# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## 3 Results and Analysis
### Surface Reflectivity Difference Ration Factor  
In Section 3.1, we evaluated the effects of snow depth on surface reflectivity, and find that it is more sensitive than the other GNSS-R observables, i.e., SNR and LES (Appendix A). Therefore, we provide an indicator in this section to illustrate its effect on the detection of snow properties. Here, we define this indicator as surface reflectivity-difference ratio factor (SR-DR factor) [36]. The factor SR-DR is defined as follows:  
\[F(t)=\frac{\Gamma(t)-\Gamma min}{\Gamma max-\Gamma min} \tag{4}\]  
where \(\Gamma(t)\) is the SR at time t, and \(\Gamma_{max}\) and \(\Gamma_{min}\) are the maximum and minimum SR in the time range, respectively. It should be noted that within the form of this equation, the snow water equivalent (SWE) can be achieved when the SWE is in quite good relationship with snow depth.  
The CYGNSS data for 2019 was studied as a case study. Figure 9 shows the SR-DR factor for the Tibetan Plateau in January, February, March, and July 2019, which has the same resolution of 25 km as SR. Analyzing the factor for the Tibetan Plateau in 2019, we found that the SR-DR factor increases in summer compared to winter for the whole region. It also changes with snow depth in winter when there is snow cover on the Tibetan Plateau.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Snow Depth (cm)** & **Surface Reflectivity (dB)** & **Soil Temperature (\({}^{\circ}\)C)** \\ \hline (c) Grassland & & & \\ \hline
2018.11 & 3.38779 & –34.03826 & –2.11510 \\
2018.12 & 6.08088 & –33.24549 & –4.36104 \\
2019.01 & 6.35053 & –33.78475 & –5.42212 \\
2019.02 & 5.02427 & –34.37498 & –4.49595 \\ \hline (d) Barren/Desert & & & \\ \hline
2018.11 & 1.86681 & –31.47885 & –1.29014 \\
2018.12 & 2.97565 & –30.19818 & –7.56332 \\
2019.01 & 3.60599 & –31.14914 & –8.23865 \\
2019.02 & 3.38220 & –30.62580 & –4.55624 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 9: The SR-DR factor on Tibetan Plateau in January (**a**), February (**b**), March (**c**), and July (**d**) of 2019.  
Figure 10 shows that the SR-DR factor for January 2019 is mainly concentrated between 0 and 0.4. In February and March 2019, SR-DR factor shows an increase, without a large change in soil moisture during this period. In July, it shows a higher value, mostly greater than 0.4. The snow depth is also lower at this time, due to the arrival of summer.  
We selected a \(1^{\circ}\times 1^{\circ}\) size area with different land cover types for the analysis of the relationship between the SR-DR factor and snow depth as follows: (a) barren or desert area (79 deg-80 degE, 33 deg-34 degN); (b) low-vegetation-covered area (81.5 deg-82.5 degE, 31.5 deg-32.5 degN); and (c) moderate-vegetation-covered area (95 deg-96 degE, 29 deg-30 degN). We removed the high-vegetation-covered area because this part of the Tibetan Plateau is small and mostly concentrated in mountainous areas with complex topography. The topographic relief and vegetation roughness may have a great impact on the effective surface reflectivity, which will make the results inaccurate. The time with snow cover on the Tibetan Plateau is concentrated from November to March of the following year, so we choose CYGNSS data from November 2018 to March 2019, and the snow depth at the coordinates of the center point of the area is used to express the change in snow accumulation. Figure 11 presents the time series of SR-DR factor versus snow depth in the research area. From the simulation, we can see that the oscillation of the snow depth in the barren area is, in general, consistent with the daily CYGNSS SR-DR factor. The differences become progressively larger as the vegetation cover level increases.  
Figure 10: Histogram of the SR–DR factor distribution on the Tibetan Plateau in January (**a**), February (**b**), March (**c**), and July (**d**) of 2019.