# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## 4 Conclusions  
Since the launch of GNSS-R satellites, many scholars have used them to study the parameters of wetland dynamics, sea surface wind speed, and soil freeze/thaw. This paper takes CYGNSS as an example to study its potential application in snow monitoring on the Tibetan Plateau. The CYGNSS data collected from January 2018 to December 2019 were processed and analyzed to calculate surface reflectivity and compare it with snow depth. Soil moisture from SMAP and temperature data from ERA5 were used to study the changes in both when the snow depth varied significantly. With the help of typical GNSS-R observables (LES, SNR, and SR), we found that SR can be a good potential parameter to indicate the snow depth. Therefore, based on the SR, the finally developed indicator, which is the SR-DR factor, shows an apparent good relationship with snow depth. This factor can represent the snow water equivalent to some extent through the ratio form in the equation. Compared with conventional remote sensing satellites, the sampled data from CYGNSS have high spatial and temporal resolution. This demonstrates the potential of satellite-based GNSS-R to monitor snow depth at a high spatial resolution. In the analysis of GNSS-R data, it is generally believed that the signal received by the receiver is mainly composed of coherent scattering along the mirror direction, but, in fact, the incoherent scattering affects the results, and their relative contribution depends on the receiver height, surface roughness, and vegetation coverage. In this paper, only the case of coherent scattering is considered. The CYGNSS was originally intended for sea surface wind field research, and many of its applications on land are exploratory studies. This paper qualitatively proves that satellite-based GNSS-R can be used for snow monitoring, but still lacks a quantitative evaluation to determine the specific value of snow depth as retrieved from SR. It can be further analyzed with other estimators in future research. In addition, the coverage of CYGNSS is mainly concentrated in the middle and low latitudes, so there is a lack of effective data in some areas of the northern Tibetan Plateau. In practical application, it may be necessary to combine the traditional remote sensing satellite data to achieve high-precision snow monitoring. [PERSON] analyzed and interpreted the data and materials, and wrote the original manuscript. [PERSON] provided the data analysis and interpretation. [PERSON] conducted the conceptualization suggestions, review, and funding. [PERSON] performed the suggestion and inspection. [PERSON] and [PERSON] conducted the suggestions and funding. All authors have read and agreed to the published version of the manuscript. Figure 11: The time series of SR–DR factor versus snow depth. (**a**) Barren or desert area; (**b**) low-vegetation-covered area; (**c**) moderate-vegetation-covered area. Funding: This research was funded by the National Natural Science Foundation of China (No. 42061057&4207402&672004017), Chifeng University, the Laboratory of National Land Space Planning and Disaster Emergency Management of Inner Mongolia (CFXYZD202006), and Innovative Teams of Studying Environmental Evolution and Disaster Emergency Management of Chifeng University in China under grant number crykykycxtd202006. Data Availability Statement: The datasets analyzed during the current study are available from NASA, [[https://podaac.jpl.nasa.gov/dataset/CYGNSS_L1_CDR_V1.0](https://podaac.jpl.nasa.gov/dataset/CYGNSS_L1_CDR_V1.0)]([https://podaac.jpl.nasa.gov/dataset/CYGNSS_L1_CDR_V1.0](https://podaac.jpl.nasa.gov/dataset/CYGNSS_L1_CDR_V1.0)) (accessed on 12 March 2021). The datasets analyzed during the current study are available in the Land Processes Distributed Active Archive Center, [[https://lpdaac.usgs.gov/products/mcd12](https://lpdaac.usgs.gov/products/mcd12) clv006/]([https://lpdaac.usgs.gov/products/mcd12](https://lpdaac.usgs.gov/products/mcd12) clv006/) (accessed on 13 January 2022).