# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 3 SAR-Based Studies and Methods to Detect Snow
### Spatial and Temporal Scale of Snow Cover Studies  
Snow covers extensive areas around the globe, including high altitudes featuring complex terrain, high latitudes, and boreal forests. It is important to get an overview of the study regions of the already conducted research. Additionally, some studies focus specifically on a certain land cover while excluding others (e.g., focusing on glaciers or masking out any forested areas). In order to identify possible research gaps or areas where SAR-based snow cover analyses are still facing major challenges, every study incorporated in this review was evaluated by its location and land cover characteristics. Figure 4 presents the result of this evaluation, breaking the study regions down into study regions' types (Figure 4a), study mountainous regions (Figure 4b), and the distribution of study regions inside the European Alps (Figure 4c). The map presented in Figure 4d visualizes the locations of the studies and the frequency with which these regions have been investigated so far. It can be observed that the majority of studies were conducted in mountainous regions, which is reasonable as these regions are frequently affected by cloud coverage so the advantage of SAR can be demonstrated. However, the relatively small number of studies conducted in forest and glacier areas was also noted although snow commonly exists in these region types. This finding suggested that the current SAR-based SCE detection studies still have further investigation potential in these regions. Regarding the hotspots of the frequently studied areas, Asian and European mountain ranges represent around 85% of available study regions, as also shown in Figure 4d. The European Alps have been studied most intensively (32%), followed by Himalaya (31%). Within the European Alps,  
Figure 3: The frequency of different SAR sensors/bands being employed for snow cover studies. most studies have been conducted in Italy and France (Figure 4c). Other global mountain ranges like the Andes, Caucas, Rocky Mountains, or Ural have merely been investigated. Within the polar regions, several studies exist for Greenland and Antarctica, where the interaction between snow cover and glaciers has been investigated. The imbalance of study areas also indicates that more research is necessary in these areas. Not only the location but also the spatial extent of the study regions is important as it can help assess the maturity and readiness of algorithms. Figure 5a illustrates the size of the study regions. Most of the studies were conducted on a local scale as visible in Figure 5a. These studies are usually limited to a specific test site, which in more than 50% of the cases is smaller than 2500 km\({}^{2}\). Only five studies investigated areas greater than 200,000 km\({}^{2}\) (the size of Alps is around 298,128 km\({}^{2}\)). One motivation for these relatively small study sites is the spatial extent of typical SAR-data footprints itself. Many studies were designed to be conducted only within the boundary of a single SAR-footprint, which limits their extent according to the coverage of the respective SAR-mission. When trying to evaluate the transferability of a study, not only the spatial extent is of interest, but also the amount of observations included. This information helps assess whether a proposed algorithm can work under different weather and snowpack conditions. The number of sensed years and the average number of observations conducted per sensed year is illustrated in Figure 5b,c, respectively. It must be noted that, even for studies we categorized as multi-year monitoring in Figure 5b, none of  
Figure 4: Overview of published SAR-based snow cover studies. (**a**) Study regions’ type; (**b**) frequency of studied mountain ranges; (**c**) distribution among countries within the Alps; (**d**) geographic overview of performed studies. them provide a consistent time-series of SCE result but only few random observations in each observation year.