|
|
Registro Completo |
Biblioteca(s): |
Embrapa Rondônia. |
Data corrente: |
10/10/2006 |
Data da última atualização: |
10/10/2006 |
Autoria: |
JULIATTI, F. C.; REIS, E. M.; OCCHIENA, E. M.; SILVA Jr, J. L.; MOURA, E. A. C.; POLIZEL, A. C. |
Título: |
Avaliação de um modelo climático de alerta da ferrugem da soja e determinação de danos baseados na incidência foliar e severidade sob inoculação artificial. |
Ano de publicação: |
2006 |
Fonte/Imprenta: |
In: REUNIAO DE PESQUISA DE SOJA DA REGIAO CENTRAL DO BRASIL, 28., 2006, Uberaba, MG. Resumos... Londrina: Embrapa Soja, Fundaçção Meridional, Fundação Triângulo, 2006. |
Páginas: |
p. 150-152 |
Série: |
(Embrapa Soja. Documentos, 272) |
Idioma: |
Português |
Palavras-Chave: |
Danos foliar; Ferrugem Asiática da Soja. |
Thesagro: |
Inoculação Artificial. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00888naa a2200229 a 4500 001 1707654 005 2006-10-10 008 2006 bl uuuu u00u1 u #d 100 1 $aJULIATTI, F. C. 245 $aAvaliação de um modelo climático de alerta da ferrugem da soja e determinação de danos baseados na incidência foliar e severidade sob inoculação artificial. 260 $c2006 300 $ap. 150-152 490 $a(Embrapa Soja. Documentos, 272) 650 $aInoculação Artificial 653 $aDanos foliar 653 $aFerrugem Asiática da Soja 700 1 $aREIS, E. M. 700 1 $aOCCHIENA, E. M. 700 1 $aSILVA Jr, J. L. 700 1 $aMOURA, E. A. C. 700 1 $aPOLIZEL, A. C. 773 $tIn: REUNIAO DE PESQUISA DE SOJA DA REGIAO CENTRAL DO BRASIL, 28., 2006, Uberaba, MG. Resumos... Londrina: Embrapa Soja, Fundaçção Meridional, Fundação Triângulo, 2006.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Rondônia (CPAF-RO) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
Registro Completo
Biblioteca(s): |
Embrapa Café. |
Data corrente: |
13/12/2019 |
Data da última atualização: |
13/12/2019 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 1 |
Autoria: |
JARAMILLO-GIRALDO, C.; FERREIRA, W. P. M.; FONSECA, H. P.; RIBEIRO, M. de F.; SILVA, L. M. R.; FERNANDES, R. B. A. |
Afiliação: |
Carolina Jaramillo-Giraldo, Empresa de Pesquisa Agropecuária de Minas Gerais - EPAMIG; WILLIAMS PINTO MARQUES FERREIRA, CNPCa; Humberto Paiva Fonseca; Marcelo de Freitas Ribeiro, Empresa de Pesquisa Agropecuária de Minas Gerais - EPAMIG; Laís Maria Rodrigues Silva, Universidade Federal de Viçosa - UFV/Departamento de Ciência do Solo; Raphael Bragança Alves Fernandes, Universidade Federal de Viçosa - UFV/Departamento de Ciência do Solo. |
Título: |
Relationship Between Spatio-Temporal Leaf Area Index and Crop Coefficient When Monitoring Coffee Plots in Brazil. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
Journal of Agricultural Science, v. 11, n. 15, p, 187-199, 2019. |
Idioma: |
Inglês |
Conteúdo: |
Robust monitoring techniques for perennial crops have become increasingly possible due to technological advances in the area of Remote Sensing (RS), and the products are available through the European Space Agency (ESA) initiative. RS data provides valuable opportunities for detailed assessments of crop conditions at plot level using high spatial, spectral, and temporal resolution. This study addresses the monitoring of coffee at the plot level using RS, analyzing the relationship between the spatio-temporal variability of the Leaf Area Index (LAI) and the crop coefficient (Kc); the Kc being a biophysical variable that integrates the potential hydrological characteristics of an agroecosystem compared to the reference crop. Daily and one-year Kc were estimated using the relation of crop evapotranspiration and reference. ESA Sentinel-2 images were pre-analyzed and atmospherically corrected, and Top-of-the-Atmosphere (TOA) reflections converted to Top-of-the-Canopy (TOC) reflectance. The TOCs resampled at the 10m resolution, and with the angles corresponding to the directional information at the time of the acquisition, the LAI was estimated using the trained neural network available in the Sentinel Application Platform (SNAP). During 75% of the monitored days, Kc ranged between 1.2 and 1.3 and, the LAI analyzed showed high spatial and temporal variability at the plot level. Based on the relationship between the biophysical variables, the LAI variable can substitute the Kc and be used to monitor the water conditions at the production area as well as analyze