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

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 consisten...

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Autores principales: Feng, Fei, Li, Xianglan, Yao, Yunjun, Liang, Shunlin, Chen, Jiquan, Zhao, Xiang, Jia, Kun, Pintér, Krisztina, McCaughey, J. Harry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4966955/
https://www.ncbi.nlm.nih.gov/pubmed/27472383
http://dx.doi.org/10.1371/journal.pone.0160150
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author Feng, Fei
Li, Xianglan
Yao, Yunjun
Liang, Shunlin
Chen, Jiquan
Zhao, Xiang
Jia, Kun
Pintér, Krisztina
McCaughey, J. Harry
author_facet Feng, Fei
Li, Xianglan
Yao, Yunjun
Liang, Shunlin
Chen, Jiquan
Zhao, Xiang
Jia, Kun
Pintér, Krisztina
McCaughey, J. Harry
author_sort Feng, Fei
collection PubMed
description 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.
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spelling pubmed-49669552016-08-18 An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations Feng, Fei Li, Xianglan Yao, Yunjun Liang, Shunlin Chen, Jiquan Zhao, Xiang Jia, Kun Pintér, Krisztina McCaughey, J. Harry PLoS One Research Article 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. Public Library of Science 2016-07-29 /pmc/articles/PMC4966955/ /pubmed/27472383 http://dx.doi.org/10.1371/journal.pone.0160150 Text en © 2016 Feng et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Feng, Fei
Li, Xianglan
Yao, Yunjun
Liang, Shunlin
Chen, Jiquan
Zhao, Xiang
Jia, Kun
Pintér, Krisztina
McCaughey, J. Harry
An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations
title An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations
title_full An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations
title_fullStr An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations
title_full_unstemmed An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations
title_short An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations
title_sort empirical orthogonal function-based algorithm for estimating terrestrial latent heat flux from eddy covariance, meteorological and satellite observations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4966955/
https://www.ncbi.nlm.nih.gov/pubmed/27472383
http://dx.doi.org/10.1371/journal.pone.0160150
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