Orbital and laboratory spectral data to optimize soil analysis
Autor(a) principal: | |
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Data de Publicação: | 2009 |
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=S0103-90162009000200015 |
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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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 |
_version_ |
1748936461439205376 |