spatial variability inside that area. Sentinel-2 products could be more useful in monitoring coffee in the farm production area. MenosRobust monitoring techniques for perennial crops have become increasingly possible due to technological advances in the area of Remote Sensing (RS), and the products are available through the European Space Agency (ESA) initiative. RS data provides valuable opportunities for detailed assessments of crop conditions at plot level using high spatial, spectral, and temporal resolution. This study addresses the monitoring of coffee at the plot level using RS, analyzing the relationship between the spatio-temporal variability of the Leaf Area Index (LAI) and the crop coefficient (Kc); the Kc being a biophysical variable that integrates the potential hydrological characteristics of an agroecosystem compared to the reference crop. Daily and one-year Kc were estimated using the relation of crop evapotranspiration and reference. ESA Sentinel-2 images were pre-analyzed and atmospherically corrected, and Top-of-the-Atmosphere (TOA) reflections converted to Top-of-the-Canopy (TOC) reflectance. The TOCs resampled at the 10m resolution, and with the angles corresponding to the directional information at the time of the acquisition, the LAI was estimated using the trained neural network available in the Sentinel Application Platform (SNAP). During 75% of the monitored days, Kc ranged between 1.2 and 1.3 and, the LAI analyzed showed high spatial and temporal variability at the plot level. Based on the relationship between the biophysical variables, the LAI variable can substitute the Kc and ... Mostrar Tudo |
Palavras-Chave: |
Satellite crop monitoring sentinel-2. |
Thesaurus NAL: |
Crop coefficient; Leaf area index. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/206996/1/Relationship-between-Spatio-Temporal.pdf
|
Marc: |
LEADER 02427naa a2200217 a 4500 001 2116841 005 2019-12-13 008 2019 bl uuuu u00u1 u #d 100 1 $aJARAMILLO-GIRALDO, C. 245 $aRelationship Between Spatio-Temporal Leaf Area Index and Crop Coefficient When Monitoring Coffee Plots in Brazil.$h[electronic resource] 260 $c2019 520 $aRobust monitoring techniques for perennial crops have become increasingly possible due to technological advances in the area of Remote Sensing (RS), and the products are available through the European Space Agency (ESA) initiative. RS data provides valuable opportunities for detailed assessments of crop conditions at plot level using high spatial, spectral, and temporal resolution. This study addresses the monitoring of coffee at the plot level using RS, analyzing the relationship between the spatio-temporal variability of the Leaf Area Index (LAI) and the crop coefficient (Kc); the Kc being a biophysical variable that integrates the potential hydrological characteristics of an agroecosystem compared to the reference crop. Daily and one-year Kc were estimated using the relation of crop evapotranspiration and reference. ESA Sentinel-2 images were pre-analyzed and atmospherically corrected, and Top-of-the-Atmosphere (TOA) reflections converted to Top-of-the-Canopy (TOC) reflectance. The TOCs resampled at the 10m resolution, and with the angles corresponding to the directional information at the time of the acquisition, the LAI was estimated using the trained neural network available in the Sentinel Application Platform (SNAP). During 75% of the monitored days, Kc ranged between 1.2 and 1.3 and, the LAI analyzed showed high spatial and temporal variability at the plot level. Based on the relationship between the biophysical variables, the LAI variable can substitute the Kc and be used to monitor the water conditions at the production area as well as analyze spatial variability inside that area. Sentinel-2 products could be more useful in monitoring coffee in the farm production area. 650 $aCrop coefficient 650 $aLeaf area index 653 $aSatellite crop monitoring sentinel-2 700 1 $aFERREIRA, W. P. M. 700 1 $aFONSECA, H. P. 700 1 $aRIBEIRO, M. de F. 700 1 $aSILVA, L. M. R. 700 1 $aFERNANDES, R. B. A. 773 $tJournal of Agricultural Science$gv. 11, n. 15, p, 187-199, 2019.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Café (CNPCa) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
Expressão de busca inválida. Verifique!!! |
|
|