Soil Moisture Estimation Based on Polarimetric Decomposition and Quantile Regression Forests

Abstract

The measurement of surface soil moisture (SSM) assists in making agricultural decisions, such as precision irrigation and flooding or drought predictions. The critical challenge for SSM estimation in vegetation-covered areas is the coupling between vegetation and surface scattering. This study proposed an SSM estimation method based on polarimetric decomposition and quantile regression forests (QRF) to overcome this problem. Model-based polarimetric decomposition separates volume scattering, double-bounce scattering, and surface scattering, while eigenvalue-based polarimetric decomposition provides additional parameters to describe the scattering mechanism. The combined use of these parameters explains the polarimetric SAR scattering information from multiple perspectives, such as vegetation, surface roughness, and SSM. As different crops differ in morphology and structure, it is essential to investigate the potential of varying polarimetric parameters to estimate SSM in areas covered by different crops. QRF, a regression method applicable to high-dimensional predictor variables, is used to estimate SSM from these parameters. In addition to the SSM estimates, QRF can also provide the predicted uncertainty intervals and quantify the importance of the different parameters in the SSM estimates. The performance of QRF in SSM estimation was tested using data from the soil moisture active passive validation experiment 2012 (SMAPVEX12) and compared with copula quantile regression (CQR). The SSM estimated by the proposed method was consistent with the in situ SSM, with the root-mean-square-error ranging from 0.037 cm3/cm3 to 0.079 cm3/cm3 and correlation coefficients ranging from 0.745 to 0.905. Meanwhile, the method proposed in this study can provide both the uncertainty of SSM estimation and the importance of different polarimetric parameters.