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The goal of this work is to assess the impact of polarimetric SAR decompositions for soil moisture retrieval, and identify the decomposition that performs best for varying vegetation covers and soil conditions. Seven polarimetric decompositions are applied to three L-band radar time-series to evaluate their relative performances for future inclusion within a soil moisture retrieval scheme. Three agricultural sites with different soil and vegetation characteristics are selected across a latitudinal gradient in America. Two time-series of quad-polarimetric data collected by the NASA&#x002F;JPL UAVSAR airborne instrument are considered for the first two sites, while quad-polarimetric images acquired by the SAOCOM-1A mission are examined for the third site. We extract a set of radar polarimetric descriptors, including the backscattering coefficients, to analyze their sensitivity to soil moisture and vegetation through correlation analysis. We also apply a simple linear regression model to each crop type and site for estimating soil moisture (or Soil Water Index) by alternatively considering a combination of the decomposition powers and of the total backscattering coefficients (  ${{\bm{\gamma }}^0},\ {{\bm{\sigma }}^0}$  ). The linear regression analysis shows that the estimates are generally comparable in terms of linear correlation and root mean square error. Results also reveal that the sensitivity of polarimetric decomposition descriptors to soil moisture and vegetation parameters depend both on crop type and area of interest, without significant differences among the various decompositions tested in this study.

Sensitivity of Polarimetric SAR Decompositions to Soil Moisture and Vegetation Over Three Agricultural Sites Across a Latitudinal Gradient