# Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR
## 5 Discussion  
The investigations demonstrate the excellent capability of the Sentinel-1 mission for operational monitoring of snowmelt areas. The proposed algorithm, based on change detection in multitemporal SAR images, represents an extension to previously applied procedures for wet snow mapping. It exploits the advanced technical features of the S1 SAR sensor. Of particular benefit for the quality of the snowmelt area maps are the high instrument stability and the high spatial resolution across the comparatively wide swath of the IW mode [21]. The electronic azimuth scanning by means of the  
Figure 7: Map of snowmelt area extent over the Alps, derived from Sentinel-1 data of 20 to 23 April 2015 (Date 1), 14 to 17 May 2015 (Date 2), 1 to 4 July 2015 (Date 3). Light blue: decrease of snow extent between Date 1 and Date 2. Dark blue: decrease of snow extent between Date 2 and Date 3; Purple: snowmelt area on Date 3. Black: water surfaces; orange: mask of layover and foreshortening. Background: S1 SAR amplitude image, \(VV\) polarizations.  
TOPSAR operation, harmonizing the performance in along-track direction and reducing scalloping effects, offers significant improvements in terms of radiometric quality compared to the conventional ScanSAR operation mode [3]. Due to the high spatial resolution of S1 SAR and the application of multichannel filtering in the snow mapping algorithm, the speckle-related uncertainty for the multi-temporal ratio images is very low.  
Another beneficial feature for snow mapping is the regular acquisition of dual polarization (_VV-VH_) IW mode data, being used as standard operation scenario over many land areas [37]. Co-polarized channel data are the preferred option for interferometric applications, a main application field of the S1 mission. On the other hand, for retrieving snowmelt areas, the added value of cross-polarized data is significant, in particular in regions with steep topography. Thanks to the moderate angular dependence of backscatter, cross-polarized data offer good separability of wet snow and snow-free surfaces also on fore-slopes. For co-polarized data, the separability decreases significantly towards low incidence angles.  
For assessing the accuracy of the S1 snow maps, we performed pixel-wise comparisons with Landsat-based snow maps. The results cannot be exactly translated into error numbers for the S1 snow maps because Landsat snow maps also show sensor specific features [35], and the dates of the Landsat and S1 snow maps differ by up to four days. Even so, the overall agreement rate between the Landsat and S1 classifications ranges from 0.95 to 0.97 for the various test sites (Table 4), confirming the good quality of both products that are derived from independent data sources. These numbers indicate significant improvements for SAR snow mapping by Sentinel-1 compared to previous snow cover products that are based on co-polarized SAR data of various satellite missions [11; 12; 13; 14].  
The comparison with Landsat snow maps reveals a slight trend for underestimation of snow extent for S1. This refers mainly to pixels with patchy snow cover where the signal contribution of snow-free surface dominates in SAR images, whereas the NDSI-based binary classifier separates the two classes at about 50% fractional snow cover [35; 36].  
For dense coniferous forest, common in the European Alps, reliable snow mapping with C-band SAR is not feasible because the radar signal is largely dominated by scattering in the canopy [38]. Therefore, we apply a land cover mask to exclude forested areas for snow retrievals over the Alps. Forests in Nordic areas are less dense, which offers the possibility to detect melting snow in open forests [39]. This could be a topic for further studies on the application of S1 for snow mapping.