# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 4 Critical Auxiliary Data Necessary to Support Detecting SCE from SAR Data
### The Influence of Land Cover (Vegetation) on Snow Detection from SAR-Data  
Another factor affecting SAR-based snow cover detection is land cover. This effect was first described by [PERSON] et al. [138] when they analyzed the backscattering coefficient values of different surface conditions depending on different types of land cover. Results clearly showed that the presence of vegetation significantly decreases the backscattering difference between wet snow and dry snow as well as bare ground. [PERSON] et al. [134] also reported that snow-covered and snow-free areas in forest regions are difficult to discriminate as the backscatter is significantly influenced by canopy [182].  
According to the model proposed by [PERSON] [183], SAR backscattering relates to the transmissivity of a forest canopy, which is influenced by stem volume. The different scattering coefficient levels of grassland, crop field and dense forest confirm these results [121; 134]. [PERSON] and [PERSON] [184] found that backscattering coefficient is continuously increasing with increasing vegetation height. Therefore, applying only one fixed backscatter threshold to classify snow cover in a region with varying land cover may lead to inaccurate results [121]. Additionally, results from polarimetric models indicate that backscatter of dry snow is strongly sensitive to the underlying surface as the backscattering originates from the snow/ground surface interface [169; 172]. [PERSON] et al. [163] concluded that stems of snowcapped shrubs and grass lead to higher volume scattering for dry snow than for wet snow. [PERSON] et al. [35] reported difficulties detecting snow cover in forested areas as the tree structure affects the scattering of the SAR signal (double-bounce with tree trunks, volume scattering with foliage and single scattering with forest floor). The seasonal phenology of trees (leaf fall before winter) also alters the scattering behavior. [PERSON] et al. [169] concluded that the presence of woodlands could increase volume scattering as well as H and \(\alpha\).  
Forest also affects InSAR-based approaches. The coherence values of InSAR are found to be commonly lower in densely vegetated regions [149; 163]. [PERSON] et al. [102] concluded that this reduction in coherence is connected to both snow cover and the presence of forests. [PERSON] and [PERSON] [185] reported that the random motion of leaves due to wind would reduce the coherence significantly.  
Aforementioned studies intensively indicate that vegetation would limit the accuracy of SAR-based snow detection approaches; however, based on previous studies there is no decisive threshold for any vegetation index such as biomass or canopy closure. As a result, many studies use masks to eliminate the influence of densely forested regions. [PERSON] and [PERSON] (126; 127) as well as [PERSON] et al. (186) masked the agriculture areas. Practically, external land cover products (149; 163; 175; 186) or land cover maps generated during the pre-processing workflow (102; 144; 187) may serve as masks. Masking of critical land cover regions; however, may reduce the size of the study region considerably. Therefore, [PERSON] et al. (134) divided the backscattering ratio map for each land cover and calculated the geometric mean for each class as its threshold; [PERSON] et al. (143) built the model for each land cover type and mapped total SCE individually.