An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations
Abstract
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.
- Authors:
-
- Beijing Normal Univ. (China). College of Global Change and Earth System Science. State Key Lab. of Remote Sensing Science
- Beijing Normal Univ. (China). School of Geography. State Key Lb. of Remote Sensing Science
- Beijing Normal Univ. (China). College of Global Change and Earth System Science. State Key Lab. of Remote Sensing Science; Univ. of Maryland, College Park, MD (United States). Dept. of Geographical Sciences
- Michigan State Univ., East Lansing, MI (United States). Center for Global Change and Earth Observations (CGCEO). Landscape Ecology & Ecosystem Science (LEES) Lab
- Szent István Univ., Gödöllő (Hungary). Inst. of Botany and Ecophysiology; Szent István Univ., Gödöllő (Hungary). MTA-SZIE Plant Ecology Research Group
- Queen's Univ., Kingston, ON (Canada). Dept. of Geography
- Publication Date:
- Research Org.:
- Univ. of Maryland, College Park, MD (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1627799
- Grant/Contract Number:
- FG02-04ER63917; FG02- 04ER63911
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS ONE
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 7; Journal ID: ISSN 1932-6203
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Science & Technology - Other Topics
Citation Formats
Feng, Fei, Li, Xianglan, Yao, Yunjun, Liang, Shunlin, Chen, Jiquan, Zhao, Xiang, Jia, Kun, Pintér, Krisztina, and McCaughey, J. Harry. An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations. United States: N. p., 2016.
Web. doi:10.1371/journal.pone.0160150.
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. United States. doi:10.1371/journal.pone.0160150.
Feng, Fei, Li, Xianglan, Yao, Yunjun, Liang, Shunlin, Chen, Jiquan, Zhao, Xiang, Jia, Kun, Pintér, Krisztina, and McCaughey, J. Harry. Fri .
"An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations". United States. doi:10.1371/journal.pone.0160150. https://www.osti.gov/servlets/purl/1627799.
@article{osti_1627799,
title = {An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations},
author = {Feng, Fei and Li, Xianglan and Yao, Yunjun and Liang, Shunlin and Chen, Jiquan and Zhao, Xiang and Jia, Kun and Pintér, Krisztina and McCaughey, J. Harry},
abstractNote = {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.},
doi = {10.1371/journal.pone.0160150},
journal = {PLoS ONE},
number = 7,
volume = 11,
place = {United States},
year = {2016},
month = {7}
}
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Fig 1: Coefficient of determination of latent heat fluxes (PT-JPL model) as a function of the number of leading EOF components. The left Y-axis is the contribution rate of covariance for each single EOF components (blue). The right Y-axis is the contribution rate of cumulative total of variance (red).
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Works referencing / citing this record:
Integrating Latent Heat Flux Products from MODIS and Landsat Data Using Multi-Resolution Kalman Filter Method in the Midstream of Heihe River Basin of Northwest China
journal, July 2019
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