Sparse-Temporal Crop Mapping with Confidence-Weighted Multi-Source Supervision in Jilin Province During 2019

Reliable crop mapping under incomplete seasonal observation is difficult because temporal evidence is sparse and public crop maps can disagree in parcel boundaries and class assignments. We investigate sparse-temporal crop mapping in Jilin Province using monthly Sentinel 1 and Sentinel 2 features acquired from June to September 2019, while treating three public crop maps as complementary supervision sources rather than independent ground truth. Their labels are harmonized into four map-derived classes and transformed into a hard pseudo target, heuristic pixel weights, soft label distributions, and agreement or conflict region codes. A full-temporal teacher then guides a sparse-temporal student through confidence-weighted losses, logit and feature distillation, and prototype regularization, allowing the same four-month interface to support different observation subsets. Under the fixed July and August setting, the STLRNet student reaches 0.792 OA, 0.742 Macro F1, and 0.596 mIoU, whereas complete June-to-September observations reach 0.821 OA. Soft pseudo labels improve OA from 0.721 to 0.751 relative to unweighted hard labels, and the student requires fewer parameters, operations, inference time, and memory than the full-temporal teacher. All metrics quantify agreement with the constructed pseudo reference, so the findings establish methodological feasibility for this Jilin 2019 case study rather than independent field accuracy or cross-region transfer.