smatra vsak iz vesolja viden in odkrit vodnat predel (sladkovodni in morski) večji kot 30m², vključno z naravnimi in umetnimi vodnimi telesi. Več informacij [tukaj](https://collections.sentinel-hub.com/global-surface-water/).\n\n**Pokritost**: Globalna pokritost med geografsko dolžino 170°E in 180°W ter med geografsko širino 80°N in 50°S.\n\n**Razpoložljivost podatkov**: 1984 - 2019, 1984 - 2020 2019.\n\n**Prostorska ločljivost**: 30 metrov.\n\n**Skupna uporaba**: Spremljanje vodnih teles za področja oskrbe z vodo, klimatskega modeliranja, ohranitve biodiverzitete in varnosti hrane."]},"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)":{"msgid":"The **Mapzen DEM** is based on the SRTM30 (Shuttle Radar Topography Mission) and [other sources]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). The bathymetry data is taken from [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). It is a static collection (independent of date) with global coverage.\n\n**Spatial resolution:** Mostly 90 m, in some areas up to 10 m.\n\nCredits: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)","msgstr":["**Mapzen DEM** temelji na SRTM30 (Shuttle Radar Topography Mission) in [drugih virih]( https://github.com/tilezen/joerd/blob/master/docs/data-sources.md). Batimetrični podatki so vzeti iz [ETOPO1](https://www.ngdc.noaa.gov/mgg/global/global.html). Gre za statično zbirko (neodvisno od datuma) z globalno pokritostjo.\n\n**Prostorska ločljivost:**Večinoma 90 m, na nekaterih območjih do 10 m.delu ZDA.\n\nVir: [Mapzen](https://github.com/tilezen/joerd/tree/master/docs)"]},"Continuous urban fabric":{"msgid":"Continuous urban fabric","msgstr":[""]},"Discontinuous urban fabric":{"msgid":"Discontinuous urban fabric","msgstr":[""]},"Industrial or commercial units":{"msgid":"Industrial or commercial units","msgstr":[""]},"Road and rail networks and associated land":{"msgid":"Road and rail networks and associated land","msgstr":[""]},"Port areas":{"msgid":"Port areas","msgstr":[""]},"Airports":{"msgid":"Airports","msgstr":[""]},"Mineral extraction sites":{"msgid":"Mineral extraction sites","msgstr":[""]},"Dump sites":{"msgid":"Dump sites","msgstr":[""]},"Construction sites":{"msgid":"Construction sites","msgstr":[""]},"Green urban areas":{"msgid":"Green urban areas","msgstr":[""]},"Sport and leisure facilities":{"msgid":"Sport and leisure facilities","msgstr":[""]},"Non-irrigated arable land":{"msgid":"Non-irrigated arable land","msgstr":[""]},"Permanently irrigated land":{"msgid":"Permanently irrigated land","msgstr":[""]},"Rice fields":{"msgid":"Rice fields","msgstr":[""]},"Vineyards":{"msgid":"Vineyards","msgstr":[""]},"Fruit trees and berry plantations":{"msgid":"Fruit trees and berry plantations","msgstr":[""]},"Olive groves":{"msgid":"Olive groves","msgstr":[""]},"Pastures":{"msgid":"Pastures","msgstr":[""]},"Annual crops associated with permanent crops":{"msgid":"Annual crops associated with permanent crops","msgstr":[""]},"Complex cultivation patterns":{"msgid":"Complex cultivation patterns","msgstr":[""]},"Land principally occupied by agriculture with significant areas of natural vegetation":{"msgid":"Land principally occupied by agriculture with significant areas of natural vegetation","msgstr":[""]},"Agro-forestry areas":{"msgid":"Agro-forestry areas","msgstr":[""]},"Broad-leaved fores":{"msgid":"Broad-leaved fores","msgstr":[""]},"Coniferous fores":{"msgid":"Coniferous fores","msgstr":[""]},"Mixed fores":{"msgid":"Mixed fores","msgstr":[""]},"Natural grasslands":{"msgid":"Natural grasslands","msgstr":[""]},"Moors and heathland":{"msgid":"Moors and heathland","msgstr":[""]},"Sclerophyllous vegetation":{"msgid":"Sclerophyllous vegetation","msgstr":[""]},"Transitional woodland-shrub":{"msgid":"Transitional woodland-shrub","msgstr":[""]},"Beaches":{"msgid":"Beaches","msgstr":[""]},"Bare rocks":{"msgid":"Bare rocks","msgstr":[""]},"Sparsely vegetated areas":{"msgid":"Sparsely vegetated areas","msgstr":[""]},"Burnt areas":{"msgid":"Burnt areas","msgstr":[""]},"Glaciers and perpetual snow":{"msgid":"Glaciers and perpetual snow","msgstr":[""]},"Inland marshes":{"msgid":"Inland marshes","msgstr":[""]},"Peat bogs":{"msgid":"Peat bogs","msgstr":[""]},"Salt marshes":{"msgid":"Salt marshes","msgstr":[""]},"Salines":{"msgid":"Salines","msgstr":[""]},"Intertidal flats":{"msgid":"Intertidal flats","msgstr":[""]},"Water courses":{"msgid":"Water courses","msgstr":[""]},"Water bodies":{"msgid":"Water bodies","msgstr":[""]},"Coastal lagoons":{"msgid":"Coastal lagoons","msgstr":[""]},"Estuaries":{"msgid":"Estuaries","msgstr":[""]},"Sea and ocean":{"msgid":"Sea and ocean","msgstr":[""]},"NODATA":{"msgid":"NODATA","msgstr":[""]},"Commercial data":{"msgid":"Commercial data","msgstr":["Komercialni Podatki"]},"Order by:":{"msgid":"Order by:","msgstr":[""]},"Location":{"msgid":"Location","msgstr":[""]},"DatasetId":{"msgid":"DatasetId","msgstr":[""]},"Title":{"msgid":"Title","msgstr":[""]},"Show evalscript":{"msgid":"Show evalscript","msgstr":[""]},"Show details":{"msgid":"Show details","msgstr":[""]},"Landsat 1-5 MSS L1":{"msgid":"Landsat 1-5 MSS L1","msgstr":[""]},"Landsat 7 ETM+ L1":{"msgid":"Landsat 7 ETM+ L1","msgstr":[""]},"Landsat 7 ETM+ L2":{"msgid":"Landsat 7 ETM+ L2","msgstr":[""]},"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 1-5 MSS** collection includes imagery produced with the Multispectral Scanner System (MSS), which was carried onboard Landsat 1 through Landsat 5 satellites. There are 4 optical bands available in 60 m resolution. Data is archived and includes global imagery since 1972. \n\n**Spatial resolution**: 68 m x 83 m (commonly resampled to 57 m, or 60 m)\n\n**Revisit time**: 18 days for Landsats 1-3 and 16 days for Landsats 4-5\n\n**Data availability**: Global, since:\n- Landsat 1 from July 1972 to January 1978\n- Landsat 2 from January 1975 to February 1982\n- Landsat 3 from March 1978 to March 1983\n- Landsat 4 from July 1982 to December 1993\n- Landsat 5 from 1984 to October 1992, and from June 2012 to January 2013\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.":{"msgid":"**Landsat 7 ETM+** includes imagery produced with the Enhanced Thematic Mapper (ETM+) sensor, which was carried onboard Landsat 7 satellite. There are 8 optical and 1 thermal infrared bands available. Global data is available since 1999, with a revisit time of 16 days. Top of the atmosphere level-1, and surface reflectance level-2 products are provided. Note that there are data gaps for all images acquired since 2003-05-30 due to sensor failure.