Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Brasileira de Ciência do Solo (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100311 |
Resumo: | ABSTRACT: Planning sustainable use of land resources requires reliable information about spatial distribution of soil physical and chemical properties related to environmental processes and ecosystemic functions. In this context, cation exchange capacity (CEC) is a fundamental soil quality indicator; however, it takes money and time to obtain this data. Although many studies have been conducted to spatially quantify soil properties on various scales and in different environments, not much is known about interactions between soil properties and environmental covariates in the Brazilian semiarid region. The goal of this study was to evaluate the efficiency of random forest and cokriging models applied to predict CEC in the Brazilian semiarid region. The covariates used to predict CEC consist of images from Landsat 5 TM and a legacy soil map (scale 1:10,000). The sample set comprises 499 samples from the topsoil layer (0.00-0.20 m), where 375 samples were used in training processes and 124 as validation samples. The cokriging model (R2 = 0.57 and RMSE = 7.22 cmolc kg-1) performed better in predicting CEC than the random forest model (R2 = 0.47 and RMSE = 7.89 cmolc kg-1). The approach used showed potential for estimating CEC content in the Brazilian semiarid region by using covariates obtained from orbital remote sensing and the legacy soil map. |
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Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Datadata mininggeostatisticsLandsat 5legacy datasoil surveyABSTRACT: Planning sustainable use of land resources requires reliable information about spatial distribution of soil physical and chemical properties related to environmental processes and ecosystemic functions. In this context, cation exchange capacity (CEC) is a fundamental soil quality indicator; however, it takes money and time to obtain this data. Although many studies have been conducted to spatially quantify soil properties on various scales and in different environments, not much is known about interactions between soil properties and environmental covariates in the Brazilian semiarid region. The goal of this study was to evaluate the efficiency of random forest and cokriging models applied to predict CEC in the Brazilian semiarid region. The covariates used to predict CEC consist of images from Landsat 5 TM and a legacy soil map (scale 1:10,000). The sample set comprises 499 samples from the topsoil layer (0.00-0.20 m), where 375 samples were used in training processes and 124 as validation samples. The cokriging model (R2 = 0.57 and RMSE = 7.22 cmolc kg-1) performed better in predicting CEC than the random forest model (R2 = 0.47 and RMSE = 7.89 cmolc kg-1). The approach used showed potential for estimating CEC content in the Brazilian semiarid region by using covariates obtained from orbital remote sensing and the legacy soil map.Sociedade Brasileira de Ciência do Solo2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100311Revista Brasileira de Ciência do Solo v.42 2018reponame:Revista Brasileira de Ciência do Solo (Online)instname:Sociedade Brasileira de Ciência do Solo (SBCS)instacron:SBCS10.1590/18069657rbcs20170183info:eu-repo/semantics/openAccessChagas,César da SilvaCarvalho Júnior,Waldir dePinheiro,Helena Saraiva KoenowXavier,Pedro Armentano MudadoBhering,Silvio BargePereira,Nilson RendeiroCalderano Filho,Brazeng2018-10-16T00:00:00Zoai:scielo:S0100-06832018000100311Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=0100-0683&lng=es&nrm=isohttps://old.scielo.br/oai/scielo-oai.php||sbcs@ufv.br1806-96570100-0683opendoar:2018-10-16T00:00Revista Brasileira de Ciência do Solo (Online) - Sociedade Brasileira de Ciência do Solo (SBCS)false |
dc.title.none.fl_str_mv |
Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data |
title |
Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data |
spellingShingle |
Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data Chagas,César da Silva data mining geostatistics Landsat 5 legacy data soil survey |
title_short |
Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data |
title_full |
Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data |
title_fullStr |
Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data |
title_full_unstemmed |
Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data |
title_sort |
Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data |
author |
Chagas,César da Silva |
author_facet |
Chagas,César da Silva Carvalho Júnior,Waldir de Pinheiro,Helena Saraiva Koenow Xavier,Pedro Armentano Mudado Bhering,Silvio Barge Pereira,Nilson Rendeiro Calderano Filho,Braz |
author_role |
author |
author2 |
Carvalho Júnior,Waldir de Pinheiro,Helena Saraiva Koenow Xavier,Pedro Armentano Mudado Bhering,Silvio Barge Pereira,Nilson Rendeiro Calderano Filho,Braz |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Chagas,César da Silva Carvalho Júnior,Waldir de Pinheiro,Helena Saraiva Koenow Xavier,Pedro Armentano Mudado Bhering,Silvio Barge Pereira,Nilson Rendeiro Calderano Filho,Braz |
dc.subject.por.fl_str_mv |
data mining geostatistics Landsat 5 legacy data soil survey |
topic |
data mining geostatistics Landsat 5 legacy data soil survey |
description |
ABSTRACT: Planning sustainable use of land resources requires reliable information about spatial distribution of soil physical and chemical properties related to environmental processes and ecosystemic functions. In this context, cation exchange capacity (CEC) is a fundamental soil quality indicator; however, it takes money and time to obtain this data. Although many studies have been conducted to spatially quantify soil properties on various scales and in different environments, not much is known about interactions between soil properties and environmental covariates in the Brazilian semiarid region. The goal of this study was to evaluate the efficiency of random forest and cokriging models applied to predict CEC in the Brazilian semiarid region. The covariates used to predict CEC consist of images from Landsat 5 TM and a legacy soil map (scale 1:10,000). The sample set comprises 499 samples from the topsoil layer (0.00-0.20 m), where 375 samples were used in training processes and 124 as validation samples. The cokriging model (R2 = 0.57 and RMSE = 7.22 cmolc kg-1) performed better in predicting CEC than the random forest model (R2 = 0.47 and RMSE = 7.89 cmolc kg-1). The approach used showed potential for estimating CEC content in the Brazilian semiarid region by using covariates obtained from orbital remote sensing and the legacy soil map. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 |
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=S0100-06832018000100311 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100311 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/18069657rbcs20170183 |
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 |
Sociedade Brasileira de Ciência do Solo |
publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência do Solo |
dc.source.none.fl_str_mv |
Revista Brasileira de Ciência do Solo v.42 2018 reponame:Revista Brasileira de Ciência do Solo (Online) instname:Sociedade Brasileira de Ciência do Solo (SBCS) instacron:SBCS |
instname_str |
Sociedade Brasileira de Ciência do Solo (SBCS) |
instacron_str |
SBCS |
institution |
SBCS |
reponame_str |
Revista Brasileira de Ciência do Solo (Online) |
collection |
Revista Brasileira de Ciência do Solo (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Ciência do Solo (Online) - Sociedade Brasileira de Ciência do Solo (SBCS) |
repository.mail.fl_str_mv |
||sbcs@ufv.br |
_version_ |
1752126521746653184 |