GNSS-R monitoring of soil moisture dynamics in areas of severe drought: example of Dahra in the Sahelian climatic zone (Senegal)

Ha, Minh Cuong; Darrozes, José; Llubes, Muriel; Grippa, Manuela; Ramillien, Guillaume; Frappart, Frédéric; Baup, Frédéric; Tagesson, Torbern; Mougin, E.; Guiro, Idrissa; Kergoat, Laurent; Nguyen, Huu Duy; Seoane, Lucía; Dufréchou, Grégory; Vu, Phuong Lan

doi:10.1080/22797254.2022.2156931

Cited by 5 publications

(1 citation statement)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ha et al developed a new method that combines the interference pattern technology with the phase unwrapping method to obtain continuous phase measurements of SNR multipath, enabling SM inversion. This method achieved good inversion results in sandy soil observation areas, with a volumetric soil moisture (VSM) range of 2.5-8%, and the correlation coefficient between the inverted SM and the actual measured SM reached 0.97 [30].…”

Section: Ground-basedmentioning

confidence: 98%

Review of GNSS-R Technology for Soil Moisture Inversion

Yang,

Mao,

Guo

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

Section: Ground-basedmentioning

confidence: 98%