Parcel-based soil moisture retrieval in karst mountainous areas via SAR-optical temporal transformation

Accurate soil moisture (SM) estimation is essential for precision agriculture; however, in southwestern China's karst mountains, terrain effects on SAR and persistent cloud contamination introduce uncertainty. We propose a parcel-constrained SAR–optical temporal reconstruction framework. Cultivated parcels are delineated from sub-meter imagery using semantic segmentation, and multi-source SAR, optical and environmental features are aggregated to reduce speckle and mixed-pixel effects. A SAR–Optical Transformer (SOT) reconstructs parcel-scale normalized difference vegetation index (NDVI) time series from SAR, which are integrated with multi-source features in a random forest for SM retrieval. The SOT achieved R2 = 0.854 and mean absolute error (MAE) = 0.065. Adding reconstructed NDVI to the SAR + environmental baseline (R2 = 0.721) increased SM R2 to 0.805 and reduced RMSE to 0.038 m3 m−3, with uncertainty decreasing by ~18%. Compared with pixel-scale retrieval (R2 = 0.762), the parcel constraint improved R2 to 0.805 and reduced uncertainty by ~29%.