Mountain river bathymetry inversion method based on remote sensing data and its application

River bathymetry (RB) is a fundamental dataset in the field of river research. However, mountainous regions often lack comprehensive data due to topographical and transportation challenges. Remote sensing technology provides an innovative method for estimating RB. In this study, the theoretical relationship between the water level and the river width is established by generalizing the channel cross-section shape. A novel RB estimation method was proposed, integrating the Hydroweb dataset and Sentinel-1 images. The impacts of exposure, reach-average length, and remote sensing observation errors on estimation accuracy were systematically analyzed. The method was applied to the Upper Yangtze River to evaluate its potential for estimating river discharge. Results reveal that: ① The estimation error of the riverbed elevation ranges from 4.00 m to 4.06 m, with the estimated cross-section representing 73.69% to 80.29% of the actual area, indicating precise RB estimation. ② Exposure rate emerges as a primary factor, significantly enhancing estimation accuracy. An appropriate reach-average length improves the estimation precision and optimal length of 10km is advised for the Upper Yangtze River. Furthermore, the accuracy of RB estimation is more susceptible to water level errors in remote sensing than to river width. ③ The method demonstrates the potential to estimate river discharge achieving a Nash efficiency coefficient of 0.92. The research outcome can provide a novel approach to RB monitoring in data-scarce regions.