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Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy

文献类型：期刊论文

作者Hao, Zhen6; Cai, Xiaobin5,7; Ge, Yong4; Foody, Giles3; Li, Xinyan2; Yin, Zhixiang1; Du, Yun; Ling, Feng5,7
刊名JOURNAL OF HYDROLOGY
出版日期2024-08-01
卷号640页码:13
关键词JRC GSW Deep learning Water area mapping Gap filling Seasonal water
ISSN号0022-1694
DOI10.1016/j.jhydrol.2024.131673
通讯作者Ling, Feng(lingf@whigg.ac.cn)
英文摘要The distribution of land surface water bodies is constantly changing. Monitoring these changes is critical for both humanity and the ecological system. The Joint Research Centre Global Surface Water (GSW) dataset is crucial in monitoring global water resources. However, about a third of this dataset suffers from gaps and invalid observations, diminishing its practical effectiveness. We first highlight the challenge of filling data gaps in seasonal water areas, a task significantly more complex than for permanent water bodies. The distinction is vital, as most water bodies are permanent or exhibit high occurrence probability, simplifying gap-filling. Focusing on addressing this, we introduce a self-supervised learning approach tailored to address data gaps in seasonal water areas. The model, trained using simulated gaps from the GSW dataset, adeptly identifies water body seasonal fluctuation patterns. Tested against 639 cloud-free Sentinel-2 images and simulated labels, the model demonstrates high accuracy, achieving F1 score of 0.83 for water and 0.75 for land in seasonal water areas. To improve dataset reliability, we implemented a quality scoring system for each filled segment, distinguishing between high and low-quality filled data and substantially mitigating uncertainty. This study stresses the need for distinct validations for seasonal waters to circumvent biases inherent in methods that do not differentiate between water body types (permanent vs. seasonal). The gap-filled dataset enables a more precise and comprehensive analysis of global water resource trends, highlighting the transformative impact of deep learning in improving the utility and reliability of large-scale environmental datasets.
WOS关键词INDEX NDWI ; LANDSAT ; CLASSIFICATION ; CLOUD ; RESOLUTION ; RESOURCES ; REMOVAL ; IMAGERY
资助项目Joint Funds of the National Natural Science Foundation of China[U22A20567] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA 2003030201] ; Natural Science Foundation of China[42171381] ; State Key Laboratory of Geodesy and Earth's Dynamics[S22L650104]
WOS研究方向Engineering ; Geology ; Water Resources
语种英语
WOS记录号WOS:001279937200001
出版者ELSEVIER
资助机构Joint Funds of the National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Natural Science Foundation of China ; State Key Laboratory of Geodesy and Earth's Dynamics
源URL[http://ir.igsnrr.ac.cn/handle/311030/207187]  
专题中国科学院地理科学与资源研究所
通讯作者Ling, Feng
作者单位1.Anhui Univ, Anhui Prov Key Lab Wetland Ecosyst Protect & Resto, Hefei 230601, Peoples R China
2.Surveying & Mapping Inst Lands & Resource, Dept Guangdong Prov, Guangzhou 510663, Peoples R China
3.Univ Nottingham, Sch Geog, Univ Pk, Nottingham NG7 2RD, England
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
5.Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, State Key Lab Geodesy & Earths Dynam, Wuhan 430071, Peoples R China
6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
7.Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, Key Lab Environm & Disaster Monitoring & Evaluat, Wuhan 430071, Hubei, Peoples R China
推荐引用方式
GB/T 7714		 Hao, Zhen,Cai, Xiaobin,Ge, Yong,et al. Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy[J]. JOURNAL OF HYDROLOGY,2024,640:13.
APA Hao, Zhen.,Cai, Xiaobin.,Ge, Yong.,Foody, Giles.,Li, Xinyan.,...&Ling, Feng.(2024).Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy.JOURNAL OF HYDROLOGY,640,13.
MLA Hao, Zhen,et al."Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy".JOURNAL OF HYDROLOGY 640(2024):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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