High-spatial and -temporal resolution snow cover products in mountain areas are important to hydrological applications. The GF-1 satellite provides multispectral images with 8-m resolution and a revisit up to 2 days, which makes it possible to produce snow cover products. However, it is challenging to extract snow cover from these images because of limited spectral bands, severe mountain shadows, and dataset-shift problem in multitemporal classification. To overcome the limitations above, this study proposes a multitemporal ensemble learning framework to extract snow cover from high-spatial-resolution images in mountain areas. The principle behind ensemble learning, i.e. learning from disagreement, is extended from single image classification to multitemporal ones. We assume that multitemporal training samples selected within time-invariant classes at the same locations can be different in feature space. Such disagreements are used in multitemporal ensemble learning to improve classification accuracy. To enhance both accuracy and diversity of the multiple classifiers trained on these samples, a joint feature selection method is suggested to select the optimal multitemporal feature space and a joint parameter optimization method is designed to ensemble classifiers trained for multitemporal images. The experiments show that the performances of multitemporal ensemble classifiers are superior to that of single classifiers, confirming the effectiveness of the proposed framework.