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Remote Sens. 2017, 9(1), 82; doi:10.3390/rs9010082

Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Global Land Cover Facility, Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
4
Department of Ecological Remote Sensing, Satellite Environment Center, Ministry of Environmental Protection, Beijing 100094, China
5
US Department of Agriculture Forest Service, Region 5, Remote Sensing Laboratory, McClellan, CA 95652, USA
6
College of Tourism and Environment, ShaanXi Normal University, Xi’an 710100, China
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Xiaofeng Li and Prasad S. Thenkabail
Received: 11 November 2016 / Revised: 28 December 2016 / Accepted: 11 January 2017 / Published: 17 January 2017
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Abstract

Lake extent is an indicator of water capacity as well as the aquatic ecological and environmental conditions. Due to the small sizes and rapid water dynamics, monitoring the extent of small lakes fluctuating between 2.5 and 30 km2 require observations with both high spatial and temporal resolutions. The paper applied an improved surface reflectance (SR) downscaling method (i.e., IMAR (Improved Modified Adaptive Regression model)) to downscale the daily SR acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra platform to a consistent 250-m resolution, and derived monthly water extent of four small lakes in the Tibetan Plateau (Longre Co, Ayonggongma Co, Ayonggama Co, and Ayongwama Co)) from 2000 to 2014. Using Landsat ETM+ acquired on the same date, the downscaled MODIS SR and identified water extent were compared to the original MODIS, observations downscaled using an early SR downscaling method (MAR (Modified Adaptive Regression model)) and Wavelet fusion. The results showed IMAR achieved the highest correlation coefficients (R2) (0.89–0.957 for SR and 0.79–0.933 for water extent). The errors in the derived water extents were significantly decreased comparing to the results of MAR and Wavelet fusion, and lakes morphometry of IMAR is more comparable to Landsat results. The detected lake extents dynamic between 2000 and 2014 were analyzed using the trend and season decomposition model (BFAST), indicating an increasing trend after 2005, and it likely had higher correlations with temperature and precipitation variation in the Tibetan region (R2: 0.598–0.728 and 0.61–0.735, respectively). View Full-Text
Keywords: downscaling; Moderate Resolution Imaging Spectroradiometer (MODIS); lake change; BFAST downscaling; Moderate Resolution Imaging Spectroradiometer (MODIS); lake change; BFAST
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Che, X.; Yang, Y.; Feng, M.; Xiao, T.; Huang, S.; Xiang, Y.; Chen, Z. Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance. Remote Sens. 2017, 9, 82.

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