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| 4. |  | BRANDAO, Z. N.; GREGO, C. R.; JORGE, L. A. de C.; MANJOLIN, R. C. Multispectral reflectance and geostatistic methods to estimate leaf nitrogen content and cotton yield. In: SILVA-MATOS, R. R. S. da; MORAES, L. F.; SILVA, F. L. de S. (org.). Desenvolvimento da pesquisa científica, tecnologia e inovação na agronomia 3. Ponta Grossa: Atena, 2022. cap. 14, p. 143-154.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Algodão; Embrapa Instrumentação. |
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| 7. | | GREGO, C. R.; TEIXEIRA, A. H. de C.; LEIVAS, J. F.; SILVA, G. B. S. da; MANJOLIN, R. C. Interpolação de dados agrometeorológicos em duas densidades amostrais no norte de Minas Gerais. Agrometeoros, Passo Fundo, v. 25, n. 1, p. 81-89, ago. 2017.| Biblioteca(s): Embrapa Territorial. |
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| 10. | | GREGO, C. R.; MANJOLIN, R. C.; NOGUEIRA, S. F.; RODRIGUES, C. A. G.; SILVA, G. B. S. da; CONCEIÇÃO, M. P. C.; HERLING, V. R. Geostatistical Analysis of NDVI in rotational and continuous grazing pastures In: INTERNATIONAL SYMPOSIUM ON GREENHOUSE GASES IN AGRICULTURE, 2., 2016, Campo Grande, MS. Proceedings... Brasília, DF: Embrapa, 2016. p. 121-125. (Embrapa Gado de Corte. Documentos, 216).| Biblioteca(s): Embrapa Territorial. |
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| 11. | | MANJOLIN, R. C.; GREGO, C. R.; NOGUEIRA, S. F.; BARIONI, L. G.; SILVA, G. B. S. da; SANTOS, P. M.; PEZZOPANE, J. R. M. Variabilidade espacial da disponibilidade de forragem em sistemas de pastejo nas estações chuvosa e seca. In: MOSTRA DE ESTAGIÁRIOS E BOLSISTAS DA EMBRAPA INFORMÁTICA AGROPECUÁRIA, 14., 2018, Campinas. Resumos expandidos... Brasília, DF: Embrapa, 2018. p. 88-93. (Embrapa Informática Agropecuária. Eventos técnicos & científicos, 1). Editores técnicos: Carla Geovana do Nascimento Macário, Carla Cristiane Osawa, Flávia Bussaglia Fiorini, Maria Fernanda Moura, Poliana Fernanda Giachetto.| Biblioteca(s): Embrapa Agricultura Digital. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Algodão. Para informações adicionais entre em contato com cnpa.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Algodão. |
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Data corrente: |
08/08/2017 |
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Data da última atualização: |
15/01/2018 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
BRANDAO, Z. N.; GREGO, C. R.; MANJOLIN, R. C. |
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Afiliação: |
ZIANY NEIVA BRANDAO, CNPA; CELIA REGINA GREGO, CNPM; RODOLFO CORREA MANJOLIN, ESTAGIÁRIO - CNPM. |
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Título: |
Geoestatistical tools and spectral measurements from AWiFs data for evaluation of N and P contents in cotton leaves. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais ... Santos: INPE, 2017. |
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Páginas: |
p. 2408-2415 |
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Idioma: |
Inglês |
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Notas: |
SBSR. |
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Conteúdo: |
Satellite images and geostatistics are useful tools to assess the nutritional status of plants, and thus, understanding the variability of cotton yield in farmers' fields. The resulting kriged maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and understanding the factors that led to crop yield. To support decisions on improving cotton yield, this study combines the conventional statistic analysis, spatial regression modeling of georreferenced data and AWiFs' vegetations indices assessment. The experiments were carried out in a 47.4 ha commercial field of Goiás state, Brazil. Multispectral satellite images at 56 m spatial resolution were collected in a rainfed cotton field in two dates, on 04/01/2011 and 04/10/2012, from AWiFS sensor during the flowering cotton stage. Measures of leaf nitrogen (N) and phosphorus (P) contents were determined over previously georreferenced central points of 70 plots, each one measuring 80X80 m. Data were analyzed using descriptive statistics and geostatistical analyses by building and setting semivariograms and kriging interpolation. Best correlation was found between IVs and nitrogen contents of cotton leaves. Results