Improving a Penman-Monteith evapotranspiration model by incorporating soil moisture control on soil evaporation in semiarid areas
作者:Sun, L (Sun, Liang)[ 1,2,3 ] ; Liang, SL (Liang, Shunlin)[ 1,2,3,4 ] ; Yuan, WP (Yuan, Wenping)[ 1,2,3 ] ; Chen, ZX (Chen, Zhongxin)[ 5 ]
INTERNATIONAL JOURNAL OF DIGITAL EARTH
卷: 6  页: 134-156  增刊: 1  特刊: SI
DOI: 10.1080/17538947.2013.783635
出版年: DEC 9 2013

摘要
Penman-Monteith (PM) theory has been successfully applied to calculate land surface evapotranspiration (ET) for regional and global scales. However, soil surface resistance, related to soil moisture, is always difficult to determine over a large region, especially in arid or semiarid areas. In this study, we developed an ET estimation algorithm by incorporating soil moisture control, a soil moisture index (SMI) derived from the surface temperature and vegetation index space. We denoted this ET algorithm as the PM-SMI. The PM-SMI algorithm was compared with several other algorithms that calculated soil evaporation using relative humidity, and validated with Bowen ratio measurements at seven sites in the Southern Great Plain (SGP) that were covered by grassland and cropland with low vegetation cover, as well as at three eddy covariance sites from AmeriFlux covered by forest with high vegetation cover. The results show that in comparison with the other methods examined, the PM-SMI algorithm significantly improved the daily ET estimates at SGP sites with a root mean square error (RMSE) of 0.91 mm/d, bias of 0.33 mm/d, and R-2 of 0.77. For three forest sites, the PM-SMI ET estimates are closer to the ET measurements during the non-growing season when compared with the other three algorithms. At all the 10 validation sites, the PM-SMI algorithm performed the best. PM-SMI 8-day ET estimates were also compared with MODIS 8-day ET products (MOD16A2), and the latter showed negligible bias at SGP sites. In contrast, most of the PM-SMI 8-day ET estimates are around the 1:1 line.

通讯作者地址: Sun, L (通讯作者)
Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Chinese Acad Sci, Inst Remote Sensing Applicat, Beijing, Peoples R China
[ 3 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
[ 4 ] Univ Maryland, Dept Geog, College Pk, MD 20742 USA
[ 5 ] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100193, Peoples R China