Nonlinear models for soil moisture sensor calibration in tropical mountainous soils
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
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 |
_version_ |
1748936466079154176 |