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dc.contributorXiao, Xiangmingen_US
dc.contributorZhao, Binen_US
dc.contributorMiyata, Akiraen_US
dc.contributorBaldocchi, Dennisen_US
dc.contributorKnox, Saraen_US
dc.contributorKang, Minseoken_US
dc.contributorShim, Kyo-Moonen_US
dc.contributorMin, Sunghyunen_US
dc.contributorChen, Bangqianen_US
dc.contributorLi, Xiangpingen_US
dc.contributorWang, Jieen_US
dc.contributorDong, Jinweien_US
dc.contributorBiradar, Chandrashekharen_US
dc.creatorXin, Fengfeien_US
dc.date2017-03-01en_US
dc.date.accessioned2017-02-23T13:20:24Z
dc.date.available2017-02-23T13:20:24Z
dc.identifierhttps://mel.cgiar.org/dspace/limiteden_US
dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0034425716304722en_US
dc.identifierhttps://www.researchgate.net/publication/312333872_Modeling_gross_primary_production_of_paddy_rice_cropland_through_analyses_of_data_from_CO2_eddy_flux_tower_sites_and_MODIS_imagesen_US
dc.identifier.citationFengfei Xin, Xiangming Xiao, Bin Zhao, Akira Miyata, Dennis Baldocchi, Sara Knox, Minseok Kang, Kyo-Moon Shim, Sunghyun Min, Bangqian Chen, Xiangping Li, Jie Wang, Jinwei Dong, Chandrashekhar Biradar. (1/3/2017). Modeling gross primary production of paddy rice cropland through analyses of data from CO2 eddy flux tower sites and MODIS images. Remote Sensing of Environment, 190, pp. 42-55.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/5915
dc.description.abstractAccurate information on the gross primary production (GPP) of paddy rice cropland is critical for assessing and monitoring rice growing conditions. The eddy co-variance technique was used to measure net ecosystem ex- change (NEE) of CO2 between paddy rice croplands and the atmosphere, and the resultant NEE data then partitioned into GPP (GPPEC) and ecosystem respiration. In this study, we first used the GPPEC data from four paddy rice flux tower sites in South Korea, Japan and the USA to evaluate the biophysical performance of three vegetation indices: Normalized Difference Vegetation Index (NDVI); Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) in terms of phenology (crop growing seasons) and GPPEC, which are derived from images taken by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. We also ran the Vegetation Photosynthesis Model (VPM), which is driven by EVI, LSWI, photosynthetically active radiation (PAR) and air temperature, to estimate GPP over multiple years at these four sites (GPPVPM). The 14 site-years of simulations show that the seasonal dynamics of GPPVPM successfully tracked the seasonal dynamics of GPPEC (R2 N 0.88 or higher). The cross-site comparison also shows that GPPVPM agreed reasonably well with the variations of GPPEC across both years and sites. The simulation results clearly demonstrate the potential of the VPM model and MODIS images for estimating GPP of paddy rice croplands in the monsoon climates of South Korea and Japan and the Mediterranean climate in California, USA. The application of VPM to regional simulations in the near future may provide crucial GPP data to support the studies of food security and cropland carbon cycle around the world.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherElsevieren_US
dc.rightsCC-BY-NC-4.0en_US
dc.sourceRemote Sensing of Environment;190,(2016) Pagination 42,55en_US
dc.subjectlight use efficiencyen_US
dc.subjectchlorophyllen_US
dc.subjectmulti-site co2 fluxesen_US
dc.subjectvegetation photosynthesis modelen_US
dc.subjectRiceen_US
dc.titleModeling gross primary production of paddy rice cropland through analyses of data from CO2 eddy flux tower sites and MODIS imagesen_US
dc.typeJournal Articleen_US
cg.creator.idBiradar, Chandrashekhar: 0000-0002-9532-9452en_US
cg.creator.ID-typeORCIDen_US
cg.contributor.centerFudan University, Institute of Biodiversity Science - IBSFUen_US
cg.contributor.centerUniversity of Oklahoma, College of Arts and Sciences - OU - CASen_US
cg.contributor.centerThe National Agriculture and Food Research Organization, Institute of Crop Science - NARO-NICSen_US
cg.contributor.centerUniversity of California, Berkeley - UCBen_US
cg.contributor.centerSeoul National University, College of Agriculture and Life Sciences, National Center for AgroMeterology - SNU -CALS - NCAMen_US
cg.contributor.centerRural Development Administration, National Institute of Agricultural Sciences - RDA - NASen_US
cg.contributor.centerChinese Academy of Tropical Agricultural Sciences - CATASen_US
cg.contributor.centerUniversity of Oklahoma, Center for Spatial Analysis - OU - CSAen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.funderIndian Council of Agricultural Research - ICARen_US
cg.contributor.projectIndia Collaborative Program: Restricted funding for breeding for resistance to abiotic stresses in pulses & for 2017/2018 - 2017/2020 - 2020/2021en_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.date.embargo-end-date2116-11-30en_US
cg.coverage.regionEastern Asiaen_US
cg.coverage.regionNorthern Americaen_US
cg.coverage.countryJPen_US
cg.coverage.countryKRen_US
cg.coverage.countryUSen_US
cg.contactxiangming.xiao@ou.eduen_US
cg.identifier.doihttps://dx.doi.org/10.1016/j.rse.2016.11.025en_US
dc.identifier.statusLimited accessen_US
mel.project.openhttp://geoagro.icarda.org/india/en_US
mel.impact-factor6.265en_US


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