# Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe
## 6 Conclusions  
The goal of Sentinel-2 constellation is to provide satellite data that can be used to map widely used land surface variables including global land cover and vegetation parameters. ESA has provided both the Sen2 Cor processor for offline processing of single L1C products to L2A products and systematic processing of L2A products using the Sen2 Cor processor integrated in the ESA Data Hub. While considerable effort has been placed in validating and comparing the offline Sen2 Cor processor, the same cannot be said for the operational processor until now. In terms of validation against reference measurements, AOT and WVP estimates from both S2C and ESA provided similar levels of agreement to AERONET estimates (RMSE from 0.06 to 0.09 for AOT and RMSE from 0.20 to 0.22 for WVP) published in other studies (RMSE \(\approx\) 0.14 and for AOT and RMSE \(\approx\) 0.30 for WVP [17; 20]). However, the ESA-AOT bias was \(\sim\)100% larger than the S2C-AOT bias for two scenes over the arid Barrax region. These differences are of similar magnitude to the typical error of existing Sentinel-2 atmospheric correction algorithms for clear sky conditions [20]. Intercomparison of AOT and WVP between S2C and ESA indicated that the differences are not substantial over granules with substantial vegetation, leading us to hypothesize that they are due to differences in the selection of dense dark vegetation targets. Indeed, the Sen2 Cor ATBD envisions atmospheric correction using nine product tiles while the offline S2C implementation uses only one tile. We recommend that both ESA and S2C L2A products provide a map of DDV targets used during processing. Considering the regional variation of sensitivity to DDV target selection, we also recommend that users be provided with either a local uncertainty estimate of BOA reflectance and AOD based on perturbation of DDVs or based on multi-algorithm intercomparisons. Intercomparison of SLC and VIs showed negligible differences between ESA and S2C except for EVI. We hypothesize that the bias between S2C and ESA EVI observed over Barrax was due to a bias in AOT. This would be consistent with the hypothesis that AOT estimates were most uncertain over arid regions with few DDV targets. We recommend that the EVI be used with caution in areas with few DDV targets since biases may change over a growing season as vegetation density changes. Intercomparison of BOA reflectance and vegetation parameters indicated systematic differences related to local slope. The RMSE in BOA reflectance of 4.9% in the red-edge observed over Harth and La Crau sites are of the same magnitude of the L1C radiometric calibration uncertainty and the RMSE  
Figure 9: The slope map and the spatial difference maps between S2C-L2A and ESA-L2A BOA vegetation biophysical parameters over Harth site (DOY: 230). relative to reference estimates based on AERONET sites and in situ measurements. Intercomparison of vegetation parameters indicated differences varied by parameter but were not negligible over rugged terrain in comparison to performance requirements (RMSE = 0.23 for LAI; RMSE = 13.27 for LAIxCab, RMSE = 0.05 for fAPAR and RMSE = 0.07 for fcover). We hypothesize that some of the differences in BOA reflectance and subsequently derived vegetation parameters are due to differences in DEMs used during the rugged terrain correction. However, it is not easy to quantify this impact of the DEM without controlling for atmospheric parameters such as AOT and WVP. One could argue that having a standard DEM across all users will eliminate the difference in downstream products due to DEM specification. However, the fact that there is substantial sensitivity of L2A products to the implemented rugged terrain correction suggests that the accuracy and precision of the correction should be quantified. We recommend that this task be accomplished by the data producers prior to considering the ESA-L2A products completely operational.