作者:Feng, F (Feng, Fei)[ 1 ] ; Li, XL (Li, Xianglan)[ 1 ] ; Yao, YJ (Yao, Yunjun)[ 2 ] ; Liu, M (Liu, Meng)[ 2,3,4 ]

PLOS ONE

卷: 12  期: 8

文献号: e0183771

DOI: 10.1371/journal.pone.0183771

出版年: AUG 24 2017

摘要

Estimating cropland latent heat flux (LE) from continental to global scales is vital to modeling crop production and managing water resources. Over the past several decades, numerous LE models were developed, such as the moderate resolution imaging spectroradiometer LE (MOD16) algorithm, revised remote sensing-based Penman-Monteith LE algorithm (RRS), the Priestley-Taylor LE algorithm of the Jet Propulsion Laboratory (PT-JPL) and the modified satellite-based Priestley-Taylor LE algorithm (MS-PT). However, these LE models have not been directly compared over the global cropland ecosystem using various algorithms. In this study, we evaluated the performances of these four LE models using 34 eddy covariance (EC) sites. The results showed that mean annual LE for cropland varied from 33.49 to 58.97 W/m(2) among the four models. The interannual LE slightly increased during 1982-2009 across the global cropland ecosystem. All models had acceptable performances with the coefficient of determination (R-2) ranging from 0.4 to 0.7 and a root mean squared error (RMSE) of approximately 35 W/m(2). MS-PT had good overall performance across the cropland ecosystem with the highest R-2, lowest RMSE and a relatively low bias. The reduced performances of MOD16 and RRS, with R-2 ranging from 0.4 to 0.6 and RMSEs from 30 to 39 W/m(2), might be attributed to empirical parameters in the structure algorithms and calibrated coefficients.

通讯作者地址: Li, XL (通讯作者)

Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.

通讯作者地址: Yao, YJ (通讯作者)

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

地址:

[ 1 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

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

[ 3 ] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China

[ 4 ] Univ Chinese Acad Sci, Beijing, Peoples R China