# Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe
## 5 Discussion  
Validation of AOT and WVP using AERONET measurements indicated similar levels of RMSE and bias reported in [PERSON] [17] and Doxani [20]. S2C-AOT exhibited systematically lower bias than ESA-AOT. While this systematic difference was not large overall, it was substantial (\(\geq\)0.15 units or \(\geq\)100% of the in situ AOT) for two dates over Barrax corresponding to DOY 169 and 227. Biases in AOT lead us to hypothesize that the S2C and ESA algorithms are using different dark dense vegetation (DDV) targets or assume different values for DDV reflectance during processing. The S2C algorithm theoretical basis document [34] specifies that the L1C input to S2C should correspond to nine 100 km \(\times\) 100 km product grid tiles centred on the nominal centre product grid tile. The offline S2C processor operates on only the data contained in the nominal centre product grid tile. This data includes granules from the same data take that extend past the grid tile boundary. However, these granules do not contain all data in adjacent tiles within the data take and it is not clear whether the DDV algorithm operates using data past the tile boundary. As such, we hypothesize that there may be different DDV targets used by the ESA-L2A and S2C-L2A products. This is especially critical in areas such as Barrax where there are potentially few DDV targets. Maps of DDV targets are not provided with ESA-L2A or S2C-L2A products so we cannot further assess this concern. A second concern is that DDV targets are used to provide an estimate of BOA reflectance initially assuming a certain (clear sky in this case) atmosphere for infrared wavelength bands. The initial DDV reflectance is then iterated, which will result in sensitivity to differences in the rugged terrain correction induced by differences in input DEM. This second source of differences between ESA-L2A and S2C-L2A is likely small since it would result in substantial differences (e.g., order 0.1) in AOT over vegetated regions while overall biases in AOT at Harth and La Crau were less than 0.02 and less than 0.05 on a scene specific basis. Intercomparison of reflectance indicated a spatially persistent difference related to the topographic slope. The S2C ATBD indicates a rugged terrain correction is applied where local slope exceeds 7\({}^{\circ}\). This correction increases the incident irradiance on the target by adding a factor proportional to the cosine squared of the local slope and the reflectance of surrounding 500 m \(\times\) 500 m. We hypothesize that the large differences in BOA reflectance observed over mountainous areas could be due to differences in DEMs used as input to ESA-L2A and S2C-L2A notwithstanding the fact that the ESA-L2A metadata indicates the same DEM was used as we used for S2C-L2A products. Testing this hypothesis is complicated by the rugged terrain correction depending on initial estimates of reflectance of surrounding pixels that will vary with spectral band and with differences in AOT between products. Irrespective of the hypothesized cause, observed differences in the BOA reflectance approach (for bands Blue, Green, Red, VRE 1 and SWIR 2) exceed (for bands VRE 2, VRE 3, NIR and SWIR 1) the uncertainty due to radiometric calibration and have comparable magnitude to the uncertainty in S2C-L2A reflectances cited in Doxani [20] over AERONET sites. Both NDVI and NDWI rely on bands for which observed differences in BOA reflectance between ESA-L2A and S2C-L2A were similar. As a result, the impact of these differences on NDVI and NDWI was negligible. Indeed, this is what we would have hoped for all bands and parameters. The larger systematic difference for EVI is likely due to the large sensitivity of the blue band to small differences in AOT [35]. For vegetation biophysical parameters, differences between ESA and S2C, especially over rugged terrain, were non negligible and in some cases exceeded 10% in comparison to the site average.