Next Article in Journal
A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR
Next Article in Special Issue
The Potential of Sentinel Satellites for Burnt Area Mapping and Monitoring in the Congo Basin Forests
Previous Article in Journal
Grassland and Cropland Net Ecosystem Production of the U.S. Great Plains: Regression Tree Model Development and Comparative Analysis
Previous Article in Special Issue
Water Constituents and Water Depth Retrieval from Sentinel-2A—A First Evaluation in an Oligotrophic Lake
Open AccessTechnical Note

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

Institute of Surveying, Remote Sensing & Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Str. 82, 1190 Vienna, Austria
Department of Civil, Environmental, Aerospace, Materials Engineering (DICAM), University of Palermo, Viale Delle Scienze, Bld. 8, 90128 Palermo, Italy
Institut National de la Recherche Agronomique—Université d’Avignon et des Pays du Vaucluse (INRA-UAPV), 228 Route de l’Aérodrome, 84914 Avignon, France
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao and Prasad S. Thenkabail
Remote Sens. 2016, 8(11), 938;
Received: 1 August 2016 / Revised: 12 October 2016 / Accepted: 6 November 2016 / Published: 11 November 2016
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). View Full-Text
Keywords: Sentinel-2; atmospheric correction; Sen2Cor; LAI; broadband HDRF Sentinel-2; atmospheric correction; Sen2Cor; LAI; broadband HDRF
Show Figures

Graphical abstract

MDPI and ACS Style

Vuolo, F.; Żółtak, M.; Pipitone, C.; Zappa, L.; Wenng, H.; Immitzer, M.; Weiss, M.; Baret, F.; Atzberger, C. Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples. Remote Sens. 2016, 8, 938.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop