Abstract
Global Navigation Satellite Systems-Reflectometry (GNSS-R) has shown unprecedented advantages to sense Soil Moisture Content (SMC) with high spatial and temporal coverage, low cost, and under all-weather conditions. However, implementing an appropriated physical basis to estimate SMC from GNSS-R is still a challenge, while previous solutions were only based on direct comparisons, statistical regressions, or time-series analyses between GNSS-R observables and external SMC products. In this paper, we attempt to retrieve SMC from GNSS-R by estimating the dielectric permittivity from Fresnel reflection coefficients. We employ Cyclone GNSS (CYGNSS) data and effectively account for the effects of bare soil roughness (BSR) and vegetation optical depth by employing ICESat-2 (Ice, Cloud, and land Elevation Satellites 2) and/or SMAP (Soil Moisture Active Passive) products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief. Our GNSS-R SMC estimates are validated by SMAP SMC products and the results provide an R-square of 0.6, Root Mean Squared Error (RMSE) of 0.05, and a zero p-value, for the 4568 test points evaluated at the eastern region of China during April 2019. The achieved results demonstrate the optimal capability and potential of this new method for converting reflectivity measurements from GNSS-R into Land Surface SMC estimates.
Highlights
In many different scientific fields, the utmost significance of Soil Moisture Content (SMC) has been pointed out as an environmental factor for land surface dynamics monitoring [1,2,3,4,5,6,7,8], such as evapotranspiration, droughts, floods, etc., while it simultaneously regulates energy and water exchange between the land and the atmosphere and other hydrological processes
In this manuscript, we have derived SMC estimates from Fresnel reflection coefficients measured by Global Navigation Satellite Systems-Reflectometry (GNSS-R), from the Cyclone Global Navigation Satellite Systems (GNSS) (CYGNSS) mission during April 2019 for the eastern region of China
We have derived SMC estimates from Fresnel reflection coefficients measured by GNSS-R, from the CYGNSS mission during April 2019 for the eastern region of China
Summary
In many different scientific fields, the utmost significance of Soil Moisture Content (SMC) has been pointed out as an environmental factor for land surface dynamics monitoring [1,2,3,4,5,6,7,8], such as evapotranspiration, droughts, floods, etc., while it simultaneously regulates energy and water exchange between the land and the atmosphere and other hydrological processes. In the same frequency domain, during the last few decades, an emerging and challenging technology based on the opportunity signal, i.e., receiver devices that take the advantage of employing existing signals from other systems, is being exploited for specific Earth observation applications such as, e.g., geoid determination, sea surface wind speed, and surface SMC [19]. Since current GNSS-R missions does not provide BSR nor vegetation canopy characteristics yet, this information need to be accessed from other existing sources, such as NASA’s Ice, Cloud, and land Elevation Satellites 2 (ICESat-2) [39], providing BSR estimates, or NASA’s Soil Moisture Active Passive (SMAP) [40], providing BSR and vegetation biophysical variables such as Vegetation Optical Depth (VOD) or Vegetation Water Content (VWC).
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