## 6 Discussion

Table 17: Relevant statistics per main biome type and the compliance matrix of the direct validation of the MCD43A3 C6 blue-sky albedo satellite retrievals (best quality pixels, Table 3) versus the ground values (RMs) from 20 GBOV stations during the 2014–2018 period. N stands for the number of samples. reasons for the higher fraction of missing data of PROBA-V SA v1.0 products compared to SPOT/VGT SA v1.0. Discrepancies between different albedo estimates can also be attributed to the different BRDF models used [7]. Moreover, the performance of the BRDF model for good clear-sky observations also depends on the number of available looks during the synthesis period and the angular distribution of the sampling. Large BRDF uncertainties are associated with snow targets (as observed for the highest albedo ranges) for which none of these parametric BRDF models were well suited [73]. PROBA-V, SPOT/VGT and MODIS are wide-FOV sensors on polar-orbiting platforms, and a low impact of discrepancies is expected, due to different sun-view configurations. However, the different compositing periods (see Table 1) could play an important role in the differences between satellite products, mainly in rapid SA variations, such as snow events. Different techniques for temporal composite approaches also affect the completeness of EO satellite products. SPOT/VGT SA v1.0 uses a recursive temporal composition approach [9], which is the main reason for the improved completeness compared to the other satellite products (C3S PROBA-V SA v1.0 and MCD43A3 C6) that are computed using classical composite schemes based on predefined temporal windows. In the last step, the broadband albedos are defined using slightly different spectral regions. The same broadband spectral regions are defined in both C3S PROBA-V SA v1.0 and SPOT/VGT SA v1.0 products, which contribute to a better agreement, whereas the MCD43A3 C6 broadband albedos are slightly differently defined. In addition, the temporal noise (i.e., large temporal variability) observed for C3S PROBA-V SA v1.0 in the NIR domain through the qualitative inspection of the temporal variations (see Section 5.1.2) has a strong relationship with the precision, since low intra-annual precision was found compared to both reference EO products in this spectral range. In terms of the inter-annual precision, improved results were found for C3S PROBA-V SA v1.0 (\(\sim\)1%) compared to C3S SA v1.0 based on SPOT/VGT SA data (2%). Regarding the accuracy with the ground measurements, PROBA-V SA v1.0 provided a similar accuracy (bias of 11.5%) to that found for C3S SPOT/VGT SA v1.0 during the validation exercise [44], where a positive bias (14%) was also reported for a different sampling (i.e., different stations and dates). PROBA-V SA v1.0 also provided a slightly worse accuracy (median error of 18.2%) than MCD43A3 C6 (median error of 11.2%). PROBA-V SA v1.0 tends to overestimate the ground values, whereas MCD43A3 C6 showed the opposite sign of the mean bias. The positive bias of PROBA-V SA v1.0 was mainly observed for forest sites (SA \(<\) 0.2) explained in the fact that the Roujean kernel [42] for geometrical scattering may not fit some cover types well, especially dense forest canopies, where it showed a weak hotspot effect [74; 75; 76]. The negative bias of MCD43A3 C6 is mainly influenced by some outliers detected in _Gobabeb_ (bare soil) and _Cabaw_ (grassland). For the _Cabaw_ case, the lower MCD43A3 C6 values are explained by the persistent cloudiness at the MODIS overpass times [56]. It is important to note that only the satellite retrievals classified as the best quality, according to QFLAGS (Table 3), were used in the direct validation.