Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123511
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dc.contributor.authorMACIEL, D. A.
dc.contributor.authorSILVA, V. A.
dc.contributor.authorALVES, H. M. R.
dc.contributor.authorVOLPATO, M. M. L.
dc.contributor.authorBARBOSA, J. P. R. A. de
dc.contributor.authorSOUZA, V. C. O.
dc.contributor.authorSANTOS, M. O.
dc.contributor.authorSILVEIRA, H. R. DE O.
dc.contributor.authorDANTAS, M. F.
dc.contributor.authorFREITAS, A. F. de
dc.contributor.authorSANTOS, J. O. DOS
dc.date.accessioned2020-06-30T11:11:04Z-
dc.date.available2020-06-30T11:11:04Z-
dc.date.created2020-06-29
dc.date.issued2020
dc.identifier.citationPlos One, v. 15, n. 3, e031019, Mar. 2020.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1123511-
dc.descriptionTraditionally, 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.
dc.language.isopor
dc.rightsopenAccesspt_BR
dc.titleLeaf water potential of coffee estimated by landsat-8 images.
dc.typeArtigo de periódico
dc.subject.thesagroPotencial Hídrico
dc.subject.thesagroÁrea Foliar
dc.subject.thesagroProdução Agrícola
dc.subject.thesagroCafé
dc.subject.nalthesaurusLeaf water potential
dc.subject.nalthesaurusPlantations
dc.subject.nalthesaurusCoffee beans
dc.subject.nalthesaurusRemote sensing
riaa.ainfo.id1123511
riaa.ainfo.lastupdate2020-07-03 -03:00:00
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0230013
dc.contributor.institutionDANIEL 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.
Aparece nas coleções:Artigo em periódico indexado (SAPC)

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