Search

As the most dynamic component of terrestrial water cycling, soil moisture plays a pivotal role in the formation, transformation, and consumption of surface water. Simultaneously, it serves as a crucial parameter influencing hydrological processes, vegetation status, and climatic conditions. Therefore, highprecision, high spatiotemporal resolution soil moisture data holds significant importance across various fields, including agriculture, forestry, and meteorology. Presently, traditional methods such as the timeconsuming and labor-intensive drying-weighing technique are inadequate for large-scale monitoring demands, highlighting the advantages of remote sensing methods. However, optical sensors are susceptible to cloud cover and vegetation obscuration, while microwave remote sensing technology faces challenges in balancing spatial and temporal resolutions. The space-borne global navigation satellite system-reflectometry (GNSS R) technology, characterized by short revisit cycles and pseudo-random sampling, presents a new opportunity for soil moisture retrieval. This paper begins by introducing the fundamental theory of soil moisture retrieval using GNSS-R and analyzes error sources during the retrieval process. This includes the scattering and attenuation effects of surface factors such as vegetation and surface roughness on reflected signals, as well as the impact of water bodies on reflectivity. Existing error correction methods and their limitations are summarized, and potential avenues for improvement are discussed. Subsequently, the research progress in recent years regarding space-borne GNSS-R soil moisture retrieval is analyzed from three perspectives: (1) The development status of space-borne GNSS-R constellation. (2)An overview of algorithms for space-borne GNSS-R soil moisture retrieval, including empirical models, semi-empirical models, and machine learning methods. (3) Advancements and directions in integrating GNSS-R with other data for soil moisture retrieval. In conclusion, based on the scattering mechanism of reflected signals, the selection of auxiliary data, and the utilization of incident angle information, this paper discusses the technical challenges faced in current soil moisture retrieval using space-borne GNSS-R technology and provides insights into future research directions. [ABSTRACT FROM AUTHOR]

Objectives: The spaceborne global navigation satellite system-reflectometry (GNSS-R) technology has been extensively employed in monitoring surface hydrology in recent years. However, research on the key factor of precipitation in the surface hydrological cycle, especially its spatiotemporal relationship with cyclone global navigation satellite system (CYGNSS) signals, remains limited. Therefore, this paper conducts a detailed and comprehensive analysis of the direct relationship between CYGNSS reflectivity and precipitation, taking into account multiple influencing factors and scale effects. Methods: This study utilizes monthly CYGNSS data and precipitation data from 2019 to 2020, taking Guangdong Province, China as the study area. It analyzes the spatiotemporal response relationship between CYGNSS signals and precipitation, while comprehensively considering the influence of different driving factors such as digital elevation model (DEM), slope, land cover type, soil type, and normalized difference vegetation index (NDVI) on the CYGNSS signal-precipitation response. The study also explores scale effects, and uses a factor detector model in the geographical detector to investigate the degree of influence of each driving factor. Results: From 2019 to 2020, the monthly average CYGNSS reflectivity and precipitation show significant seasonal differences and nearly synchronous variations, validating the potential of CYGNSS signals for precipitation monitoring. The positive correlation between CYGNSS signals and precipitation strengthens with increasing spatial scale. For example, at small scale (1 km), the positive correlation between CYGNSS signals and precipitation is 21.00% (significant at the 0.05 level), while at medium scale (3 km) and large scale (9 km), it reaches 26.84% and 40.36% respectively, highlighting the advantage of CYGNSS in monitoring precipitation at larger scales. Additionally, the study found that at different spatial scales, regions with lower DEM and slope (indicating relatively flat terrain), areas with land cover type of cropland, and regions with lower to moderate vegetation coverage exhibited a stronger positive correlation between CYGNSS signals and precipitation. However, the influence of soil type on the relationship between CYGNSS signals and precipitation response varied across different scales. The driving factors 􀆳 influence on the relationship between CYGNSS signals and precipitation is ranked as follows: DEM>NDVI>slope>land cover type>soil type, and their influence becomes stronger with increasing scale. Conclusions: This study elaborates on the response relationship between CYGNSS signals and regional precipitation, providing useful references for further utilizing GNSS-R technology to monitor precipitation and facilitating accurate assessment of surface hydrological changes. The results demonstrate that certain spatiotemporal response relationships exist between CYGNSS signals and precipitation across different scales, and various driving factors have certain influences on their response relationships. [ABSTRACT FROM AUTHOR]