Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regression

Detalhes bibliográficos
Autor(a) principal: Oliveira,Luiz Felipe Ramalho de
Data de Publicação: 2020
Outros Autores: Santana,Reynaldo Campos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000601402
Resumo: ABSTRACT: Leaf hyperspectral reflectance has been used to estimate nutrient concentrations in plants in narrow bands of the electromagnetic spectrum. The aim of this study was to estimate leaf nutrient concentrations using leaf hyperspectral reflectance and verify the variable selection methods using the partial least squares regression (PLSR). Two studies were carried out using stands with Eucalyptus clones. Study I was established in Eucalyptus stands with three clones, classifying leaves into five colour patterns using the Munsell chart for plant tissues. Immediately after leaf collection, leaf reflectance was read and the chemical analysis was performed. Study II was carried out in commercial clonal stands of Eucalyptus performing the same leaf sampling and chemical analysis as used in Study I. All leaf reflectance spectra were smoothed and three more pre-processing procedures were applied. In addition, three methods of PLSR were tested. The first derivative was more accurate for predicting nitrogen ( R c v 2 = 0.95), phosphorous ( R c v 2 = 0.93), and sulphur concentration ( R c v 2 = 0.85). The estimates for concentrations of calcium ( R c v 2 = 0.81), magnesium ( R c v 2 = 0.22), and potassium ( R c v 2 = 0.76) were more accurate using the logarithm transformation. Only the estimates for iron concentrations were performed with higher accuracy ( R c v 2 = 0.35) using the smoothed reflectance. The copper concentrations were more accurate ( R c v 2 = 0.78) using the logarithm transformation. Concentrations of boron ( R c v 2 = 0.68) and manganese ( R c v 2 = 0.79) were more accurate using the first derivative, while zinc ( R c v 2 = 0.31) concentration was most accurate using the second derivative.
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spelling Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regressionremote sensingtree monitoringmodellingvariable selectionABSTRACT: Leaf hyperspectral reflectance has been used to estimate nutrient concentrations in plants in narrow bands of the electromagnetic spectrum. The aim of this study was to estimate leaf nutrient concentrations using leaf hyperspectral reflectance and verify the variable selection methods using the partial least squares regression (PLSR). Two studies were carried out using stands with Eucalyptus clones. Study I was established in Eucalyptus stands with three clones, classifying leaves into five colour patterns using the Munsell chart for plant tissues. Immediately after leaf collection, leaf reflectance was read and the chemical analysis was performed. Study II was carried out in commercial clonal stands of Eucalyptus performing the same leaf sampling and chemical analysis as used in Study I. All leaf reflectance spectra were smoothed and three more pre-processing procedures were applied. In addition, three methods of PLSR were tested. The first derivative was more accurate for predicting nitrogen ( R c v 2 = 0.95), phosphorous ( R c v 2 = 0.93), and sulphur concentration ( R c v 2 = 0.85). The estimates for concentrations of calcium ( R c v 2 = 0.81), magnesium ( R c v 2 = 0.22), and potassium ( R c v 2 = 0.76) were more accurate using the logarithm transformation. Only the estimates for iron concentrations were performed with higher accuracy ( R c v 2 = 0.35) using the smoothed reflectance. The copper concentrations were more accurate ( R c v 2 = 0.78) using the logarithm transformation. Concentrations of boron ( R c v 2 = 0.68) and manganese ( R c v 2 = 0.79) were more accurate using the first derivative, while zinc ( R c v 2 = 0.31) concentration was most accurate using the second derivative.Escola Superior de Agricultura "Luiz de Queiroz"2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000601402Scientia Agricola v.77 n.6 2020reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/1678-992x-2018-0409info:eu-repo/semantics/openAccessOliveira,Luiz Felipe Ramalho deSantana,Reynaldo Camposeng2020-01-17T00:00:00Zoai:scielo:S0103-90162020000601402Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2020-01-17T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regression
title Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regression
spellingShingle Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regression
Oliveira,Luiz Felipe Ramalho de
remote sensing
tree monitoring
modelling
variable selection
title_short Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regression
title_full Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regression
title_fullStr Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regression
title_full_unstemmed Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regression
title_sort Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regression
author Oliveira,Luiz Felipe Ramalho de
author_facet Oliveira,Luiz Felipe Ramalho de
Santana,Reynaldo Campos
author_role author
author2 Santana,Reynaldo Campos
author2_role author
dc.contributor.author.fl_str_mv Oliveira,Luiz Felipe Ramalho de
Santana,Reynaldo Campos
dc.subject.por.fl_str_mv remote sensing
tree monitoring
modelling
variable selection
topic remote sensing
tree monitoring
modelling
variable selection
description ABSTRACT: Leaf hyperspectral reflectance has been used to estimate nutrient concentrations in plants in narrow bands of the electromagnetic spectrum. The aim of this study was to estimate leaf nutrient concentrations using leaf hyperspectral reflectance and verify the variable selection methods using the partial least squares regression (PLSR). Two studies were carried out using stands with Eucalyptus clones. Study I was established in Eucalyptus stands with three clones, classifying leaves into five colour patterns using the Munsell chart for plant tissues. Immediately after leaf collection, leaf reflectance was read and the chemical analysis was performed. Study II was carried out in commercial clonal stands of Eucalyptus performing the same leaf sampling and chemical analysis as used in Study I. All leaf reflectance spectra were smoothed and three more pre-processing procedures were applied. In addition, three methods of PLSR were tested. The first derivative was more accurate for predicting nitrogen ( R c v 2 = 0.95), phosphorous ( R c v 2 = 0.93), and sulphur concentration ( R c v 2 = 0.85). The estimates for concentrations of calcium ( R c v 2 = 0.81), magnesium ( R c v 2 = 0.22), and potassium ( R c v 2 = 0.76) were more accurate using the logarithm transformation. Only the estimates for iron concentrations were performed with higher accuracy ( R c v 2 = 0.35) using the smoothed reflectance. The copper concentrations were more accurate ( R c v 2 = 0.78) using the logarithm transformation. Concentrations of boron ( R c v 2 = 0.68) and manganese ( R c v 2 = 0.79) were more accurate using the first derivative, while zinc ( R c v 2 = 0.31) concentration was most accurate using the second derivative.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000601402
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000601402
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1678-992x-2018-0409
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Escola Superior de Agricultura "Luiz de Queiroz"
publisher.none.fl_str_mv Escola Superior de Agricultura "Luiz de Queiroz"
dc.source.none.fl_str_mv Scientia Agricola v.77 n.6 2020
reponame:Scientia Agrícola (Online)
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Scientia Agrícola (Online)
collection Scientia Agrícola (Online)
repository.name.fl_str_mv Scientia Agrícola (Online) - Universidade de São Paulo (USP)
repository.mail.fl_str_mv scientia@usp.br||alleoni@usp.br
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