Using SAOCOM data and Bayesian Inference to estimate soil dielectric constant in agricultural soils

ARELLANA, JAVIER; FRANCO, MARIANO; GRINGS, FRANCISCO

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Año: 2023

1545-598X

Soil moisture is a key geophysical variable that can be estimated using remote sensing techniques by making use of the known relation between soil backscattering and dielectric constant in the microwave regime. However, since SAR systems observations depends on geometrical and dielectrical surface parameters (besides instrument parameters like operation frequency, incidence angle and received/transmitted polarization), the uncertainties associated to a given retrieval scheme are difficult to evaluate. In this paper, these uncertainties associated to the estimation of soil dielectric constant from a single quadpol SAR image are studied using a physically based interaction model (i.e. a two-layer version of the SPM model at second order) coupled with a Bayesian approach. The overall scheme was validated using SAOCOM quad-pol data and in situ soil dielectric constant measurements in experimental agricultural plots in Argentina. Both a theoretical end-to-end experiments and actual retrieval from real SAR data were implemented. From the simulations, the intrinsic ambiguities in the estimations of soil dielectric constant from a single image were studied, and the benefits of using two images with different incidence angles were discussed. Finally, by analyzing SAOCOM data using the proposed retrieval scheme, soil dielectric constant were estimated and compared with in situ measurements, with and RMSE error of ≤ 2.