文献类型： 外文期刊
作者： Yang, Changzhi 1 ; Mao, Kebiao 1 ; Guo, Zhonghua 1 ; Shi, Jiancheng 3 ; Bateni, Sayed M. 4 ; Yuan, Zijin 2 ;
作者机构： 1.Ningxia Univ, Sch Phys Elect Elect Engn, Yinchuan 750021, Peoples R China
2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
3.Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China
4.Univ Hawaii Manoa, Dept Civil Environm & Construct Engn, Honolulu, HI 96822 USA
5.Univ Hawaii Manoa, Water Resources Res Ctr, Honolulu, HI 96822 USA
关键词： remote sensing; GNSS-R; soil moisture; machine learning; transfer learning
期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）
ISSN：
年卷期： 2024 年 16 卷 7 期
页码：
收录情况： SCI
摘要： Soil moisture (SM) is an important parameter in water cycle research. Rapid and accurate monitoring of SM is critical for hydrological and agricultural applications, such as flood detection and drought characterization. The Global Navigation Satellite System (GNSS) uses L-band microwave signals as carriers, which are particularly sensitive to SM and suitable for monitoring it. In recent years, with the development of Global Navigation Satellite System-Reflectometry (GNSS-R) technology and data analysis methods, many studies have been conducted on GNSS-R SM monitoring, which has further enriched the research content. However, current GNSS-R SM inversion methods mainly rely on auxiliary data to reduce the impact of non-target parameters on the accuracy of inversion results, which limits the practical application and widespread promotion of GNSS-R SM monitoring. In order to promote further development in GNSS-R SM inversion research, this paper aims to comprehensively review the current status and principles of GNSS-R SM inversion methods. It also aims to identify the problems and future research directions of existing research, providing a reference for researchers. Firstly, it introduces the characteristics, usage scenarios, and research status of different GNSS-R SM observation platforms. Then, it explains the mechanisms and modeling methods of various GNSS-R SM inversion research methods. Finally, it highlights the shortcomings of existing research and proposes future research directions, including the introduction of transfer learning (TL), construction of small models based on spatiotemporal analysis and spatial feature fusion, and further promoting downscaling research.
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