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Accurate information on rice planting areas and spatial distribution is critical for agricultural management in China; however, mapping efforts in regions like Sichuan Province are severely constrained by persistent cloud cover and fragmented terrain. Existing phenology-based methods and coarse-resolution products often fail to provide precise paddy localizations or usable training labels in such complex environments. To address these limitations, this study proposes a robust framework integrating optical and Synthetic Aperture Radar (SAR) imagery. The methodology employs a phenology-driven strategy for rapid candidate area annotation, coupled with an asymmetric feature extraction mechanism that incorporates a Multi-Scale Semantic Enhancement Module (MSEM) and a Category-Balanced Feature Fusion Module (CBFM) to facilitate adaptive and effective cross-modal fusion. Applying this framework to the Tianfu New Area (2019–2023) yielded 10-meter resolution rice distribution maps with a rice Intersection over Union (IoU) of 83.31% and a statistical correlation (R 2) of 0.972. These results demonstrate the framework’s capacity for selective multi-source fusion and cost-effective sampling, facilitating precise rice mapping in challenging agricultural landscapes.

Fine-scale mapping of rice distribution in cloud-prone regions: a multi-scale asymmetric fusion network framework based on Sentinel-1/2 imagery