# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## 3 Results and Analysis  
### Comparison of Surface Reflectivity and Parameters on the Tibetan Plateau  
The CYGNSS daily SR and snow depth were analyzed and compared under different land cover types. The snow depth in the Tibetan Plateau region was calculated using the long-term series of daily snow depth dataset in China (1979-2019). Figure 6 shows the time series of CYGNSS SR and snow depth, and we assess the ability of CYGNSS data to monitor changes in snow cover depth by comparing them. The SR varies with the surface dielectric constant, so there are differences in SR for different ground objects. The snow on the Tibetan Plateau is mainly produced from October to April each year, during which the Tibetan region enters a period of high snow frequency, and SR increases as the snow depth increases. A portion of the SMAP data was lost between June 2019 and July 2019, due to data sampling issues with the satellite. Figure 6 shows the variation of CYGNSS reflectivity (blue left axis), which is consistent with the oscillation of snow depth (red right axis) during the months with snow accumulation. In addition, the part of the anomalous oscillation is mainly caused by the attenuation and volume scattering of vegetation. The change trend of SR and snow depth is closest in the high-vegetation-covered area. In the low-vegetation-covered area and barren/desert area, the CYGNSS time series fluctuates more by the noise, but shows an overall increasing trend. Figure 6: The time series of SR (blue left axis) versus snow depth (red right axis) compared under different land cover type on Tibetan Plateau. (**a**) High-vegetation-covered area; (**b**) moderate-vegetation-covered area; (**c**) low-vegetation-covered area; (**d**) barren or desert area. The analysis results show that the CYGNSS SR has a certain correlation with the variation in snow depth, which can be used for snow monitoring on the Tibetan Plateau. Soil moisture is an important factor affecting SR. To exclude its influence on the results, we plotted the time series of soil moisture using the SMAP surface soil moisture data and examined it with the variation of snow depth over time, in order to investigate the correlation between them. It can be seen from Figure 7 that the interannual variation of soil moisture is closely related to the variation in snow cover, and the soil moisture content fluctuates greatly when the snow cover melts. The low-vegetation-covered area has the largest change in soil moisture. In addition, when the snow cover on Tibetan Plateau is obvious (November 2018-February 2019), the value of soil moisture under different land cover types fluctuates around 0.1 cm\({}^{3}\)/cm\({}^{3}\), and does not change significantly. Therefore, we learn that it is not the change in soil moisture that causes the oscillation of SR.  
To further verify the effect of snow depth on the SR, we also used the ERA5 soil temperature data to obtain the monthly average soil temperature over this four month period (Figure 8). We found that there is no inversion from negative to positive values of surface temperature in most areas of the Tibetan Plateau during this period. The northern region shows the relatively large temperature variation. However, as this region is beyond the detection zone of CYGNSS, we exclude the effect of surface temperature variations. Figure 7: The time series of soil moisture (blue left axis) versus snow depth (red right axis) compared under different land cover type on Tibetan Plateau. (**a**) High-vegetation-covered area; (**b**) moderate-vegetation-covered area; (**c**) low-vegetation-covered area; (**d**) barren or desert area. Table 2 shows the mean values of snow depth, SR, and soil temperature for the four different land cover types over a four month period. It can be seen that the trends of snow depth and SR over time are approximately the same when the soil temperature is negative without more significant changes.