A dual-mode UAV radar synergistic observation system and processing framework for high-resolution farmland imaging and soil moisture retrieval

Lightweight UAV-borne microwave radar systems exhibit significant potential for coordinated, multi-dimensional observation in precision agriculture. However, limitations in platform payload and instability during low-altitude flight make it challenging for existing systems to simultaneously achieve high-resolution farmland mapping and retrieval of microscopic physical parameters within a single radar platform. To address this challenge, this study develops a dual-mode microwave radar cooperative observation system—featuring both side-looking and downward-looking configurations—mounted on an unmanned aerial vehicle (UAV). A systematic data processing framework is also established for high-resolution farmland imaging and soil moisture retrieval. In the side-looking synthetic aperture radar (SAR) imaging mode, an improved range Doppler algorithm (RDA), integrating RTK-assisted two-step compensation and minimum entropy autofocus (MEA), is proposed to effectively mitigate trajectory distortions. In addition, the scale-invariant feature transform (SIFT) algorithm is employed to achieve geometrically consistent mosaicking of large-area farmland radar images. In the downward-looking quantitative retrieval mode, a height-adaptive correction and time-window truncation strategy based on edge detection is introduced to suppress interference caused by vertical height fluctuations and to extract canopy- and soil-related backscattering features. A random forest (RF) model is then constructed for soil moisture retrieval. Experimental results under complex farmland conditions demonstrate robust performance in both modes. In the side-looking mode, the structural similarity index (SSIM) of SAR image mosaics reaches 0.889, with a root mean square error (RMSE) of 0.443 pixels. In the downward-looking mode, the height-corrected RF retrieval model was evaluated using four-fold cross-validation, yielding RMSE values of 0.0213, 0.0110, and 0.0101 cm³ /cm³ in Zones I–III, respectively, with corresponding correlation coefficients (R) of 0.6898, 0.7270, and 0.6308. The height-correction ablation further confirmed that interface alignment improved retrieval stability and reduced the influence of UAV vertical motion on radar feature extraction. The proposed framework can support field-scale soil moisture mapping, irrigation nonuniformity diagnosis, and precision irrigation decision-making in agricultural water management.