Integrated In-season Rapeseed Mapping and Flowering Retrieval: A Multi-task Transformer Framework Using Sentinel-1 and Sentinel-2

As a globally vital oilseed, rapeseed necessitates precise in-season mapping to support field management. Furthermore, the accurate retrieval of peak flowering dates is critical for yield estimation, as this phenological stage directly correlates with crop productivity. While state-of-the-art methods have advanced both crop mapping and phenology retrieval, existing approaches predominantly address these tasks in isolation, thereby neglecting their inherent phenological interdependence. Specifically, in-season mapping is often confounded by early-season phenological heterogeneity across fields and regions, whereas flowering retrieval typically relies on the prerequisite of an accurate a priori crop map. To address these limitations, this study introduces a multi-task Transformer-based framework that simultaneously maps rapeseed and retrieves peak flowering dates using Sentinel-1 and Sentinel-2 time series. Reliable training samples were automatically generated via phenology-based rules applied to cloud-free time series. To enhance the robustness against cloud contamination, a data augmentation strategy was introduced that masks Sentinel-2 observations using real-cloud temporal masks to simulate realistic data unavailability. The proposed architecture integrates a dual-task framework with adaptive loss weighting to dynamically balance learning gradients between tasks. Extensive validation across 13 European countries, covering a flowering gradient of up to two months, demonstrates that the proposed method achieves an F1-score of 0.89 for rapeseed mapping four months prior to harvest, and a Mean Absolute Error (MAE) of 6 days for peak flowering retrieval. These results substantially outperform both conventional sequential single-task baselines and specialized state-of-the-art methods. Furthermore, independent validation against phenological records from the German Weather Service (DWD) further confirm the robustness of the proposed method in flowering retrieval. To provide interpretable insights into the model's effectiveness, we analyzed Transformer attention maps and band importance. These visualizations substantiate that the multi-task model effectively extracts task-shared spectral-temporal features, offering a clear and interpretable basis for its enhanced generalization. Overall, this study presents a practical, scalable solution for integrated, large-scale rapeseed monitoring, demonstrating a robust framework that is adaptable to the integrated monitoring of other crops.