Soil moisture retrieval using space-borne GNSS reflectometry: a comprehensive review

Rahmani, Mina; Asgari, J.; Asgarimehr, Milad

doi:10.1080/01431161.2022.2128927

Cited by 10 publications

(2 citation statements)

References 75 publications

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“…It is the first GNSS-R pathfinder mission consisting of eight Low Earth Orbiting (LEO) micro-satellites. CYGNSS was launched in December 2016 with the main objective of near-surface ocean wind and currents monitoring (e.g., [34], [35], [36]); however, it can also be used as tool for the remote sensing of other environmental features, including but not limited to, inland water monitoring (e.g., [37], [38], [39]), soil moisture, wetlands and biomass observation ( [40], [41], [42]), freeze/thaw state retrieval ( [43], [44]), and mesoscale ocean eddies observations [45]. Mayer and Ruf [46] proposed the potential of CYGNSS for conducting altimetry over lake ice and using the transparency of ice to GNSS L-band signals to infer ice thickness.…”

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confidence: 99%

Potential of GNSS-R for the Monitoring of Lake Ice Phenology

Ghiasi,

Duguay,

Murfitt

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This study introduces the first use of Global Navigation Satellite System Reflectometry (GNSS-R) for monitoring lake ice phenology. This is demonstrated using Qinghai Lake, Tibetan Plateau, as a case study. Signal-to-Noise Ratio (SNR) values obtained from the Cyclone GNSS (CYGNSS) constellation over four ice seasons (2018 to 2022) were used to examine the impact of lake surface conditions on reflected GNSS signals during open water and ice cover seasons. A moving t-test algorithm was applied to time-varying SNR values allowing for the detection of lake ice at daily temporal resolution. Good agreement was achieved between ice phenology records derived from CYGNSS data and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The CYGNSS timings for freeze-up, i.e., the period starting with the first appearance of ice on the lake (freeze-up start; FUS) until the lake becomes fully ice covered (freeze-up end; FUE), as well as those for breakup, i.e., the period beginning with the first pixel of open water (breakup start; BUS) and ending when the whole lake becomes ice-free (breakup end; BUE), were validated against the phenology dates derived from MODIS images. Mean absolute errors are 7, 5, 10, 4 and 5 days for FUS, FUE, BUS, BUE and ice cover duration, respectively. Observations revealed the sensitivity of GNSS reflected signals to surface melt prior to the appearance of open water conditions as determined from MODIS, which explains the larger difference of 10 days for BUS.

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Section: > Replace This Line With Your Manuscript Id Number (Double-c...mentioning

confidence: 99%

Potential of GNSS-R for the Monitoring of Lake Ice Phenology

Ghiasi,

Duguay,

Murfitt

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Meanwhile, its monitored area covers the entire surface below the antenna, and the size of monitored area can be changed by adjusting the antenna height and selecting the incident angle. In addition, the GNSS-R remote sensing method does not require specialized transmitting equipment, which has the potential to lower input expenses [14][15][16][17].…”

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confidence: 99%

Review of GNSS-R Technology for Soil Moisture Inversion

Yang,

Mao,

Guo

et al. 2024

Remote Sensing

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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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