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

Ling, Feng; Li, Xinyan; Foody, Giles M.; Boyd, Doreen; Ge, Yong; Li, Xiaodong; Du, Yun

Authors

Feng Ling

Xinyan Li

GILES FOODY giles.foody@nottingham.ac.uk
Professor of Geographical Information

DOREEN BOYD doreen.boyd@nottingham.ac.uk
Professor of Earth Observation

Yong Ge

Xiaodong Li

Yun Du



Abstract

© 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) 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.

Citation

Ling, F., Li, X., Foody, G. M., Boyd, D., Ge, Y., Li, X., & Du, Y. (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. https://doi.org/10.1016/j.isprsjprs.2020.08.008

Journal Article Type Article
Acceptance Date Aug 10, 2020
Online Publication Date Aug 20, 2020
Publication Date Oct 1, 2020
Deposit Date Aug 28, 2020
Publicly Available Date Aug 21, 2021
Journal ISPRS Journal of Photogrammetry and Remote Sensing
Print ISSN 0924-2716
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 168
Pages 141-152
DOI https://doi.org/10.1016/j.isprsjprs.2020.08.008
Keywords MODIS, Sub-pixel analysis, Surface water, Reservoir area
Public URL https://nottingham-repository.worktribe.com/output/4854952
Publisher URL https://www.sciencedirect.com/science/article/abs/pii/S092427162030215X
Additional Information This article is maintained by: Elsevier; Article Title: Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information; Journal Title: ISPRS Journal of Photogrammetry and Remote Sensing; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.isprsjprs.2020.08.008; Content Type: article; Copyright: © 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

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