Water body extraction from remote sensing images in complex backgrounds is crucial for environmental monitoring, disaster management, and urban planning. Although existing water body extraction algorithms for remote sensing images offer robust tools for monitoring, they still face challenges, including the inability to extract fine water bodies and the frequent omission of water body edges in complex backgrounds such as dense vegetation, varying terrain, or cloud interference. In response, we introduce an architecture named WatNet to enhance the precision of water body extraction in complex environments. WatNet comprises three main modules: the global multi-attention fusion module (GMAF), the water forward network module (WFN), and the edge focus attention module (EFA). The GMAF module enhances the model’s capability to capture global information through multi-head self-attention and convolutional attention modules, improving the overall feature extraction of water bodies. The WFN module utilizes depth-wise separable convolution and attention mechanisms to enhance the capture of local features in fine water bodies. The EFA module significantly improves the clarity and accuracy of water body boundaries through refined edge detection. Experiments on the LoveDA Remote Sensing Land Cover (LoveDA), Qinghai–Tibet Plateau (QTPL), and Wuhan dense labeling (WHDLD) datasets show that WatNet outperforms the mainstream methods in precision, recall, overall accuracy (OA), F1 score, and mean Intersection over Union (mIoU). On the LoveDA dataset, WatNet’s mIoU improved by 1.24% compared with the second-best method, by 0.4% on the QTPL dataset, and by 0.51% on the WHDLD dataset. These results validate the WatNet’s effectiveness and robustness in water body extraction tasks under various environmental conditions.