Leaf water potential of coffee estimated by landsat-8 images.

Detalhes bibliográficos
Autor(a) principal: MACIEL, D. A.
Data de Publicação: 2020
Outros Autores: 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
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123511
https://doi.org/10.1371/journal.pone.0230013
Resumo: 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.
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spelling Leaf water potential of coffee estimated by landsat-8 images.Potencial HídricoÁrea FoliarProdução AgrícolaCaféLeaf water potentialPlantationsCoffee beansRemote sensingTraditionally, 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.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.MACIEL, D. A.SILVA, V. A.ALVES, H. M. R.VOLPATO, M. M. L.BARBOSA, J. P. R. A. deSOUZA, V. C. O.SANTOS, M. O.SILVEIRA, H. R. DE O.DANTAS, M. F.FREITAS, A. F. deSANTOS, J. O. DOS2020-06-30T11:11:04Z2020-06-30T11:11:04Z2020-06-292020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlePlos One, v. 15, n. 3, e031019, Mar. 2020.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123511https://doi.org/10.1371/journal.pone.0230013porinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2020-06-30T11:11:11Zoai:www.alice.cnptia.embrapa.br:doc/1123511Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542020-06-30T11:11:11falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542020-06-30T11:11:11Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Leaf water potential of coffee estimated by landsat-8 images.
title Leaf water potential of coffee estimated by landsat-8 images.
spellingShingle Leaf water potential of coffee estimated by landsat-8 images.
MACIEL, D. A.
Potencial Hídrico
Área Foliar
Produção Agrícola
Café
Leaf water potential
Plantations
Coffee beans
Remote sensing
title_short Leaf water potential of coffee estimated by landsat-8 images.
title_full Leaf water potential of coffee estimated by landsat-8 images.
title_fullStr Leaf water potential of coffee estimated by landsat-8 images.
title_full_unstemmed Leaf water potential of coffee estimated by landsat-8 images.
title_sort Leaf water potential of coffee estimated by landsat-8 images.
author MACIEL, D. A.
author_facet 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
author_role author
author2 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
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv 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.
dc.contributor.author.fl_str_mv 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
dc.subject.por.fl_str_mv Potencial Hídrico
Área Foliar
Produção Agrícola
Café
Leaf water potential
Plantations
Coffee beans
Remote sensing
topic Potencial Hídrico
Área Foliar
Produção Agrícola
Café
Leaf water potential
Plantations
Coffee beans
Remote sensing
description 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.
publishDate 2020
dc.date.none.fl_str_mv 2020-06-30T11:11:04Z
2020-06-30T11:11:04Z
2020-06-29
2020
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Plos One, v. 15, n. 3, e031019, Mar. 2020.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123511
https://doi.org/10.1371/journal.pone.0230013
identifier_str_mv Plos One, v. 15, n. 3, e031019, Mar. 2020.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123511
https://doi.org/10.1371/journal.pone.0230013
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
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institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
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repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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