The conventional fuzzy C-means clustering algorithm is weak in terms of noise suppression in remote sensing image water segmentation and requires the manual setting of excessive parameters. To solve this problem, a fuzzy clustering remote sensing image water segmentation method combined with a gravity model was proposed. First, the fuzzy membership matrix obtained using the fuzzy C-means clustering algorithm was used as the initial membership matrix of the algorithm. Then, the ratio of the local standard deviation of the pixel gray value to the local mean was calculated. Furthermore, the normalized ratio was used as a weighting factor to reflect the influence of neighboring pixels on the central pixel. Finally, combined with the spatial attraction model, a tradeoff weighting factor was introduced in the relationship between the local space and fuzzy membership, which could simultaneously consider the spatial distance between the central pixel and its neighboring pixels and the difference in category membership. This factor can adaptively and accurately estimate spatial constraints using the neighboring pixels and fully adapt to the image content. Experimental results show that compared with the conventional fuzzy C-means clustering algorithm and related representative algorithms developed recently, the segmentation performance of the proposed algorithm is optimum. The proposed algorithm exhibits the highest segmentation accuracy, with a maximum of 97.1%, and the false alarm rate is reduced by approximately 15%--30%.