Journal
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
Volume 119, Issue 8, Pages 4521-4545Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1002/2013JD020864
Keywords
latent heat flux; evapotranspiration; Bayesian model averaging method; simple model averaging method
Categories
Funding
- U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program [DE-FG02-04ER63917, DE-FG02-04ER63911]
- CFCAS
- NSERC
- BIOCAP
- Environment Canada
- NRCan
- CarboEuropeIP
- FAO-GTOS-TCO
- iLEAPS
- Max Planck Institute for Biogeochemistry
- National Science Foundation
- University of Tuscia
- Universite Laval
- U.S. Department of Energy
- High-Tech Research and Development Program of China [2013AA122801]
- Natural Science Fund of China [41201331, 41101310, 41101313, 41301353, 41205104]
- National Basic Research Program of China [2012CB955302]
- Fundamental Research Funds for the Central Universities [2013YB34]
- High Resolution Earth Observation Systems of National Science and Technology Major Projects [05-Y30B02-9001-13/15-9]
- National Aeronautics and Space Administration
Ask authors/readers for more resources
Accurate estimation of the satellite-based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite-based global terrestrial LE estimation by merging five process-based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product algorithm, the revised remote-sensing-based Penman-Monteith LE algorithm, the Priestley-Taylor-based LE algorithm, the modified satellite-based Priestley-Taylor LE algorithm, and the semi-empirical Penman LE algorithm. We validated the BMA method using data for 2000-2009 and by comparison with a simple model averaging (SA) method and five process-based algorithms. Validation data were collected for 240 globally distributed eddy covariance tower sites provided by FLUXNET projects. The validation results demonstrate that the five process-based algorithms used have variable uncertainty and the BMA method enhances the daily LE estimates, with smaller root mean square errors (RMSEs) than the SA method and the individual algorithms driven by tower-specific meteorology and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data provided by the NASA Global Modeling and Assimilation Office (GMAO), respectively. The average RMSE for the BMA method driven by daily tower-specific meteorology decreased by more than 5 W/m(2) for crop and grass sites, and by more than 6 W/m(2) for forest, shrub, and savanna sites. The average coefficients of determination (R-2) increased by approximately 0.05 for most sites. To test the BMA method for regional mapping, we applied it for MODIS data and GMAO-MERRA meteorology to map annual global terrestrial LE averaged over 2001-2004 for spatial resolution of 0.05 degrees. The BMA method provides a basis for generating a long-term global terrestrial LE product for characterizing global energy, hydrological, and carbon cycles.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available