Robust Crop Classification Model Based on Asynchronous SAR and Optical Temporal Feature Fusion

In-season crop identification requires accurate, timely, and reliable predictions from asynchronous SAR and optical observations that accumulate progressively during early and middle calendar checkpoints. This study proposes a progressive SAR–optical fusion framework with selective risk control for parcel-level crop monitoring. A Gated Cross-Attention Temporal Network (GCAT) integrates complementary Sentinel-1 and Sentinel-2 time-series information, while random-prefix training and real day-of-year encoding accommodate asynchronous and unequal-length observations. Probability calibration and a sequential ACCEPT/DEFER strategy further allow uncertain predictions to be deferred until a predefined acceptance criterion is satisfied. Experiments on PASTIS-R show that GCAT achieves a full-season Macro-F1 of 80.57 ± 0.83% across five official folds, outperforming unimodal and multimodal fusion baselines. In the primary Fold-2 evaluation, GCAT provides larger gains than the Sentinel-2-only model at early- and mid-season operational checkpoints, reaches acceptance 7.55 days earlier on average among jointly accepted parcels, and increases accepted coverage. Cross-year evaluation on Brandenburg shows that the classification and earliness advantages of multimodal fusion are partially retained, whereas the confidence–risk relationship degrades markedly. These results highlight the value of SAR–optical fusion for earlier in-season decisions while emphasizing the need for renewed calibration and reliability assessment across years.