[1]窦明,史玉仙,屈凌波,等.基于Landsat影像的大型水体水下地形分区反演[J].郑州大学学报(工学版),2026,47(XX):1-8.[doi:10. 13705 / j. issn. 1671-6833. 2025. 05. 020]
　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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基于Landsat影像的大型水体水下地形分区反演()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年XX
页码:
1-8
栏目:
出版日期:
2026-09-10

文章信息/Info

Title:
Zonal Inversion of Underwater Topography of Large Water Bodies Based on Landsat Images
作者:
窦明1; 2 ;  史玉仙1 ;  屈凌波2 ;  王继华3 ;  邢澳琪2
1. 郑州大学 水利与交通学院,河南 郑州 450001;2. 郑州大学 生态与环境学院,河南 郑州 450001;3.河南省自然资源监测和国土整治院,河南 郑州 450016
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
关键词:
水下地形反演;  Landsat 遥感影像;  BP 神经网络模型;  多波段随机森林模型;  丹江口水库
Keywords:
underwater topography inversion;  Landsat remote sensing imagery;  BP neural network model;  multiband random forest model;  Danjiangkou Reservoir
分类号:
P237TP79
DOI:
10. 13705 / j. issn. 1671-6833. 2025. 05. 020
文献标志码:
A
摘要:
针对缺资料大型水体水下地形获取困难的问题，以丹江口水库为研究对象，提出了一种基于Landsat遥感影像和水深分区的大型水体水下地形反演方法，分别采用水位线克里金插值法和四种水深反演模型（单波段、双波段比值、BP神经网络、多波段随机森林）对丹江口水库浅水区和深水区水下地形进行反演，并评价其反演精度。结果显示，浅水区水下地形反演效果良好（均方根误差 RMSE=2.553 m）；深水区反演中，汉库水域采用多波段随机森林模型表现最佳（RMSE=2.428 m），丹库水域采用BP神经网络模型表现最佳（RMSE=1.599 m）；不同反演模型精度针对不同水深和不同区域具有差异性，多波段随机森林模型在深水水下地形反演上存在优势。研究结果可为缺资料大型水体提供一种快捷的地形资料收集方法。
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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备注/Memo

备注/Memo:
收稿日期:2025-04-11;修订日期:2025-06-16
基金项目:河南省重大科技专项项目(221100320200) ;院士团队科研启动项目(13432340370)
作者简介:窦明(1975— ),男,山东桓台人,郑州大学教授,博士,主要从事水资源与水环境研究,E-mail:dou_ming@163.com。
更新日期/Last Update: 2026-01-15