Título:	Improved modeling of gross primary production and transpiration of sugarcane plantations with time-series Landsat and Sentinel-2 images.
Autoria:	CELIS, J. XIAO, X. WHITE, P. M. CABRAL, O. M. R. FREITAS, H. C.
Afiliação:	JORGE 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.
Ano de publicação:	2023
Referência:	Remote Sensing, v. 16, n. 1, article 46, 2023.
Conteúdo:	Abstract: 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.
Thesagro:	Sensoriamento Remoto Cana de Açúcar Satélite Transpiração Vegetal Fotossíntese
NAL Thesaurus:	Sugarcane Remote sensing Transpiration Photosynthesis
ISSN:	2072-4292
Digital Object Identifier:	http://dx.doi.org/10.3390/rs16010046
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPMA)