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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
09/10/2020 |
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Data da última atualização: |
25/08/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
XIN, F.; XIAO, X.; CABRAL, O. M. R.; WHITE JUNIOR, P. M.; GUO, H.; MA, J.; LI, B.; ZHAO, B. |
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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. |
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Título: |
Understanding the land surface phenology and gross primary production of sugarcane plantations by eddy flux measurements, MODIS images, and data-driven models. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing, v. 12, n. 14, article 2186, 2020. |
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Páginas: |
p. 1-20. |
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DOI: |
https://doi.org/10.3390/rs12142186 |
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Idioma: |
Inglês |
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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. MenosAbstract: 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 wel... Mostrar Tudo |
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Palavras-Chave: |
CO2; Eddy covariance flux tower; MODIS images; Vegetation photosynthesis model. |
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Thesagro: |
Cana de Açúcar; Dióxido de Carbono; Fenologia; Sensoriamento Remoto. |
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Thesaurus Nal: |
Carbon dioxide; Eddy covariance; Photosynthesis; Remote sensing; Sugarcane; Vegetation index. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/216551/1/Cabral-Understanding-Land-2020.pdf
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Marc: |
LEADER 03091naa a2200397 a 4500
001 2125400
005 2021-08-25
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/rs12142186$2DOI
100 1 $aXIN, F.
245 $aUnderstanding the land surface phenology and gross primary production of sugarcane plantations by eddy flux measurements, MODIS images, and data-driven models.$h[electronic resource]
260 $c2020
300 $ap. 1-20.
520 $aAbstract: 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.
650 $aCarbon dioxide
650 $aEddy covariance
650 $aPhotosynthesis
650 $aRemote sensing
650 $aSugarcane
650 $aVegetation index
650 $aCana de Açúcar
650 $aDióxido de Carbono
650 $aFenologia
650 $aSensoriamento Remoto
653 $aCO2
653 $aEddy covariance flux tower
653 $aMODIS images
653 $aVegetation photosynthesis model
700 1 $aXIAO, X.
700 1 $aCABRAL, O. M. R.
700 1 $aWHITE JUNIOR, P. M.
700 1 $aGUO, H.
700 1 $aMA, J.
700 1 $aLI, B.
700 1 $aZHAO, B.
773 $tRemote Sensing$gv. 12, n. 14, article 2186, 2020.
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