\n\n**Spatial resolution**: 30 meter, 15 meter for a panchromatic band\n\n**Revisit time**: 16 days\n\n**Data availability**: global, since April 1999\n\n**Common Usage**: Monitoring of vegetation, ice and water resources, change detection and the creation of land use - land cover maps.","msgstr":[""]},"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)":{"msgid":"**Landsat 7 ETM+ Level-1** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm/)","msgstr":[""]},"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).":{"msgid":"**Landsat 7 ETM+ Level-2** \n\nLearn more [here](https://docs.sentinel-hub.com/api/latest/data/landsat-etm-l2/).","msgstr":[""]},"Green (500-600 nm)\t":{"msgid":"Green (500-600 nm)\t","msgstr":[""]},"Red (600-700 nm)":{"msgid":"Red (600-700 nm)","msgstr":[""]},"Ultra Red (700-800 nm)":{"msgid":"Ultra Red (700-800 nm)","msgstr":[""]},"Near Infrared (NIR) (800-1100 nm)":{"msgid":"Near Infrared (NIR) (800-1100 nm)","msgstr":[""]},"Panchromatic (520-900 nm)":{"msgid":"Panchromatic (520-900 nm)","msgstr":[""]},"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).":{"msgid":"# Thermal Visualization\n\nThis thermal visualization is based on band B06 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, band B06 reports on the ground itself, which is often much hotter. Thermal band B06 is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meter.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites) and [here](https://custom-scripts.sentinel-hub.com/landsat-7-etm/thermal/).","msgstr":[""]},"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_1 Visualization\n\nThis thermal visualization is based on band B06_VCID_1 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_1 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. As its dinamic range is wider than that of B06_VCID_2, it is less likely to oversaturate over hot areas. \n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).":{"msgid":"# Thermal B06_VCID_2 Visualization\n\nThis thermal visualization is based on band B06_VCID_2 (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). At the central wavelength of 10400-12500 nm it measures in the thermal infrared, or TIR. Instead of measuring the temperature of the air, like weather stations do, B06_VCID_2 reports on the ground itself, which is often much hotter. It is useful in providing surface temperatures and is collected with a 60-meter resolution, resampled to 30-meters. Its dinamic range is narrower than that of B06_VCID_1, which means it is more likely to oversaturate over hot areas, but in turn has slightly higher radiometric sensitivity.\n\n\n\nMore info [here](https://www.usgs.gov/faqs/why-do-landsat-7-level-1-products-contain-two-thermal-bands?qt-news_science_products=0#qt-news_science_products).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is Ultra Red band 3 (700 - 800 nm), which is particularly useful for distinguishing vegetation boundaries between land and water and various landforms. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-ultrared/).","msgstr":[""]},"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).":{"msgid":"# False color composite\n\nA false color composite uses at least one non-visible wavelength to image Earth. The false color composite using near infrared, red and green bands is very popular (a band is a region of the electromagnetic spectrum; a satellite sensor can image Earth in different bands). The false colour composite is most commonly used to assess plant density and health, since plants reflect near infrared and green light, while they absorb red. Cities and exposed ground are grey or tan, and water appears blue or black. In this case, the NIR band used in the red channel is the NIR band 4 (800 - 1100 nm), which penetrates atmospheric haze, emphasizes vegetation, and distinguishes between land and water. \n\n\n\nMore info [here](https://eos.com/find-satellite/landsat-5-mss/) and [here](https://custom-scripts.sentinel-hub.com/landsat-1-5-mss/false-color-nir/).","msgstr":[""]},"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)":{"msgid":"# True color composite\n\nSensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. True color composite uses visible light bands red, green and blue in the corresponding red, green and blue color channels, resulting in a natural colored product, that is a good representation of the Earth as humans would see it naturally.\n\n\n\nMore info [here.](https://custom-scripts.sentinel-hub.com/landsat-7-etm/true-color//)","msgstr":[""]},"Max. cloud coverage:":{"msgid":"Max. cloud coverage:","msgstr":["Maksimalna oblačnost:"]},"Order name":{"msgid":"Order name","msgstr":["Ime naročila"]},"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.":{"msgid":"ORDER USING PRODUCTS IDS\n\nSearch for data and add products to you order by clicking on \"Add to Order\" buttons. This will add product IDs to your Order Options under \"Added Products (by ID).