气候变化领域集成服务门户(CCPortal): Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning

CCPortal

DOI	10.1080/17538947.2022.2069873
	Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning
	Yang, Hong; Guo, Hengliang; Dai, Wenhao; Nie, Bingkang; Qiao, Baojin; Zhu, Liping
通讯作者	Guo, HL (通讯作者)
发表日期	2022
ISSN	1753-8947
EISSN	1753-8955
起始页码	789
结束页码	812
卷号	15 期号:1
英文摘要	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 (R (2)) 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 km(3) 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	TIBETAN PLATEAU SATELLITE IMAGERY DEPTH REGRESSION MODELS RETRIEVAL LIDAR SVM
英文关键词	Remote sensing inversion; lake bathymetry; Sentinel-2; machine learning (ML); random forest (RF); water storage
语种	英语
WOS研究方向	Physical Geography ; Remote Sensing
WOS类目	Geography, Physical ; Remote Sensing
WOS记录号	WOS:000790565000001
来源期刊	INTERNATIONAL JOURNAL OF DIGITAL EARTH
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/260597
推荐引用方式 GB/T 7714	Yang, Hong,Guo, Hengliang,Dai, Wenhao,et al. Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning[J]. 中国科学院青藏高原研究所,2022,15(1).
APA	Yang, Hong,Guo, Hengliang,Dai, Wenhao,Nie, Bingkang,Qiao, Baojin,&Zhu, Liping.(2022).Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning.INTERNATIONAL JOURNAL OF DIGITAL EARTH,15(1).
MLA	Yang, Hong,et al."Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning".INTERNATIONAL JOURNAL OF DIGITAL EARTH 15.1(2022).