Month-Aware Optical and SAR Temporal Fusion for Growing Season Crop Classification

Growing season crop classification supports agricultural monitoring, yet cloud-affected optical observations, fragmented fields, and uncertainty in mapped labels complicate reliable evaluation. Public crop maps also differ in class definitions and spatial detail, so agreement with one map does not establish independent ground accuracy. This study presents MAOSTFNet, a month-aware temporal fusion network that combines Sentinel-2 optical data and Sentinel-1 SAR data from June through September 2019 in Jilin Province. Twelve optical features and four SAR features are embedded separately, mixed by a spatial gate, and aggregated with calendar month embeddings and temporal attention. Dense decoding predicts four crop classes, while an auxiliary crop-class boundary head adds a spatial structure objective. The fixed evaluation uses corrected SAR features, grid-grouped subsets, seed 42, one checkpoint rule, a shared test mask, and one metric implementation. MAOSTFNet achieved 0.947 OA, 0.933 AA, 0.912 Kappa, 0.937 Macro F1, and 0.882 mIoU, with margins of 0.016, 0.019, 0.025, 0.014, and 0.024 over the strongest comparator. Removing August reduced Macro F1 by 0.050, and masking optical input reduced it by 0.080. Across test-support crop-coverage thresholds from 0 to 20 percent, Macro F1 ranged from 0.920 to 0.944 and remained highest among five evaluated implementations. These scores describe fixed-seed agreement with mapped labels in Jilin Province in 2019. The main support is crop-enriched, adjacent subsets have no exclusion buffer, and the results do not establish independent ground accuracy or geographically separated generalization.