# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 6 Conclusions  
Snow cover extent is one of the most important cryogenic components, as it controls global radiation balance, hydrological behavior, vegetation coverage and affects human activities. Spaceborne SAR offers the capability to quantify snow cover conditions even under clouded or nighttime conditions, which enables a better understanding of the global snow cover dynamics. Moreover, its polarizations and phase data provide valuable information about the snowpack characteristics. Three mainstream SAR-based approaches to map snow cover have been pursued in last three decades: (1) Detecting wet snow based on SAR backscattering behavior; (2) PolSAR technique inverting the scattering mechanism of the targeted snow type; and (3) the coherence value calculated from InSAR techniques enabling estimation of total SCE. After evaluating relevant studies published within the last three decades, we draw the following conclusions for monitoring snow cover using spaceborne SAR:  
1. C-band SAR based algorithms dominate the studies, but the recent prosperity of X-band SAR provides a promising option. Due to the long-term preference of the C-band wavelength and its better capability to detect snow when compared to L-band SAR, C-band SAR has the longest history and is utilized for snow cover detection more often than any other sensor. However, many recent studies have proven that X-band is more suitable to detect dry snow; considering the amount of new and planned X-band missions, an increase in popularity of X-band based snow cover detection algorithms therefore can be expected for the near future. 2. Most studies focused on mountainous regions, especially the European Alps (32%) and the Asian Himalaya (31%), leading to an imbalanced distribution of study sites. The relatively small size of the study sites also implies the lack of utility of the recent wide-swath sensing mode. 3. The majority of studies investigated snow cover for one year with an average of two observations within this year to account for the dynamics of the snowpack. These temporal aspect-limited studies indicate that there is still a gap in understanding the long-term capability of SAR-based algorithms to detect snow consistently. 4. For detecting wet SCE, the majority of studies relied on backscattering-based approaches. More than 55% of the reviewed studies only detected wet snow, with 82% of those studies applying a backscattering-based approach proposed by [PERSON] et al. in 2000. However, we observed a recent increase in studies relying on InSAR- and PolSAR-based algorithms especially for the detection of dry and total SCE. 5. This review confirms the importance of ancillary data such as a DEM, a land cover map as well as meteorological data as additional inputs into SAR-based snow detection algorithms. Based on the DEM data, information about LIA, SAR shadow and layover can be derived; land cover information is useful to mitigate the negative effects of vegetated areas on the classification accuracy, and the actual snow melting conditions can be inferred from meteorological data. 6. Commonly employed classification methods shifted from supervised ML approach towards more sophisticated DL approaches, and the maturity of optical-based snow cover products enables a selection of suitable training samples for supervised classifications. 7. Technical advances in recently launched SAR missions such as wider sensing swaths, shorter revisit times and quad-polarization make SAR-based snow cover detection more promising. These technical developments and the mainstream SAR-based algorithms complement each other well, as the extended coverage can increase the efficiency of the classification, the shortened revisiting time can support InSAR-based approaches to sustain more usable coherence, and the quad-polarization can enrich the information decomposed by PolSAR-based techniques. 8. The difficulty of SCE detection in vegetated land cover regions is recently addressed but further exploration of PolInSAR and TomoSAR techniques should be investigated.