Quantification of soil water content by machine learning using enhanced high-resolution ERT

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The accurate acquisition of soil water content is a fundamental cornerstone of research into hydrological processes and agricultural engineering. Electrical Resistivity Tomography (ERT) has been validated for hydrological studies and soil monitoring. The establishment of a quantitative relationship between ERT resistivity data and soil water content is usually based on rock physics models. However, the applicability of such models in complex environments and the acquisition of the relevant parameters pose a certain challenge. In addition, the spatial resolution of ERT limits its application in soil moisture assessment. Therefore, a machine learning-based approach is proposed in this study to determine the quantitative relationship between resistivity and soil water content. We investigate the integration of three machine learning models (KNN, RF, XGBOOST) with ERT to predict the water content of clay soils. The results show that the RF model achieves an R2 of 0.92 with an RMSE of 0.41. To improve the ERT resolution, a new data collection method (MRU) is introduced in this study by increasing the density of ERT data collection. A comparative analysis is conducted between traditional ERT data collection methods and the MRU approach in terms of soil water content prediction accuracy. The results show that the MRU method of data collection improves the accuracy of soil water content prediction by an average of 57% compared to traditional methods. This study confirms the feasibility of using machine learning models to establish mappings between resistance and water content and shows that the MRU data collection method for ERT effectively improves the accuracy of predicting soil water content. These results provide a new perspective for hydrological process research and agricultural monitoring technology.