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dc.contributorXiao, Xiangmingen_US
dc.contributorWagle, Pradeepen_US
dc.contributorZhang, Gelien_US
dc.contributorZhou, Yutingen_US
dc.contributorJin, Cuien_US
dc.contributorTorn, Margareten_US
dc.contributorMeyers, Tildenen_US
dc.contributorSuyker, Andrewen_US
dc.contributorWang, Junbangen_US
dc.contributorYan, Huiminen_US
dc.contributorBiradar, Chandrashekharen_US
dc.contributorMoore III, Berrienen_US
dc.creatorDong, Jinweien_US
dc.identifier.citationJinwei Dong, Xiangming Xiao, Pradeep Wagle, Geli Zhang, Yuting Zhou, Cui Jin, Margaret Torn, Tilden Meyers, Andrew Suyker, Junbang Wang, Huimin Yan, Chandrashekhar Biradar, Berrien Moore III. (1/6/2015). Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought. Remote Sensing of Environment, 162, pp. 154-168.en_US
dc.description.abstractAccurate estimation of gross primary production (GPP) is critical for understanding ecosystem response to climate variability and change. Satellite-based diagnostic models, which use satellite images and/or climate data as input, are widely used to estimate GPP. Many models used the Normalized Difference Vegetation Index (NDVI) to estimate the fraction of absorbed photosynthetically active radiation (PAR) by vegetation canopy (FPARcanopy) and GPP. Recently, the Enhanced Vegetation Index (EVI) has been increasingly used to estimate the fraction of PAR absorbed by chlorophyll (FPARchl) or green leaves (FPARgreen) and to provide more accurate estimates of GPP in such models as the Vegetation Photosynthesis Model (VPM), Temperature and Greenness (TG) model, Greenness and Radiation (GR) model, and Vegetation Index (VI) model. Although these EVI-based models perform well under non-drought conditions, their performances under severe droughts are unclear. In this study, we run the four EVI-based models at three AmeriFlux sites (rainfed soybean, irrigated maize, and grassland) during drought and non-drought years to examine their sensitivities to drought. As all the four models use EVI for FPAR estimate, our hypothesis is that their different sensitivities to drought are mainly attributed to the ways they handle light use efficiency (LUE), especially water stress. The predicted GPP from these four models had a good agreement with the GPP estimated from eddy flux tower in non-drought years with root mean squared errors (RMSEs) in the order of 2.17 (VPM), 2.47 (VI), 2.85 (GR) and 3.10 g C m−2 day−1 (TG). But their performances differed in drought years, the VPM model performed best, followed by the VI, GR and TG, with the RMSEs of 1.61, 2.32, 3.16 and 3.90 g C m−2 day−1 respectively. TG and GR models overestimated seasonal sum of GPP by 20% to 61% in rainfed sites in drought years and also overestimated or underestimated GPP in the irrigated site. This difference in model performance under severe drought is attributed to the fact that the VPMuses satellite-based Land Surface Water Index (LSWI) to address the effect of water stress (deficit) on LUE and GPP,while the other three models do not have such a mechanism. This study suggests that it is essential for these models to consider the effect of water stress on GPP, in addition to using EVI to estimate FPAR, if these models are applied to estimate GPP under drought conditions.en_US
dc.sourceRemote Sensing of Environment;162,(2015) Pagination 154,168en_US
dc.subjectdrought light use efficiency (lue)en_US
dc.subjectvegetation photosynthesis model (vpm)en_US
dc.subjecttemperature and greenness (tg)en_US
dc.subjectmodel greenness and radiation (gr) modelen_US
dc.subjectvegetation index (vi) modelen_US
dc.subjectSoya beanen_US
dc.titleComparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe droughten_US
dc.typeJournal Articleen_US
cg.creator.idBiradar, Chandrashekhar: 0000-0002-9532-9452en_US
cg.contributor.centerUniversity of Oklahoma, Center for Spatial Analysis - OU - CSAen_US
cg.contributor.centerUniversity of Oklahoma, College of Arts and Sciences - OU - CASen_US
cg.contributor.centerUniversity of Oklahoma - OUen_US
cg.contributor.centerBerkeley Lab - Lawrence Berkeley National Laboratory - lblen_US
cg.contributor.centerNational Oceanic and Atmospheric Administration, Atmospheric Turbulence and Diffusion Division - NOAA-ATDDen_US
cg.contributor.centerUniversity of Nebraska-Lincoln - UNLen_US
cg.contributor.centerChinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research - cas-igsnrren_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerUniversity of Oklahoma, College of Atmospheric and Geographic Science - OU - AGSen_US
cg.contributor.crpCGIAR Research Program on Dryland Systems - DSen_US
cg.contributor.funderNot Applicableen_US
cg.coverage.regionNorthern Americaen_US
dc.identifier.statusLimited accessen_US

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