Nonlinear models for soil moisture sensor calibration in tropical mountainous soils

Detalhes bibliográficos
Autor(a) principal: Silva,Bárbara Pereira Christofaro
Data de Publicação: 2022
Outros Autores: Tassinari,Diego, Silva,Marx Leandro Naves, Silva,Bruno Montoani, Curi,Nilton, Rocha,Humberto Ribeiro da
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000401402
Resumo: ABSTRACT Electromagnetic sensors are widely used to monitor soil water content (θ); however, site-specific calibrations are necessary for accurate measurements. This study compares regression models used for calibration of soil moisture sensors and investigates the relation between soil attributes and the adjusted parameters of the specific calibration equations. Undisturbed soil samples were collected in the A and B horizons of two Ultisols and two Inceptisols from the Mantiqueira Range in Southeastern Brazil. After saturation, the Theta Probe ML2X was used to obtain the soil dielectric constant (ε). Several readings were made, ranging from saturation to oven-dry. After each reading, the samples were weighted to calculate θ (m3 m–3). Fourteen regression models (linear, linearized, and nonlinear) were adjusted to the calibration data and checked for their residue distribution. Only the exponential model with three parameters met the regression assumptions regarding residue distribution. The stepwise regression was used to obtain multiple linear equations to estimate the adjusted parameters of the calibration model from soil attributes, with silt and clay contents providing the best relations. Both the specific and the general calibrations performed well, with RMSE values of 0.02 and 0.03 m3 m–3, respectively. Manufacturer calibration and equations from the literature were much less accurate, reinforcing the need to develop specific calibrations.
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spelling Nonlinear models for soil moisture sensor calibration in tropical mountainous soilssoil dielectric constantsoil water contentmodel selectiondielectric-based sensorABSTRACT Electromagnetic sensors are widely used to monitor soil water content (θ); however, site-specific calibrations are necessary for accurate measurements. This study compares regression models used for calibration of soil moisture sensors and investigates the relation between soil attributes and the adjusted parameters of the specific calibration equations. Undisturbed soil samples were collected in the A and B horizons of two Ultisols and two Inceptisols from the Mantiqueira Range in Southeastern Brazil. After saturation, the Theta Probe ML2X was used to obtain the soil dielectric constant (ε). Several readings were made, ranging from saturation to oven-dry. After each reading, the samples were weighted to calculate θ (m3 m–3). Fourteen regression models (linear, linearized, and nonlinear) were adjusted to the calibration data and checked for their residue distribution. Only the exponential model with three parameters met the regression assumptions regarding residue distribution. The stepwise regression was used to obtain multiple linear equations to estimate the adjusted parameters of the calibration model from soil attributes, with silt and clay contents providing the best relations. Both the specific and the general calibrations performed well, with RMSE values of 0.02 and 0.03 m3 m–3, respectively. Manufacturer calibration and equations from the literature were much less accurate, reinforcing the need to develop specific calibrations.Escola Superior de Agricultura "Luiz de Queiroz"2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000401402Scientia Agricola v.79 n.4 2022reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/1678-992x-2020-0253info:eu-repo/semantics/openAccessSilva,Bárbara Pereira ChristofaroTassinari,DiegoSilva,Marx Leandro NavesSilva,Bruno MontoaniCuri,NiltonRocha,Humberto Ribeiro daeng2021-07-20T00:00:00Zoai:scielo:S0103-90162022000401402Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2021-07-20T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Nonlinear models for soil moisture sensor calibration in tropical mountainous soils
title Nonlinear models for soil moisture sensor calibration in tropical mountainous soils
spellingShingle Nonlinear models for soil moisture sensor calibration in tropical mountainous soils
Silva,Bárbara Pereira Christofaro
soil dielectric constant
soil water content
model selection
dielectric-based sensor
title_short Nonlinear models for soil moisture sensor calibration in tropical mountainous soils
title_full Nonlinear models for soil moisture sensor calibration in tropical mountainous soils
title_fullStr Nonlinear models for soil moisture sensor calibration in tropical mountainous soils
title_full_unstemmed Nonlinear models for soil moisture sensor calibration in tropical mountainous soils
title_sort Nonlinear models for soil moisture sensor calibration in tropical mountainous soils
author Silva,Bárbara Pereira Christofaro
author_facet Silva,Bárbara Pereira Christofaro
Tassinari,Diego
Silva,Marx Leandro Naves
Silva,Bruno Montoani
Curi,Nilton
Rocha,Humberto Ribeiro da
author_role author
author2 Tassinari,Diego
Silva,Marx Leandro Naves
Silva,Bruno Montoani
Curi,Nilton
Rocha,Humberto Ribeiro da
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Silva,Bárbara Pereira Christofaro
Tassinari,Diego
Silva,Marx Leandro Naves
Silva,Bruno Montoani
Curi,Nilton
Rocha,Humberto Ribeiro da
dc.subject.por.fl_str_mv soil dielectric constant
soil water content
model selection
dielectric-based sensor
topic soil dielectric constant
soil water content
model selection
dielectric-based sensor
description ABSTRACT Electromagnetic sensors are widely used to monitor soil water content (θ); however, site-specific calibrations are necessary for accurate measurements. This study compares regression models used for calibration of soil moisture sensors and investigates the relation between soil attributes and the adjusted parameters of the specific calibration equations. Undisturbed soil samples were collected in the A and B horizons of two Ultisols and two Inceptisols from the Mantiqueira Range in Southeastern Brazil. After saturation, the Theta Probe ML2X was used to obtain the soil dielectric constant (ε). Several readings were made, ranging from saturation to oven-dry. After each reading, the samples were weighted to calculate θ (m3 m–3). Fourteen regression models (linear, linearized, and nonlinear) were adjusted to the calibration data and checked for their residue distribution. Only the exponential model with three parameters met the regression assumptions regarding residue distribution. The stepwise regression was used to obtain multiple linear equations to estimate the adjusted parameters of the calibration model from soil attributes, with silt and clay contents providing the best relations. Both the specific and the general calibrations performed well, with RMSE values of 0.02 and 0.03 m3 m–3, respectively. Manufacturer calibration and equations from the literature were much less accurate, reinforcing the need to develop specific calibrations.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
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=S0103-90162022000401402
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000401402
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1678-992x-2020-0253
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.79 n.4 2022
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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