\"\n\nORDER USING QUERY\n\nYour order will be based on your AOI and time range, without searching for data and adding products to your order. Especially useful for ordering time-series data.\nIt's possible for some products to be partially covered by clouds, despite the cloud coverage % information being 0.","msgstr":[""]},"Collection ID":{"msgid":"Collection ID","msgstr":[""]},"Ordered products will be clipped to the selected area.":{"msgid":"Ordered products will be clipped to the selected area.","msgstr":["Naročeni produkti bodo izrezani v skladu z označeno površino"]},"Set an approximate order limit to prevent undesired large area requests.":{"msgid":"Set an approximate order limit to prevent undesired large area requests.","msgstr":[""]},"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.":{"msgid":"Harmonization is not yet supported for surface reflectance products, thus this field must be explicitly set to NONE if productBundle is analytic_sr or analytic_sr_udm2.","msgstr":[""]},"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.":{"msgid":"When you click \"Create Order\", your order will be created. At this stage, the order will not go through and no quota will be substracted. This will happen when you confirm the order. Before you do, you will be able to review the requested quota and decide if you would like to proceed.","msgstr":[""]},"Select":{"msgid":"Select","msgstr":["Izberi"]},"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.":{"msgid":"Note that it is technically possible to order more PlanetScope data than your purchased quota. Make sure your order is in line with the Hectares under Management (HUM) model to avoid overage fees.","msgstr":[""]},"Layer default":{"msgid":"Layer default","msgstr":[""]},"Speckle Filter":{"msgid":"Speckle Filter","msgstr":[""]},"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.":{"msgid":"Speckle filtering is only applied at zoom levels 12 and above for IW and zoom levels 8 and above for EW acquisition. Zoom levels outside this range will render without speckle filtering, even if it is set.","msgstr":[""]},"Speckle filtering not applied. Zoom in to apply speckle filtering.":{"msgid":"Speckle filtering not applied. Zoom in to apply speckle filtering.","msgstr":[""]},"Processing parameters":{"msgid":"Processing parameters","msgstr":["Parametri procesiranja"]},"Draw rectangular area of interest for image downloads":{"msgid":"Draw rectangular area of interest for image downloads","msgstr":[""]},"Draw polygonal area of interest for image downloads":{"msgid":"Draw polygonal area of interest for image downloads","msgstr":[""]},"Search options":{"msgid":"Search options","msgstr":["Možnosti iskanja"]},"Order options":{"msgid":"Order options","msgstr":["Možnosti naročil"]},"My orders":{"msgid":"My orders","msgstr":["Moja naročila"]},"My quotas":{"msgid":"My quotas","msgstr":["Moje kvote"]},"Use current display area":{"msgid":"Use current display area","msgstr":["Uporabi trenutno prikazano območje"]},"Draw rectangular area of interest":{"msgid":"Draw rectangular area of interest","msgstr":[""]},"Draw polygonal area of interest":{"msgid":"Draw polygonal area of interest","msgstr":[""]},"Cloud cover":{"msgid":"Cloud cover","msgstr":["Pokritost z oblaki"]},"Constellation":{"msgid":"Constellation","msgstr":["Ozvezdje"]},"Processing level":{"msgid":"Processing level","msgstr":["Nivo procesiranja"]},"Snow cover":{"msgid":"Snow cover","msgstr":[""]},"Incidence angle":{"msgid":"Incidence angle","msgstr":[""]},"Coverage":{"msgid":"Coverage","msgstr":["Pokritost"]},"Shadow percent":{"msgid":"Shadow percent","msgstr":[""]},"Pixel resolution":{"msgid":"Pixel resolution","msgstr":["Ločjivost v pikslih"]},"Hide details":{"msgid":"Hide details","msgstr":["Skrij podrobnosti"]},"Product id":{"msgid":"Product id","msgstr":[""]},"Accuisition date":{"msgid":"Accuisition date","msgstr":[""]},"Prepare order":{"msgid":"Prepare order","msgstr":["Pripravi naročilo"]},"From":{"msgid":"From","msgstr":["Od"]},"To":{"msgid":"To","msgstr":["Do"]},"Provider":{"msgid":"Provider","msgstr":["Ponudnik"]},"Used km":{"msgid":"Used km","msgstr":[""]},"No quotas available":{"msgid":"No quotas available","msgstr":[""]},"Unable to get quotas: ${ err.message }":{"msgid":"Unable to get quotas: ${ err.message }","msgstr":[""]},"Refresh quotas":{"msgid":"Refresh quotas","msgstr":[""]},"Purchased km":{"msgid":"Purchased km","msgstr":[""]},"Advanced options":{"msgid":"Advanced options","msgstr":["Napredne možnosti"]},"Product bundle":{"msgid":"Product bundle","msgstr":[""]},"Max. Cloud Coverage":{"msgid":"Max. Cloud Coverage","msgstr":[""]},"Min. Off Nadir":{"msgid":"Min. Off Nadir","msgstr":[""]},"Max. Off Nadir":{"msgid":"Max. Off Nadir","msgstr":[""]},"Min. Sun Elevation":{"msgid":"Min. Sun Elevation","msgstr":[""]},"Max. Sun Elevation":{"msgid":"Max. Sun Elevation","msgstr":[""]},"Sensor":{"msgid":"Sensor","msgstr":["Senzor"]},"Processing Level":{"msgid":"Processing Level","msgstr":["Nivo procesiranja"]},"Snow Coverage":{"msgid":"Snow Coverage","msgstr":["Pokritost s snegom"]},"Incidence Angle":{"msgid":"Incidence Angle","msgstr":[""]},"add":{"msgid":"add","msgstr":["dodaj"]},"remove":{"msgid":"remove","msgstr":["odstrani"]},"Show results on map":{"msgid":"Show results on map","msgstr":["Prikaži rezultate na zemljevidu"]},"Order type":{"msgid":"Order type","msgstr":["Tip naročila"]},"Order limit":{"msgid":"Order limit","msgstr":[""]},"Order limit (km2)":{"msgid":"Order limit (km2)","msgstr":[""]},"Harmonize data":{"msgid":"Harmonize data","msgstr":[""]},"Planet API Key":{"msgid":"Planet API Key","msgstr":[""]},"Your