Abstract
Abstract. To allow for a robust and automatic exploitation of Sentinel-2 data, Analysis Ready Data (ARD) products are requested by most users. The processors of ARD products take care of the common burdens necessary for most applications, that include precise orthorectification, cloud detection and atmospheric correction steps, as well as the generation of periodic syntheses of cloud free surface reflectances. The French Theia land data center, and the German Earth Observation Center (EOC) started delivering Sentinel-2 surface reflectance products to users in 2016 in France and 2019 in Germany respectively. Both centers produce and distribute these data sets in near real time, over large regions requested by French users such as Western Europe, Maghreb, Sahel, Madagascar… Theia’s and EOC products include an instantaneous surface reflectance product (Level-2A), and a monthly cloud free synthesis of surface reflectance (Level-3A). This article shortly describes the methods used to generate the Level-2A products with the MAJA processor, and the Level-3A products with theWASP processor. The MAJA processor is based on multi-temporal methods, that use the slow variation of surface reflectance to detect clouds and estimate aerosol depth, while WASP, thanks to the quality of MAJA cloud mask, calculates a weighted average of all the cloud free observations over 45 days, every month. The article also provides validation results for Level-2A and Level-3A products, resulting from comparison with in-situ data and with other methods. A last section gives first insights from the monitoring of user uptake of the distributed products.
Highlights
In the early decades of remote sensing data science, users would spend a large amount of their time devoted to remote sensing at ortho-rectifying, calibrating, correcting atmospheric effects and obtaining cloud free surface reflectance images
∗ Corresponding author (MAJA), which is the result of a common effort of the French and German space agencies, the Centre National d’Etudes Spatiales (CNES) and the Deutsches Zentrum fur Luft- und Raumfahrt (DLR), and of the Centre d’Etudes Spatiales de la BIOsphere (CESBIO)
Thanks to the multi-temporal criteria, Multi-temporal Atmospheric Correction and Cloud Screening software (MACCS)-ATCOR Joint Algorithm (MAJA) retrieves aerosol optical depth (AOD) even above arid landscapes, with a reduced accuracy compared to AOD retrieved over vegetation
Summary
In the early decades of remote sensing data science, users would spend a large amount of their time devoted to remote sensing at ortho-rectifying, calibrating, correcting atmospheric effects and obtaining cloud free surface reflectance images. The French Land Data center, Theia, at CNES, and the Earth Observation Center (EOC), at DLR, produce and distribute Level-2A products (L2A) thanks to the MACCS-ATCOR Joint Algorithm (MAJA), which is the result of a common effort of the French and German space agencies, the Centre National d’Etudes Spatiales (CNES) and the Deutsches Zentrum fur Luft- und Raumfahrt (DLR), and of the Centre d’Etudes Spatiales de la BIOsphere (CESBIO). Compared to classical processors based on multi-spectral relations to detect clouds and estimate aerosol content, MAJA involves multi-temporal criteria, which assume that land surface reflectance tends to change slowly as compared to the atmospheric effects due to clouds and aerosols. MAJA is the only Level-2A processor using multi-temporal criteria, it is using more information to detect clouds and aerosols than the other methods
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