CORDIS OpenAire Sygma HAL INRAE Bielefeld Academic Search Engine (BASE) UnpayWall ProdInra DOAJ Crossref Microsoft Academic Graph European Union Open Data Portal http://www.mdpi.com/2072-4292/8/11/938/pdf https://doi.org/10.3390/rs8110938 Remote Sensing Open Access 2016-11-11 2020-05-27 <p>This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency￢ﾀﾙs (ESA) Sen2Cor algorithm, the platform processes ESA￢ﾀﾙs Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).</p> 550 reflectance télédétection Science 0211 other engineering and technologies 02 engineering and technology 7. Clean energy remote sensing Traitement du signal et de l'image atmospheric correction remote sensing;sentinel-2;atmospheric correction;Sen2Cor;LAI;broadband HDRF [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing 9. Industry and infrastructure sentinel-2 Q Signal and Image processing 04 agricultural and veterinary sciences broadband HDRF 620 LAI atmosphère Sen2Cor 13. Climate action 0401 agriculture, forestry, and fisheries Sentinel-2 [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing donnée satellitaire Francesco Vuolo, Mateusz Żółtak, Claudia Pipitone, Luca Zappa, Hannah Wenng, Markus Immitzer, Marie Weiss, Frederic Baret, Clement Atzberger, 2016-11-11 journalpaper <p>This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency￢ﾀﾙs (ESA) Sen2Cor algorithm, the platform processes ESA￢ﾀﾙs Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).</p> Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples 10.3390/rs8110938