Multi‑Sensor Deep Learning for Smallholder Paddy Rice Mapping with Sentinel‑1 SAR and Sentinel‑2 Optical Imagery
This study demonstrates that an ensemble of U-Net and PSPNet deep learning models, trained on fused multi-temporal Sentinel-1 SAR and Sentinel-2 optical data, achieves high-accuracy mapping of fragmented smallholder paddy rice fields by leveraging multi-scale contextual reasoning and phenological growth stages.