Planet API key":{"msgid":"Your Planet API key","msgstr":[""]},"Create order":{"msgid":"Create order","msgstr":["Ustvari naročilo"]},"Created orders (Not confirmed)":{"msgid":"Created orders (Not confirmed)","msgstr":[""]},"Running orders":{"msgid":"Running orders","msgstr":[""]},"Finished orders":{"msgid":"Finished orders","msgstr":["Zaključena naročila"]},"Created at":{"msgid":"Created at","msgstr":["Ustvarjeno ob"]},"Confirmed at":{"msgid":"Confirmed at","msgstr":["Potrjeno ob"]},"Size":{"msgid":"Size","msgstr":["Velikost"]},"Status":{"msgid":"Status","msgstr":["Status"]},"All input parameters":{"msgid":"All input parameters","msgstr":["Vsi vhodni parametri"]},"Order ID":{"msgid":"Order ID","msgstr":[""]},"Hide ${ property } values":{"msgid":"Hide ${ property } values","msgstr":[""]},"Show ${ property } values":{"msgid":"Show ${ property } values","msgstr":[""]},"Confirm":{"msgid":"Confirm","msgstr":["Potrdi"]},"Delete":{"msgid":"Delete","msgstr":["Izbriši"]},"Show coverage":{"msgid":"Show coverage","msgstr":["Prikaži pokritost"]},"Show data":{"msgid":"Show data","msgstr":["Prikaži podatke"]},"No orders found":{"msgid":"No orders found","msgstr":["Ni naročil"]},"Error confirming order":{"msgid":"Error confirming order","msgstr":["Napaka pri potrjevanju naročila"]},"Error deleting order":{"msgid":"Error deleting order","msgstr":["Napaka pri brisanju naročila"]},"Confirm order":{"msgid":"Confirm order","msgstr":["Potrdi naročilo"]},"Are you sure you want to confirm this order?":{"msgid":"Are you sure you want to confirm this order?","msgstr":["Ali ste prepričani, da želite potrditi to naročilo?"]},"Delete order":{"msgid":"Delete order","msgstr":["Izbriši naročilo"]},"Are you sure you want to delete this order?":{"msgid":"Are you sure you want to delete this order?","msgstr":["Ali ste prepričani, da želite izbrisati to naročilo?"]},"Refresh orders":{"msgid":"Refresh orders","msgstr":["Osveži naročila"]},"Creating order":{"msgid":"Creating order","msgstr":["Ustvarjam naročilo"]},"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.":{"msgid":"Are you sure you want to create an order without a Collection ID? \nWhen you confirm your order a new collection will be created automatically.","msgstr":[""]},"Order size (approx)":{"msgid":"Order size (approx)","msgstr":[""]},"Create a new collection":{"msgid":"Create a new collection","msgstr":[""]},"Manual Entry":{"msgid":"Manual Entry","msgstr":["Ročni vnos"]},"Your collections":{"msgid":"Your collections","msgstr":[""]},"Upload data":{"msgid":"Upload data","msgstr":["Prenos podatkov"]},"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package":{"msgid":"Enter a Planet API key, that you received via email after purchasing a Planet PlanetScope Sentinel Hub Package","msgstr":[""]},"Show preview":{"msgid":"Show preview","msgstr":[""]},"Please login to gain access to it":{"msgid":"Please login to gain access to it","msgstr":[""]},"The theme you are trying to access is private":{"msgid":"The theme you are trying to access is private","msgstr":[""]},"Continue without logging in":{"msgid":"Continue without logging in","msgstr":[""]},"e.g.: My planet data":{"msgid":"e.g.: My planet data","msgstr":[""]},"Settings":{"msgid":"Settings","msgstr":["Nastavitve"]},"Sky / Atmosphere":{"msgid":"Sky / Atmosphere","msgstr":["Nebo / atmosfera"]},"Sun":{"msgid":"Sun","msgstr":["Sonce"]},"Sun time (UTC)":{"msgid":"Sun time (UTC)","msgstr":["Čas sonca (UTC)"]},"Sun projected shadows":{"msgid":"Sun projected shadows","msgstr":["Sončne projicirane sence"]},"Shading parameters":{"msgid":"Shading parameters","msgstr":[""]},"Shadow parameters":{"msgid":"Shadow parameters","msgstr":["Parametri senčenja"]},"Ambient factor":{"msgid":"Ambient factor","msgstr":["Faktor ambienta"]},"Diffuse factor":{"msgid":"Diffuse factor","msgstr":["Faktor difuzije"]},"Specular factor":{"msgid":"Specular factor","msgstr":["Faktor zrcalnosti"]},"Specular power":{"msgid":"Specular power","msgstr":["Stopnja zrcalnosti"]},"Shadow visibility":{"msgid":"Shadow visibility","msgstr":["Vidnost sence"]},"Shadow rendering distance":{"msgid":"Shadow rendering distance","msgstr":["Razdalja risanja senc"]},"Shadow map size":{"msgid":"Shadow map size","msgstr":["Velikost slike senčenja"]},"Parameters":{"msgid":"Parameters","msgstr":["Parametri"]},"Local time on computer":{"msgid":"Local time on computer","msgstr":["Lokalni čas na računalniku"]},"Edit":{"msgid":"Edit","msgstr":["Uredi"]},"Reset values":{"msgid":"Reset values","msgstr":["Ponastavi vrednosti"]},"Current time":{"msgid":"Current time","msgstr":["Trenutni čas"]},"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.":{"msgid":"Double clicking with the left mouse button on the camera console resets the camera view to North and down looking position.","msgstr":["Dvoklik z levim gumbom miške na konzoli za kamero ponastavi pogled kamere, tako da gleda proti severu in navzdol."]},"Vertical terrain scaling":{"msgid":"Vertical terrain scaling","msgstr":["Vertikalno skaliranje terena"]},"Camera lens flare effect (with the sun)":{"msgid":"Camera lens flare effect (with the sun)","msgstr":["Odsev leč kamere (pri soncu)"]},"Image download in compare mode is currently available only for basic image download.":{"msgid":"Image download in compare mode is currently available only for basic image download.","msgstr":[""]},"you can only download an image while visualizing or comparing":{"msgid":"you can only download an image while visualizing or comparing","msgstr":[""]},"you need to compare at least 2 layers":{"msgid":"you need to compare at least 2 layers","msgstr":["primerjati morate vsaj 2 sloja"]},"Rotate around the clicked point":{"msgid":"Rotate around the clicked point","msgstr":["Rotacija okoli kliknjene točke"]},"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.":{"msgid":"When checked, moving the mouse while the middle button is pressed, \nrotates the world around the clicked point, \notherwise the camera rotates around itself. \nAlt key, being pressed when starting to rotate, toggles this behavior.","msgstr":["Kadar je ta opcija vklopljena, premikanje miške, medtem ko je srednji gumb pritisnjen, premika svet okoli kliknjene točke. V nasprotnem primeru kamera rotira okoli lastne osi. \nČe je pred začetkom rotiranja pritisnjena tipka Alt, se obnašanje invertira."]