The Surface Water and Ocean Topography (SWOT) satellite mission, jointly developed by NASA and several international collaboration agencies, aims to achieve high-resolution two-dimensional observations of global surface water. Equipped with the advanced Ka-band radar interferometer (KaRIn), it significantly enhances the ability to monitor surface water and provides a new data source for obtaining large-scale water surface elevation (WSE) data at high temporal and spatial resolution. However, the accuracy and applicability of its scientific data products for inland water bodies still require validation. This study obtained three scientific data products from the SWOT satellite between August 2023 and December 2024: the Level 2 KaRIn high-rate river single-pass vector product (L2_HR_RiverSP), the Level 2 KaRIn high-rate lake single-pass vector product (L2_HR_LakeSP), and the Level 2 KaRIn high-rate water mask pixel cloud product (L2_HR_PIXC). These were compared with in situ water level data to validate their accuracy in retrieving inland water levels across eight different regions in the middle and lower reaches of the Yangtze River (MLRYR) and to evaluate the applicability of each product. The experimental results show the following: (1) The inversion accuracy of L2_HR_RiverSP and L2_HR_LakeSP varies significantly across different regions. In some areas, the extracted WSE aligns closely with the in situ water level trend, with a coefficient of determination ($R^2$) exceeding 0.9, while in other areas, the $R^2$ is lower (less than 0.8), and the error compared to in situ water levels is larger (with Root Mean Square Error (RMSE) greater than 1.0 m). (2) This study proposes a combined denoising method based on the Interquartile Range (IQR) and Adaptive Statistical Outlier Removal (ASOR). Compared to the L2_HR_RiverSP and L2_HR_LakeSP products, the L2_HR_PIXC product, after denoising, shows significant improvements in all accuracy metrics for water level inversion, with $R^2$ greater than 0.85, Mean Absolute Error (MAE) less than 0.4 m, and RMSE less than 0.5 m. Overall, the SWOT satellite demonstrates the capability to monitor inland water bodies with high precision, especially through the L2_HR_PIXC product, which shows broader application potential and will play an important role in global water dynamics monitoring and refined water resource management research.

Accurate terrain modeling from high-resolution digital surface models (DSM) is critical for geosciences, geology, geomorphology, earthquake studies, and applied geology. However, existing filtering methods such as progressive morphological filtering (PMF), cloth simulation filtering (CSF), and progressive TIN densification (TIN) often struggle with complex topography and urban structures, leading to either excessive ground loss or incomplete object removal. Furthermore, some of these algorithms are only specialized for point cloud data and are not optimized for grid data. To address these limitations, we propose an iterative morphological filtering (IMF) algorithm that introduces a binary surface edge-segmentation strategy. The method refines object–ground separation by combining iterative morphological operations with block-based graph-cut stitching, thus enhancing continuity and accuracy in challenging terrain. Validation on UAV-derived DSM over the Haihe Basin in China and the ISPRS Vaihingen dataset shows that IMF achieves notable accuracy improvements: the Vaihingen test areas yielded an average Type I error of 8.93%, Type II error of 3.09%, overall accuracy of 80.85%, and Kappa coefficient of 0.7524, while the Haihe Basin test areas achieved Type I and II errors of 2.22% and 1.87%, overall accuracy of 89.32%, and a Kappa coefficient of 0.8706. These results demonstrate that IMF outperforms conventional methods by reducing both Type I and Type II errors, producing terrains highly consistent with real conditions. This innovation provides a robust and scalable solution for digital elevation models (DEM) generation from gridded DSM, offering significant value for large-scale environmental monitoring and flood risk assessment.

The difficulty in accurately determining channel Manning’s roughness has long constrained the accuracy of river flood simulation. At present, Manning’s roughness is mainly assigned using empirical approaches such as lookup tables and visual comparison, which are insufficient to represent the spatial heterogeneity of roughness in natural rivers. Remote sensing provides valuable data support for flood simulation in areas with sparse hydrological gauges. Remote-sensing observations are objective and can be acquired over large areas, but they are temporally discontinuous, whereas hydrodynamic models provide physically meaningful and temporally continuous simulations. These two information sources are therefore naturally complementary, providing a new avenue for Manning’s roughness inversion through remote sensing and hydrodynamic model coupling. To address these challenges, this study proposes a new remote sensing and hydrodynamic model coupling framework centered on roughness inversion. SAR-derived inundation extent is used as a spatial constraint, and an objective function together with particle swarm optimization (PSO) drives an iterative closed loop between the one-dimensional HEC-RAS model and the remote-sensing constraint to autonomously approach the optimal roughness parameters and improve the reliability of flood simulation. To bridge the scale mismatch between one-dimensional hydrodynamic outputs and two-dimensional SAR observations, the HEC-RAS cross-section geometry TIN structure is used to spatialize the simulated water surface, enabling direct comparison between simulated and SAR-observed inundation extents. The Le’an River in Wuyuan, Jiangxi Province, China, was selected as the test reach to evaluate the applicability and reliability of the framework under sparse-gauge conditions. The model was calibrated using the SAR-derived inundation extent, and simulated water levels were subsequently compared with rating-curve-derived reference levels. Under the SAR inundation constraint, the agreement between simulated and observed inundation extents improved, with IoU increasing from 0.763 under the initial parameter set to 0.789. Comparison with rating-curve-derived reference water levels further showed that the optimized Manning’s roughness parameters improved water-level simulation, reducing RMSE from 0.811 m to 0.594 m and increasing NSE from 0.950 to 0.973. These results demonstrate the effectiveness of the proposed method when continuous hydrological observations are unavailable. The framework provides an automated remote-sensing-constrained approach for Manning’s roughness inversion in hydrodynamic models of small and medium-sized rivers with limited in situ hydrological data.