Improved modeling of gross primary production and transpiration of sugarcane plantations with time-series Landsat and Sentinel-2 images.

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dc.contributorJORGE CELIS, UNIVERSITY OF OKLAHOMA; XIANGMING XIAO, UNIVERSITY OF OKLAHOMA; PAUL M. WHITE, UNITED STATES DEPARTMENT OF AGRICULTURE; OSVALDO MACHADO RODRIGUES CABRAL, CNPMA; HELBER C. FREITAS, UNIVERSIDADE ESTADUAL PAULISTA.
dc.creatorCELIS, J.
dc.creatorXIAO, X.
dc.creatorWHITE, P. M.
dc.creatorCABRAL, O. M. R.
dc.creatorFREITAS, H. C.
dc.date2024-02-02T14:32:35Z
dc.date2024-02-02T14:32:35Z
dc.date2024-02-02
dc.date2023
dc.date.accessioned2026-07-07T05:09:56Z
dc.descriptionAbstract: Sugarcane 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.
dc.identifierRemote Sensing, v. 16, n. 1, article 46, 2023.
dc.identifier2072-4292
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1161567
dc.identifierhttp://dx.doi.org/10.3390/rs16010046
dc.identifier.urihttp://hdl.handle.net/123456789/482600
dc.languageeng
dc.rightsopenAccess
dc.subjectSensoriamento Remoto
dc.subjectCana de Açúcar
dc.subjectSatélite
dc.subjectTranspiração Vegetal
dc.subjectFotossíntese
dc.subjectSugarcane
dc.subjectRemote sensing
dc.subjectTranspiration
dc.subjectPhotosynthesis
dc.titleImproved modeling of gross primary production and transpiration of sugarcane plantations with time-series Landsat and Sentinel-2 images.
dc.typeArtigo de periódico

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