What question did this study set out to answer?

The aim is to enhance the accuracy of water body extraction from SWOT data by developing a robust denoising framework.

March 1, 2026Open Access

SDNet: a multi-scale SWOT framework for denoising and quality-control optimization in inland water body extraction

HXHaojun XuJining Normal University JWJianjun WuHunan University of Science and Technology JYJiaqi YaoJining Normal University

Key Points

The aim is to enhance the accuracy of water body extraction from SWOT data by developing a robust denoising framework.
Developed a transformer-based multi-scale framework called SDNet.
Evaluated accuracy of water body extraction across different scales.
Conducted cross-validation with ICESat-2 altimetry data to assess errors.
Performed seasonal hydrological analysis on varying water body types.
SDNet improved Water Body Intersection Rate (WIR) and Background Intersection Rate (BIR) by up to 45.96% for large water bodies.
Achieved increases in WIR by 2.83% for medium and a factor of 3.57 for small water bodies.
Median altimetry errors ranged from 0.117 m to 0.181 m at 100 m resolution, maintaining sub-meter accuracy.

Abstract

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.

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