Leaf water potential of coffee estimated by landsat-8 images

Detalhes bibliográficos
Autor(a) principal: Maciel, Daniel Andrade
Data de Publicação: 2020
Outros Autores: Silva, Vânia Aparecida, Alves, Helena Maria Ramos, Volpato, Margarete Marin Lordelo, Barbosa, João Paulo Rodrigues Alves de, Souza, Vanessa Cristina Oliveira de, Santos, Meline Oliveira, Silveira, Helbert Rezende de Oliveira, Dantas, Mayara Fontes, Freitas, Ana Flávia de, Carvalho, Gladyston Rodrigues, Santos, Jacqueline Oliveira dos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
DOI: 10.1371/journal.pone.0230013.
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/42730
Resumo: Traditionally, water conditions of coffee areas are monitored by measuring the leaf water potential (ΨW) 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 ΨW 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 ΨW estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate ΨW 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 imagesLeaf water potentialVegetation indicesCoffee plantingDrought monitoringPotencial hídrico foliarÍndices de vegetaçãoCafeiculturaSeca - MonitoramentoTraditionally, water conditions of coffee areas are monitored by measuring the leaf water potential (ΨW) 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 ΨW 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 ΨW estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate ΨW from Landsat-8/OLI imagery, contributing to drought monitoring in the coffee area leading to cost reduction to the producers.PLOS2020-08-31T17:41:53Z2020-08-31T17:41:53Z2020-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMACIEL, D. A. et al. Leaf water potential of coffee estimated by landsat-8 images. PLoS ONE, [S. I.], v. 15, n. 3, e0230013. DOI: https://doi.org/10.1371/journal.pone.0230013.http://repositorio.ufla.br/jspui/handle/1/42730Plos Onereponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessMaciel, Daniel AndradeSilva, Vânia AparecidaAlves, Helena Maria RamosVolpato, Margarete Marin LordeloBarbosa, João Paulo Rodrigues Alves deSouza, Vanessa Cristina Oliveira deSantos, Meline OliveiraSilveira, Helbert Rezende de OliveiraDantas, Mayara FontesFreitas, Ana Flávia deCarvalho, Gladyston RodriguesSantos, Jacqueline Oliveira doseng2020-08-31T17:42:07Zoai:localhost:1/42730Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2020-08-31T17:42:07Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)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
Leaf water potential of coffee estimated by landsat-8 images
Maciel, Daniel Andrade
Leaf water potential
Vegetation indices
Coffee planting
Drought monitoring
Potencial hídrico foliar
Índices de vegetação
Cafeicultura
Seca - Monitoramento
Maciel, Daniel Andrade
Leaf water potential
Vegetation indices
Coffee planting
Drought monitoring
Potencial hídrico foliar
Índices de vegetação
Cafeicultura
Seca - Monitoramento
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
Leaf water potential of coffee estimated by landsat-8 images
title_full_unstemmed Leaf water potential of coffee estimated by landsat-8 images
Leaf water potential of coffee estimated by landsat-8 images
title_sort Leaf water potential of coffee estimated by landsat-8 images
author Maciel, Daniel Andrade
author_facet Maciel, Daniel Andrade
Maciel, Daniel Andrade
Silva, Vânia Aparecida
Alves, Helena Maria Ramos
Volpato, Margarete Marin Lordelo
Barbosa, João Paulo Rodrigues Alves de
Souza, Vanessa Cristina Oliveira de
Santos, Meline Oliveira
Silveira, Helbert Rezende de Oliveira
Dantas, Mayara Fontes
Freitas, Ana Flávia de
Carvalho, Gladyston Rodrigues
Santos, Jacqueline Oliveira dos
Silva, Vânia Aparecida
Alves, Helena Maria Ramos
Volpato, Margarete Marin Lordelo
Barbosa, João Paulo Rodrigues Alves de
Souza, Vanessa Cristina Oliveira de
Santos, Meline Oliveira
Silveira, Helbert Rezende de Oliveira
Dantas, Mayara Fontes
Freitas, Ana Flávia de
Carvalho, Gladyston Rodrigues
Santos, Jacqueline Oliveira dos
author_role author
author2 Silva, Vânia Aparecida
Alves, Helena Maria Ramos
Volpato, Margarete Marin Lordelo
Barbosa, João Paulo Rodrigues Alves de
Souza, Vanessa Cristina Oliveira de
Santos, Meline Oliveira
Silveira, Helbert Rezende de Oliveira
Dantas, Mayara Fontes
Freitas, Ana Flávia de
Carvalho, Gladyston Rodrigues
Santos, Jacqueline Oliveira dos
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Maciel, Daniel Andrade
Silva, Vânia Aparecida
Alves, Helena Maria Ramos
Volpato, Margarete Marin Lordelo
Barbosa, João Paulo Rodrigues Alves de
Souza, Vanessa Cristina Oliveira de
Santos, Meline Oliveira
Silveira, Helbert Rezende de Oliveira
Dantas, Mayara Fontes
Freitas, Ana Flávia de
Carvalho, Gladyston Rodrigues
Santos, Jacqueline Oliveira dos
dc.subject.por.fl_str_mv Leaf water potential
Vegetation indices
Coffee planting
Drought monitoring
Potencial hídrico foliar
Índices de vegetação
Cafeicultura
Seca - Monitoramento
topic Leaf water potential
Vegetation indices
Coffee planting
Drought monitoring
Potencial hídrico foliar
Índices de vegetação
Cafeicultura
Seca - Monitoramento
description Traditionally, water conditions of coffee areas are monitored by measuring the leaf water potential (ΨW) 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 ΨW 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 ΨW estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate ΨW 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 2020-08-31T17:41:53Z
2020-08-31T17:41:53Z
2020-03
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv MACIEL, D. A. et al. Leaf water potential of coffee estimated by landsat-8 images. PLoS ONE, [S. I.], v. 15, n. 3, e0230013. DOI: https://doi.org/10.1371/journal.pone.0230013.
http://repositorio.ufla.br/jspui/handle/1/42730
identifier_str_mv MACIEL, D. A. et al. Leaf water potential of coffee estimated by landsat-8 images. PLoS ONE, [S. I.], v. 15, n. 3, e0230013. DOI: https://doi.org/10.1371/journal.pone.0230013.
url http://repositorio.ufla.br/jspui/handle/1/42730
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv PLOS
publisher.none.fl_str_mv PLOS
dc.source.none.fl_str_mv Plos One
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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dc.identifier.doi.none.fl_str_mv 10.1371/journal.pone.0230013.