Review of GNSS-R Technology for Soil Moisture Inversion

Author:

Yang Changzhi12,Mao Kebiao12ORCID,Guo Zhonghua1,Shi Jiancheng3ORCID,Bateni Sayed M.4ORCID,Yuan Zijin2

Affiliation:

1. School of Physics/Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China

2. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

3. National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China

4. Department of Civil, Environmental, and Construction Engineering, and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA

Abstract

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.

Funder

National Key R&D Program of China

Publisher

MDPI AG

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