Optical and SAR Data Fusion Based on Transformer for Rice Identification: A Comparative Analysis from Early to Late Integration

The accurate identification of rice fields through remote sensing is critical for agricultural monitoring and global food security. While optical and Synthetic Aperture Radar (SAR) data offer complementary advantages for crop mapping—spectral richness from optical imagery and all-weather capabilities from SAR—their integration remains challenging due to heterogeneous data characteristics and environmental variability. This study systematically evaluates three Transformer-based fusion strategies for rice identification: Early Fusion Transformer (EFT), Feature Fusion Transformer (FFT), and Decision Fusion Transformer (DFT), designed to integrate optical-SAR data at the input level, feature level, and decision level, respectively. Experiments conducted in Arkansas, USA—a major rice-producing region with complex agroclimatic conditions—demonstrate that EFT achieves superior performance, with an overall accuracy (OA) of 98.33% and rice-specific Intersection over Union (IoU_rice) of 83.47%, surpassing single-modality baselines (optical: IoU_rice = 75.78%; SAR: IoU_rice = 66.81%) and alternative fusion approaches. The model exhibits exceptional robustness in cloud-obstructed regions and diverse field patterns, effectively balancing precision (90.98%) and recall (90.35%). These results highlight the superiority of early-stage fusion in preserving complementary spectral–structural information, while revealing limitations of delayed integration strategies. Our work advances multi-modal remote sensing methodologies, offering a scalable framework for operational agricultural monitoring in challenging environments.