## 3 Results
### Comparison with In Situ Measurements from Weather Stations  
The temporal validation of the reconstructed series was achieved by comparison with Snow Depth data measured by weather stations, the results of which are shown in Figure 9. Producer and User Accuracies of the two classes are shown in the Supplementary material. All three approaches have almost equal behavior at all stations, except for Locana Rosone and Rosone, both of which are in the valley bottom, at 700 m altitude, about 240 m apart, where the performances of the Snow class are low, especially for A1. This issue may stem from the fact that the valley floors are heavily shaded, so the training dataset, as well as the validation against the S2 themselves, has a low capacity in correctly detecting snow. The overall accuracy is almost the same for the three approaches: always above 90% up to about 95%, in all the weather stations analyzed, except for the Rosone station, where A1 shows a slightly worst performance. On closer analysis, the Snow class metrics reveal that the behavior between A1 and the other two approaches diverges in the Locana Rosone and Rosone stations, which are characterized by proximity to each other and lower altitude (both at about 700 m a.s.l.). The other stations, on the contrary, are all above 1500 m a.s.l. Clearly, the location of the weather stations equipped with snow gauges is linked to the presence of snow, so they are typically located in open areas at high altitudes, and, thus, the spatial representativeness of the data obtained for analysis must be considered. Snow's  
Figure 8: Example of the snowline (SWL) elevation and pixels predicted against the real S2 scene acquired on 24 June 2016. **Top left**: natural color S2 with close-up area. **A1–A3**: close-up with real S2 SCE map in background and SWL pixels and elevation computed, respectively, with A1, A2, and A3 approaches. PA is always around 90%, except for the two aforementioned weather stations, where A1 reaches higher values than A2 and A3. Considering [PERSON]'s UA, A1 shows the worst performances, with values less than 20%, while A2 and A3 values only decrease to 50-60%. The F1-score confirms the worst behavior of A1 compared to A2 and A3 for Locana Rosone and Rosone stations, while for all the other stations the performances are virtually equal. A1, therefore, has a higher Producer Accuracy of the Snow class than A2 and A3, but, at the same time, of the times that it indicates the presence of snow, this is true at a lower rate than A2 and A3 (lower User Accuracy). In other words, at the Locana Rosone and Rosone stations, all three overestimate the snow class; A1 to a greater extent than A2 and A3. The metrics of the No-Snow class are also essentially the same for the three approaches at all stations. Finally, the snow phenology parameters were tested against weather stations (Table 5). The FSD is estimated with an RMSE of 17.2 days. In absolute values, the prediction is wrong by 7.9 days, while on average the error is 2.5 days. The LSD is predicted with an RMSE of 8.7 days, while the days of difference between the predicted and the observed is 5.5 in absolute value, or, on average, 2 days. Finally, the SCD shows an RMSE of 18.9 days, and a difference of 11 days in absolute value and 0.5 days on average. These values are total for all stations. As pointed out by other authors [90; 91], this validation approach based on the comparison of two spatially different types of data (bi-dimensional 20 \(\times\) 20 m pixels on the one hand and uni-dimensional point data on the other) could lead to errors due to inaccuracies in satellite images georeferencing and co-registration, and, in addition, point data may not be representative of the pixel. However, weather stations represent useful temporally \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **RMSE (Days)** & **MAE (Days)** & **MBE (Days)** \\ \hline FSD & 17.2 & 7.9 & 2.5 \\ LSD & 8.7 & 5.5 & 2.0 \\ SCD & 18.9 & 11.0 & 0.5 \\ \hline \hline \end{tabular}
\end{table}