李占胜等:On Uncertainties of the Priestley-Taylor/LST-Fc Feature Space Method to Estimate Evapotranspiration: Case Study in an Arid/Semiarid Region in Northwest China
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2015-03-06
On Uncertainties of the Priestley-Taylor/LST-Fc Feature Space Method to Estimate Evapotranspiration: Case Study in an Arid/Semiarid Region in Northwest China
作者:Li, ZS (Li, Zhansheng)[ 1,2 ] ; Jia, L (Jia, Li)[ 1,3 ] ; Lu, J (Lu, Jing)[ 1 ]
REMOTE SENSING
卷: 7  期: 1  页: 447-466
DOI: 10.3390/rs70100447
出版年: JAN 2015
 
摘要
Accurate evapotranspiration (ET) estimation is very crucial for water resource management, particularly for the arid and semi-arid region. The remote sensing-based Priestley-Taylor method (RS-PT method) can estimate ET at regional scale, using the feature space of remotely sensed land surface temperature (LST) and vegetation index (VI). This study evaluates the RS-PT feature space method over an arid and semi-arid region in northwest China using satellite data from the moderate-resolution space-borne sensor Advanced Along-Track Scanning Radiometer (AATSR), the observations from the high-resolution airborne sensor Wide-angle Infrared Dual-mode line/area Array Scanner (WiDAS) and ground measurements of heat fluxes collected in summer 2008. The results show that the mean difference for latent heat flux (LE) estimates resulting from different domain sizes is 69.5 W/m(2). When using high-resolution images from airborne measurements, the dry boundary is strongly affected by the pixels of impervious surfaces, which lead to a mean difference of 15.36 W/m(2) for LE estimates. In addition, the physically based Surface Energy Balance Index (SEBI) model is used to analyze the accuracy of dry/wet boundaries in the RS-PT method. Compared with the SEBI-estimated relative evaporative fraction (Lambda r), the RS-PT method underestimated Lambda r by similar to 0.11. For the RS-PT method, the uncertainty in the determination of the dry/wet boundaries has a significant impact on the accuracy of the ET estimate, not only depending on the size of the area to build the feature space, but also on the land covers.
 
通讯作者地址: Jia, L (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[ 2 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[ 3 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China