Orbital and laboratory spectral data to optimize soil analysis

Detalhes bibliográficos
Autor(a) principal: Fiorio,Peterson Ricardo
Data de Publicação: 2009
Outros Autores: Demattê,José Alexandre M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162009000200015
Resumo: Traditional soil analyses are time-consuming with high cost and environmental risks, thus the use of new technologies such as remote sensing have to be estimulated. The purpose of this work was to quantify soil attributes by laboratory and orbital sensors as a non-destructive and a non-pollutant method. The study area was in the region of Barra Bonita, state of São Paulo, Brazil, in a 473 ha bare soil area. A sampling grid was established (100 × 100 m), with a total of 474 locations and a total of 948 soil samples. Each location was georeferenced and soil samples were collected for analysis. Reflectance data for each soil sample was measured with a laboratory sensor (450 to 2,500 nm). For the same locations, reflectance data was obtained from a TM-Landsat-5 image. Multiple linear regression equations were developed for 50% of the samples. Two models were developed: one for spectroradiometric laboratory data and the second for TM-Landsat-5 orbital data. The remaining 50% of the samples were used to validate the models. The test compared the attribute content quantified by the spectral models and that determined in the laboratory (conventional methods). The highest coefficients of determination for the laboratory data were for clay content (R² = 0.86) and sand (R² = 0.82) and for the orbital data (R² = 0.61 and 0.63, respectively). By using the present methodology, it was possible to estimate CEC (R² = 0.64) by the laboratory sensor. Laboratory and orbital sensors can optimize time, costs and environment pollutants when associated with traditional soil analysis.
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spelling Orbital and laboratory spectral data to optimize soil analysisremote sensingsoil attributessoil reflectanceTraditional soil analyses are time-consuming with high cost and environmental risks, thus the use of new technologies such as remote sensing have to be estimulated. The purpose of this work was to quantify soil attributes by laboratory and orbital sensors as a non-destructive and a non-pollutant method. The study area was in the region of Barra Bonita, state of São Paulo, Brazil, in a 473 ha bare soil area. A sampling grid was established (100 × 100 m), with a total of 474 locations and a total of 948 soil samples. Each location was georeferenced and soil samples were collected for analysis. Reflectance data for each soil sample was measured with a laboratory sensor (450 to 2,500 nm). For the same locations, reflectance data was obtained from a TM-Landsat-5 image. Multiple linear regression equations were developed for 50% of the samples. Two models were developed: one for spectroradiometric laboratory data and the second for TM-Landsat-5 orbital data. The remaining 50% of the samples were used to validate the models. The test compared the attribute content quantified by the spectral models and that determined in the laboratory (conventional methods). The highest coefficients of determination for the laboratory data were for clay content (R² = 0.86) and sand (R² = 0.82) and for the orbital data (R² = 0.61 and 0.63, respectively). By using the present methodology, it was possible to estimate CEC (R² = 0.64) by the laboratory sensor. Laboratory and orbital sensors can optimize time, costs and environment pollutants when associated with traditional soil analysis.Escola Superior de Agricultura "Luiz de Queiroz"2009-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162009000200015Scientia Agricola v.66 n.2 2009reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/S0103-90162009000200015info:eu-repo/semantics/openAccessFiorio,Peterson RicardoDemattê,José Alexandre M.eng2009-03-31T00:00:00Zoai:scielo:S0103-90162009000200015Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2009-03-31T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Orbital and laboratory spectral data to optimize soil analysis
title Orbital and laboratory spectral data to optimize soil analysis
spellingShingle Orbital and laboratory spectral data to optimize soil analysis
Fiorio,Peterson Ricardo
remote sensing
soil attributes
soil reflectance
title_short Orbital and laboratory spectral data to optimize soil analysis
title_full Orbital and laboratory spectral data to optimize soil analysis
title_fullStr Orbital and laboratory spectral data to optimize soil analysis
title_full_unstemmed Orbital and laboratory spectral data to optimize soil analysis
title_sort Orbital and laboratory spectral data to optimize soil analysis
author Fiorio,Peterson Ricardo
author_facet Fiorio,Peterson Ricardo
Demattê,José Alexandre M.
author_role author
author2 Demattê,José Alexandre M.
author2_role author
dc.contributor.author.fl_str_mv Fiorio,Peterson Ricardo
Demattê,José Alexandre M.
dc.subject.por.fl_str_mv remote sensing
soil attributes
soil reflectance
topic remote sensing
soil attributes
soil reflectance
description Traditional soil analyses are time-consuming with high cost and environmental risks, thus the use of new technologies such as remote sensing have to be estimulated. The purpose of this work was to quantify soil attributes by laboratory and orbital sensors as a non-destructive and a non-pollutant method. The study area was in the region of Barra Bonita, state of São Paulo, Brazil, in a 473 ha bare soil area. A sampling grid was established (100 × 100 m), with a total of 474 locations and a total of 948 soil samples. Each location was georeferenced and soil samples were collected for analysis. Reflectance data for each soil sample was measured with a laboratory sensor (450 to 2,500 nm). For the same locations, reflectance data was obtained from a TM-Landsat-5 image. Multiple linear regression equations were developed for 50% of the samples. Two models were developed: one for spectroradiometric laboratory data and the second for TM-Landsat-5 orbital data. The remaining 50% of the samples were used to validate the models. The test compared the attribute content quantified by the spectral models and that determined in the laboratory (conventional methods). The highest coefficients of determination for the laboratory data were for clay content (R² = 0.86) and sand (R² = 0.82) and for the orbital data (R² = 0.61 and 0.63, respectively). By using the present methodology, it was possible to estimate CEC (R² = 0.64) by the laboratory sensor. Laboratory and orbital sensors can optimize time, costs and environment pollutants when associated with traditional soil analysis.
publishDate 2009
dc.date.none.fl_str_mv 2009-04-01
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=S0103-90162009000200015
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162009000200015
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0103-90162009000200015
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.66 n.2 2009
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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