# 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
#### 3.3.1 Wet SCE Detection  
The most commonly used approach to derive the extent of wet snow cover is by exploiting the backscatter coefficient. As outlined in Section 2.2, the backscatter coefficient drops significantly when a snowpack starts to melt, therefore containing liquid water, which decreases the dielectric constant. The first algorithm to exploit this behavior was published by [PERSON] and [PERSON] in 1995 and 2000 [126; 127]. They relied on two SAR images (one is sensed during the snow-covered period \(\sigma^{0}_{us}\), and the other is a reference image \(\sigma^{0}_{ref}\) which is sensed in either the snow-free or the dry-snow period) featuring the same imaging geometry (i.e., repeat pass pair) as well as a digital elevation model (DEM). The main workflow comprises pre-processing and wet snow mapping as illustrated in Figure 6. For pre-processing, the two images are calibrated, coregistered, multilooked, speckle-filtered, the scattering coefficient is transformed to a logarithmic scale (dB), the ratio between the two observations is calculated (see Equation (1)), and finally the result is geocoded. The geocoding is based on the DEM and produces a SAR layover mask, a shadow mask, and a LIA map. After pre-processing, the geocoded ratio map is classified based on a threshold to derive the binary wet snow extent. A threshold of -3 dB has proven to be robust enough to achieve satisfactory results, and was used in many subsequent studies. \[\frac{\sigma^{0}_{us}}{\sigma^{0}_{ref}}<-3\;\mathrm{dB},\;\mathrm{wet\;snow} \tag{1}\]  
Figure 5: Spatial and temporal overview of published SAR-based snow cover extent (SCE) detection studies. (**a**) The spatial extent of studies; (**b**) number of sensed years; (**c**) average number of observations per sensed year. Note that none of the studies categorized as multi-year monitoring in (**b**) provide a time-series of SCE results, but only few random observations for each year. Thus the average observations conducted per sensed year of each study are illustrated in (**c**). As this method is easy to implement, it was applied successfully to most SAR sensors and study regions. Consequently, the backscattering coefficient-based method is known as \"Nagler's method\". Yet, this method uses only a single equation with a fixed threshold to retrieve the binary result; this result, however, is limited as the random noise of SAR would inevitably degrade the single image as mentioned in Section 2.1 and the binary snow results do not meet the real condition. Several improvements have therefore been achieved for \"[PERSON]'s method\":  
* Mapping of snow cover fraction Binary snow cover products (i.e., a pixel is flagged as either snow covered or snow free) contain uncertainties especially along the transition between snow covered and snow free areas. Traditionally, a pixel containing more than 50% of snow cover would be considered fully snow covered in a binary product [128; 129; 130]. Thus, an approach to derive fractional snow cover information from SAR-data is required. Therefore, [PERSON] and [PERSON] [131] utilized a sigmoid-function based on the assumption that each pixel is a mixture of dry snow, wet snow and snow-free surface. This approach was later refined into different forms [132; 133]. * Refined reference image selection Selecting a suitable image for representing the snow-free (or dry-snow) ground surface backscattering conditions is critical, as the ratio between this reference image and the observation containing the wet snow accounts for the accuracy of the wet snow detection. It is important to ensure that the reference image selection is selected carefully. Since [PERSON]'s first publication [126], soil moisture has been frequently proven to bias single reference images [134; 135]. Thus, [PERSON] et al. [136] suggested reference scenes acquired under dry snow conditions recorded during wintertime. Figure 6: The overall workflow of the backscattering-based algorithm to detect wet snow.