},"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.":{"msgid":"Map overlay is disabled when AOI is specified. Remove your AOI in order to use this option.","msgstr":[""]},"Exported image(s) will include datasource and date, zoom scale and branding.":{"msgid":"Exported image(s) will include datasource and date, zoom scale and branding.","msgstr":[""]},"Enable captions in order to write a description.":{"msgid":"Enable captions in order to write a description.","msgstr":[""]},"Add a short description to the exported image.":{"msgid":"Add a short description to the exported image.","msgstr":[""]},"Layer does not have any legend data.":{"msgid":"Layer does not have any legend data.","msgstr":[""]},"Exported image will include legend.":{"msgid":"Exported image will include legend.","msgstr":[""]},"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.":{"msgid":"The **ESA WorldCover** product is the first global land cover map at 10 m resolution based on both Sentinel-1 and Sentinel-2 data. More information [here](https://esa-worldcover.org/).\n\n**Coverage**: Global coverage.\n\n**Data Availability**: 2020.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Development of novel services to help with preserving biodiversity, food security, carbon assessment and climate modelling.","msgstr":[""]},"Land cover classification":{"msgid":"Land cover classification","msgstr":[""]},"Based on the last band of the custom script.":{"msgid":"Based on the last band of the custom script.","msgstr":[""]},"You need to login to use this functionality.":{"msgid":"You need to login to use this functionality.","msgstr":[""]},"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.":{"msgid":"The **Seasonal Trajectories** product is a filtered time series of Plant Phenology Index (PPI) provided yearly on a 10-daily basis. It is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The Seasonal Trajectories PPI is derived through fitting a smoothing and gap filling function to the yearly time-series raw PPI values generated from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/seasonal-trajectories/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated every 10 days.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology monitoring, such as tracking green canopy foliage dynamics through time.","msgstr":[""]},"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.":{"msgid":"The **Vegetation Indices** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The product is comprised of 4 raw Vegetation Indices generated near real-time (NRT) from Sentinel-2 satellite observations. More information [here](https://collections.sentinel-hub.com/vegetation-indices/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since October 2016, updated daily. \n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Plant phenology assessment and monitoring, including vegetation cover, density, productivity and health.","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-1/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.":{"msgid":"The **Corine Land Cover Accounting Layers** are status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system. The modification combines CLC status and change layers in the 100 m raster in order to create homogeneous quality time series of CLC / CLC-change layers for accounting purposes. The CLC inventory consists of 44 land cover and land use classes derived from a series of satellite missions since it was first established. More information [here](https://collections.eurodatacube.com/corine-land-cover-accounting-layers/).\n\n**Coverage**: Europe (EEA39 region).\n\n**Data Availability**: Since 2000, updated every 6 years. Data available for 2000, 2006, 2012 and 2018.\n\n**Spatial resolution**: 100 meters.\n\n**Common Usage**: Land use and land cover monitoring, analysis and change prediction for various applications, including environment, agriculture, transport and spatial planning.","msgstr":[""]},"Corine Land Cover Accounting Layer":{"msgid":"Corine Land Cover Accounting Layer","msgstr":[""]},"Plant Phenology Index":{"msgid":"Plant Phenology Index","msgstr":[""]},"Quality Flag":{"msgid":"Quality Flag","msgstr":[""]},"Normalized Difference Vegetation Index":{"msgid":"Normalized Difference Vegetation Index","msgstr":[""]},"Fraction of Absorbed Photosynthetically Active Radiation":{"msgid":"Fraction of Absorbed Photosynthetically Active Radiation","msgstr":[""]},"Leaf Area Index":{"msgid":"Leaf Area Index","msgstr":[""]},"Day of start-of-season":{"msgid":"Day of start-of-season","msgstr":[""]},"Day of end-of-season":{"msgid":"Day of end-of-season","msgstr":[""]},"Day of maximum-of-season":{"msgid":"Day of maximum-of-season","msgstr":[""]},"Vegetation index value at SOSD":{"msgid":"Vegetation index value at SOSD","msgstr":[""]},"Vegetation index value at EOSD":{"msgid":"Vegetation index value at EOSD","msgstr":[""]},"Vegetation index value at MAXD":{"msgid":"Vegetation index value at MAXD","msgstr":[""]},"Average vegetation index value of minima on left and right sides of each