# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 5 Discussion
### The Advances of SCE-Detection by SAR  
Conventional backscattering-based approaches were subject to criticism for using a hard threshold to classify snow cover, as numerous factors may affect the backscattering value. For instance, in a warm winter more frequent melting/refreezing cycles would lead to bigger grain sizes and thus influencing the backscattering coefficient [196]; sudden changes of air temperature or heavy snowfall events may cause snow metamorphism, altering the snowpack conditions [188]. Variations of soil moisture may also disturb the backscattering coefficient values [197]. Moreover, the geometry of two images needs to be identical to preserve similar LIAs [197]. If thresholds are set too low/high, the final SCE would be under/overestimated. However, backscattering-based algorithms also show one great advantage, i.e., they do not require training samples for classification. This fact avoids the manual selection of a classifier and saves time for the classification, which is an asset for an automated processing over longer periods or large regions. In contrast to backscattering-based approaches, PolSAR-based algorithms have the merit of less LIA dependency. Hence, theoretically they are more suitable for mountainous terrain. Another benefit is the availability of additional information such as scattering angle and degree of randomness inherent to the polarimetric data. Furthermore, PolSAR technique only requires one image, which increases the sensing frequency and real-time monitoring capabilities. The different ways to decompose the covariance matrix, however, results in countless derived parameters; a suitable selection of included parameters and a proper filtering of redundant data therefore is critical. Moreover, the process of decomposition is more time-consuming when compared to backscattering-based approaches as it involves more matrix calculations. InSAR-based algorithms are supported by the maturity of available InSAR processing software. The main challenge is the revisit interval of the SAR sensor and the sometimes rapid change of snow cover conditions (depending on location, weather conditions, and season). Nevertheless, it can be postulated that InSAR-based approaches may become more promising in the near future, as the revisit time of SAR sensors has enhanced significantly. Overall, the comparison of mainstream SAR-based approaches is shown in Table 3, which provides guidance for choosing the appropriate algorithm based on the targeted snow cover type and SAR image availability. In addition to the aforementioned three main SAR-based approaches, other more elaborate possibilities should be further investigated, such as the information theoretic snow detection algorithm (ITSDA) proposed by [PERSON] et al. (2016). A fusion of the three mainstream SAR-based approaches is another option: [PERSON] et al. (2016) facilitated the information derived from all three mainstream technique to a SVM classifier to map both dry and wet SCE; [PERSON] et al. (2014) utilized RF to map total SCE based on backscatter, InSAR coherence, and PolSAR H/\(\sqrt{\alpha}\) parameters in five study areas around the globe. The total and wet SCE estimated by [PERSON] et al. (2014) is illustrated in Figure 11, which demonstrates the great value of fusing all three main SAR-based approaches to derive the holistic (total + wet) SCE. The holistic SCE enables not only investigating the dynamics of SCE and snowpack by comparing different seasons' SCE conditions but also the potential of further integration with conventional optical sensor-based cloud-affected SCE results. When it comes to the ML classification method, considering the recent advances in deep learning (DL) algorithms and computer hardware, there have been few attempts utilizing the neural network to detect snow in SAR imagery, such as [PERSON] et al. (2018) and [PERSON] et al. (2019), which showed a classification accuracy that was comparable to conventional ML algorithms.