Título:	Understanding the land surface phenology and gross primary production of sugarcane plantations by eddy flux measurements, MODIS images, and data-driven models.
Autoria:	XIN, F. XIAO, X. CABRAL, O. M. R. WHITE JUNIOR, P. M. GUO, H. MA, J. LI, B. ZHAO, B.
Afiliação:	FENGFEI 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.
Ano de publicação:	2020
Referência:	Remote Sensing, v. 12, n. 14, article 2186, 2020.
Páginas:	p. 1-20.
Conteúdo:	Abstract: 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.
Thesagro:	Cana de Açúcar Dióxido de Carbono Sensoriamento Remoto Fenologia
NAL Thesaurus:	Sugarcane Carbon dioxide Eddy covariance Photosynthesis Vegetation index Remote sensing
Palavras-chave:	CO2 Eddy covariance flux tower MODIS images Vegetation photosynthesis model
Digital Object Identifier:	https://doi.org/10.3390/rs12142186
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPMA)