Soil Moisture Retrieval Method Based on Fusion of GNSS‑R and Radiometer Data

Abstract:

Soil moisture is a significant variable that influences agricultural productivity and climate dynamics. However, conventional GNSS?reflection (GNSS?R) methods for soil moisture retrieval often suffer from limited accuracy and insufficient capability to capture deep data features. A hybrid deep learning model that integrates convolutional neural networks (CNN) and long short?term memory (LSTM) networks for soil moisture retrieval was proposed to improve these issues. This model can fully leverage the advantages of CNN in feature extraction and LSTM in time series modeling, especially the complementarity in multi?source data fusion. By incorporating GNSS?R reflected signals along with brightness temperature (TB) and the integrated attenuation coefficient related to soil roughness and vegetation cover (TR) obtained from the ELBARA?Ⅱ radiometer, the proposed model enabled a more comprehensive representation of soil moisture dynamics. Compared with the conventional GNSS?R retrieval model and classical machine learning methods (such as support vector machine (SVM), multilayer perceptron (MLP), random forest (RF)), the proposed model achieved higher correlation coefficients (R) and lower root mean square errors (RMSE). The results verified that the proposed model offered superior accuracy, robustness, and generalization performance, making it well?suited for continuous soil moisture monitoring in fixed regions. This work demonstrated the potential of deep neural networks for enhancing remote sensing data fusion and improving the accuracy of soil moisture retrieval.