Development of an Innovative Physical-Geometry-Based Soil Moisture Retrieval Method for CYGNSS Constellations
Global Navigation Satellite System-Reflectometry (GNSS-R) has evolved as an innovative remote sensing technique for Earth surface monitoring over the past three decades. While the Cyclone Global Navigation Satellite System (CYGNSS) constellation was initially designed to observe tropical cyclones, r...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11026779/ |
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| Summary: | Global Navigation Satellite System-Reflectometry (GNSS-R) has evolved as an innovative remote sensing technique for Earth surface monitoring over the past three decades. While the Cyclone Global Navigation Satellite System (CYGNSS) constellation was initially designed to observe tropical cyclones, recent studies highlight its untapped potential for soil moisture retrieval. In this work, we first analyze the physical scattering mechanisms of bare soil and vegetation, focusing on their distinct interactions with GNSS signals. A novel soil moisture retrieval method is proposed based on a zero-order scattering model. Surface reflectivity (SR) derived from CYGNSS is influenced by vegetation cover, surface roughness, and soil texture. To mitigate these effects, roughness-vegetation (R-V) correction factors are introduced. By removing R-V contributions from the SR, Fresnel reflectivity—directly linked to soil dielectric properties—is isolated for moisture estimation. In addition, simulations reveal that observation geometry significantly impacts SR variations; this geometric dependency is explicitly incorporated to refine retrieval accuracy. Validation demonstrates that integrating R-V correction and geometric adjustments reduces the root-mean-square error (RMSE) of soil moisture estimates from 0.0794 to 0.0357, marking a 55% improvement. This physics-based approach enhances CYGNSS-derived soil moisture precision and holds promise for advancing sustainable water resource management and meteorological studies through high-resolution, all-weather monitoring. |
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| ISSN: | 1939-1404 2151-1535 |