Deep Learning Approaches to Soil Moisture Retrieval in Tropical Regions: Integrating GNSS-R and SAR Data for High-Resolution Mapping

Accurate high-resolution soil moisture mapping in tropical regions remains challenging due to persistent cloud cover, dense vegetation, and the coarse spatial resolution of existing satellite products. This study presents a deep learning ensemble framework integrating Cyclone Global Navigation Satellite System (CYGNSS) reflectometry, Sentinel-1 Synthetic Aperture Radar (SAR) dual-polarization data (VV and VH), and auxiliary environmental layers—including rainfall, land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), slope, and bulk density—to produce 500m resolution soil moisture estimates for Nigeria. An aggressive SAR downsampling strategy was implemented to reduce 20GB Sentinel-1 files to manageable dimensions while preserving critical radiometric information, enabling efficient processing across the entire study domain. The ensemble model, optimally weighting a deep neural network (70%), multi-layer perceptron (15%), and XGBoost (15%), was trained on 33,208 SMAP reference points, achieving strong performance (R²=0.925). The trained model was subsequently deployed across 6.1 million 500m grid cells covering Nigeria. The resulting soil moisture map yielded a mean value of 0.295 cm³/cm³ and successfully captured spatial gradients from the humid southern regions (0.32–0.45 cm³/cm³) to the arid northern zones (0.12–0.25 cm³/cm³). The framework achieved a 64-fold resolution improvement over native SMAP products while maintaining accuracy (RMSE=0.032 cm³/cm³). Feature importance analysis identified CYGNSS backscatter, SAR VH polarization, and NDVI as the dominant predictors, highlighting the complementary value of combining active and passive remote sensing observations. This computationally tractable framework demonstrates the feasibility of operational-scale, high-resolution soil moisture monitoring across data-sparse tropical regions. The methodology is scalable and transferable to other cloud-affected regions, offering a pathway toward improved agricultural monitoring, drought prediction, and water resource management in developing nations where in situ observations are limited.