The unique geographic environment, diverse ecosystems, and complex landforms of the Qinghai–Tibet Plateau make accurate land cover classification a significant challenge in plateau earth sciences. Given advancements in machine learning and satellite remote sensing technology, this study investigates whether emerging ensemble learning classifiers and submeter-level stereoscopic images can significantly improve land cover classification accuracy in the complex terrain of the Qinghai–Tibet Plateau. This study utilizes multitemporal submeter-level GF-7 stereoscopic images to evaluate the accuracy of 11 typical ensemble learning classifiers (representing bagging, boosting, stacking, and voting strategies) and 3 classification datasets (single-temporal, multitemporal, and feature-optimized datasets) for land cover classification in the loess hilly area of the Eastern Qinghai–Tibet Plateau. The results indicate that compared to traditional single strong classifiers (such as CART, SVM, and MLPC), ensemble learning classifiers can improve land cover classification accuracy by 5% to 9%. The classification accuracy differences among the 11 ensemble learning classifiers are generally within 1% to 3%, with HistGBoost, LightGBM, and AdaBoost-DT achieving a classification accuracy comparable to CNNs, with the highest overall classification accuracy (OA) exceeding 93.3%. All ensemble learning classifiers achieved better classification accuracy using multitemporal datasets, with the classification accuracy differences among the three classification datasets generally within 1% to 3%. Feature selection and feature importance evaluation show that spectral bands (e.g., the summer near-infrared (NIR-S) band), topographic factors (e.g., the digital elevation model (DEM)), and spectral indices (e.g., the summer resident ratio index (RRI-S)) significantly contribute to the accuracy of each ensemble learning classifier. Using feature-optimized datasets, ensemble classifiers can improve classification efficiency. This study preliminarily confirms that GF-7 images are suitable for land cover classification in complex terrains and that using ensemble learning classifiers and multitemporal datasets can improve classification accuracy.

Land use/land cover (LULC) data are essential for Earth science research. Due to the high fragmentation and heterogeneity of landscapes, machine learning-based LULC classification frequently emphasizes results such as classification accuracy, efficiency, and variable importance analysis. However, this approach often overlooks the intermediate processes, and LULC mapping that relies on a single classifier typically does not yield satisfactory results. In this paper, to obtain refined LULC classification products at the watershed scale and improve the accuracy and efficiency of watershed-scale mapping, we propose a subregion ensemble learning classification framework. The Huangshui River watershed, located in the transition belts between the Qinghai-Tibet Plateau and Loess Plateau, is chosen as the case study area, and Sentinel-2A/B multi-temporal data are selected for ensemble learning classification. Using the proposed method, the block classification scale is analyzed and illustrated at the watershed, and the classification accuracy and efficiency of the new method are compared and analyzed against three ensemble learning methods using several variables. The proposed watershed-scale ensemble learning framework has better accuracy and efficiency for LULC mapping and has certain advantages over the other methods. The method proposed in this study provides new ideas for watershed-scale LULC mapping technology.