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基于多光谱数据的内陆湖水度估计模型的开发研究

Junzhen Meng¹, Yunfei Wang¹, Wenkai Liu¹

¹College of Surveying and Feo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.

Sensors (Basel, Switzerland)

|April 12, 2025

概括

这项研究开发了一种新的机器学习方法,用于通过遥感来估计内陆湖泊的水深. 该模型显著提高了中小型湖泊的精度,有助于水资源管理.

科学领域:

地球和环境科学地球和环境科学
遥感遥感遥感遥感
水文学的水文学

背景情况:

湖泊对区域经济和生态系统至关重要.
准确的水深数据对于管理湖泊资源和生态稳定至关重要.
现有用于内陆湖深度估计的遥感模型缺乏统一的评估和一致的性能.

研究的目的:

系统地比较内陆湖水深估计的数值和机器学习模型.
提出一种基于机器学习的新方法,用于构建内陆湖泊准确的水深估计模型.
整合机器学习与多谱遥感数据,以提高深度估计.

主要方法:

数字模型 (MLR) 和机器学习模型 (随机森林,BP神经网络,AdaBoost) 的系统比较.
开发一种新的基于机器学习的方法来估计内陆湖泊的水深.
机器学习算法的集成与多谱遥感数据.

主要成果:

机器学习模型 (RF,BP,AdaBoost) 在准确度方面表现优于MLR模型 (R2高达0.88而不是0.59).
拟议的方法在机器学习模型中提高了精度 (R2高达0.92).
开发的模型特别适用于中小型湖泊,提供高精度的水下地形图.

结论:

机器学习模型显著提高了基于遥感的内陆湖泊水深估计的准确性.
拟议的方法为中小型湖泊的水深估计提供了更精确和适用的方法.
该研究为快速,高精度的水文数据采集提供了宝贵的工具,支持有效的湖泊水资源管理.

关键词:

度估计的估计方法随机的森林随机的森林反射率提取方法的方法遥感指数遥感指数遥感指数水下3D地形地图

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