| |
|
|
| Registros recuperados : 243 | |
| 126. |  | DANTAS, M. E.; SHINZATO, E.; ADAMY, A.; TEIXEIRA, W. G.; LUMBRERAS, J. F. Análise integrada das paisagens do estado de Rondônia. In: LUMBRERAS, J. F.; SILVA, L. M. da; ANJOS, L. H. C. dos; OLIVEIRA, V. A. de; WADT, P. G. S.; PEREIRA, M. G.; DELARMELINDA-HONORÉ, E. A.; BURITY, K. T. L. (Ed.). Guia de campo da XII Reunião Brasileira de Classificação e Correlação de Solos: RCC de Rondônia. Brasília, DF: Embrapa, 2019. E-book. cap. 4.| Biblioteca(s): Embrapa Solos. |
|    |
| 128. | | EIRA, M.; FAZUOLI, L. C.; GUERREIRO FILHO, O.; SILVAROLA, M.; DANTAS, M.; REIS, R. Aumento da variabilidade genética de café no Brasil. In: SANTOS, M. G. B. dos. (Org). Artigos técnicos divulgados na mídia: coletânea 2007. Brasília, DF: Embrapa Recursos Genéticos e Biotecnologia, 2007. (Embrapa Recursos Genéticos e Biotecnologia. Documentos, 244). Publicado em 16/03/2007. Disponível em: O Cafezal, ocafezal160307.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
| |
| Registros recuperados : 243 | |
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Café. |
|
Data corrente: |
29/06/2020 |
|
Data da última atualização: |
03/07/2020 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
Autoria: |
MACIEL, D. A.; SILVA, V. A.; ALVES, H. M. R.; VOLPATO, M. M. L.; BARBOSA, J. P. R. A. de; SOUZA, V. C. O.; SANTOS, M. O.; SILVEIRA, H. R. DE O.; DANTAS, M. F.; FREITAS, A. F. de; SANTOS, J. O. DOS. |
|
Afiliação: |
DANIEL ANDRADE MACIEL, INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS; VÂNIA APARECIDA SILVA, EPAMIG; HELENA MARIA RAMOS ALVES, CNPCa; MARGARETE MARIN LORDELO VOLPATO, EPAMIG; JOÃO PAULO RODRIGUES ALVES DE BARBOSA, UNIVERSIDADE FEDERAL DE LAVRAS; VANESSA CRISTINA OLIVEIRA SOUZA, UNIVERSIDADE FEDERAL DE ITAJUBÁ; MELINE OLIVEIRA SANTOS, EPAMIG; HELBERT REZENDE DE OLIVEIRA SILVEIRA, EPAMIG; MAYARA FONTES DANTAS, EPAMIG; ANA FLÁVIA DE FREITAS, EPAMIG; JACQUELINE OLIVEIRA DOS SANTOS, EPAMIG. |
|
Título: |
Leaf water potential of coffee estimated by landsat-8 images. |
|
Ano de publicação: |
2020 |
|
Fonte/Imprenta: |
Plos One, v. 15, n. 3, e031019, Mar. 2020. |
|
DOI: |
https://doi.org/10.1371/journal.pone.0230013 |
|
Idioma: |
Português |
|
Conteúdo: |
Traditionally, water conditions of coffee areas are monitored by measuring the leaf water potential throughout a pressure pump. However, there is a demand for the development of technologies that can estimate large areas or regions. In this context, the objective of this study was to estimate the WW by surface reflectance values and vegetation indices obtained from the Landsat-8/OLI sensor in Minas Gerais-Brazil Several algorithms using OLI bands and vegetation indexes were evaluated and from the correlation analysis, a quadratic algorithm that uses the Normalized Difference Vegetation Index (NDVI) performed better, with a correlation coefficient (R2) of 0.82. Leave-One-Out Cross-Validation (LOOCV) was performed to validate the models and the best results were for NDVI quadratic algorithm, presenting a Mean Absolute Percentage Error (MAPE) of 27.09% and an R2 of 0.85. Subsequently, the NDVI quadratic algorithm was applied to Landsat-8 images, aiming to spatialize the WW estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate WW from Landsat-8/OLI imagery, contributing to drought monitoring in the coffee area leading to cost reduction to the producers. |
|
Thesagro: |
Área Foliar; Café; Potencial Hídrico; Produção Agrícola. |
|
Thesaurus NAL: |
Coffee beans; Leaf water potential; Plantations; Remote sensing. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/214268/1/Leaf-water-petential.pdf
|
|
Marc: |
LEADER 02267naa a2200349 a 4500
001 2123511
005 2020-07-03
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1371/journal.pone.0230013$2DOI
100 1 $aMACIEL, D. A.
245 $aLeaf water potential of coffee estimated by landsat-8 images.$h[electronic resource]
260 $c2020
520 $aTraditionally, water conditions of coffee areas are monitored by measuring the leaf water potential throughout a pressure pump. However, there is a demand for the development of technologies that can estimate large areas or regions. In this context, the objective of this study was to estimate the WW by surface reflectance values and vegetation indices obtained from the Landsat-8/OLI sensor in Minas Gerais-Brazil Several algorithms using OLI bands and vegetation indexes were evaluated and from the correlation analysis, a quadratic algorithm that uses the Normalized Difference Vegetation Index (NDVI) performed better, with a correlation coefficient (R2) of 0.82. Leave-One-Out Cross-Validation (LOOCV) was performed to validate the models and the best results were for NDVI quadratic algorithm, presenting a Mean Absolute Percentage Error (MAPE) of 27.09% and an R2 of 0.85. Subsequently, the NDVI quadratic algorithm was applied to Landsat-8 images, aiming to spatialize the WW estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate WW from Landsat-8/OLI imagery, contributing to drought monitoring in the coffee area leading to cost reduction to the producers.
650 $aCoffee beans
650 $aLeaf water potential
650 $aPlantations
650 $aRemote sensing
650 $aÁrea Foliar
650 $aCafé
650 $aPotencial Hídrico
650 $aProdução Agrícola
700 1 $aSILVA, V. A.
700 1 $aALVES, H. M. R.
700 1 $aVOLPATO, M. M. L.
700 1 $aBARBOSA, J. P. R. A. de
700 1 $aSOUZA, V. C. O.
700 1 $aSANTOS, M. O.
700 1 $aSILVEIRA, H. R. DE O.
700 1 $aDANTAS, M. F.
700 1 $aFREITAS, A. F. de
700 1 $aSANTOS, J. O. DOS
773 $tPlos One$gv. 15, n. 3, e031019, Mar. 2020.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Café (CNPCa) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
