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| 11. |  | KHAN, A. S.; SILVA, L. M. R.; MESQUITA, T. C.; MAYORGA, R. D.; SILVA, D. N. Adoção de tecnologia na cultura do caupi no estado do Ceará. Revista Econômica do Nordeste, Fortaleza, v. 25, n. 1, p. 47-92, jan./mar. 1994.| Biblioteca(s): Embrapa Semiárido. |
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| 15. | | KHAN, A. S.; CRUZ, J. A. N.; SILVA, L. M. R.; LIMA, P. V. P. S. Efeito da seca sobre a produção, renda e o emprego agrícola na microrregião geográfica de Brejo Santo e no Estado do Ceará. Revista Econômica do Nordeste, Fortaleza, v.36, n.2, p.242-262, 2005.| Biblioteca(s): Embrapa Caprinos e Ovinos; Embrapa Semiárido. |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
13/12/2019 |
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Data da última atualização: |
13/12/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
JARAMILLO-GIRALDO, C.; FERREIRA, W. P. M.; FONSECA, H. P.; RIBEIRO, M. de F.; SILVA, L. M. R.; FERNANDES, R. B. A. |
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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. |
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Título: |
Relationship Between Spatio-Temporal Leaf Area Index and Crop Coefficient When Monitoring Coffee Plots in Brazil. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Journal of Agricultural Science, v. 11, n. 15, p, 187-199, 2019. |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Satellite crop monitoring sentinel-2. |
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Thesaurus NAL: |
Crop coefficient; Leaf area index. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/206996/1/Relationship-between-Spatio-Temporal.pdf
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Marc: |
LEADER 02427naa a2200217 a 4500
001 2116841
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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.
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Embrapa Café (CNPCa) |
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