A Novel Framework for Winter Crop Mapping Using Sample Generation Automatically and Bayesian-Optimized Machine Learning

Timely and accurate winter crop distribution maps are crucial for agricultural monitoring, food security, and sustainable land use planning. However, conventional methods relying on field surveys are labor-intensive, costly, and difficult to scale across large regions. To address these limitations, this study presents an automated winter crop mapping framework that integrates phenology-based sample generation and machine learning classification using time-series Sentinel-2 imagery. The Winter Crop Index (WCI) is developed to capture seasonal vegetation dynamics, and the Otsu algorithm is employed to automatically extract reliable training samples. These samples are then used to train three widely used machine learning classifiers—Random Forest (RF), a Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)—with hyperparameters optimized via Bayesian optimization. The framework was validated in three diverse agricultural regions in China: the Erhai Basin in Yunnan Province, Shenzhou City in Hebei Province, and Jiangling County in Hunan Province. The experimental results demonstrate that the combination of the WCI and Otsu enables a reliable initial classification, facilitating the generation of high-quality training samples. XGBoost achieved the best performance in the Erhai Basin and Shenzhou City, with overall accuracies of 0.9238 and 0.9825 and F1-scores of 0.9233 and 0.9823, respectively. In contrast, the SVM performed best in Jiangling County, yielding an overall accuracy of 0.9574 and an F1-score of 0.9525. The proposed approach enables high-precision winter crop mapping without reliance on manually collected samples, demonstrating strong generalizability and providing a promising solution for large-scale, automated agricultural monitoring.