season":{"msgid":"Average vegetation index value of minima on left and right sides of each season","msgstr":[""]},"Season amplitude (MAXV – MINV)":{"msgid":"Season amplitude (MAXV – MINV)","msgstr":[""]},"Length of Season (number of days between start and end)":{"msgid":"Length of Season (number of days between start and end)","msgstr":[""]},"Slope of the greening up period":{"msgid":"Slope of the greening up period","msgstr":[""]},"Slope of the senescent period":{"msgid":"Slope of the senescent period","msgstr":[""]},"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD":{"msgid":"Seasonal productivity. The growing season integral computed as the sum of all daily values between SOSD and EOSD","msgstr":[""]},"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.":{"msgid":"Total productivity. The growing season integral computed as sum of all daily values minus their base level value.","msgstr":[""]},"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).":{"msgid":"# Corine Land Cover - Accounting\n\n\n\nThis script visualises CORINE Land Cover (CLC) Accounting Layers according to the official CORINE Land Cover color scheme. CLC Accounting Layers are CLC status layers modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. For more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/corine_land_cover_accounting_layer/).","msgstr":[""]},"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).":{"msgid":"# ESA WorldCover Map\n\n\n\nThe WorldCover product displays a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/worldcover/).","msgstr":[""]},"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).":{"msgid":"# Yearly Time-Series of the Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. It is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Seasonal Trajectories PPI product is a filtered yearly time series of PPI, providing the vegetation status for each pixel on a regular 10-day time step. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/st-ppi/).","msgstr":[""]},"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Amplitude Parameter\n\n\n\nThis layer visualizes the seasonal amplitude parameter of the VPP (Vegetation Phenology and Productivity) parameter. It is calculated as a difference between the MAXV (season maximum value) and MINV (season minimum value) parameters. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-amplitude-ampl/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# End of Season Values\n\n\n\nThis layer visualizes the EOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. EOSV is a PPI (plant phenology index) value of the end-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-end-of-season-value-eosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Maximum Value\n\n\n\nThis layer visualizes the MAXV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MAXV represents the PPI (plant phenology index) value at the day of the season maximum. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-maximum-value-maxv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Season Minimum Value\n\n\n\nThis layer visualizes the MINV parameter of the VPP (Vegetation Phenology and Productivity) parameter. MINV represents the average PPI (plant phenology index) value of the minima on the left and right sides of each season. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-season-minimum-value-minv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Senescent Period\n\n\n\nThis layer visualizes the RSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. RSLOPE represents the slope of the PPI (plant phenology index) of the senescent period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-senescent-period-rslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Start of Season Values\n\n\n\nThis layer visualizes the SOSV parameter of the VPP (Vegetation Phenology and Productivity) parameter. SOSV represents the PPI (plant phenology index) value of the start-of-season day. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-start-of-season-value-sosv/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Seasonal Productivity Parameter\n\n\n\nThis layer visualizes the SPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. SPROD represents seasonal productivity. It is calculated as the sum of all daily PPI (plant phenology index) values between SOSD (start-of-season day) and EOSD (end-of-season day). Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-seasonal-productivity-sprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Total Productivity Parameter\n\n\n\nThis layer visualizes the TPROD parameter of the VPP (Vegetation Phenology and Productivity) parameter. TPROD represents total productivity. It is calculated as a sum of all daily PPI (plant phenology index) values, minus their base level value. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-total-productivity-tprod/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).":{"msgid":"# Fraction of Absorbed Photosynthetically Active Radiation\n\n\n\n\nFAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The index describes only the green parts of the canopy and is very useful for assessing the primary productivity of canopies. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-fapar/).","msgstr":[""]},"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).":{"msgid":"# Leaf Area Index\n\n\nLAI is defined as one half of the total area of photosynthetically active elements of the canopy per unit horizontal ground area. The LAI provided by HRVPP corresponds to actual LAI of all the canopy layers, including all green contributors. Practically, the LAI quantifies the thickness of the vegetation cover. Deeper green colors indicate thicker vegetation cover. