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| 77. |  | BUSO, G. C. S.; BRONDANI, R. V.; AMARAL, Z.; REIS, A. M. M.; FERREIRA, M. E. A battery of SSR markers for genetic analysis of peppers (Capsicum spp.) In: SIMPOSIO DE RECURSOS GENETICOS PARA AMERICA LATINA E CARIBE, 2., 1999, Brasilia, DF. Anais... Brasilia: Embrapa Recursos Geneticos e Biotecnologia, 1999. CD-ROM.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 79. | | MANTOVANI, J. R.; CRUZ, M. C. P. da; FERREIRA, M. E.; BARBOSA, J. C. Comparação de procedimentos de quantificação de nitrato em tecido vegetal. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 40, n. 1, p. 53-59, jan. 2005 Título em inglês: Comparison of procedures for nitrate determination in vegetable tissue.| Biblioteca(s): Embrapa Unidades Centrais. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
17/12/2019 |
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Data da última atualização: |
17/12/2019 |
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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: |
OLIVEIRA, B. S. de; FERREIRA, M. E.; COUTINHO, A. C.; ESQUERDO, J. C. D. M. |
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Afiliação: |
BERNARD S. DE OLIVEIRA, Mauro Borges Statistic and Socioeconomic Institute (IMB); MANUEL E. FERREIRA, UFG; ALEXANDRE CAMARGO COUTINHO, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA. |
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Título: |
Phenological-metric algorithm for mapping soybean in savanna biome in Brazil. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Australian Journal of Crop Science, v. 13, n. 09, p. 1456-1466, Sept. 2019. |
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DOI: |
10.21475/ajcs.19.13.09.p1541 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract. Agricultural expansion in Brazil is still intense for commodities (such soybeans and corn), mostly cultivated over large portions of the Cerrado biome. Therefore, the development and application of techniques based on remote sensing to map crop areas at a regional level, in a dynamic and more precise way is urgently necessary. In this context, the objective of this study is the improvement of techniques for mapping soybean crops in Brazil, through an analysis of the Centro Goiano mesoregion of Goiás state (a core area of Cerrado), using a time series of Enhanced Vegetation Index (EVI) images provided by TERRA/MODIS orbital sensor, in a test period between 2002 and 2010. Despite their proven quality, MODIS EVI images already contain atmospheric interferences inherent to the acquisition process, such as the presence of clouds. Thus, a set of methods to minimize such artifacts was applied to the data of this study. In general, the methodological procedures comprise of (1) the application of the pixel reliability band aiming to remove pixels contaminated by clouds; (2) the use of contaminated pixel estimates (excluded from the time series); (3) application of an interpolation filter to fill the void pixels in each scene, obtaining continuous and smoothed spectral-temporal profiles for each land use classes; and (4) the classification of agricultural areas using a specific algorithm for crops in the Cerrado region of Goiás. The areas reconstituted in the images matched neighboring pixels, maintaining good coherence with the original data. Likewise, areas mapped with soybeans had a high correlation with official IBGE census data, with a global accuracy value of 78%, and Pearson Correlation coefficient of 0.64. The application of this technique to other imagery sensors (such as RapidEye, Landsat 8 and Sentinel 2) is highly encouraged due a better spatial and temporal resolution (when applied together in a temporal image cube), ensuring more efficient crop monitoring in Brazil. MenosAbstract. Agricultural expansion in Brazil is still intense for commodities (such soybeans and corn), mostly cultivated over large portions of the Cerrado biome. Therefore, the development and application of techniques based on remote sensing to map crop areas at a regional level, in a dynamic and more precise way is urgently necessary. In this context, the objective of this study is the improvement of techniques for mapping soybean crops in Brazil, through an analysis of the Centro Goiano mesoregion of Goiás state (a core area of Cerrado), using a time series of Enhanced Vegetation Index (EVI) images provided by TERRA/MODIS orbital sensor, in a test period between 2002 and 2010. Despite their proven quality, MODIS EVI images already contain atmospheric interferences inherent to the acquisition process, such as the presence of clouds. Thus, a set of methods to minimize such artifacts was applied to the data of this study. In general, the methodological procedures comprise of (1) the application of the pixel reliability band aiming to remove pixels contaminated by clouds; (2) the use of contaminated pixel estimates (excluded from the time series); (3) application of an interpolation filter to fill the void pixels in each scene, obtaining continuous and smoothed spectral-temporal profiles for each land use classes; and (4) the classification of agricultural areas using a specific algorithm for crops in the Cerrado region of Goiás. The areas reconstituted in the images matched ... Mostrar Tudo |
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Palavras-Chave: |
Enhanced Vegetation Index; Índice de vegetação EVI; Séries temporais. |
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Thesagro: |
Cerrado; Sensoriamento Remoto; Soja. |
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Thesaurus NAL: |
Agriculture; Remote sensing; Soybeans; Time series analysis; Vegetation index. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02927naa a2200301 a 4500
001 2117065
005 2019-12-17
008 2019 bl uuuu u00u1 u #d
024 7 $a10.21475/ajcs.19.13.09.p1541$2DOI
100 1 $aOLIVEIRA, B. S. de
245 $aPhenological-metric algorithm for mapping soybean in savanna biome in Brazil.$h[electronic resource]
260 $c2019
520 $aAbstract. Agricultural expansion in Brazil is still intense for commodities (such soybeans and corn), mostly cultivated over large portions of the Cerrado biome. Therefore, the development and application of techniques based on remote sensing to map crop areas at a regional level, in a dynamic and more precise way is urgently necessary. In this context, the objective of this study is the improvement of techniques for mapping soybean crops in Brazil, through an analysis of the Centro Goiano mesoregion of Goiás state (a core area of Cerrado), using a time series of Enhanced Vegetation Index (EVI) images provided by TERRA/MODIS orbital sensor, in a test period between 2002 and 2010. Despite their proven quality, MODIS EVI images already contain atmospheric interferences inherent to the acquisition process, such as the presence of clouds. Thus, a set of methods to minimize such artifacts was applied to the data of this study. In general, the methodological procedures comprise of (1) the application of the pixel reliability band aiming to remove pixels contaminated by clouds; (2) the use of contaminated pixel estimates (excluded from the time series); (3) application of an interpolation filter to fill the void pixels in each scene, obtaining continuous and smoothed spectral-temporal profiles for each land use classes; and (4) the classification of agricultural areas using a specific algorithm for crops in the Cerrado region of Goiás. The areas reconstituted in the images matched neighboring pixels, maintaining good coherence with the original data. Likewise, areas mapped with soybeans had a high correlation with official IBGE census data, with a global accuracy value of 78%, and Pearson Correlation coefficient of 0.64. The application of this technique to other imagery sensors (such as RapidEye, Landsat 8 and Sentinel 2) is highly encouraged due a better spatial and temporal resolution (when applied together in a temporal image cube), ensuring more efficient crop monitoring in Brazil.
650 $aAgriculture
650 $aRemote sensing
650 $aSoybeans
650 $aTime series analysis
650 $aVegetation index
650 $aCerrado
650 $aSensoriamento Remoto
650 $aSoja
653 $aEnhanced Vegetation Index
653 $aÍndice de vegetação EVI
653 $aSéries temporais
700 1 $aFERREIRA, M. E.
700 1 $aCOUTINHO, A. C.
700 1 $aESQUERDO, J. C. D. M.
773 $tAustralian Journal of Crop Science$gv. 13, n. 09, p. 1456-1466, Sept. 2019.
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