融合多源遥感的缺资料河槽地形构建与验证

Construction and validation of data-scarce river channel topography using integrated multi-source remote sensing

摘要

【目的】河槽地形数据是开展水文监测的基础，尤其在缺资料地区现有方法难以实现较高精度的河槽地形数据获取，成为水文监测的瓶颈之一。以金沙江奔子栏水文站上下游7 km河段为研究区，利用融合多源遥感的方法构建河槽地形，为地形复杂河段及缺资料地区的地形建模提供新的技术路径和解决方案。【方法】基于无人机获取的河段数字高程模型(UAV-DEM)和水文站实测大断面高程数据，通过融合地表水及海洋地形卫星(SWOT)水面测高数据和高分辨率遥感影像提取的水边线数据枯季至汛期水位波动范围内的河槽地形，同时对比枯水期高分7号立体像对数据生成的河槽地形数据(GF7-DEM)。【结果】结果表明：(1)对比校正前后GF7-DEM精度指标，RMSE由0.717 8 m降至0.601 9 m,MAE由0.561 5 m降至0.476 2 m,STD由0.717 5 m降至0.596 m,R²由0.909 5提高至0.930 1,校正后GF7-DEM精度改善，大尺度河槽地形表达能力较好。(2)多源遥感构建地形RBF-DEM、EBK-DEM和IDW-DEM与UAV-DEM对比均显示良好空间一致性，其中RBF方法表现最佳，具有最低误差(MAE=1.063 9 m,RMSE=1.330 5 m)和最低离散性(STD=1.284 9 m),相关性也较高。(3)在大尺度河槽地形表达上，校正后GF7-DEM(RMSE=0.601 9 m,MAE=0.476 2 m,STD=0.596 m,R²=0.930 1)效果优于RBF-DEM(RMSE=1.330 5 m,MAE=1.063 9 m,STD=1.284 9 m,R²=0.792 9),具备更高的准确性和稳定性，但在缺资料地区GF7数据不完备时RBF-DEM可以为地形构建提供更为有效的替代方案。【结论】GF7-DEM对于河槽地形的表征能力较强，但其对数据获取的要求较高，需使用枯季数据生成DEM,且覆盖度有限。相比之下，SWOT卫星数据丰富，二者互为补充，共同构成缺资料地区地形获取的方法，为基于遥感的河道径流监测、河道水动力过程模拟、数字孪生流域、河流生境演变等诸多领域的研究应用和管理提供基础支撑。

Abstract

[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.

关键词

缺资料河段 / 地形构建 / GF-7卫星 / SWOT卫星 / 精度验证 / 水文 / 无人机 / 多源遥感

Key words

data-scarce river segments / topographic construction / GF-7 satellite / SWOT satellite / accuracy validation / hydrology / unmanned aerial vehicle / multi-source remote sensing

基金资助

国家自然科学基金项目(长江水科学研究联合基金)(U2240202)

国家自然科学基金项目(52394233)

国家重点研发计划(2018YFC0506904)