Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products:System Use and Examples

Claudia Pipitone, Markus Immitzer, Francesco Vuolo, Clement Atzberger, Mateusz Zóltak, Luca Zappa, Hannah Wenng, Claudia Pipitone, Hannah Wenng, Mateusz Zóltak, Hannah Wenng, Frederic Baret, Marie Weiss

Research output: Contribution to journalArticlepeer-review

125 Citations (Scopus)


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’s (ESA) Sen2Cor algorithm, the platform processes ESA’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).
Original languageEnglish
Number of pages16
JournalRemote Sensing
Publication statusPublished - 2016

All Science Journal Classification (ASJC) codes

  • General Earth and Planetary Sciences


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