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Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information

文献类型：期刊论文

作者Ling, Feng4; Li, Xinyan1,4; Foody, Giles M.2; Boyd, Doreen2; Ge, Yong3; Li, Xiaodong4; Du, Yun4
刊名ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
出版日期2020-10-01
卷号168页码:141-152
ISSN号0924-2716
关键词MODIS Sub-pixel analysis Surface water Reservoir area
DOI10.1016/j.isprsjprs.2020.08.008
英文摘要Information on the temporal variation of surface water area of reservoirs is fundamental for water resource management and is often monitored by satellite remote sensing. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is an attractive data source for the routine monitoring of reservoirs, however, the accuracy is often limited due to the negative impacts associated with its coarse spatial resolution and the effects of cloud contamination. Methods have been proposed to solve these two problems independently but it remains challenging to address both problems simultaneously. To overcome this, this paper proposes a new approach that aims to monitor reservoir surface water area variations accurately and timely from daily MODIS images by exploring sub-pixel scale information. The proposed approach used estimates of reservoir water areas obtained from cloud-free and relatively fine spatial resolution Landsat images and water fraction images by spectral unmixing of coarse MODIS imagery as reference data. For each MODIS pixel, these reference reservoir water areas and their corresponding pixel water fractions were used to construct a linear regression equation, which in turn may be applied to predict the time series of reservoir water areas from daily MODIS water fraction images. The proposed approach was assessed with 21 reservoirs, where the correlation coefficients between reservoir water areas predicted by the common pixel-based analysis method and altimetry water levels were all less than 0.5. With the proposed sub-pixel analysis method, the resultant correlation coefficients were much improved, with eleven values larger than 0.5 including six values larger than 0.8 and the highest value of 0.94. The results show that the proposed sub-pixel analysis method is superior to the pixel based analysis method. The proposed method makes it possible to directly estimate the whole reservoir water area from, potentially, an individual cloud-free MODIS pixel, and is a promising way to improve the accuracy in the usability of MODIS images for the monitoring of reservoir surface water area variations.
WOS关键词TIME-SERIES ; ALTIMETRY DATA ; INDEX NDWI ; LAKE ; LAND ; STORAGE ; DELINEATION ; DATASET ; TM
资助项目Hubei Provincial Natural Science Foundation for Innovation Groups[2019CFA019] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA 2003030201] ; National Science Fund for Distinguished Young Scholars[41725006] ; Hubei Province Natural Science Fund for Distinguished Young Scholars[2018CFA062] ; Youth Innovation Promotion Association CAS[2017384]
WOS研究方向Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者ELSEVIER
WOS记录号WOS:000567932300010
资助机构Hubei Provincial Natural Science Foundation for Innovation Groups ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Science Fund for Distinguished Young Scholars ; Hubei Province Natural Science Fund for Distinguished Young Scholars ; Youth Innovation Promotion Association CAS
源URL[http://ir.igsnrr.ac.cn/handle/311030/156817]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.Univ Chinese Acad Sci, Beijing 100864, Peoples R China
2.Univ Nottingham, Sch Geog, Univ Pk, Nottingham NG7 2RD, England
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, Key Lab Environm & Disaster Monitoring & Evaluat, Wuhan 430077, Hubei, Peoples R China
推荐引用方式
GB/T 7714		 Ling, Feng,Li, Xinyan,Foody, Giles M.,et al. Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2020,168:141-152.
APA Ling, Feng.,Li, Xinyan.,Foody, Giles M..,Boyd, Doreen.,Ge, Yong.,...&Du, Yun.(2020).Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,168,141-152.
MLA Ling, Feng,et al."Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 168(2020):141-152.

入库方式： OAI收割

来源：地理科学与资源研究所

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