An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations

作者:Feng, F (Feng, Fei)[ 1 ] ; Li, XL (Li, Xianglan)[ 1 ] ; Yao, YJ (Yao, Yunjun)[ 2 ] ; Liang, SL (Liang, Shunlin)[ 2,3 ] ; Chen, JQ (Chen, Jiquan)[ 4 ] ; Zhao, X (Zhao, Xiang)[ 2 ] ; Jia, K (Jia, Kun)[ 2 ] ; Pinter, K (Pinter, Krisztina)[ 5,7 ] ; McCaughey, JH (McCaughey, J. Harry)[ 6 ]

卷: 11  期: 7

文献号: e0160150

DOI: 10.1371/journal.pone.0160150

出版年: JUL 29 2016

摘要

Accurate estimation of latent heat flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types.

通讯作者地址: Li, XL (通讯作者)

Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

通讯作者地址: Yao, YJ (通讯作者)

Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 3 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA

[ 4 ] Michigan State Univ, CGCEO, Landscape Ecol & Ecosyst Sci LEES Lab, E Lansing, MI 48823 USA

[ 5 ] Szent Istvan Univ, Inst Bot & Ecophysiol, Pater Ku 1, H-2100 Godollo, Hungary

[ 6 ] Queens Univ, Dept Geog, Mackintosh Corry Hall,Room E112, Kingston, ON, Canada

[ 7 ] MTA SZIE Plant Ecol Res Grp, H-2103 Godollo, Hungary