气候变化领域集成服务门户(CCPortal): Bathymetric Inversion and Mapping of Two Shallow Lakes Using Sentinel-2 Imagery and Bathymetry Data in the Central Tibetan Plateau

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DOI	10.1109/JSTARS.2022.3177227
	Bathymetric Inversion and Mapping of Two Shallow Lakes Using Sentinel-2 Imagery and Bathymetry Data in the Central Tibetan Plateau
	Yang, Hong; Ju, Jianting; Guo, Hengliang; Qiao, Baojin; Nie, Bingkang; Zhu, Liping
通讯作者	Zhu, LP (通讯作者)
发表日期	2022
ISSN	1939-1404
EISSN	2151-1535
起始页码	4279
结束页码	4296
卷号	15
英文摘要	High-accuracy lake bathymetry and mapping are crucial for estimating lake water storage on the Tibetan Plateau (TP). In this article, we constructed traditional empirical (TE) models and machine learning (ML) models to compare the prediction accuracy and remote sensing bathymetric mapping performance by using Sentinel-2 satellite imagery and in situ measured water depth from Caiduochaka (CK) and QiXiang Co in the central TP. We analyzed the relationship between the band reflectance and depth and explored the universality of the model in different lakes. The results indicated that when using the TE model, the mean absolute percentage error (MAPE) varied between 14.5% and 26.5% for the test dataset at different study sites. When using the ML models, the MAPE varied between 7.6% and 18.9%, and it was the better choice overall. For the test dataset of the random forest model with the highest accuracy, in the CK with the maximum depth of approximately 16 m, the mean absolute error (MAE) and root-mean-square error (RMSE) were 0.54 and 0.89 m, and the precision was the highest with an MAE of 1.13 m and RMSE of 1.67 m in QiXiang Co with a maximum depth of approximately 28 m, whereas the portability of the model was not satisfactory. Overall, the results indicated that the ML model can obtain bathymetric maps with high accuracy, good visual performance, and reliability, outperforming the TE model. It can be used effectively for deriving accurate and updated high-resolution bathymetric maps for shallow lakes.
关键词	MULTISPECTRAL SATELLITE IMAGERY WATER DEPTH NEURAL-NETWORKS AIRBORNE LIDAR REGRESSION RETRIEVAL MODEL
英文关键词	Lakes; Satellites; Remote sensing; Bathymetry; Data models; Analytical models; Earth; Bathymetric mapping; machine learning (ML); remote sensing depth inversion; Sentinel-2; shallow lake
语种	英语
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000808062300002
来源期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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/260596
推荐引用方式 GB/T 7714	Yang, Hong,Ju, Jianting,Guo, Hengliang,et al. Bathymetric Inversion and Mapping of Two Shallow Lakes Using Sentinel-2 Imagery and Bathymetry Data in the Central Tibetan Plateau[J]. 中国科学院青藏高原研究所,2022,15.
APA	Yang, Hong,Ju, Jianting,Guo, Hengliang,Qiao, Baojin,Nie, Bingkang,&Zhu, Liping.(2022).Bathymetric Inversion and Mapping of Two Shallow Lakes Using Sentinel-2 Imagery and Bathymetry Data in the Central Tibetan Plateau.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,15.
MLA	Yang, Hong,et al."Bathymetric Inversion and Mapping of Two Shallow Lakes Using Sentinel-2 Imagery and Bathymetry Data in the Central Tibetan Plateau".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 15(2022).