Multi-Modal sensing for soil moisture mapping: Integrating drone-based ground penetrating radar and RGB-thermal imaging with deep learning

Abstract

Precise estimation of soil moisture is vital for refining irrigation practices, enhancing crop productivity, and promoting sustainable water use management. This study integrates Ground Penetrating Radar (GPR) and RGB-Thermal imaging datasets to enhance soil moisture prediction throughout the maize growing season, assessing moisture content at 10 and 30-cm soil depths. By leveraging the complementary strengths of GPR for subsurface moisture detection and RGB-Thermal imagery for surface and canopy analysis, we addressed common issues such as underestimation and overestimation often encountered with standalone datasets, including the weaknesses of GPR signal and its attenuation for deeper soil moisture monitoring as well as RGB-Thermal sensor lack in dealing with canopy, covering the soil surface. Advanced machine learning models, including ANN, AdaBoost, and PLS, were applied to evaluate the effects of thermal, structural and spectral variables on accuracy of moisture estimation. The best-performing model, the ANN trained with variables extracted from the 1D-CNN network, achieved an R² of 0.83 and an RMSE of 1.9 % at 10 cm depth. At 30 cm, the same model achieved an R² of 0.79 and an RMSE of 3.2 %, showing robust performance even at deeper soil levels. These results demonstrate the significant improvement in model performance when GPR data is integrated with RGB-Thermal data, reducing prediction errors in both high and low moisture regimes. Thermal variables, particularly Land Surface Temperature, exhibited a strong correlation with moisture content, especially at shallower depths. However, GPR variables were essential for detecting subsurface moisture at 30 cm depth, where RGB-Thermal data alone showed limitations. The integration of GPR and RGB-Thermal data resulted in a more comprehensive and accurate soil moisture estimation model, offering significant potential for optimizing water use in agricultural systems.