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| Registros recuperados : 7 | |
| 2. |  | ZHANG, D.; XIAO, X.; WEI, Y.; WU, Z. Ecological effects on fibre production of Zhongwei goats. In: INTERNATIONAL CONFERENCE ON GOATS, 6., 1996, Beijing, China. Proceedings... Beijing, China: International Academic, 1996. v. 1, p. 441-443.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 3. | | ZHANG, D.; XIAO, X.; WEI, Y.; WU, Z.; LU, Y. Production characteristics of Zhongwei goats. In: INTERNATIONAL CONFERENCE ON GOATS, 6., 1996, Beijing, China. Proceedings... Beijing, China: International Academic, 1996. v. 2, p. 870-872.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 6. | | XIN, F.; XIAO, X.; CABRAL, O. M. R.; WHITE JUNIOR, P. M.; GUO, H.; MA, J.; LI, B.; ZHAO, B. Understanding the land surface phenology and gross primary production of sugarcane plantations by eddy flux measurements, MODIS images, and data-driven models. Remote Sensing, v. 12, n. 14, article 2186, 2020. p. 1-20.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 7. | | HURTT, G.; XIAO, X.; KELLER, M.; PALACE, M.; ASNER, G. P.; BRASWELL, R.; BRONDÍZIO, E. S.; CARDOSO, M.; CARVALHO, C. J. R.; FEARON, M. G.; GUILD, L.; HAVEN, S.; HETRICK, S.; MOORE III, B.; NOBRE, C.; READ, J. M.; SÁ, T.; SCHLOSS, A.; VOURLITIS, G.; WICKEL, A. J. IKONOS imagery for the large scale biosphere-atmosphere experiment in Amazonia (LBA). Remote Sensing of Environment, v. 88, p.111-127, 2003. il.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| Registros recuperados : 7 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
|
Data corrente: |
02/02/2024 |
|
Data da última atualização: |
02/02/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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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. |
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Título: |
Improved modeling of gross primary production and transpiration of sugarcane plantations with time-series Landsat and Sentinel-2 images. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Remote Sensing, v. 16, n. 1, article 46, 2023. |
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ISSN: |
2072-4292 |
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DOI: |
http://dx.doi.org/10.3390/rs16010046 |
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Idioma: |
Inglês |
|
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. |
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Thesagro: |
Cana de Açúcar; Fotossíntese; Satélite; Sensoriamento Remoto; Transpiração Vegetal. |
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Thesaurus NAL: |
Photosynthesis; Remote sensing; Sugarcane; Transpiration. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1161567/1/Cabral-Improved-modeling-2023.pdf
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Marc: |
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022 $a2072-4292
024 7 $ahttp://dx.doi.org/10.3390/rs16010046$2DOI
100 1 $aCELIS, J.
245 $aImproved modeling of gross primary production and transpiration of sugarcane plantations with time-series Landsat and Sentinel-2 images.$h[electronic resource]
260 $c2023
520 $aAbstract: 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.
650 $aPhotosynthesis
650 $aRemote sensing
650 $aSugarcane
650 $aTranspiration
650 $aCana de Açúcar
650 $aFotossíntese
650 $aSatélite
650 $aSensoriamento Remoto
650 $aTranspiração Vegetal
700 1 $aXIAO, X.
700 1 $aWHITE, P. M.
700 1 $aCABRAL, O. M. R.
700 1 $aFREITAS, H. C.
773 $tRemote Sensing$gv. 16, n. 1, article 46, 2023.
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Embrapa Meio Ambiente (CNPMA) |
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