Accurately characterizing river water level dynamics is essential for understanding watershed hydrological processes, assessing flood risk, and supporting refined water resource management. Launched in December 2022, the Surface Water and Ocean Topography (SWOT) satellite carries the Ka-band Radar Interferometer (KaRIn), enabling wide swath, high resolution observations of inland water surface elevation (WSE) globally, marking a new era of high precision hydrologic remote sensing. However, under complex observational geometries and heterogeneous surface conditions, the accuracy of SWOT derived WSE may degrade, manifested as a reduction in both geolocation and elevation accuracy. This degradation arises from both observation geometry and environmental heterogeneity, including cross track distance, layover effects, terrain slope, water area, and water surface brightness. Here we examine the main stem of the Yangtze River using available SWOT WSE observations from 2023 to 2024, with temporally matched daily in situ water levels from six hydrological stations as reference. We use cross track distance as a controlling variable and quantify how layover and environmental heterogeneity modulate both systematic bias and random dispersion, thereby characterizing the dominant controls governing spatial accuracy degradation. Results show that (1) SWOT captures seasonal and longitudinal water level variations along the Yangtze River, with R 2 values of 0.86 to 0.97 and RMSE of 0.15 to 0.42 m; (2) WSE error increases approximately linearly with cross track distance at an average rate of (6 to 9) × 10 -6 m/m, and the increase is amplified under strong layover conditions; and (3) environmental heterogeneity further shapes the error distribution, with steep terrain (greater than 15°) and small water area (less than 20,000 m 2 ) showing larger random fluctuations and more pronounced systematic deviations. Overall, this study provides basin scale quantitative evidence that the spatial degradation of SWOT WSE accuracy is jointly controlled by observation geometry and environmental heterogeneity, and it clarifies their relative roles in shaping error magnitude and spatial structure. These findings support the development of multi factor error correction approaches and improve the reliability of SWOT applications in large basin hydrological modeling, flood monitoring, and data assimilation. • SWOT WSE validated at six Yangtze sites (R 2 =0.86–0.97; RMSE=0.15–0.42 m). • Geometric factors (cross-track, layover) dominate accuracy degradation. • Environmental factors (slope, water area, brightness) amplify WSE errors. • Coupled geometric-environmental effects cause RMSE up to ±0.6 m. • Findings guide SWOT correction and hydrodynamic model calibration.
Zhao et al. (Sun,) studied this question.