Rule-based training sample generation using Sentinel-2 GCVI time series for winter wheat mapping

Timely and accurate winter wheat mapping is essential for agricultural monitoring and food security. However, efficiently acquiring high-quality training data for supervised classification remains a challenge. In this study, we developed a rule-based method to automatically generate training samples using Sentinel-2 green chlorophyll vegetation index (GCVI) time series. Then, the key phenological periods were identified through feature importance analysis, and spectral features from these periods were used with a Random Forest (RF) classifier to produce 10 m resolution winter wheat distribution maps for Hengshui, Kaifeng, and Xiangyang in 2022 and 2023. To evaluate temporal transferability, the automatically generated training samples from 2022 to 2023 were transferred to subsequent years, enabling winter wheat mapping for 2023 and 2024 based on cross-year training data. Accuracy assessments showed that the proposed method achieved high performance, with average overall accuracy (OA) of 96.04 ± 1.97 % and 94.81 ± 2.14 % in 2022 and 2023, respectively, and average F1 scores of 91.21 % and 90.83 %. The winter wheat maps generated using transferred samples also demonstrated good temporal transferability, and maintained high accuracy, with average OA of 94.06 ± 2.19 % in 2023 and 94.58 ± 2.01 % in 2024. Area estimates from stratified random sampling showed that winter wheat planting areas in Hengshui, Kaifeng, and Xiangyang were 290.8 ± 16.82, 199.16 ± 10.65, and 318.05 ± 44.16 thousand hectares (kha) in 2022, increasing to 379.34 ± 19.75, 209.27 ± 12.68, and 342.02 ± 42.81 kha in 2023, respectively. Compared with existing winter wheat products, the map generated in this study achieved higher classification accuracy and finer spatial detail. Overall, this study provides a practical and effective approach for automatic training sample generation in winter wheat mapping, and offers valuable guidance for large-scale, long-term agricultural monitoring.