## 3 Results
### Comparison with Real Sentinel-2 SCE Maps
The Macro-F1 score reaches the maximum value around 2500 m a.s.l., where both classes are more equally represented, while, at the extremes, where one or the other class is practically never present, the metric shows low performance. This occurs because the training dataset is skewed toward one of the two classes and, therefore, overestimates them, rather than correctly identifying the minority class when present. Regarding slope areas  
Figure 5: Accuracy metrics for Snow and No-Snow classes of the classification’s performances against an independent dataset of S2 scenes. F1 = F1-score, OA = Overall Accuracy, DJF = December-January-February, MAM = March-April-May, JJA = June-July-August, SON = September-October-November. A1 = blue line, A2 = red line, A3 = yellow line. with low slopes, i.e., less than 5 degrees, they are mainly distributed in the valley bottoms, i.e., at lower elevations, just as higher slopes correspond to higher elevations. This explains the similar trend among Macro-F1 metrics with respect to elevation and slope. Finally, the different exposure does not affect, in all three cases, the performance of the algorithm, which is high for all exposures, except the Flat one. This is because, in both A2 and A3, the watersheds are masked with a fixed layer during the transition from the NDSI map to the binary snow map, while, in A1, the determination of snow cover is directly determined. Slope and aspect maps of the study area are available in the Supplementary Materials. Figure 7 shows the prediction accuracy of the predicted snowline (SWL) elevation against the independent dataset of real S2 scenes, with RMSE, MAE, and MBE reported in Table 4. The SWL altitude is computed as the lower altitude at which the snow covers above 50% of the altitudinal band. Being the snow map of 20 m resolution, each altitudinal band investigated is of 20 m in elevation. The SWL pixels are the edges of the snow patches retrieved (Figure 8). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RMSE (m)** & **MAE (m)** & **MBE (m)** \\ \hline
**A1** & 162 & 85 & 50 \\
**A2** & 162 & 98 & 29 \\
**A3** & 223 & 113 & 44 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Bias Error (MBE) of the predicted snowline elevation. Figure 6: Macro-F1 score of the predicted snow cover maps according to elevation (**left plot**), slope (**middle plot**) and aspect (**right plot**). A2 (red) and A3 (yellow) are almost coincident in the three graphs. (Fl = Flat, N = North, NE = North-East, E = East, SE = South-East, S = South, SW = South-West, W = West, NW = North-West). Figure 7: Scatterplots of the predicted and observed snowline elevation (SWL) (m a.s.l.) for the three approaches (**A1–A3**). In 25% of times (11 times out of 44 observations), the snowline is predicted with an elevation error of 0 m. However, the results show that there are outliers in the time series, up to an estimation error of 680 m. This suggests that the overall time series is reliable, but the single predicted scenes are not always, and it would be useful to integrate a prediction check based on contiguous days into the algorithm to eliminate these outliers.