Field-scale Soil Moisture Retrieval from Drone-Based L-band Radiometry with Optical and Thermal Infrared Priors

Accurate field-scale near-surface soil moisture is essential for infiltration, runoff generation, land-atmosphere exchange, and agricultural water management. Drone-based low-frequency (L-band) radiometry offers a promising intermediate scale between in situ measurements and satellite observations, but retrieval remains ill-posed because brightness temperature depends jointly on soil dielectric properties, vegetation attenuation, surface temperature, and sub-footprint heterogeneity. This study develops an uncertainty-aware Bayesian retrieval framework that integrates unmanned aerial vehicle (UAV) dual-polarized L-band brightness temperature with red-green-blue (RGB) and thermal infrared (TIR) information through footprint-consistent priors. Optical fraction vegetation cover, thermal state, and texture descriptors are used to constrain vegetation optical depth (τ) and its uncertainty at the scale of the radiometric footprint. The method was evaluated over heterogeneous cropland in Pengzhou, China, using independent calibration (4 scenes, about 1.3 ha) and validation datasets (13 scenes, about 12.6 ha). The proposed approach reduced root mean square error (RMSE) from about 0.057 to about 0.024 m3 m−3 and largely eliminated the systematic dry bias of the conventional τ–ω inversion. The uncertainty diagnostics showed that footprint-scale texture was more strongly associated with τ-related uncertainty than with posterior soil-moisture variance or absolute retrieval error. Overall, the results indicate that physically informed multi-source priors can improve both accuracy metrics and interpretability for field-scale hydrological soil-moisture observation.