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: | 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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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 |
_version_ |
1822220684454002688 |
dc.identifier.doi.none.fl_str_mv |
10.1371/journal.pone.0230013. |