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: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1097748 |
Resumo: | 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 cmol c kg-1) performed better in predicting CEC than the random forest model (R2= 0.47 and RMSE = 7.89 cmol c 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 data.Mineração de dadosGeoestatísticaLandsat 5SoloLevantamentoReconhecimento do SoloSoil surveysPlanning 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 cmol c kg-1) performed better in predicting CEC than the random forest model (R2= 0.47 and RMSE = 7.89 cmol c 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.CESAR DA SILVA CHAGAS, CNPS; WALDIR DE CARVALHO JUNIOR, CNPS; Helena Saraiva Koenow Pinheiro, Universidade Federal Rural do Rio de Janeiro; Pedro Armentano Mudado Xavier, Universidade Federal Rural do Rio de Janeiro; SILVIO BARGE BHERING, CNPS; NILSON RENDEIRO PEREIRA, CNPS; BRAZ CALDERANO FILHO, CNPS.CHAGAS, C. da S.CARVALHO JUNIOR, W. dePINHEIRO, H. S. K.XAVIER, P. A. M.BHERING, S. B.PEREIRA, N. R.CALDERANO FILHO, B.2018-11-28T23:39:54Z2018-11-28T23:39:54Z2018-10-1820182018-11-28T23:39:54Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleRevista Brasileira de Ciência do Solo, v. 42, article e0170183, 2018.http://www.alice.cnptia.embrapa.br/alice/handle/doc/109774810.1590/18069657rbcs20170183enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2018-11-28T23:40:00Zoai:www.alice.cnptia.embrapa.br:doc/1097748Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542018-11-28T23:40falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542018-11-28T23:40Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)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, C. da S. Mineração de dados Geoestatística Landsat 5 Solo Levantamento Reconhecimento do Solo Soil surveys |
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, C. da S. |
author_facet |
CHAGAS, C. da S. CARVALHO JUNIOR, W. de PINHEIRO, H. S. K. XAVIER, P. A. M. BHERING, S. B. PEREIRA, N. R. CALDERANO FILHO, B. |
author_role |
author |
author2 |
CARVALHO JUNIOR, W. de PINHEIRO, H. S. K. XAVIER, P. A. M. BHERING, S. B. PEREIRA, N. R. CALDERANO FILHO, B. |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
CESAR DA SILVA CHAGAS, CNPS; WALDIR DE CARVALHO JUNIOR, CNPS; Helena Saraiva Koenow Pinheiro, Universidade Federal Rural do Rio de Janeiro; Pedro Armentano Mudado Xavier, Universidade Federal Rural do Rio de Janeiro; SILVIO BARGE BHERING, CNPS; NILSON RENDEIRO PEREIRA, CNPS; BRAZ CALDERANO FILHO, CNPS. |
dc.contributor.author.fl_str_mv |
CHAGAS, C. da S. CARVALHO JUNIOR, W. de PINHEIRO, H. S. K. XAVIER, P. A. M. BHERING, S. B. PEREIRA, N. R. CALDERANO FILHO, B. |
dc.subject.por.fl_str_mv |
Mineração de dados Geoestatística Landsat 5 Solo Levantamento Reconhecimento do Solo Soil surveys |
topic |
Mineração de dados Geoestatística Landsat 5 Solo Levantamento Reconhecimento do Solo Soil surveys |
description |
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 cmol c kg-1) performed better in predicting CEC than the random forest model (R2= 0.47 and RMSE = 7.89 cmol c 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 |
2018-11-28T23:39:54Z 2018-11-28T23:39:54Z 2018-10-18 2018 2018-11-28T23:39:54Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Revista Brasileira de Ciência do Solo, v. 42, article e0170183, 2018. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1097748 10.1590/18069657rbcs20170183 |
identifier_str_mv |
Revista Brasileira de Ciência do Solo, v. 42, article e0170183, 2018. 10.1590/18069657rbcs20170183 |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1097748 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
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EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503465796370432 |