A Region-Adaptive Phenology-Aware Network for Perennial Cash Crop Mapping Using Multi-Source Time-Series Remote Sensing

Monitoring and identifying perennial cash crops is essential for optimizing agricultural resource allocation and supporting sustainable rural development. However, cross-regional recognition remains challenging due to cloud contamination, irregular mountainous topography, and climatic-driven phenological shifts. To address these issues, we propose a Region-Adaptive Multi-Head Phenology-Aware Network (RAM-PAMNet) that incorporates three key innovations. First, a Multi-source Temporal Attention Fusion (MTAF) module dynamically fuses Sentinel-1 SAR and Sentinel-2 optical time series to enhance temporal consistency and cloud robustness. Second, a Region-Aware Module (RAM) encodes topographic and climatic factors to adaptively adjust phenological windows across regions. Third, a Multi-Head Phenology-Aware Module (MHA-PAM) captures short-, mid-, and long-term phenological rhythms while integrating region-modulated attention for adaptive feature learning. The model was trained and validated in Changde, Hunan (694 patches; augmented to 2776; 70%/15%/15% split) and independently tested in Yaan, Sichuan (574 patches), two regions with contrasting elevation, terrain complexity, and hydrothermal regimes. RAM-PAMNet achieved an OA of 83.3%, mean F1 of 78.8%, and mIoU of 65.4% in Changde, and maintained strong generalization in Yaan with an mIoU of 59.2% and a DecayRate of 9.5, outperforming all baseline models. These results demonstrate that RAM-PAMNet effectively mitigates regional phenological mismatches and improves perennial crop mapping across heterogeneous environments. The proposed framework provides an interpretable and region-adaptive solution for large-scale monitoring of tea, citrus, and grape.