The increasing frequency of water anomalies poses severe threats to aquatic ecosystem stability, water resource security, and socioeconomic sustainability. Rapid and accurate identification of these events is critical for early warning and informed environmental decision-making. However, existing type identification methods often fail to balance high precision with rapid inference since many depend on hand-crafted feature engineering or computationally intensive post-classification procedures that slow processing and increase confusion among anomalies with similar spectral characteristics. Therefore, in this study, a deep multisphere decision boundaries (DMSDB) method for unified, fine-grained identification of diverse water anomalies is developed. The method first involves constructing a compact 5-dimensional feature space by deriving physically interpretable indices from Sentinel-2 imagery. Then, a modified VM-UNet-v2 network is employed to extract water anomaly response features (WARFs) that capture the spatial–spectral characteristics of each anomaly. Finally, a multi-sphere-contrast loss function optimizes class-specific decision boundaries, compacting intraclass features while increasing the separation between spectrally similar anomaly categories. The validation results demonstrate that DMSDB achieves an average F1 score of 0.8789 and an mIoU of 0.7932, enabling efficient large-scale inference, and completing classification over a 10,000 km² scene within 33 s. These results highlight the method’s potential for effective or ground-based rapid environmental monitoring, supporting timely and category-specific responses to aquatic disturbances.

Accurate delineation of glacier extent is crucial for monitoring glacier degradation in the context of global warming. Satellite remote sensing with high spatial and temporal resolution offers an effective approach for large-scale glacier mapping. However, persistent cloud cover limits its application on the Tibetan Plateau, leading to substantial omissions in glacier identification. Therefore, this study proposed a novel sub-cloudy glacier extraction model (SCGEM) designed to extract glacier boundaries from cloud-affected satellite images. First, the cloud-insensitive characteristics of topo-graphic (Topo.), synthetic aperture radar (SAR), and temporal (Tempo.) features were investigated for extracting glaciers under cloud conditions. Then, a transformer-based generative adversarial network (GAN) was proposed, which incorporates an image reconstruction and an adversarial branch to improve glacier extraction accuracy under cloud cover. Experimental results demonstrated that the proposed SCGEM achieved significant improvements with an IoU of 0.7700 and an F1 score of 0.8700. The Topo., SAR, and Tempo. features all contributed to glacier extraction in cloudy areas, with the Tempo. features contributing the most. Ablation studies further confirmed that both the adversarial training mechanism and the multi-task architecture contributed notably to improving the extraction accuracy. The proposed architecture serves both to data clean and enhance the extraction of glacier texture features.