# Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR
## 3 Methods
### Comparison of Sentinel-1 and Landsat Snow Maps  
For discussing the performance of S1 snow classification, we use the Landsat snow maps as reference. Landsat snow maps are frequently used as benchmarks for cross-sensor snow map validation [36] so that the S1 snow maps can be evaluated in relation to common snow-products. The three study areas for comparison of Landsat and S1 snow maps cover mountainous terrain. The two Alpine areas extend over an elevation range of about 3500 m, more than twice the range at the Trollaskagi Peninsula where the highest peak reaches 1538 m in elevation. In the Alpine study areas, the snowline on the selected dates was located above the tree line. The Iceland area lacks a forest cover. Dates in late spring were selected for the intercomparison when the snow cover was melting up to the highest elevations. Suitable Landsat images are not available on exactly the same day as the S1 images. For the Iceland area (A1), the time difference is only one day, whereas for the Alpine areas the Landsat images were acquired 3 days later (A2), and 4 days earlier (A3), respectively, than the S1 image (Tables 2 and 3). For the area A1, the impact of the time difference is probably very small, as the good agreement between the Landsat and S1 snow maps shows (Figure 3). Figures 3-5 show the binary Landsat and S1 snow maps at 100 m pixel size. In order to match the grid size of the S1 maps for the statistical comparison, the binary 30 m Landsat snow pixels are aggregated into pixels of 100 m size. This provides an estimate of fractional snow cover at 100 m scale, representing an upper estimate because the \(NDSI\) threshold value of 0.4, used for the binary snow classification, corresponds to a cut-off value of about 50% snow cover [35; 36]. For the statistical comparison (Table 4), we generate a binary Landsat snow map with 100 m pixel size by assigning all pixels with fractional snow extent \(\geq\) 75 % to the snow class. This map is also used for defining snow/snow-free areas to extract the S1 backscatter ratios for Figure 2. Areas with clouds in Landsat images and areas with layover, foreshortening (S1 local incidence angle \(\theta<15^{\circ}\)), backslopes with \(\theta>75^{\circ}\), and dense forests in S1 images are masked out. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Area** & **Date \& Time UTC** & **Orbit Heading** & **Track Nr.** & \(\theta\) \\ \hline A1, Trollaskagi, Iceland & 26 June 2015 07:41 & Descending & 9 & 40\({}^{\circ}\) \\ A2, Otztal Alps, Austria & 2 June 2015 05:26 & Descending & 168 & 37\({}^{\circ}\) \\ A3, Swiss Alps & 7 June 2015 05:35 & Descending & 66 & 42\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Information on Sentinel-1 images used for comparison with Landsat snow maps. The incidence angle, \(\theta\), refers to a horizontal surface in the center of each area. Figure 4: Snow map of the Ötztal mountain region, Eastern Alps of Austria and Italy. (**Left**) Snow extent (blue) derived from Landsat-7 image of 5 June 2015; yellow: clouds; white: water; background: Landsat-7 ETM+ bands 5, 4, 3. (**Right**) Wet snow extent (blue) derived from Sentinel-1 image of 2 June 2015; background: S1 amplitude image, \(VV\)-channel; orange: mask for layover and foreshortening. Figure 3: Snow map of Tröllaskagi Peninsula, Iceland. (**Left**) Snow extent (blue) derived from Landsat-8 image of 27 June 2015; white: water; background: Landsat-8 OLI bands 6, 5, 4. (**Right**) Wet snow extent (blue) derived from Sentinel-1 image of 26 June 2015; background: S1 amplitude image, \(VV\)-channel; orange: mask for layover and foreshortening.