Mapping agricultural soil water content using multi-feature ensemble learning of GPR data

Soil water content (SWC) is significant for understanding and evaluating the conditions of soils and plants. Since traditional methods such as time domain reflectometry (TDR) and neutron probes have significant drawbacks such as limitations in spatial resolution, detection depth, efficiency, and non-destruction, ground penetrating radar (GPR) has become a potential method in SWC estimation. Many features extracted from GPR data in the time and frequency domain have been proven to be sensitive to the SWC and can further achieve the estimation of it. However, the methods based on these features are easy to be interfered with by noise and the heterogeneity in soils. This article aims to solve this problem by including more features and integrating these features for a joint estimation. Firstly, we study the relationships between SWC and seven features extracted from GPR data. Consequently, we propose to include new features, i.e. the loss tangent feature and the time-frequency features, in the SWC inversion. Secondly, we achieve the multi-feature ensemble learning based on the Adaboost R. method, which largely enhances the accuracy of SWC inversions compared to the single-feature estimations. During the numerical test, we establish the stochastic medium to model the heterogeneity in the real soil. The test verifies the effectiveness and the robustness of the proposed method. Finally, a field experiment is performed on the transition zone of no-tillage and deep-ploughing croplands. A 2-D SWC map is obtained which distinctly presents the SWC difference between the two regions. Our study provides a new approach to improve the accuracy of SWC estimation using GPR.