Enhancing Soil Moisture Estimation with Convolutional Neural Networks

The measurement of soil water content plays a crucial role in the management of water resources, particularly in agriculture, which consumes over 80% of the managed water supply. An accurate and efficient model for measuring water content can significantly enhance agricultural water usage efficiency, impacting irrigation management, yield prediction, and drought response strategies positively. In this paper, we develop a convolutional neural network (CNN) designed to address the inverse scattering problem by analyzing the complex reflection coefficient to estimate the volumetric water content (VWC) based on electromagnetic properties. The network is trained and tested with a synthetic dataset modeling the multilayered soil. Our results demonstrate the proposed method’s efficiency in estimating soil volumetric water content, showcasing its potential as a tool for improving water resource management in agricultural settings.