Systematic validation of Sentinel-2 THEIA L2A products

=> Very recently, Camille Desjardins (from CNES), who is handling the validation of the L2A products generated by THEIA, has set up a systematic validation of the products delivered by MAJA, with the help of an operational service from CNES (OT/PE) (Bruno Besson, and Nicolas Guilleminot from Thales Services, using tools developed by Aurélie Courtois, also from Thales) Systematically, a comparison of AOT and water vapour is made for every Sentinel-2 L2A product from THEIA which observes one of the sites of the Aeronet network. Both plots below show the results obtained during the month of February, for the Aerosol Optical Thickness (left), and for the water vapour content (right). Blue dots correspond to validations in ideal conditions (low cloud amount, no gap filling, and quality assured Aeronet data (Level 2.0). The red dots allow degraded conditions, and most of them correspond to the unavailability, yet, of version 2.0 Aeronet data. As data are processed in near real time, and level 2.0 data are made available a few months later, these plots rely mainly on Level 1.5 data, which are more prone to errors (such as a calibration drift… or the presence of a spider in the instrument tubes). 

Aerosol optical thickness validation of Sentinel-2 L2A for all Aeronet match-ups gathered in February 2018	Water vapour validation of Sentinel-2 L2A for all Aeronet match-ups gathered in February 2018 (in g/cm2)

The results are pretty good ! The water vapour results are as usual, with a very accurate determination for low water vapour content, and an overestimation when there is too much water vapour. This is due to the very simple model we use, which supposes the water vapour is above the over atmospheric layers and not mixed. But as we use the same model to perform the atmospheric correction, these errors should be really small. Regarding AOT, we are getting much enhanced results compared to what we had usually. It is probably related to the continuous improvements we bring to MAJA and its parameters, and to the increased repetitivity of observations due to the availability of S2A and S2B working at full capacity. As our multi-temporal methods assume that the surface reflectance does not change from one image to the next one, the more frequent observations, the better results. But it is probably not the only reason. Our aerosol estimates are usually good when the aerosol type present in the atmosphere is the same as the one we specified in MAJA processor: a continental model. When the aerosol model is wrong, often in the case of dust, our AOT estimates are too low. These cases correspond to the points in the lower part of the AOT diagram. They are probably much less frequent in winter, as the ground is more wet, and the dust is less easily blown by the wind. In a few months, the version 3 of MAJA will be installed in MUSCATE, and this version will use an aerosol type variable with time and location, thanks to the use of aerosol forecasts from Copernicus Atmosphere Monitoring Service. This was explained in Bastien Rouquié’s post, and should significantly improve our results.