# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 3 SAR-Based Studies and Methods to Detect Snow
### Algorithms Utilized to Investigate the Different Snow Cover Types  
After reviewing the advancements in detecting snow cover from SAR data within the last three decades, a comprehensive overview of the different mapping approaches for different snow cover types that can be observed relying on these approaches emerged, which is presented in Figure 9. When analyzing the snow type, the review revealed that more than half of the available studies focused on the retrieval of wet snow only, while 30% of the studies aimed at both, wet and dry snow (Figure 9a). Less than 16% of the studies were designed to retrieve total SCE or dry snow only. This mismatch does not indicate that dry snow is less important than wet snow but it clearly shows that the detection of dry snow from SAR-data is still challenging.  
The different approaches to detect wet, dry, and total SCE (Figure 9a) can further be divided: For wet snow (Figure 9b), 82% of studies employed \"Nagler's method\", while InSAR- and PolSAR-based approaches account for 7% and 10%, respectively. Regarding dry snow (Figure 9c), more than 60% of the studies relied on topographic empirical determination, which is an approach frequently employed together with \"[PERSON]'s method\" (see Section 3.3.3). Another 21% and 10% of the studies utilized PolSAR (including PCVE) and InSAR techniques, respectively. The remaining 7% of the studies applied logic conditional rules to identify dry snow extent. When it comes to the detection of total SCE, (Figure 9d), 77% of the reviewed studies relied on PolSAR while only 23% applied InSAR techniques. In summary, when reviewing Figure 9, the predominance of PolSAR and InSAR for dry and total snow cover detection becomes obvious, while backscattering-based approaches are clearly the methods of choice to detect wet snow.  
Figure 8: The overall workflow of polarimetric SAR (PolSAR)-based algorithms to detect wet and dry snow cover, including (1) machine learning classification trained by external SCE result; (2) the calculation of seasonal parameters’ difference before thresholding, and (3) the direct thresholding.  
Machine Learning Classification Methods for Advanced SAR Information Analysis  
Additionally to the aforementioned algorithms, ML is commonly performed on advanced SAR-derived information especially derived from PolSAR-based approaches. Because SAR images can contain a considerable amount of noise, the derived PolSAR parameters are often challenging to be manually extracted. As PolSAR-based approaches are a comparatively new technique, the number of studies employing ML classification is small (only 19%). Owing to the maturity of optical-based snow cover monitoring techniques and products, training samples for supervised algorithms are available. Thus, supervised algorithms are slightly more common than unsupervised approaches.  
The most commonly employed supervised ML classification approach is the support vector machine (SVM), which is well known for its ability to solve high-dimensional non-linear classifications with only small samples [173]. Compared to maximum likelihood and decision tree methods, SVM produced higher snow cover classification accuracies [165] and thus was utilized for both InSAR-based [149; 163] and PolSAR-based studies [165; 174]. However, random forest (RF) also attracts attention for its simplicity and low computation load [143].