Accurately delineating inland water bodies and monitoring surface water dynamics are crucial for hydrological research and climate adaptation. Surface water and ocean topography (SWOT) satellites has significantly improved global surface water observation capabilities. However, in complex inland environments, SWOT data are often affected by stripe noise and quality control (QC) marker failures, which can easily lead to water body extraction errors or the omission of narrow water bodies. To address these issues, we developed SDNet, a transformer-based multi-scale framework. This design suppresses high-frequency stripe noise while preserving fine-scale hydrological boundaries, thus significantly improving the reliability of water body extraction. Experimental results for different water bodies show that: (1) SDNet achieved high accuracy across water bodies of different scales. Compared to the QC-based classification, the Water Body Intersection Rate (WIR) and Background Intersection Rate (BIR) of our method increased by 23.68% and 45.96%, respectively, for large water bodies. WIR further increased by 2.83% for medium-scale water bodies and by a factor of 3.57 for small water bodies. (2) Cross-validation using ICESat-2 altimetry data showed that the SWOT altimetry error was positively correlated with cross-track distance. The median errors ranged from 0.117 m to 0.181 m at 100 m resolution and 0.111 m to 0.170 m at 250 m resolution, with the mean absolute error remaining in the sub-meter range. (3) Seasonal hydrological analysis revealed distinct response patterns across different water body types to water level changes. Natural lakes are mainly driven by climate processes, while controlled reservoirs exhibit multi-peak dynamic characteristics dominated by human regulation. These findings provide a scalable solution for multi-scale water body monitoring, contributing valuable support to hydrological research, flood risk assessment, and climate adaptation strategies.
Xu et al. (2026) studied this question.