Soil-Permittivity Estimation Under Grassland Using Machine-Learning and Polarimetric Decomposition Techniques

Abstract

The estimation of soil permittivity under fully covered grassland is a challenging task that can be approached by either model-based polarimetric decomposition techniques or data-driven machine-learning (ML) methods. In this study, we test the benefits and limitations of those techniques when individually or jointly applied to estimate the permittivity of the top soil (lower than 5-cm depth) from the L-band full-polarimetric SAR data. Training (needed for the ML approach) and reference data for accuracy assessment are based on the soil-permittivity measurements from an $in\\,\\, situ$ sensor network. The applied polarimetric decomposition approaches are unable to estimate high soil-permittivity ranges (permittivity higher than 25–30) under the full-cover grassland leading to an underestimation compared with the $in\\,\\, situ$ values. Purely data-driven ML techniques, here a case-adapted Random Forest (RF) architecture, applied directly to the SAR data achieve similar results as the decomposition approaches, where estimation quality mostly depends on the quality of the training set. The combination of both techniques works best and is able to represent high soil-permittivity ranges. The joint estimation decreases the mean absolute error drastically compared with applying any of the two approaches alone (i.e., from 4.88 and 4.99 of an informed physical model and ML on SAR only to 3.35).