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dc.contributor.authorXIN, F.
dc.contributor.authorXIAO, X.
dc.contributor.authorCABRAL, O. M. R.
dc.contributor.authorWHITE JUNIOR, P. M.
dc.contributor.authorGUO, H.
dc.contributor.authorMA, J.
dc.contributor.authorLI, B.
dc.contributor.authorZHAO, B.
dc.date.accessioned2020-10-10T09:14:56Z-
dc.date.available2020-10-10T09:14:56Z-
dc.date.created2020-10-09
dc.date.issued2020
dc.identifier.citationRemote Sensing, v. 12, n. 14, article 2186, 2020.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1125400-
dc.descriptionAbstract: Sugarcane (complex hybrids of Saccharum spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (GPPEC) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and GPPEC data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane GPPEC and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the GPPEC and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (GPPVPM). The seasonal dynamics of the GPPVPM and GPPEC at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.
dc.language.isoeng
dc.rightsopenAccesseng
dc.subjectCO2
dc.subjectEddy covariance flux tower
dc.subjectMODIS images
dc.subjectVegetation photosynthesis model
dc.titleUnderstanding the land surface phenology and gross primary production of sugarcane plantations by eddy flux measurements, MODIS images, and data-driven models.
dc.typeArtigo de periódico
dc.subject.thesagroCana de Açúcar
dc.subject.thesagroDióxido de Carbono
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroFenologia
dc.subject.nalthesaurusSugarcane
dc.subject.nalthesaurusCarbon dioxide
dc.subject.nalthesaurusEddy covariance
dc.subject.nalthesaurusPhotosynthesis
dc.subject.nalthesaurusVegetation index
dc.subject.nalthesaurusRemote sensing
dc.format.extent2p. 1-20.
riaa.ainfo.id1125400
riaa.ainfo.lastupdate2020-10-14 -03:00:00
dc.identifier.doihttps://doi.org/10.3390/rs12142186
dc.contributor.institutionFENGFEI XIN, Fudan University; XIANGMING XIAO, University of Oklahoma; OSVALDO MACHADO RODRIGUES CABRAL, CNPMA; PAUL M WHITE JUNIOR, ARS-USDA; HAIQIANG GUO, Fudan University; JUN MA, Fudan University; BO LI, Fudan University; BIN ZHAO, Fudan University.
Aparece en las colecciones:Artigo em periódico indexado (CNPMA)

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