Abstract
In this paper, an algorithm to retrieve surface soil moisture from GNSS-R (Global Navigaton Satellite System Reflectometry) observations is presented. Surface roughness and vegetation effects are found to be the most critical ones to be corrected. On one side, the NASA SMAP (Soil Moisture Active and Passive) correction for vegetation opacity (multiplied by two to account for the descending and ascending passes) seems too high. Surface roughness effects cannot be compensated using in situ measurements, as they are not representative. An ad hoc correction for surface roughness, including the dependence with the incidence angle, and the actual reflectivity value is needed. With this correction, reasonable surface soil moisture values are obtained up to approximately a 30° incidence angle, beyond which the GNSS-R retrieved surface soil moisture spreads significantly.
Highlights
The first evidence of soil moisture (SM) signatures in GPS reflected signals date back to 1996 [1]
That the value of the b parameter directly determined at the satellite scale “is nearly identical to what was proposed for crops in the ESA SMOS (Soil Moisture and Ocean Salinity mission) algorithm, but half as large as what is currently used by Soil Moisture Active Passive (SMAP)” [29], and
Due to the limited accuracy and coarse sampling of the soil surface roughness, the results show a large disparity between the ARIEL and the GNSS-R SM retrievals
Summary
The first evidence of soil moisture (SM) signatures in GPS reflected signals date back to 1996 [1]. YeYlleolwlowrercetactnagnlgelecocrorrersepspononddsstotoththeesseeccoonnddqquuaaddrraanntt ooff zzoooomm rreeggiioonnooffFFiigguurree33..NNoortrhthWWesetstcocronrenre:r: Remot4e 1S4◦e1n4°s74.’723'0332”30N",N1, 2,0,0◦x°55F00O’'44R”"EEP;;ESSEooRuuRtthhEVEEIaaEssWtt ccoorrnneerr::4411°◦4466'5’65"6N”N, 0,°05◦15'517’5"E7”, Ed,isdtaisntcaen=ce2=.9 2k.m9 .km At this stage, it is important to highlight two potential error sources in these parameterizations: that the value of the b parameter directly determined at the satellite scale “is nearly identical to what was proposed for crops in the ESA SMOS (Soil Moisture and Ocean Salinity mission) algorithm, but half as large as what is currently used by SMAP” [29], and the current values of the h parameter may be too smooth (low) for crop regions, as reported in [20] for the South Fork SMAP Core Validation Site in the Corn Belt state of Iowa.
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