Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration
作者:Yao, YJ (Yao, Yunjun)[ 1 ] ; Liang, SL (Liang, Shunlin)[ 1,2 ] ; Zhao, SH (Zhao, Shaohua)[ 3 ] ; Zhang, YH (Zhang, Yuhu)[ 4 ] ; Qin, QM (Qin, Qiming)[ 5 ] ; Cheng, J (Cheng, Jie)[ 1 ] ; Jia, K (Jia, Kun)[ 1 ] ; Xie, XH (Xie, Xianhong)[ 1 ] ; Zhang, NN (Zhang, Nannan)[ 1,6 ] ; Liu, M (Liu, Meng)[ 1,7 ]
REMOTE SENSING
卷: 6  期: 1  页: 880-904
DOI: 10.3390/rs6010880
出版年: JAN 2014

摘要
Satellite-based vegetation indices (VIs) and Apparent Thermal Inertia (ATI) derived from temperature change provide valuable information for estimating evapotranspiration (LE) and detecting the onset and severity of drought. The modified satellite-based Priestley-Taylor (MS-PT) algorithm that we developed earlier, coupling both VI and ATI, is validated based on observed data from 40 flux towers distributed across the world on all continents. The validation results illustrate that the daily LE can be estimated with the Root Mean Square Error (RMSE) varying from 10.7 W/m(2) to 87.6 W/m(2), and with the square of correlation coefficient (R-2) from 0.41 to 0.89 (p < 0.01). Compared with the Priestley-Taylor-based LE (PT-JPL) algorithm, the MS-PT algorithm improves the LE estimates at most flux tower sites. Importantly, the MS-PT algorithm is also satisfactory in reproducing the inter-annual variability at flux tower sites with at least five years of data. The R-2 between measured and predicted annual LE anomalies is 0.42 (p = 0.02). The MS-PT algorithm is then applied to detect the variations of long-term terrestrial LE over Three-North Shelter Forest Region of China and to monitor global land surface drought. The MS-PT algorithm described here demonstrates the ability to map regional terrestrial LE and identify global soil moisture stress, without requiring precipitation information.

通讯作者地址: Yao, YJ (通讯作者)
Beijing Normal Univ, State Key Lab Remote Sensing Sci, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
[ 2 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
[ 3 ] Minist Environm Protect, Environm Satellite Ctr, Beijing 100094, Peoples R China
[ 4 ] Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China
[ 5 ] Peking Univ, Inst Remote Sensing, Beijing 100871, Peoples R China
[ 6 ] Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China
[ 7 ] Chengdu Univ Technol, Coll Earth Sci, Chengdu 610059, Peoples R China