# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China  
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1 Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China 12 School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China 21 Shanghai Space Science Center, Chinese Academy of Sciences (NSSC/CAS), Beijing 100190, China 12 University of Chinese Academy of Sciences, Shanghai 200030, China 21 Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China 1  
[PERSON]  
3 National Space Science Center, Chinese Academy of Sciences (NSSC/CAS), Beijing 100190, China 34 Beijing Key Laboratory of Space Environment Exploration, Chinese Academy of Sciences, Beijing 100190, China 45 Key Laboratory of Science and Technology on Space Environment Situational Awareness, Chinese Academy of Sciences (CAS), Beijing 100190, China 5  
[PERSON]  
6 School of Resources, Environment and Architectural Engineering, Chifeng University, Chifeng 024000, China 612 University of Chinese Academy of Sciences, Beijing 100049, China 21 Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 2  
systems of the East Asian region. Therefore, the research on snowpack in this area is of great hydrological and climatic importance [3; 4]. The Tibetan Plateau is one of the main snow distribution areas in China, and is also an important pastoral area in China. The development of local agriculture and animal husbandry is also closely related to the change in snow cover. As a result, the study of snow cover on the Tibetan Plateau is of great significance for the sustainable development of the regional ecological environment, and agriculture and animal husbandry [5; 6; 7]. The Tibetan Plateau is a rough land, and has uneven terrain with an average altitude of more than 4000 m. The ground observation stations are rare with the uneven spatial distribution, and the observation time is discontinuous, which cannot meet the needs of research on large-scale snow distribution characteristics [8]. Satellite observations are an effective way to monitor snow cover. Space-borne optical passive sensors provide useful information for monitoring snow cover, but cannot work in high altitude and cloudy areas, due to the limitation of cloud cover [9; 10]. Passive microwave remote sensing data are one of the main means of monitoring snow changes, as this method can penetrate through the porous soil layer, cloud, fog, and so on. It can penetrate a certain depth of surface to obtain information on the surface physical parameters. Therefore, microwave remote sensing is the best technology to obtain regional snow depth and snow water equivalent monitoring [11; 12; 13]. Global Navigation Satellite System-Reflectometry (GNSS-R), using the reflected signals from navigation satellites, was used for remote sensing in recent years [14; 15; 16]. This method mainly works in the L-band, with strong penetrability, so it is essentially a microwave remote sensing technology. It has the advantages of low cost, low power consumption, and high spatial and temporal resolution, due to the continuous use of navigation satellite signals as a signal source, which does not require the development of a special transmitter. It is a useful supplement to the traditional polar orbit satellites [17]. In 2004 and 2014, the United Kingdom Disaster Monitoring Constellation (UK-DMC) and Techdemosat-1 (TDS-1) test satellites were launched for GNSS-R remote sensing research [18]. In December 2016, the Cyclone Global Navigation Satellite System (CYGNSS) was launched by the National Aeronautics and Space Administration (NASA). The main scientific goal of the system is to measure the ocean surface wind field information during tropical storms and hurricanes [19]. It also provides an important opportunity for the study of land surface parameters, such as soil moisture, vegetation, flood inundation, and wetland monitoring [20; 21; 22; 23].