姚云军等:Evaluation of three satellite-based latent heat flux algorithms over forest ecosystems using eddy covariance data
被阅读 981 次
2015-10-23
Evaluation of three satellite-based latent heat flux algorithms over forest ecosystems using eddy covariance data
作者:Yao, YJ (Yao, Yunjun)[ 1 ] ; Zhang, YH (Zhang, Yuhu)[ 2 ] ; Zhao, SH (Zhao, Shaohua)[ 3 ] ; Li, XL (Li, Xianglan)[ 1 ] ; Jia, K (Jia, Kun)[ 1 ]
ENVIRONMENTAL MONITORING AND ASSESSMENT
卷: 187  期: 6
文献号: 382
DOI: 10.1007/s10661-015-4619-y
出版年: JUN 2015
 
摘要
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 PenmanMonteith 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/m(2)) and mixed forest (MF) (0.62, 25.3 W/m(2)) sites, the RRS-PM algorithm has high performance over evergreen broadleaf forest (EBF) (0.4, 28.1 W/m(2)) sites, and the UMD-SEMI algorithm has high performance over both deciduous needleleaf forest (DNF) (0.78, 17.1 W/m(2)) and evergreen needleleaf forest (ENF) (0.51, 28.1 W/m(2)) 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.
 
通讯作者地址: Zhang, YH (通讯作者)
Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China
[ 3 ] Minist Environm Protect, Satellite Environm Ctr, Beijing 100094, Peoples R China