## 6 Discussion  
C3S SA v1.0 based on PROBA-V provides continuity to the CDRs of global albedo products in the C3S from December 2013 onwards. Previously, SA products were derived from SPOT/VGT (1998-2014) and NOAA/AVHRR (1981-2005) data. Good spatio-temporal consistency in the transition from SPOT/VGT to PROBA-V for both black-sky and white-sky albedos, with mean biases below \(\pm\)3% for the overlap period, was found. However, the comparison of C3S PROBA-V SA v1.0 with MCD43A3 C6 SA showed lower spatio-temporal consistency between satellite products with mean biases up to 13%. C3S PROBA-V SA v1.0 (and MCD43A3 C6) displayed more gaps (typically between 10% and 20%) than C3S SPOT/VGT v1.0. Different aspects of the retrieval methodology play important roles in the existing discrepancies between the satellite products under study (C3S PROBA-V SA v1.0, C3 SPOT/VGT SA v1.0 and MCD43A3 C6) starting from the different input data and different atmospheric correction methods; including the BRDF parameterization, temporal compositing and angular integration; and finalizing with the narrow to broadband conversion. Regarding the input data, each sensor works in different spectral channels (see Table 2). The SPOT/VGT and PROBA-V channels provide very similar spectral characteristics in the Blue, Red and NIR bands, whereas significant differences are found for the SWIR channel. The central wavelengths of the MODIS spectral bands are comparable to the corresponding bands for PROBA-V and VGT, albeit the MODIS spectral bands are narrower. These differences could translate into reflectance discrepancies in regions with high absorption features like in the visible domain, where large uncertainty values were found between pairs of products. Each satellite processing chain uses its own method for cloud/shadow screening and atmospheric correction according to the spatial, spectral and directional capabilities of each instrument. Cloud or snow contamination is the main reason for missing data in the EO products derived from optical onboard satellite sensors. The conservative PROBA-V cloud detection algorithm [72] is one of the \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**PROBA-V SA v1.0 vs. GBOV RM**} \\ \hline  & **Forest** & **Crops** & **Grass/Shrubs** & **Desert** \\ \hline
**Number of Samples (stations)** & 608 (8) & 488 (6) & 316 (3) & 303 (3) \\ \hline
**Median Error (Median Error \%)** & 0.037 (27.0\%) & 0.036 (18.3\%) & 0.015 (7.8\%) & 0.037 (16.7\%) \\
**Bias (Bias \%)** & 0.030 (22.4\%) & 0.014 (7.4\%) & 0.008 (4.2\%) & 0.023 (10.4\%) \\
**RMSD (RMSD \%)** & 0.044 (32.6\%) & 0.044 (22.4\%) & 0.024 (12.2\%) & 0.038 (17.2\%) \\ \hline
**\% optimal (GCOS)** & 6.1 & 12.4 & 38.9 & 15.5 \\
**\% target (C3S KPI)** & 13.8 & 25.8 & 59.5 & 30.4 \\
**\% threshold** & 22.9 & 38.7 & 75.3 & 63.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Relevant statistics per main biome type and the compliance matrix of the direct validation of the C3S PROBA-V SA v1.0 blue-sky albedo satellite retrievals (best quality pixels, Table 3) versus the ground values (RMs) from 20 GBOV stations during the 2014–2018 period. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**MCD43A3 C6 vs. GBOV RM**} \\ \hline  & **Forest** & **Crops** & **Grass/Shrubs** & **Desert** \\ \hline
**Number of Samples (stations)** & 608 (8) & 488 (6) & 316 (3) & 303 (3) \\ \hline
**Median Error (Median Error \%)** & 0.015 (12.1\%) & 0.021 (11.3\%) & 0.020 (11.5\%) & 0.017 (8.4\%) \\
**Bias (Bias \%)** & 0.003 (2.8\%) & \(-\)0.012 (6.7\%) & \(-\)0.027 (15.3\%) & \(-\)0.013 (6.5\%) \\
**RMSD (RMSD \%)** & 0.031 (25.2\%) & 0.051 (20.8\%) & 0.042 (24.1\%) & 0.036 (17.8\%) \\ \hline
**\% optimal (GCOS)** & 21.5 & 25.4 & 18.4 & 30.7 \\
**\% target (C3S KPI)** & 41.1 & 44.5 & 43.4 & 58.1 \\
**\% threshold** & 58.9 & 53.5 & 52.8 & 68.6 \\ \hline \hline \end{tabular}
\end{table}