A GNSS-R Soil Moisture Inversion Method Based on Multimodal Feature Fusion for the Yellow River Delta

As an emerging remote sensing technology, Global Navigation Satellite System Reflectometry (GNSS-R) offers moderate penetration capabilities and supports all-weather observations. It has been widely applied in soil moisture retrieval. However, GNSS-R-based soil moisture inversion faces significant challenges due to the marked heterogeneity, complex feature associations, and difficulty in unified modeling among Delay-Doppler Map (DDM) imagery, time delay waveforms, and auxiliary features. To address these issues, this study introduces a multi-modal fusion strategy combined with a deep learning approach that possesses strong nonlinear modeling capabilities, and proposes a novel GNSS-R soil moisture retrieval model termed the Multi-modal Cross-Branch Fusion Network (MCBF-Net). MCBF-Net consists of three parallel branches designed to process different modalities of input data: a one-dimensional convolutional neural network (1D CNN) module for efficiently extracting features from time delay waveforms; a multi-scale fusion Transformer module for capturing the global features of DDM images; and a backpropagation (BP) neural network module that leverages its powerful nonlinear mapping capabilities to deeply learn complex patterns in the auxiliary data. Additionally, a multi-modal feature fusion module and a fusion loss function are embedded within the tri-branch architecture to ensure effective integration of features across modalities and to enhance the synergy among the network branches. Through this design, the model enables deep and comprehensive feature extraction and fusion from time delay waveforms, DDM imagery, and auxiliary information, significantly improving the accuracy of soil moisture retrieval. This study uses the Yellow River Delta region as the experimental area, focusing on the urgent needs of ecological environmental protection and sustainable agricultural development in the region. The soil moisture observation data provided by the Soil Moisture Active Passive (SMAP) satellite were used to validate the effectiveness of the proposed method. The retrieved results achieved a root mean square error (RMSE) of 0.0284 cm³/cm³ and a correlation coefficient (R) of 0.9232, indicating high accuracy and reliability. Furthermore, the study analyzed the spatiotemporal distribution characteristics of soil moisture in the Yellow River Delta, offering a feasible and efficient solution for regional-scale soil moisture inversion and refined monitoring.