DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery
文献类型:期刊论文
作者 | Hao, Zhen4,5; Foody, Giles3; Ge, Yong2; Cai, Xiaobin1,5; Du, Yun5; Ling, Feng1,5 |
刊名 | JOURNAL OF HYDROLOGY
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出版日期 | 2024-07-01 |
卷号 | 638页码:13 |
关键词 | Surface Water Area Estimation Landsat Mission Imagery Small Water Bodies Monitoring River Discharge Inversion DeepWaterFraction (DWF) |
ISSN号 | 0022-1694 |
DOI | 10.1016/j.jhydrol.2024.131512 |
英文摘要 | Surface water area estimation is essential for understanding global environmental dynamics, yet it presents significant challenges, particularly when dealing with small water bodies like ponds and narrow width rivers. Surface water areas for these small bodies are often inaccurately represented by existing methods due to the spatial resolution limitations in commonly used remote sensing images. This study introduces DeepWaterFraction (DWF), a deep learning approach, to estimate percent surface water area from Landsat mission imagery. DWF is trained with a self-training method, which creates training data by upscaling remote sensing images and water map labels to a lower resolution, enabling the creation of a large-scale, global coverage training dataset. DWF demonstrates superior accuracy in estimating areas for small water bodies compared to several existing methods for surface water area estimation, with a pixel-wise root mean squared error of 14.3 %. Specifically, it reduces error rates by 54.3 % for water bodies with a minimum area of 0.001 km(2) and by 22.6 % for those with a minimum area of 0.01 km(2). DWF's application in global river discharge inversion is also explored, showcasing its capability to capture width variations in narrow rivers (<90 m) better than existing methods, and its robustness across environments including wetland, tree covers, and urban areas. Even for wider rivers (>150 m), DWF's performance remains superior, as its ability to accurately quantify mixed water pixel areas effectively reflects discharge variations when the variation area is small. We find that self-training is an effective strategy for generating extensive global training datasets for water mapping, with a high upscaling factor being critical for ensuring label accuracy. This study presents a step forward in the accurate global mapping of water resources. |
WOS关键词 | SATELLITE IMAGES ; TIME-SERIES ; INDEX ; MODIS ; CLASSIFICATION ; RESERVOIRS ; LAKES ; EXTRACTION ; WIDTH ; EMISSIONS |
资助项目 | Joint Funds of the National Natural Science Foundation of China[U22A20567] ; 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:001260095000001 |
出版者 | ELSEVIER |
资助机构 | Joint Funds of the National Natural Science Foundation of China ; Natural Science Foundation of China ; State Key Laboratory of Geodesy and Earth's Dynamics |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/207785] ![]() |
专题 | 资源与环境信息系统国家重点实验室_外文论文 |
通讯作者 | Ling, Feng |
作者单位 | 1.Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, State Key Lab Geodesy & Earths Dynam, Wuhan 430071, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Nottingham, Sch Geog, Univ Pk, Nottingham NG7 2RD, England 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.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,Foody, Giles,Ge, Yong,et al. DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery[J]. JOURNAL OF HYDROLOGY,2024,638:13. |
APA | Hao, Zhen,Foody, Giles,Ge, Yong,Cai, Xiaobin,Du, Yun,&Ling, Feng.(2024).DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery.JOURNAL OF HYDROLOGY,638,13. |
MLA | Hao, Zhen,et al."DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery".JOURNAL OF HYDROLOGY 638(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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