## 3 Results  
### Comparison with Real Sentinel-2 SCE Maps  
Spatial validation of the predicted images was achieved by comparison with S2 images. All of the images with cloud cover between 10% and 15% were selected, which were excluded from the RF training but still characterized by high accuracy (>96%). A total of 44 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & **Elevation** & **Slope** & \multirow{2}{*}{**Aspect**} & \multicolumn{2}{c}{**UTM Coordinates (m)**} \\ \cline{3-5}  & **(m a.s.L)** & & & **Nothing** & **Easting** \\ \hline Ceresole Reale & 1573 & 4.6 & SW & 5,032,244 & 362,681 \\ Ceresole Villa & 1581 & 2.5 & SE & 5,033,408 & 360,081 \\ Eugio & 1900 & 12.9 & NE & 5,035,124 & 378,243 \\ Lago Serrü & 2283 & 12.4 & N & 5,035,792 & 354,154 \\ Lago Agnel & 2304 & 8.2 & E & 5,036,613 & 354,538 \\ Lago Valsoera & 2365 & 7.8 & SE & 5,038,103 & 374,395 \\ Locana Rosone & 700 & 4.9 & E & 5,032,556 & 376,343 \\ Rosone & 701 & 5.8 & S & 5,032,323 & 376,293 \\ Telessio & 1940 & 17.5 & NW & 5,037,970 & 372,845 \\ Val Soera & 2412 & 21.8 & W & 5,038,281 & 374,628 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Location and topographic features of the weather stations used for validation (CRS: WGS84 UTM 32N). F1 = Flat, N = North, NE = North-East, E = East, SE = South-East, S = South, SW = South-West, W = West, NW = North-West. scenes, spread over the various months between 2015 and 2021, met these requirements and were used for the comparison, the results of which are shown in Figure 5. Producer and User Accuracies of the two classes are shown in the Supplementary Materials. In general, the A1 approach reaches the highest overall accuracy (A1 = 93.6%), closely followed by the other two approaches (A2 and A3 = 92.6%). Even considering the performance in individual seasons, there is no outstanding approach compared to others. Snow and No-Snow classes have an opposite behavior: the critical seasons for one class are those in which the other achieves better metrics, and vice versa. The summer season (JJA = June-July-August) is the one which presents the lowest performances for Snow (range values for A1, A2, and A3: PA = 0.81-0.84, UA = 0.89, F1 = 0.85-0.86), while the No-Snow class presents very high values (range values: PA = 0.98, UA = 0.96-0.97, F1 = 0.97). The contrary happens during the winter season (DJF = December-January-February), during which the Snow class has better performances (range values: PA = 0.95-0.96, UA = 0.94-0.95, F1 = 0.95) compared to the No-Snow class (range values: PA = 0.82-0.86, UA = 0.87-0.88, F1 = 0.84-0.87). Unexpectedly, an intermediate behavior occurs in spring (MAM = March-April-May) and autumn (SON = September-October-November), which correspond, respectively, to the snow melting and accumulation phase, in which instable snow behavior is expected, and, thus, is harder to predict. During spring, the Snow keeps high metric values (range values: PA = 0.93-0.95, UA = 0.95, F1 = 0.94-0.95), slightly higher than those for No-Snow (range values: PA = 0.91-0.93, UA = 0.89-0.91, F1 = 0.91). Finally, in fall, the Snow presents lower performances (range values: PA = 0.86-0.88, UA = 0.89-0.91, F1 = 0.88-0.90) than No-Snow (range values: PA = 0.95-0.96, UA = 0.93-0.94, F1 = 0.94-0.95). The results show that the trend of metrics by class goes hand in hand with their representativeness: in winter and spring, the snow class is the most extensive and reaches higher values, while the Non-Snow class, limited to a few pixels, has lower values. In contrast, in summer and autumn, when the situation is reversed and snow-covered territory is limited, the opposite occurs. Figure 6 shows the algorithm's performances with respect to different elevations, slopes, and aspects. A2 and A3 perform about the same in all three cases, while A1 shows a slightly different behavior, in particular considering slopes below 10 degrees and the Flat aspect. In all cases, A1 reaches higher performances compared to the other two approaches.