Remote sensing observations of surface water are vital for effective water resource management and sustainable development. Unsupervised classification holds promise for automating large-scale surface water detection, and it helps solve the difficult problem of sample collection in supervised classification. Here, we refined the water identification rule for automated surface water extraction on the basis of multidimensional clustering and a supervised classifier. We subsequently comprehensively investigated the classification performance of k-means, hierarchical clustering, and spectral clustering with 57 different feature combinations (features consisting of two bands, B8 and B12, along with four water indices: the automated water extraction index (AWEI), multiband water index (MBWI), normalized difference water index (NDWI), and modified normalized difference water index (MNDWI)) in eight challenging scenarios in China. These comparative experiments were performed from both pixel-based and object-based perspectives. The results show that pixel-based hierarchical clustering, which uses the optimal feature combination of B8, the NDWI, and the MBWI, is the algorithm with the best overall performance, with kappa coefficients exceeding 0.9 in each scene. The object-based hierarchical clustering using the optimal feature combination B8, B12, MNDWI, and MBWI achieves a kappa coefficient exceeding 0.85 in almost all scenes. This algorithm is suitable for scenes without small water bodies. In addition, the improved surface water identification rule increases the applicability of the automated surface water extraction method, which is based on multidimensional clustering in scenes with snow cover. Finally, we compared the performance of multidimensional clustering algorithms with that of the modified Otsu thresholding method and the multi-index threshold-based algorithm and found that the former has an advantage in automated surface water extraction.