姚云军等:Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations
被阅读 1757 次
2014-06-13

Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations
作者:Yao, YJ (Yao, Yunjun)[ 1 ] ; Liang, SL (Liang, Shunlin)[ 1,2 ] ; Li, XL (Li, Xianglan)[ 1 ] ; Hong, Y (Hong, Yang)[ 3,4,5 ] ; Fisher, JB (Fisher, Joshua B.)[ 6 ] ; Zhang, NN (Zhang, Nannan)[ 7 ] ; Chen, JQ (Chen, Jiquan)[ 8 ] ; Cheng, J (Cheng, Jie)[ 1 ] ; Zhao, SH (Zhao, Shaohua)[ 9 ] ; Zhang, XT (Zhang, Xiaotong)[ 1 ] ; Jiang, B (Jiang, Bo)[ 1 ] ; Sun, L (Sun, Liang)[ 10 ] ; Jia, K (Jia, Kun)[ 1 ] ; Wang, KC (Wang, Kaicun)[ 11 ] ; Chen, Y (Chen, Yang)[ 11 ] ; Mu, QZ (Mu, Qiaozhen)[ 12 ] ; Feng, F (Feng, Fei)[ 1 ]
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
卷: 119  期: 8  页: 4521-4545
DOI: 10.1002/2013JD020864
出版年: APR 27 2014

摘要
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.

通讯作者地址: Yao, YJ (通讯作者)
Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
[ 1 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
[ 3 ] Univ Oklahoma, Sch Civil Engn & Environm Sci, Norman, OK 73019 USA
[ 4 ] Tsinghua Univ, Dept Hydraul Engn, Beijing 100084, Peoples R China
[ 5 ] Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China
[ 6 ] CALTECH, Jet Prop Lab, Pasadena, CA USA
[ 7 ] Hohai Univ, Sch Earth Sci & Engn, Nanjing, Jiangsu, Peoples R China
[ 8 ] Univ Toledo, Dept Environm Sci, Toledo, OH 43606 USA
[ 9 ] Minist Environm Protect, Environm Satellite Ctr, Beijing, Peoples R China
[ 10 ] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100193, Peoples R China
[ 11 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China
[ 12 ] Univ Montana, Dept Ecosyst & Conservat Sci, Numer Terradynam Simulat Grp, Missoula, MT 59812 USA