OAR@ICRISATAccuracies of Soil Moisture Estimations Using a Semi-Empirical Model over Bare Soil Agricultural Croplands from Sentinel-1 SAR DataHAnil KumarauthorGNicoauthorMIrshad AhmedauthorA MWhitbreadauthorThis study describes a semi-empirical model developed to estimate volumetric soil moisture ( v ϑ) in bare soils during the dry season (March–May) using C-band (5.42 GHz) synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m spatial resolution. The semi-empirical model was developed using backscatter coefficient (σ° dB) and in situ soil moisture collected from Siruguppa taluk (sub-district) in the Karnataka state of India. The backscatter coefficients 0 VV σ and 0 VH σ were extracted from SAR images at 62 geo-referenced locations where ground sampling and volumetric soil moisture were measured at a 10 cm (0–10 cm) depth using a soil core sampler and a standard gravimetric method during the dry months (March–May) of 2017 and 2018. A linear equation was proposed by combining 0 VV σ and 0 VH σ to estimate soil moisture. Both localized and generalized linear models were derived. Thirty-nine localized linear models were obtained using the 13 Sentinel-1 images used in this study, considering each polarimetric channel Co-Polarization (VV) and Cross-Polarization(VH) separately, and also their linear combination of VV + VH. Furthermore, nine generalized linear models were derived using all the Sentinel-1 images acquired in 2017 and 2018; three generalized models were derived by combining the two years (2017 and 2018) for each polarimetric channel; and three more models were derived for the linear combination of 0 VV σ and 0 VH σ . The above set of equations were validated and the Root Mean Square Error (RMSE) was 0.030 and 0.030 for 2017 and 2018, respectively, and 0.02 for the combined years of 2017 and 2018. Both localized and generalized models were compared with in situ data. Both kind of models revealed that the linear combination of 0 VV σ + 0 VH σ showed a significantly higher R2 than the individual polarimetric channels.Remote SensingGIS Techniques/Remote SensingSoil Science2020-05MDPIArticle

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