A method for improving winter wheat mapping accuracy based on multi-temporal feature fusion and stacking ensemble...

Winter wheat is a strategic staple crop underpinning national food security in China, making large-scale and accurate remote sensing mapping essential for arable land management and agricultural regulation. However, in regions such as Jiangsu Province, characterized by highly heterogeneous and fragmented agricultural landscapes, conventional remote sensing classification methods are often limited by inadequate feature representation and weak discriminative capability, resulting in suboptimal mapping accuracy. To address these challenges, this study develops a high-accuracy winter wheat mapping framework that integrates multi-temporal feature fusion and stacked ensemble learning. The Sentinel-2 time-series imagery is employed as the primary data source. Temporal profiles are reconstructed using Savitzky-Golay filtering to suppress noise while preserving phenological dynamics. The multi-dimensional feature set is constructed by combining spectral band reflectance, spectral indices, and texture metrics to capture spatio-temporal crop growth patterns. Then, A stacked ensemble learning architecture is implemented, incorporating four base classifiers: Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and Gradient Tree Boosting (GTB). Subsequently, the optimized meta-learner is applied to the outputs of these base classifiers to enhance generalization capacity and model robustness. Experimental results demonstrate that the integrated feature fusion strategy significantly improves classification performance compared to single-feature configurations. The optimized stacked model achieves an Overall Accuracy (OA) of 94.74% with a Kappa coefficient of 0.9283, substantially outperforming all individual classifiers. Winter wheat distribution maps for 2021-2023 show strong consistency with statistical yearbook data, with OA of 95.31%, 94.83%, and 94.74%, and Kappa coefficients of 0.9300, 0.9272, and 0.9283, respectively, confirming the temporal stability and transferability of our model. This study establishes a robust and scalable remote sensing identification framework suitable for complex agricultural landscapes, providing methodological support for regional crop monitoring, dynamic cultivated land management, and food security assessment.