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-lai/#).","msgstr":[""]},"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).":{"msgid":"# Normalized Difference Vegetation Index\n\n\n\nNDVI quantifies vegetation photosynthetic capacity by measuring the difference between the Near-Infrared (NIR) (which vegetation strongly reflects) and red spectral bands (which vegetation absorbs). It is commonly used to monitor vegetation cover and density. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ndvi/).","msgstr":[""]},"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).":{"msgid":"# Plant Phenology Index\n\n\n\nPPI (Plant Phenology Index) is a physically-based vegetation index derived from radiative transfer equation and is calculated from red and Near-Infrared (NIR) spectral bands. PPI is linearly related to the green leaf area index, and can be used to track canopy green foliage dynamics and therefore provides an efficient approach to retrieving plant phenology. Learn more [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vi-ppi/#) and [here](https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-vegetation-indices/).","msgstr":[""]},"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.":{"msgid":"The **Vegetation Phenology and Productivity Parameters** product is part of the Copernicus Land Monitoring Service (CLMS), pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. The VPP product is comprised of 13 parameters that describe specific stages of the seasonal vegetation growth cycle. These parameters are extracted from Seasonal Trajectories of the Plant Phenology Index (PPI) derived from Sentinel-2 satellite observations.\nMore information [here](https://collections.sentinel-hub.com/vegetation-phenology-and-productivity-parameters-season-2/).\n\n**Coverage**: Europe (EEA39 region) from longitude from 25°W to 45°E and latitude 26°N to 72°N.\n\n**Data Availability**: Since January 2017, updated annually.\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Detailed assessment of the impacts of human or climate change on the ecosystem through monitoring of vegetation responses to disturbances, e.g. droughts, storms, insect infestations, and to human influence from global to local levels.","msgstr":[""]},"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).":{"msgid":"# Slope of the Greening Period\n\n\n\nThis layer visualizes the LSLOPE parameter of the VPP (Vegetation Phenology and Productivity) parameter. LSLOPE represents the slope of the PPI (plant phenology index) of the greening-up period. Find more information [here](https://custom-scripts.sentinel-hub.com/copernicus_services/vpp-slope-of-greening-up-period-lslope/) and [here](https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity).","msgstr":[""]},"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).":{"msgid":"**General** \nThe \"Commercial data\" tab allows you to search, purchase, and visualize Commercial Third-Party data.\n\n**Available constellations** \nWe currently offer data from 4 different commercial data providers:\n- Planet [Planet scope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/) (4 bands, 3m resolution)\n- Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) (5 bands, 0.5m - 2m resolution)\n- Airbus [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) (5 bands, 1.5m - 6m resolution)\n- Maxar [WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/) (5 bands, 0.5m - 2m resolution)\n\nAs the term \"commercial\" implies, the data comes at a cost, which means **in addition to your existing Sentinel Hub subscription, you will need to purchase quota** for the data you are interested in.\n\n**Quota** \nCheck *My quota* to see how much quota you have for each of the constellations. You can purchase quota through the [Sentinel Hub Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing).\n\n**Purchase** \nTo purchase commercial data, you must:\n- search for the data (*Search options*),\n- select a product from the results (*Results*),\n- add the product to your order,\n- specify where to save the order (*Order options*),\n- review the order and confirm it (*Created orders (not confirmed)* in *My orders*). Your order will now be listed under *Running orders* and move to *Finished orders* once the data has been purchased and ingested.\n\n**More information** \nFor more information on ordering commercial data ( Third Party Data Import ), please see the [Sentinel Hub Documentation Page](https://docs.sentinel-hub.com/api/latest/api/data-import/).","msgstr":[""]},"You can only view data in 3D while visualizing a collection.":{"msgid":"You can only view data in 3D while visualizing a collection.","msgstr":[""]},"2D map view":{"msgid":"2D map view","msgstr":[""]},"Not supported in 3D mode.":{"msgid":"Not supported in 3D mode.","msgstr":[""]},"Exported image will include legend":{"msgid":"Exported image will include legend","msgstr":[""]},"Image width [px]:":{"msgid":"Image width [px]:","msgstr":[""]},"Image height [px]:":{"msgid":"Image height [px]:","msgstr":[""]},"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d-YOUR-INSTANCEID-HERE&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.":{"msgid":"Browse, visualise and analyze Very High Resolution (VHR) data directly in EO Browser, tapping into global archives of Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) and [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) as well as [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nObserve the planet at resolutions starting at 3 meters and all the way up to 0.5 meters for a cost down to 0.9 EUR per km².\n\n![High resolution imagery example.