融合多源遥感的缺资料河槽地形构建与验证OA

[Objective]Topographic data of river channels is fundamental for hydrological monitoring.However,in data-scarce regions,existing method struggle to obtain high-accuracy river channel information,which has become a key bottleneck for hydrological observation.Using a 7-km river segment upstream and downstream of the Benzilan hydrological station on the Jinsha River as the study area,this study applies an integrated multi-source remote sensing approach to construct river channel topography,aiming to provide new technical pathways and solutions for terrain modelling in complex river segments and data-scarce settings.[Methods]Based on an unmanned-aerial-vehicle-derived digital elevation model(UAV-DEM)and measured cross-sectional elevation data from the hydrological station,river channel topography within water-level fluctuation ranges from dry and flood seasons was constructed by integrating surface water elevation data from the Surface Water and Ocean Topography(SWOT)satellite and shoreline data extracted from high-resolution remote sensing imagery.Additionally,comparative analysis was conducted with the river channel topographic data(GF7-DEM)generated from GF-7 stereo imagery during the dry season.[Results]The result showed that after correction,the GF7-DEM showed improved accuracy,with RMSE reduced from 0.717 8 m to 0.601 9 m,MAE reduced from 0.561 5 m to 0.476 2 m,and STD from 0.717 5 m to 0.596 m,and R2 increased from 0.909 5 to 0.930 1,indicating enhanced representation capability for large-scale river channel morphology.(2)The multi-source remote sensing-derived DEMs-RBF-DEM,EBK-DEM,and IDW-DEM all showed good spatial consistency when compared with UAV-DEM.The RBF method showed optimal performance,achieving the lowest errors(MAE=1.063 9 m,RMSE=1.330 5 m),the least dispersion(STD=1.284 9 m),and the relatively high correlation.(3)For large-scale river channel morphology representation,the corrected GF7-DEM(RMSE=0.601 9 m,MAE=0.476 2 m,STD=0.596 m,and R2=0.930 1)outperformed the RBF-DEM(RMSE=1.330 5 m,MAE=1.063 9 m,STD=1.284 9 m,and R2=0.792 9)in both accuracy and stability.However,when GF7 data were incomplete,the RBF-DEM served as a more effective alternative for terrain reconstruction in data-scarce regions.[Conclusion]GF7-DEM demonstrates strong capability in representing river channel topography,but it has high data acquisition requirements,necessitating the use of dry-season data for DEM generation and offering limited spatial coverage.In contrast,SWOT satellite data are abundant.Together with GF7-DEM,they complement each other to form an approach for acquiring topographic data in data-scarce regions.This provides fundamental support for research applications and management in different fields,including remote sensing-based runoff monitoring,river hydrodynamic process simulation,digital twin watersheds,river habitat evolution.