作者:Yao, YJ (Yao, Yunjun)[ 1 ] ; Liang, SL (Liang, Shunlin)[ 1,2 ] ; Yu, J (Yu, Jian)[ 1 ] ; Zhao, SH (Zhao, Shaohua)[ 3 ] ; Lin, Y (Lin, Yi)[ 4 ] ; Jia, K (Jia, Kun)[ 1 ] ; Zhang, XT (Zhang, Xiaotong)[ 1 ] ; Cheng, J (Cheng, Jie)[ 1 ] ; Xie, XH (Xie, Xianhong)[ 1 ] ; Sun, L (Sun, Liang)[ 5 ]  ; Wang, XY (Wang, Xuanyu)[ 1 ] ; Zhang, LL (Zhang, Lilin)[ 1 ] 

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

卷: 56  页: 1-12

DOI: 10.1016/j.jag.2016.10.009

出版年: APR 2017

摘要

Accurate estimates of terrestrial latent heat of evaporation (LE) for different biomes are essential to assess energy, water and carbon cycles. Different satellite-based Priestley-Taylor (PT) algorithms have been developed to estimate LE in different biomes. However, there are still large uncertainties in LE estimates for different PT algorithms. In this study, we evaluated differences in estimating terrestrial water flux in different biomes from three satellite-based PT algorithms using ground-observed data from eight eddy covariance (EC) flux towers of China. The results reveal that large differences in daily LE estimates exist based on EC measurements using three PT algorithms among eight ecosystem types. At the forest (CBS) site, all algorithms demonstrate high performance with low root mean square error (RMSE) (less than 16 W/m(2)) and high squared correlation coefficient (R-2) (more than 0.9). At the village (HHV) site, the ATI-PT algorithm has the lowest RMSE (13.9 W/m(2)), with bias of 2.7 W/m(2) and R-2 of 0.66. At the irrigated crop (HHM) site, almost all models algorithms underestimate LE, indicating these algorithms may not capture wet soil evaporation by parameterization of the soil moisture. In contrast, the SM-PT algorithm shows high values of R2 (comparable to those of ATI-PT and VPD-PT) at most other (grass, wetland, desert and Gobi) biomes. There are no obvious differences in seasonal LE estimation using MODIS NDVI and LAI at most sites. However, all meteorological or satellite-based water-related parameters used in the PT algorithm have uncertainties for optimizing water constraints. This analysis highlights the need to improve PT algorithms with regard to water constraints. (C) 2016 Elsevier B.V. All rights reserved.

通讯作者地址: Yao, YJ (通讯作者)

Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA

[ 3 ] Minist Environm Protect, Satellite Environm Ctr, Beijing 100094, Peoples R China

[ 4 ] Peking Univ, Inst Remote Sensing & GIS, Beijing 100871, Peoples R China

[ 5 ] USDA ARS, Hydrol & Remote Sensing Lab, Beltsville, MD USA