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, contains Pleiades data processed by Sentinel Hub\n\nWhat you need: \n- An active Sentinel Hub subscription to search the metadata. If you don't have an account yet: [Sign up](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Pre-purchased quota for any of the constellations. Go to [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) to establish a subscription and purchase commercial data plans.","msgstr":["Brskaj, prikaži in analiziraj podatke zelo visoke ločljivosti (VHR) kar v EO Browserju, in dostopaj do globalnih arhivov Planet [PlanetScope](https://docs.sentinel-hub.com/api/latest/data/planet-scope/), Airbus [Pleiades](https://docs.sentinel-hub.com/api/latest/data/airbus/pleiades/) in [SPOT](https://docs.sentinel-hub.com/api/latest/data/airbus/spot/) ter [Maxar WorldView](https://docs.sentinel-hub.com/api/latest/data/maxar/world-view/). \n\nOpazuj planet v ločljivosti vse od 3 metrov do kar 0.5 metra, za ceno od 0.9 EUR na km².\n\n![Primer slike v visoki ločljivosti.](${ process.env.REACT_APP_ROOT_URL }commercial-data-previews/high-res-image-example.png)\n\n© CNES (2020), Distribution AIRBUS DS, vsebuje podatke iz Pleiades procesirane s pomočjo Sentinel Hub\n\nKaj potrebuješ: \n- Aktivno Sentinel Hub naročnino za iskanje metapodatkov. Če računa še nimaš: [Prijava](https://services.sentinel-hub.com/oauth/subscription?param_domain_id=1¶m_redirect_uri=https://apps.sentinel-hub.com/dashboard/oauthCallback.html¶m_state=%2F¶m_scope=¶m_client_id=30cf1d69-af7e-4f3a-997d-0643d660a478&domainId=1).\n- Vnaprej zakupljeno kvoto za katero koli od konstelacij. Obišči [Dashboard](https://apps.sentinel-hub.com/dashboard/#/account/billing) za vzpostavitev naročnine in nakup paketa komercialnih podatkov."]},"DEM instance":{"msgid":"DEM instance","msgstr":[""]},"Type":{"msgid":"Type","msgstr":[""]},"No layers found for date":{"msgid":"No layers found for date","msgstr":[""]},"Creating and editing a timelapse is not supported on mobile.":{"msgid":"Creating and editing a timelapse is not supported on mobile.","msgstr":[""]},"Add layers from pins":{"msgid":"Add layers from pins","msgstr":[""]},"Visualisations":{"msgid":"Visualisations","msgstr":[""]},"Min. tile coverage":{"msgid":"Min. tile coverage","msgstr":[""]},"Edit timelapse":{"msgid":"Edit timelapse","msgstr":[""]},"fps":{"msgid":"fps","msgstr":[""]},"Transition:":{"msgid":"Transition:","msgstr":[""]},"Fade":{"msgid":"Fade","msgstr":[""]},"None":{"msgid":"None","msgstr":[""]},"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.":{"msgid":"Through Norway’s International Climate & Forests Initiative, anyone can now access Planet’s high-resolution, \nanalysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, \ncombat climate change, conserve biodiversity, and facilitate sustainable development. \n\n**Data availability:** world's tropics, September 2015 - August 2020 biannually, from September 2020 monthly.","msgstr":[""]},"Currently only collections on services.sentinel-hub are supported.":{"msgid":"Currently only collections on services.sentinel-hub are supported.","msgstr":[""]},"Built-up probability [0 - 100 %] at 10 m spatial resolution":{"msgid":"Built-up probability [0 - 100 %] at 10 m spatial resolution","msgstr":[""]},"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).":{"msgid":"# The Global Human Settlement Layer GHS-BUILT-S2 \n\n\n\n\n\nThe Global Human Settlement Layer GHS-BUILT-S2 is a global map of built-up areas (expressed as probabilities from 0 to 100 %) at 10 m spatial resolution. It was derived from a Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks.\n\nThis script visualises the built-up probabilities stretched to 0-255.\n\nFor more information about the layer, visit [this website](https://custom-scripts.sentinel-hub.com/copernicus_services/global-human-settlement-layer-ghs-built-s2/).","msgstr":[""]},"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.":{"msgid":"The **Global Human Settlement** (GHS) framework produces global maps of built-up areas, population density and settlements to monitor human presence on Earth over time.\n\n**Coverage**: Global coverage with longitude from 180°W to 180°E and latitude from 72°N to 56°S\n\n**Data Availability**: Reference year 2018\n\n**Spatial resolution**: 10 meters.\n\n**Common Usage**: Knowledge of population distribution and density has a number of applications, including disaster risk management or the study and management of urbanisation processes, not only but also in relation to the challenges of climate change and environmental degradation.","msgstr":[""]},"Resampling kernel":{"msgid":"Resampling kernel","msgstr":[""]},"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)":{"msgid":"Select the resampling kernel to use: \n- 4x4 cubic convolution (CC), \n- nearest neighbour (NN), \n- or the proprietary MTF kernel (MTF)","msgstr":[""]},"4x4 cubic convolution":{"msgid":"4x4 cubic convolution","msgstr":[""]},"nearest neighbour":{"msgid":"nearest neighbour","msgstr":[""]},"proprietary MTF kernel":{"msgid":"proprietary MTF kernel","msgstr":[""]},"Width":{"msgid":"Width","msgstr":[""]},"Height":{"msgid":"Height","msgstr":[""]},"Format":{"msgid":"Format","msgstr":[""]},"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.":{"msgid":"For optimisation reasons MPEG4 output format will always be used for generating a timelapse with transition \"fade\", even when GIF is selected.","msgstr":[""]},"Could not generate timelapse animation file. Try using lower resolution or fewer frames.":{"msgid":"Could not generate timelapse animation file. Try using lower resolution or fewer frames.","msgstr":[""]}}}}
\ No newline at end of file