Official URL: https://doi.org/10.1111/ejss.13414

Data derived from Synthetic Aperture Radar (SAR) are widely employed to predict soil properties, particularly soil moisture and soil carbon content. However, few studies address the use of microwave sensors for soil texture retrieval and those that do are typically constrained to bare soil conditions. Here, we test two statistical modelling approaches – linear (with and without interaction terms) and tree-based models, namely compositional linear regression model (LRM) and Random Forest (RF) – and both non-geophysical (e.g. surface soil moisture, topographic etc) and geophysical-based (electromagnetic, magnetic and radiometric) covariates to estimate soil texture (sand %, silt % and clay %), using microwave remote sensing data (ESA Sentinel 1). The statistical models evaluated explicitly consider the compositional nature of soil texture and were evaluated with leave-one-out cross validation (LOOCV). Our findings indicate that both modelling approaches yielded better estimates when fitted without the geophysical covariates. Based on the Nash-Sutcliffe efficiency coefficient (NSE), LRM slightly outperformed RF, with NSE values for sand, silt, and clay of 0.94, 0.62, and 0.46, respectively; for RF, the NSE values were 0.93, 0.59, and 0.44. When interaction terms were included, RF was found to outperform LRM. The inclusion of interactions in the LRM resulted in a decrease in NSE value and an increase in the size of the residuals. Findings also indicate that the use of radar derived variables (e.g. VV, VH, RVI) alone were not able to predict soil particle size without the aid of other covariates. Our findings highlight the importance of explicitly considering the compositional nature of soil texture information in statistical analysis and regression modelling. As part of the continued assessment of microwave remote sensing data (e.g. ESA Sentinel-1) for predicting topsoil particle-size, we intend to test surface scattering information derived from the dual-polarimetric decomposition technique and integrate that predictor into the models in order to deal with the effects of vegetation cover on topsoil backscattering.