# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 3 SAR-Based Studies and Methods to Detect Snow
### Employed Methods to Monitor Snow Cover with SAR Data
#### 3.3.3 Wet and Dry SCE Detection  
Initially, when polarimetric SAR (PolSAR) techniques were still immature, it was concluded that SAR is unable to detect dry snow ([PERSON], 1998; [PERSON], 1999; [PERSON], 2000). Nevertheless, as dry snow detection is crucial for applications like SWE estimation or other hydrological applications, empirical or topographical rules were applied to predict the dry snow based on the wet snow extent. [PERSON] and [PERSON] (1998) presumed regions with elevations higher than the extent of the wet snow were dry snow. This method was modified ([PERSON], 1999; [PERSON], 2000; [PERSON], 2000) and further improved by including measurements of air temperature ([PERSON], 1999; [PERSON], 2000). Another approach is based on presumption of snow status change with time as shown in [PERSON] et al. (2000) and [PERSON] et al. (2000). However, those wet snow based approaches to estimate the dry snow extent have proven inaccurate. Studies following [PERSON]'s method to derive dry SCE found large overestimations ([PERSON], 1999; [PERSON], 2000). In addition, [PERSON] and [PERSON] (2000) stated their algorithm could face problems in early spring when dry snow extent could be extensive while wet snow still being absent. Moreover, these approaches do not account for the influence of wind to redistribute snow ([PERSON], 2000). Consequently, an alternative approach, i.e., PolSAR technique, was explored to detect wet and dry SCE directly from SAR imagery. [PERSON] (2000) proposed a depolarization approach, i.e., calculating the ratio of cross- and co-polarization of airborne AIRSAR; [PERSON] and [PERSON] (2000) used SIR-C/X-SAR to test multi-frequency and multi-polarization. Early spaceborne SAR sensors; however, were generally only equipped with single polarization ability, such as ERS-1/2 (VV) and Radarsat-1 (HH). Dual polarization mode became available after the launch of ENVISAT-ASAR in 2002, offering new possibilities to explore the potential of multi-polarization repeat-pass methods for snow cover mapping. Figure 7: The overall workflow of interferometric SAR (InSAR)-based algorithms to detect total snow cover. Master and slave image refer to the two single look complex (SLC) SAR images used to generate an InSAR pair. The key of PolSAR-based wet and dry snow cover detection is to extract the geometrical scattering characteristics of ground features by decomposing the received SAR signal. The backscatter from each ground feature is composed by various unique signal-feature interactions; PolSAR decomposition therefore offers the possibility to reveal how a particular surface feature reflects the incoming SAR signal and what physical characteristics that feature might have. For instance, the frozen forest canopy leads to high correlation of the polarization due to surface backscattering during wintertime, while low correlation results from the snow-covered ground surface. Once the snow becomes wet, the polarization correlates again [159]. The development of PolSAR techniques led to numerous decomposition methods to derive PolSAR parameters. The most commonly employed decomposition parameters include Pauli decomposition [160] and H/A/\(\overline{\alpha}\)[160]. They decompose the coherence matrix in different ways and thus form different parameters, such as former's single/odd, double/even bounce and volume scattering. The H/A/\(\overline{\alpha}\) decomposition can even reveal the scattering angle and degree of randomness of the sensed target. For detail decomposition theory and examples can refer to [161; 162]. However, as there are countless decomposition indexes, selecting the most feasible parameter is challenging. [PERSON] et al. [163] calculated the Jeffreys--Matusita (J--M) distances [164] to filter feasible parameters for distinguishing dry and wet snow. [PERSON] et al. [165] plotted the normalized value of each parameter of each land cover type to observe their overlap. The parameters showing fewer overlapping of dry and wet snow were chosen.