Leaf water potential of coffee estimated by landsat-8 images.
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , , , , |
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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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 |
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Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
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EMBRAPA |
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EMBRAPA |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
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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