# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 5 Discussion
### Overall Trajectory of Spaceborne SAR-Based SCE Detection and Future Possibilities  
According to the available studies reviewed in the present paper, the overall trajectory of spaceborne SAR-based SCE detection may be summarized into three phases as depicted in Figure 12.  
After the launch of ERS-1 in 1991 and before the launch of ASAR in 2002, the repeat-pass imagery of ERS-1 allowed for a bi-temporal image analysis. Using this data source, [PERSON] and [PERSON] developed their pioneer wet SCE mapping algorithm based on backscattering information [127]. The same applies to their dry SCE detection approach [126] as well as [PERSON] et al. 's total SCE mapping with InSAR technique [147]. Moreover, merging different flight paths for achieving higher mapping accuracy [127] and calculating fractional SCE detection [138] were also proposed. In this phase, most of the available studies only utilized backscattering or InSAR techniques. New breakthroughs were achieved in the second phase after the launch of ASAR and before the launch of Sentinel-1 in 2014. Dual/quad-polarization, multi-band, and constellations of SAR sensors significantly stimulated the development of new snow cover detection methods. The rich information derived from PolSAR became achievable. For dealing with the overwhelming amount of parameters from this new source of information (Appendix A), machine learning classifications were introduced. In addition, various improvements of wet SCE mapping algorithms were achieved, including the combination with optical-based SCE [151], and proposing an algorithm with a multi-year usable reference image optimized for forest regions [139]. Figure 12: Overall development of algorithms and methods relying on spaceborne SAR data for snow cover extent (SCE) detection. The three phases denote the different main algorithms based on available SCE detection algorithms and SAR data. Topic has already shifted into the third phase since the launch of Sentinel-1 in 2014. In the hardware aspect, the fine spatial/temporal resolution and freely accessible Sentinel-1 offer an opportunity for long-term SAR-based SCE detection. Together with the prosperity of emerging X-band SAR sensors, more SAR-based studies are foreseeable such as integration and comparison of SCE information achieved from different bands of SAR sensors. In addition, the Sentinel series' satellites constellations significantly improve the potential of multi-sensor fusion as demonstrated in [PERSON] et al. [221]. In the algorithm aspect, thanks to the all land cover applicable total SCE detection approach proposed by [PERSON] et al. [143], it largely solves the previous studies' limitation that SAR-based approaches cannot detect snow accurately in vegetated regions and can only monitor wet SCE. This provides a great opportunity for future studies to integrate and complement SAR-based SCE with conventional optical-sensor-based cloud-affected SCE results to achieve more comprehensive SCE dynamics information in both spatial (conquering the influence of polar darkness and frequent cloud coverage, with \(\sim\)20 m spatial resolution) and temporal (long time-series, daily temporal resolution) aspects. Another key development is DL, although only few studies exploited Sentinel-1 in combination with DL techniques so far, it is foreseeable that there will be further studies utilizing it to solve the current technical challenges, including the influence of different filtering algorithms, impact of different sensing geometry, less classification accuracy in the forest regions, and fusion of heterogeneous sensors' such as Light detection and ranging (Lidar) as well as passive sensors. In validation aspect; the need of proper validation for SAR-based SCE--especially wet SCE--still needs to be addressed. Most of the previous studies did not provide reliable validation due to the limitation of sparse in situ measurements.