This study presents the first systematic evaluation of the capability of the Surface Water and Ocean Topography (SWOT) satellite Level-2 High Rate Pixel Cloud (L2HRPIXC) product for retrieving topography in reservoir drawdown zones under varying terrain conditions in arid and semi-arid regions. Three representative reservoirs in southern Xinjiang, China—characterized by plain, canyon, and pocket-shaped canyon morphologies—were selected to establish a terrain-dependent validation framework. A novel multi-feature clustering strategy integrating elevation and radar backscatter coefficients was explored to reduce the misclassification of wet mudflats as water pixels in the PIXC product, aiming to improve DEM accuracy in reservoir drawdown zones. Based on this framework, multi-cycle SWOT-derived digital elevation models (DEMs) were generated and quantitatively evaluated against high-resolution unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) DEMs. Results demonstrate a strong terrain dependency in SWOT-derived elevation accuracy. In low-relief environments, sub-meter accuracy is achieved, with the root mean square error (RMSE) below 0. 25 m, confirming the suitability of SWOT for high-precision monitoring. However, errors increase significantly in steep and complex terrains, reaching up to ±6 m, primarily due to interferometric decorrelation, geometric distortion, and slope-induced biases. Despite these limitations, multi-temporal observations exhibit generally similar spatial error patterns across terrains, indicating reasonable repeatability under the tested conditions. This study reveals the performance boundaries of SWOT-derived DEMs in dynamic land–water transition zones and provides a robust methodological framework for improving DEM extraction in similar environments. The findings contribute to advancing the application of SWOT data in hydrological monitoring and geomorphological analysis at regional scales.
Peng et al. (2026) studied this question.