indicated that NDVI, MSAVI and SAVI were the best indices to estimate P contents at cotton peak flowering. Identifications of spatial differences were possible using geostatistical methods with remote sensing data obtained from medium resolution satellite images, allowing to identify distinct nutritional needs and growth status of canopy to cotton plants. MenosSatellite images and geostatistics are useful tools to assess the nutritional status of plants, and thus, understanding the variability of cotton yield in farmers' fields. The resulting kriged maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and understanding the factors that led to crop yield. To support decisions on improving cotton yield, this study combines the conventional statistic analysis, spatial regression modeling of georreferenced data and AWiFs' vegetations indices assessment. The experiments were carried out in a 47.4 ha commercial field of Goiás state, Brazil. Multispectral satellite images at 56 m spatial resolution were collected in a rainfed cotton field in two dates, on 04/01/2011 and 04/10/2012, from AWiFS sensor during the flowering cotton stage. Measures of leaf nitrogen (N) and phosphorus (P) contents were determined over previously georreferenced central points of 70 plots, each one measuring 80X80 m. Data were analyzed using descriptive statistics and geostatistical analyses by building and setting semivariograms and kriging interpolation. Best correlation was found between IVs and nitrogen contents of cotton leaves. Results indicated that NDVI, MSAVI and SAVI were the best indices to estimate P contents at cotton peak flowering. Identifications of spatial differences were possible using geostatistical methods with remote sensing data obtained from medium resolution satellite images, allowing to identify dist... Mostrar Tudo |
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Palavras-Chave: |
Ìndices de vegetação; Spatial variability. |
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Thesagro: |
Agricultura de Precisão; Algodão; Sensoriamento remoto. |
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Thesaurus NAL: |
Cotton; Precision agriculture; Remote sensing. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02391nam a2200253 a 4500
001 2073757
005 2018-01-15
008 2017 bl uuuu u01u1 u #d
100 1 $aBRANDAO, Z. N.
245 $aGeoestatistical tools and spectral measurements from AWiFs data for evaluation of N and P contents in cotton leaves.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais ... Santos: INPE$c2017
300 $ap. 2408-2415
500 $aSBSR.
520 $aSatellite images and geostatistics are useful tools to assess the nutritional status of plants, and thus, understanding the variability of cotton yield in farmers' fields. The resulting kriged maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and understanding the factors that led to crop yield. To support decisions on improving cotton yield, this study combines the conventional statistic analysis, spatial regression modeling of georreferenced data and AWiFs' vegetations indices assessment. The experiments were carried out in a 47.4 ha commercial field of Goiás state, Brazil. Multispectral satellite images at 56 m spatial resolution were collected in a rainfed cotton field in two dates, on 04/01/2011 and 04/10/2012, from AWiFS sensor during the flowering cotton stage. Measures of leaf nitrogen (N) and phosphorus (P) contents were determined over previously georreferenced central points of 70 plots, each one measuring 80X80 m. Data were analyzed using descriptive statistics and geostatistical analyses by building and setting semivariograms and kriging interpolation. Best correlation was found between IVs and nitrogen contents of cotton leaves. Results indicated that NDVI, MSAVI and SAVI were the best indices to estimate P contents at cotton peak flowering. Identifications of spatial differences were possible using geostatistical methods with remote sensing data obtained from medium resolution satellite images, allowing to identify distinct nutritional needs and growth status of canopy to cotton plants.
650 $aCotton
650 $aPrecision agriculture
650 $aRemote sensing
650 $aAgricultura de Precisão
650 $aAlgodão
650 $aSensoriamento remoto
653 $aÌndices de vegetação
653 $aSpatial variability
700 1 $aGREGO, C. R.
700 1 $aMANJOLIN, R. C.
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