# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 5 Discussion
### Reliability of Current Validation Approaches  
As shown in Figure 10, around half of the available studies employed snow cover classifications based on optical sensors as ground truth to validate the SAR-based snow cover results. This approach, however, can encounter several problems. First, for studies only aiming at wet snow monitoring, the date selection of optical images is critical. Usually, late spring is selected when the snowpack theoretically melts even in the highest elevation zones [140]. This would ensure that the SAR-based snow cover mapping detects the entire snow cover extent. However, it cannot be guaranteed that no dry snow is left.  
Additionally, acquisition times of SAR and optical images differ. The resulting temporal gap leads to uncertainties, because melting processes, sudden snowfall or precipitation events all may result in different SCE in the optical and SAR data [175]. Most authors, however, selected the nearest sensed image pair, despite the time difference (e.g., 14 days between PALSAR and Landsat [169], 14 days between Radarsat-2 and Landsat [118], six days between PALSAR and AVNIR-2 [211]). Even though some satellites equipped with both SAR and optical sensors at the same platform (e.g., the Advanced Land Observation Satellite (ALOS)) may acquire both images at the same time, the presence of cloud may still hinder the optical observation.  
Additionally, the difference between day and night might lead to great variations, as proven by studies investigating the top layer of a snowpack, which might refreeze after a cloud-free night with low temperatures. Such a refreeze event increases the surface backscattering significantly [67; 186] and therefore would cause a high contrast between morning and evening observations [118]. Field surveys also revealed that the infiltration of rain may cause both, thick ice crusts within the snowpack as well as larger snow grains [184]. On top of that, differing magnitudes of temperature change in each elevation zone [65] impede the possibility to compensate its influence on the snowpack.  
When validating SAR-based snow cover classifications with products derived from optical data, the accuracy of these reference datasets is of importance. Such products are often calculated applying a threshold of 0.4 NDSI, which might cause an underestimation as only pixels containing more than 50% of snow will be selected [212; 213]. Some researchers therefore used 0.7 as a threshold to allow only fully snow covered pixels for detection [134; 186]. In addition, limiting the optical-based snow cover detection to NDSI alone may introduce errors. It is generally advised to include additional tests, such as Landsat TM band4 \(\geq\) 0.11 [214] and SPOT band3 \(\geq\) 0.11 [215] or using an NDSI-NDVI threshold to address dense forest regions [216]. Even though the need for these additional tests is well-known in the optical snow cover community, SAR-based studies often fail to include these tests. Finally, as the spatial resolutions of SAR and optical images differ, the comparison procedures generally involve resampling operations [133], i.e., aggregating the higher resolution snow cover product to match the coarser one. Nevertheless, the definition of SCE in the coarser pixel may affect the validation significantly [140].  
To overcome the constraints related to comparisons between optical and SAR-based snow cover classifications, a promising alternative approach is regional or global scale weather forecasting and/or snow models as suggested in [PERSON] et al. [23]. Although these models are criticized for their comparatively low spatial resolution, dependency on empirical parameter setting [217; 218] and the oversimplification of the snowpack [44], they can provide useful information such as ground temperature and snow accumulation amount [96; 144]. As the complexity and spatial resolution of such models advance, they may constitute a notable alternative for future validation approaches of SAR-based snow cover products.