title: 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.affiliation: State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Feng
authors.affiliation: State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China.
authors.forename: Xianglan
authors.initials: X
authors.lastname: Li
authors.affiliation: State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, China.
authors.forename: Yunjun
authors.initials: Y
authors.lastname: Yao
authors.affiliation: 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.
authors.forename: Shunlin
authors.initials: S
authors.lastname: Liang
authors.affiliation: 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.
authors.forename: Jiquan
authors.initials: J
authors.lastname: Chen
authors.affiliation: State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhao
authors.affiliation: State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, China.
authors.forename: Kun
authors.initials: K
authors.lastname: Jia
authors.affiliation: Institute of Botany and Ecophysiology, Szent István University, 2100 Páter K.u.1., Gödöllő, Hungary.|MTA-SZIE Plant Ecology Research Group, 2103, Gödöllő, Hungary.
authors.forename: Krisztina
authors.initials: K
authors.lastname: Pintér
authors.affiliation: Department of Geography, Queen's University, Mackintosh-Corry Hall, Room E112, Kingston, Ontario, Canada.
authors.forename: J Harry
authors.initials: JH
authors.lastname: McCaughey
mesh_terms: D000465:Algorithms; D017753:Ecosystem; D036262:Empirical Research; D004784:Environmental Monitoring / Q000379:methods*; D006358:Hot Temperature; D063809:Satellite Imagery; D018531:Spacecraft