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Remote Sensing
Volume 17
Issue 22
10.3390/rs17223753
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Inferring River Channel Geometry Based on Multi-Satellite Datasets and Hydraulic Modeling

by
Youcan Feng
1,2,3,4,*,
Junhui Liu
1,2,3,4,
Xin Huang
5,
Shaohua Zhao
6,
Donghe Ma
7,
Seungyub Lee
8 and
Ruibo Cao
1,2,3,4
1
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China
2
Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China
3
College of New Energy and Environment, Jilin University, Changchun 130021, China
4
Hydraulic Engineering Research Institute, Jilin University, Changchun 130021, China
5
College of Water Conservancy and Environmental Engineering, Changchun Institute of Technology, Changchun 130012, China
6
Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment/Key Laboratory of Satellite Remote Sensing, Ministry of Environmental Protection, Beijing 100094, China
7
China Water Northeastern Investigation, Design and Research Company, Changchun 130021, China
8
Department of Civil and Environmental Engineering, Hanam University, 70 Hannam-ro, Daedeok-gu, Daejeon 34430, Republic of Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(22), 3753; https://doi.org/10.3390/rs17223753
Submission received: 28 September 2025 / Revised: 6 November 2025 / Accepted: 17 November 2025 / Published: 18 November 2025
Versions Notes

Abstract

Channel geometry, e.g., riverbed elevation and channel width, is the fundamental input for hydrodynamic simulations and conveys critical information for understanding fluvial processes. In remote or data-scarce areas, however, traditional field surveys face financial and technical challenges for providing enough spatiotemporal coverage. This study proposes an innovative method integrating multi-source satellite data (Sentinel-2 and ICESat-2) and hydraulic modeling to derive channel geometry for part of the Nen River, China. Both channel width (R2 = 0.98, RMSE = 35.41 m) and bottom elevation (R2 = 0.86, RMSE = 1.77 m, PBIAS = −0.61%) are well predicted. The satellite-derived channel geometry results in an overall good simulation of 1D flows through the 5-yr period in terms of peak magnitudes and timings, with the NSE value of 0.94, RMSE of 207.76 m3/s, and PBIAS of 6.19%. The 2D inundation driven by the derived channel geometry achieved accurate hydrodynamic responses. However, for the channel bend with complicated flow regimes, the satellite-derived channel terrains tend to generate more different flow rates due to the hypothesized rectangular channel. This proposed method provides a promising way to derive river bathymetry in both low-gradient and high-slope regions where precise river topography is difficult to obtain.
Keywords: remote sensing; channel geometry; rating curve; Muskingum method; ICESat-2 remote sensing; channel geometry; rating curve; Muskingum method; ICESat-2

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MDPI and ACS Style

Feng, Y.; Liu, J.; Huang, X.; Zhao, S.; Ma, D.; Lee, S.; Cao, R. Inferring River Channel Geometry Based on Multi-Satellite Datasets and Hydraulic Modeling. Remote Sens. 2025, 17, 3753. https://doi.org/10.3390/rs17223753

AMA Style

Feng Y, Liu J, Huang X, Zhao S, Ma D, Lee S, Cao R. Inferring River Channel Geometry Based on Multi-Satellite Datasets and Hydraulic Modeling. Remote Sensing. 2025; 17(22):3753. https://doi.org/10.3390/rs17223753

Chicago/Turabian Style

Feng, Youcan, Junhui Liu, Xin Huang, Shaohua Zhao, Donghe Ma, Seungyub Lee, and Ruibo Cao. 2025. "Inferring River Channel Geometry Based on Multi-Satellite Datasets and Hydraulic Modeling" Remote Sensing 17, no. 22: 3753. https://doi.org/10.3390/rs17223753

APA Style

Feng, Y., Liu, J., Huang, X., Zhao, S., Ma, D., Lee, S., & Cao, R. (2025). Inferring River Channel Geometry Based on Multi-Satellite Datasets and Hydraulic Modeling. Remote Sensing, 17(22), 3753. https://doi.org/10.3390/rs17223753

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