Automated Inversion of Manning’s Roughness and Flood Simulation in a One-Dimensional Hydrodynamic Model Constrained by SAR-Derived Inundation Extent: A Case Study of the Le’an River in Wuyuan

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.