Evaluation of three satellite-based latent heat flux algorithms over forest ecosystems using eddy covariance data

  • Yunjun Yao
  • Yuhu ZhangEmail author
  • Shaohua Zhao
  • Xianglan Li
  • Kun Jia


We have evaluated the performance of three satellite-based latent heat flux (LE) algorithms over forest ecosystems using observed data from 40 flux towers distributed across the world on all continents. These are the revised remote sensing-based Penman-Monteith LE (RRS-PM) algorithm, the modified satellite-based Priestley-Taylor LE (MS-PT) algorithm, and the semi-empirical Penman LE (UMD-SEMI) algorithm. Sensitivity analysis illustrates that both energy and vegetation terms has the highest sensitivity compared with other input variables. The validation results show that three algorithms demonstrate substantial differences in algorithm performance for estimating daily LE variations among five forest ecosystem biomes. Based on the average Nash-Sutcliffe efficiency and root-mean-squared error (RMSE), the MS-PT algorithm has high performance over both deciduous broadleaf forest (DBF) (0.81, 25.4 W/m2) and mixed forest (MF) (0.62, 25.3 W/m2) sites, the RRS-PM algorithm has high performance over evergreen broadleaf forest (EBF) (0.4, 28.1 W/m2) sites, and the UMD-SEMI algorithm has high performance over both deciduous needleleaf forest (DNF) (0.78, 17.1 W/m2) and evergreen needleleaf forest (ENF) (0.51, 28.1 W/m2) sites. Perhaps the lower uncertainties in the required forcing data for the MS-PT algorithm, the complicated algorithm structure for the RRS-PM algorithm, and the calibrated coefficients of the UMD-SEMI algorithm based on ground-measured data may explain these differences.


Latent heat flux Forest ecosystems Revised remote sensing-based Penman-Monteith LE algorithm Modified satellite-based Priestley-Taylor LE algorithm Semi-empirical Penman LE algorithm 



The authors thank Dr. Liang Sun, Dr. Xianhong Xie, Dr. Jie Cheng, Dr. Xiaotong Zhang, Dr. Bo Jiang, and Dr. Xiang Zhao from Beijing Normal University, China, for their suggestions. This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by the CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval, Environment Canada, and US Department of Energy and the database development and technical support from the Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California, Berkeley, and the University of Virginia. This work was also partially supported by the National Science and Technology Support Plan During the 12th Five-year Plan Period of China (No.2012BAC19B03 and 2013BAC10B01), the Natural Science Fund of China (41201331 and 41301353), and the Fundamental Research Funds for the Central Universities (2013YB34 and 2013YB42).


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yunjun Yao
    • 1
  • Yuhu Zhang
    • 2
    Email author
  • Shaohua Zhao
    • 3
  • Xianglan Li
    • 1
  • Kun Jia
    • 1
  1. 1.State Key Laboratory of Remote Sensing Science, School of GeographyBeijing Normal UniversityBeijingChina
  2. 2.College of Resource Environment and TourismCapital Normal UniversityBeijingChina
  3. 3.Satellite Environment CenterMinistry of Environmental ProtectionBeijingChina

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