22 May 2015 Temporal-spatial variation of evapotranspiration in the Yellow River Delta based on an integrated remote sensing model
He Li, Zhongxin Chen, Zhiwei Jiang, Liang Sun, Ke Liu, Bin Liu
Author Affiliations +
Abstract
Evapotranspiration (ET) is a key component in a water budget and energy balance study. In this study, moderate resolution imaging spectroradiometer (MODIS) data was used to estimate land surface ET in the Yellow River Delta, China. The ET estimation is based on an integrated model of the surface energy balance algorithm for land and two-source energy balance (TSEB). Compared with the pan data of meteorological stations and the simulated results from the original TSEB, the accuracy of the estimated ET is acceptable. Using the supervised classification method and the Landsat Thematic Mapper images, we obtained the seven-category land cover maps for the years of 2002, 2005, and 2008. The maps were used in analyzing the temporal-spatial variation of regional ET. The regional ET exhibits obvious spatial patterns: highest in the freshwater, while lowest in the other land. The spatial variation of ET in the study area is highly influenced by land cover types. The temporal variation of average monthly ET shows a characteristic unimodal curve, and the interannual change of the ET is small. In addition to precipitation, groundwater, runoff, and seawater are the other factors influencing the ET in this area. This study shows that the integrated remote sensing model is effective in estimating the land surface ET at a regional scale.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2015/$25.00 © 2015 SPIE
He Li, Zhongxin Chen, Zhiwei Jiang, Liang Sun, Ke Liu, and Bin Liu "Temporal-spatial variation of evapotranspiration in the Yellow River Delta based on an integrated remote sensing model," Journal of Applied Remote Sensing 9(1), 096047 (22 May 2015). https://doi.org/10.1117/1.JRS.9.096047
Published: 22 May 2015
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Cited by 4 scholarly publications.
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KEYWORDS
Data modeling

Integrated modeling

MODIS

Vegetation

Atmospheric modeling

Remote sensing

Earth observing sensors

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