Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

被引:14
作者
Che, Xianghong [1 ,2 ]
Yang, Yaping [1 ]
Feng, Min [3 ]
Xiao, Tong [4 ]
Huang, Shengli [5 ]
Xiang, Yang [6 ]
Chen, Zugang [1 ,2 ]
机构
[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] Univ Maryland, Dept Geog Sci, Global Land Cover Facil, College Pk, MD 20742 USA
[4] Minist Environm Protect, Dept Ecol Remote Sensing, Satellite Environm Ctr, Beijing 100094, Peoples R China
[5] US Forest Serv, USDA, Remote Sensing Lab, Reg 5, Mcclellan, CA 95652 USA
[6] ShaanXi Normal Univ, Coll Tourism & Environm, Xian 710100, Peoples R China
基金
美国国家科学基金会;
关键词
downscaling; Moderate Resolution Imaging Spectroradiometer (MODIS); lake change; BFAST; REMOTE-SENSING IMAGE; WATER INDEX NDWI; FUSION; DELINEATION; TEMPERATURE; RESOLUTION; TRENDS; CHINA; AREA;
D O I
10.3390/rs9010082
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 km(2) 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 (R-2) (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 (R-2: 0.598-0.728 and 0.61-0.735, respectively).
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页数:21
相关论文
共 58 条
[1]   Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis [J].
Aiazzi, B ;
Alparone, L ;
Baronti, S ;
Garzelli, A .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2002, 40 (10) :2300-2312
[2]  
[Anonymous], PACKAGE BFAST
[3]  
[Anonymous], 2007, THE NATIONAL DISASTE, P54
[4]  
[Anonymous], 2010, J GEOPHYS RES OCEANS
[5]   Global inland water monitoring from multi-mission altimetry [J].
Berry, PAM ;
Garlick, JD ;
Freeman, JA ;
Mathers, EL .
GEOPHYSICAL RESEARCH LETTERS, 2005, 32 (16) :1-4
[6]   Evapotranspiration from an Olive Orchard using Remote Sensing-Based Dual Crop Coefficient Approach [J].
Cammalleri, C. ;
Ciraolo, G. ;
Minacapilli, M. ;
Rallo, G. .
WATER RESOURCES MANAGEMENT, 2013, 27 (14) :4877-4895
[7]   Downscaling MODIS Surface Reflectance to Improve Water Body Extraction [J].
Che, Xianghong ;
Feng, Min ;
Jiang, Hao ;
Song, Jia ;
Jia, Bei .
ADVANCES IN METEOROLOGY, 2015, 2015
[8]  
[车向红 Che Xianghong], 2015, [地球信息科学学报, Journal of Geo-Information Science], V17, P99
[9]  
Ding L., 2006, Geomat. Spat. Inf. Technol, V29, P25
[10]  
Downing JA, 2010, LIMNETICA, V29, P9