Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning  

作　　者：Hong Yang Hengliang Guo Wenhao Dai Bingkang Nie Baojin Qiao Liping Zhu 

机构地区：[1]School of Chemistry,Zhengzhou University,Zhengzhou,People’s Republic of China [2]National Supercomputing Center in Zhengzhou,Zhengzhou University,Zhengzhou,People’s Republic of China [3]School of Geoscience and Technology,Zhengzhou University,Zhengzhou,People’s Republic of China [4]Key Laboratory of Tibetan Environment Changes and Land Surface Processes,Institute of Tibetan Plateau Research,Chinese Academy of Sciences,Beijing,People’s Republic of China [5]CAS Center for Excellence in Tibetan Plateau Earth Sciences,Beijing,People’s Republic of China

出　　处：《International Journal of Digital Earth》2022年第1期789-812,共24页国际数字地球学报（英文）

基　　金：supported by 2020 Science and technology project of innovation ecosystem construction,National Supercomputing Zhengzhou center-Research on Key Technologies of intelligent fine prediction based on big data analysis[grant number:201400210800];Second Tibetan Plateau Scientific Expedition and Research(STEP)[grant number:2019QZKK0202];the CAS Alliance of Field Observation Stations[grant number:KFJ-SW-YW038];CAS Strategic Priority Research Program:[Grant Number XDA19020303,XDA20020100];the Ministry of Science and Technology of China Project[grant number:2018YFB05050000];National Natural Science Foundation of China project[grant number:41831177,41901078].

摘　　要：Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau.In this study,combined with satellite images and bathymetric data,we comprehensively evaluate the accuracy of a multi-factor combined linear regression model(MLR)and machine learning models,create a depth distribution map and compare it with the spatial interpolation,and estimate the change of water level and water storage based on the inverted depth.The results indicated that the precision of the random forest(RF)was the highest with a coefficient of determination(R2)value(0.9311)and mean absolute error(MAE)values(1.13 m)in the test dataset and had high reliability in the overall depth distribution.The water level increased by 9.36 m at a rate of 0.47 m/y,and the water storage increased by 1.811 km3 from 1998 to 2018 based on inversion depth.The water level change was consistent with that of the Shuttle Radar Topography Mission(SRTM)method.Our work shows that this method may be employed to study the water depth distribution and its changes by combining with bathymetric data and satellite imagery in shallow lakes.

关 键 词：Remote sensing inversion lake bathymetry Sentinel-2 machine learning(ML) random forest(RF) water storage 

分 类 号：P33[天文地球—水文科学] TP7[水利工程—水文学及水资源]