Zonal Inversion of Underwater Topography of Large Water Bodies Based on Landsat Images

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Zonal Inversion of Underwater Topography of Large Water Bodies Based on Landsat Images

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[1]DOU Ming,SHI Yuxian,QU Lingbo,et al.Zonal Inversion of Underwater Topography of Large Water Bodies Based on Landsat Images[J].Journal of Zhengzhou University (Engineering Science),2026,47(XX):1-8.[doi:10.13705/j.issn.1671-6833.2025.05.020]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 XX Page number: 1-8 Column: Public date: 2026-09-10

Title:: Zonal Inversion of Underwater Topography of Large Water Bodies Based on Landsat Images

Author(s):: DOU Ming¹; 2 ; SHI Yuxian¹ ; QU Lingbo² ; WANG Jihua³ ; XING Aoqi²; 1. School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China ; 2. School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China; 3. Henan Provincial Natural Resources Monitoring and land Improvement Institute, Zhengzhou 450000, China

Keywords:: underwater topography inversion; Landsat remote sensing imagery; BP neural network model; multiband random forest model; Danjiangkou Reservoir

CLC:: P237TP79

DOI:: 10.13705/j.issn.1671-6833.2025.05.020

Abstract:: To address the difficulty of obtaining underwater topography data for large water bodies with insufficient data, Danjiangkou Reservoir was selected as the study area, and a retrieval method based on Landsat remote sensing imagery and water depth zoning was proposed. The underwater topography of the shallow and deep water areas of the reservoir was reconstructed using the waterline kriging interpolation method and four water depth inversion models (single-band, dual-band ratio, BP neural network, and multi-band random forest), and the inversion accuracy was evaluated. The results showed that the underwater topography inversion in the shallow water area performed well (Root Mean Square Error, RMSE=2.553 m). In the deep water area, the multi-band random forest model performed best in the Han Reservoir area (RMSE=2.428 m), while the BP neural network model performed best in the Dan Reservoir area (RMSE=1.599 m). The accuracy of different inversion models varied across different depths and regions, with the multi-band random forest model demonstrating advantages in deep-water topography inversion. The findings provide a rapid method for collecting topographic data for large water bodies with insufficient data.

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Last Update: 2026-01-15