Synergistic Use of Multitemporal SAR and Optical Data with Random Forest Approach for Rice Mapping in Bangladesh

Mst. Farida Perveen, Sravan Shrestha, Abdullah Al Mehadi, Saima Kazmina, Amio Al Saem

Accurate in-season crop mapping is vital for national food security, yet traditional
ground-based estimation methods in Bangladesh often lack precision. This study develops a robust machine-learning framework to map Boro rice in the Manikganj district by integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery. To overcome the challenge of seasonal cloud cover, Sentinel-1 VH polarization was utilized to identify unique backscatter reductions during the transplantation phase (late February to early March) caused by specular reflection from flooded fields. By combining these SAR signatures with high-resolution spectral indices (NDVI, EVI, and BSI) from Sentinel-2 during peak growth stages, the research captures the distinct phenological signatures of paddy rice. A pixel-based Random Forest algorithm was applied within the Google Earth Engine (GEE) platform, utilizing about 200 field-verified reference points per sub-district (1,385 in total) for training and validation. The results demonstrated high classification performance, with overall accuracies ranging from 88.1% to 94.9% across the seven sub-districts. Furthermore, a comparison with official government statistics revealed a minimal total area difference of only 2.34%, underscoring the reliability of this integrated satellite approach for operational crop monitoring.