Temporal Upscaling and Reconstruction of Thermal Remotely Sensed Instantaneous Evapotranspiration

作者:Xu, TR (Xu, Tongren)[ 1,2 ] ; Liu, SM (Liu, Shaomin)[ 1,2 ] ; Xu, L (Xu, Lu)[ 1,2,3 ] ; Chen, YJ (Chen, Yujie)[ 1,2,4 ] ; Jia, ZZ (Jia, Zhenzhen)[ 1,2 ] ; Xu, ZW (Xu, Ziwei)[ 1,2 ] ; Nielson, J (Nielson, Jeffrey)[ 5,6 ]

REMOTE SENSING

卷: 7  期: 3  页: 3400-3425

DOI: 10.3390/rs70303400

出版年: MAR 2015

摘要

Currently, thermal remote sensing-based evapotranspiration (ET) models can only calculate instantaneous ET at the time of satellite overpass. Five temporal upscaling methods, namely, constant evaporative fraction (ConEF), corrected ConEF (CorEF), diurnal evaporative fraction (DiEF), constant solar radiation ratio (SolRad), and constant reference evaporative fraction (ConET(r)F), were selected to upscale the instantaneous ET to daily values. Moreover, five temporal reconstruction approaches, namely, data assimilation (ET_EnKF and ET_SCE_UA), surface resistance (ET_SR), reference evapotranspiration (ET_ETrF), and harmonic analysis of time series (ET_HANTS), were used to produce continuous daily ET with discrete clear-sky daily ET values. For clear-sky daily ET generation, SolRad and ConET(r)F produced the best estimates. In contrast, ConEF usually underestimated the daily ET. The optimum method, however, was found by combining SolRad and ConET(r)F, which produced the lowest root-mean-square error (RMSE) values. For continuous daily ET production, ET_ETrF and ET_SCE_UA performed the best, whereas the ET_SR and ET_HANTS methods had large errors. The annual ET distributions over the Beijing area were calculated with these methods. The spatial ET distributions from ET_ETrF and ET_SCE_UA had the same trend as ETWatch products, and had a smaller RMSE when compared with ET observations derived from the water balance method.

通讯作者地址: Liu, SM (通讯作者)

Beijing Normal Univ, Res Ctr Remote Sensing & GIS, State Key Lab Remote Sensing Sci, 19 Xinjiekouwai St, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Res Ctr Remote Sensing & GIS, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China

[ 3 ] Natl Lib China, Dept Informat Technol, Beijing 100081, Peoples R China

[ 4 ] Yangzhou Environm Monitoring Ctr, Yangzhou 225007, Jiangsu, Peoples R China

[ 5 ] Univ Hawaii Manoa, Dept Civil & Environm Engn, Honolulu, HI 96822 USA

[ 6 ] Univ Hawaii Manoa, Water Resource Res Ctr, Honolulu, HI 96822 USA