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DOI:	10.1371/journal.pone.0160150
论文题名:	An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations
作者:	Fei Feng; Xianglan Li; Yunjun Yao; Shunlin Liang; Jiquan Chen; Xiang Zhao; Kun Jia; Krisztina Pintér; J. Harry McCaughey
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2016
发表日期:	2016-7-29
卷:	11, 期:7
语种:	英语
英文关键词:	Algorithms ; Forests ; Ecosystems ; Remote sensing ; Latent heat ; Latitude ; Solar radiation ; Covariance
英文摘要:	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.
URL:	http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0160150&type=printable
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资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/23547
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

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作者单位:

State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China;State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China;State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, China;State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, China;Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, United States of America;Landscape Ecology & Ecosystem Science (LEES) Lab, Center for Global Change and Earth Observations (CGCEO), Michigan State University, East Lansing, MI, 48823, United States of America;State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, China;State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, China;Institute of Botany and Ecophysiology, Szent István University, 2100 Páter K.u.1., Gödöllő, Hungary;MTA-SZIE Plant Ecology Research Group, 2103, Gödöllő, Hungary;Department of Geography, Queen’s University, Mackintosh-Corry Hall, Room E112, Kingston, Ontario, Canada

Recommended Citation:

Fei Feng,Xianglan Li,Yunjun Yao,et al. An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations[J]. PLOS ONE,2016-01-01,11(7)

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