# Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR
## 3 Methods
### Comparison of Sentinel-1 and Landsat Snow Maps
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Area**} & \multicolumn{4}{c}{\(R_{up}\)} & \multicolumn{4}{c}{\(R_{uh}\)} & \multicolumn{4}{c}{\(R_{c}\)} \\ \cline{2-11}  & & **S1-S** & **S1-F** & _AR_ & **S1-S** & **S1-F** & _AR_ & **S1-S** & **S1-F** & _AR_ \\ \hline \multirow{4}{*}{**A1**} & **LS-S** & 92.8 & 7.2 & & 92.6 & 7.4 & & 94.6 & 5.4 & \\  & **LS-F** & 2.0 & 98.0 & & 0.2 & 99.8 & & 0.2 & 99.8 & \\  & & & & 0.954 & & & 0.962 & & & 0.972 \\ \hline \multirow{4}{*}{**A2**} & **LS-S** & 80.7 & 19.3 & & 94.5 & 6.4 & & 94.7 & 5.3 & \\  & **LS-F** & 4.2 & 95.8 & & 5.3 & 94.7 & & 3.2 & 97.8 & \\  & & & & 0.882 & & & 0.946 & & & 0.962 \\ \hline \multirow{4}{*}{**A3**} & **LS-S** & 72.9 & 27.1 & & 93.6 & 6.4 & & 92.2 & 7.8 & \\  & **LS-F** & 6.5 & 93.5 & & 4.6 & 95.4 & & 3.1 & 96.9 & \\ \cline{1-1}  & & & & 0.832 & & & 0.945 & & & 0.946 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix for the classes snow (S) and snow-free (F) in the 3 study areas, for snow classification based on Landsat (LS) and Sentinel-1 (S1) data. S1 results are shown for snow maps based on the ratios \(R_{vv}\), \(R_{uh}\), and \(R_{c}\). _AR_ —overall agreement rate (\(0.0\leq AR\leq 1.0\)). Figure 5: Snow map of Swiss Alps (Berne, Valais). (**Top**) Snow extent (blue) derived from Landsat-7 image of 3 June 2015; background: Landsat ETH+ bands 5, 4, 3; yellow: clouds; white: water. (**Bottom**) Snow extent (blue) derived from Sentinel-1 image of 7 June 2015; background: amplitude image, \(VV\)-channel; orange: mask for layover and foreshortening. For compiling the confusion matrix between Landsat and S1 snow classification (Table 4), the total pixel number is normalized according to the Landsat-based classification result, assigning the value 100 to each class. This normalization enables a direct comparison of the performance for the different study areas and for the different ratio images. The overall agreement rate (\(0.0\leqslant AR\leqslant 1.0\)) shows for areas A1 and A2 the best agreement for the combined ratio (\(R_{c}\)) and a small decline for \(R_{vh}\). For A3, the performance of \(R_{c}\) and \(R_{vh}\) is the same. The drop-off in the \(AR\) of \(R_{vv}\) is significant for the areas A2 and A3. We tested the application of different threshold values for segmentation in the \(R_{vv}\) images, but this does not result in improvements. The lower performance of \(R_{vv}\) for snow detection is in accordance with the smaller difference between the ratios of snow-covered and snow-free areas, in particular at lower incidence angles (Figure 2). Trollaskagi Peninsula has the highest overall agreement rate (0.97), followed by Ozttal Alps (0.96) and Swiss Alps (0.95). The time difference of three (Otztal), respectively four (Swiss Alps) days, between the Landsat and S1 snow maps may play a role. Moreover, the two Alpine areas have steeper topography than the Trollaskagi Peninsula, as is obvious from the higher fractions of layover and foreshortening in Figures 4 and 5_versus_ Figure 3. The impact of topography seems to be substantial for the \(R_{vv}\) based classification, as indicated by the lower AR values for Ozttal and the Swiss areas.