Long-term spatial distributions and trends of the latent heat fluxes over the global cropland ecosystem using multiple satellite-based models

Feng, Fei; Li, Xianglan; Yao, Yunjun; Liu, Meng

doi:10.1371/journal.pone.0183771

Title: Long-term spatial distributions and trends of the latent heat fluxes over the global cropland ecosystem using multiple satellite-based models

Journal Article · Thu Aug 24 00:00:00 EDT 2017 · PLoS ONE

DOI:https://doi.org/10.1371/journal.pone.0183771· OSTI ID:1627837

Feng, Fei ^[1];

^[1]; Yao, Yunjun ^[2]; Liu, Meng ^[3]

Beijing Normal Univ. (China). College of Global Change and Earth System Science. State Key Lab. of Remote Sensing Science
Beijing Normal Univ. (China). School of Geography. State Key Lab. of Remote Sensing Science
Beijing Normal Univ. (China). School of Geography. State Key Lab. of Remote Sensing Science; Chinese Academy of Sciences (CAS), Beijing (China). Inst. of Geographic Sciences and Natural Resources Research; Univ. of Chinese Academy of Sciences, Beijing (China)

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/m2 among the four models. The inter-annual LE slightly increased during 1982– 2009 across the global cropland ecosystem. All models had acceptable performances with the coefficient of determination (R2 ) ranging from 0.4 to 0.7 and a root mean squared error (RMSE) of approximately 35 W/m2 . MS-PT had good overall performance across the cropland ecosystem with the highest R2 , lowest RMSE and a relatively low bias. The reduced performances of MOD16 and RRS, with R2 ranging from 0.4 to 0.6 and RMSEs from 30 to 39 W/m2 , might be attributed to empirical parameters in the structure algorithms and calibrated coefficients.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725; AC02-05CH11231

OSTI ID:: 1627837

Journal Information:: PLoS ONE, Vol. 12, Issue 8; ISSN 1932-6203

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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