Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Oklahoma-
Autor(es): dc.contributorUnited States Department of Agriculture-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorCelis, Jorge-
Autor(es): dc.creatorXiao, Xiangming-
Autor(es): dc.creatorWhite, Paul M.-
Autor(es): dc.creatorCabral, Osvaldo M. R.-
Autor(es): dc.creatorFreitas, Helber C.-
Data de aceite: dc.date.accessioned2025-08-21T22:10:38Z-
Data de disponibilização: dc.date.available2025-08-21T22:10:38Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/rs16010046-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/305092-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/305092-
Descrição: dc.descriptionSugarcane croplands account for ~70% of global sugar production and ~60% of global ethanol production. Monitoring and predicting gross primary production (GPP) and transpiration (T) in these fields is crucial to improve crop yield estimation and management. While moderate-spatial-resolution (MSR, hundreds of meters) satellite images have been employed in several models to estimate GPP and T, the potential of high-spatial-resolution (HSR, tens of meters) imagery has been considered in only a few publications, and it is underexplored in sugarcane fields. Our study evaluated the efficacy of MSR and HSR satellite images in predicting daily GPP and T for sugarcane plantations at two sites equipped with eddy flux towers: Louisiana, USA (subtropical climate) and Sao Paulo, Brazil (tropical climate). We employed the Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM) with C4 photosynthesis pathway, integrating vegetation index data derived from satellite images and on-ground weather data, to calculate daily GPP and T. The seasonal dynamics of vegetation indices from both MSR images (MODIS sensor, 500 m) and HSR images (Landsat, 30 m; Sentinel-2, 10 m) tracked well with the GPP seasonality from the EC flux towers. The enhanced vegetation index (EVI) from the HSR images had a stronger correlation with the tower-based GPP. Our findings underscored the potential of HSR imagery for estimating GPP and T in smaller sugarcane plantations.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionCenter for Earth Observation and Modeling School of Biological Sciences University of Oklahoma-
Descrição: dc.descriptionAgriculture Research Service Sugarcane Research Unit United States Department of Agriculture-
Descrição: dc.descriptionEmbrapa Meio Ambiente-
Descrição: dc.descriptionFaculty of Sciences Universidade Estadual Paulista-
Descrição: dc.descriptionFaculty of Sciences Universidade Estadual Paulista-
Descrição: dc.descriptionFAPESP: 2014/24452-0-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcrop-
Palavras-chave: dc.subjectmodel-
Palavras-chave: dc.subjectphotosynthesis-
Palavras-chave: dc.subjectprecision farming-
Palavras-chave: dc.subjectremote sensing-
Título: dc.titleImproved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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