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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
18/10/2018 |
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Data da última atualização: |
28/11/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
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. |
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Afiliação: |
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. |
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Título: |
Mapping soil cation exchange capacity in a semiarid region through predictive models and covariates from remote sensing data. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Revista Brasileira de Ciência do Solo, v. 42, article e0170183, 2018. |
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DOI: |
10.1590/18069657rbcs20170183 |
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Idioma: |
Inglês |
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Conteúdo: |
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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Palavras-Chave: |
Geoestatística; Landsat 5; Mineração de dados. |
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Thesagro: |
Levantamento; Reconhecimento do Solo; Solo. |
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Thesaurus Nal: |
Soil surveys. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/184698/1/Mapping-Soil-Cation-Exchange.pdf
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Marc: |
LEADER 02267naa a2200289 a 4500
001 2097748
005 2018-11-28
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1590/18069657rbcs20170183$2DOI
100 1 $aCHAGAS, C. da S.
245 $aMapping soil cation exchange capacity in a semiarid region through predictive models and covariates from remote sensing data.$h[electronic resource]
260 $c2018
520 $aPlanning 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.
650 $aSoil surveys
650 $aLevantamento
650 $aReconhecimento do Solo
650 $aSolo
653 $aGeoestatística
653 $aLandsat 5
653 $aMineração de dados
700 1 $aCARVALHO JUNIOR, W. de
700 1 $aPINHEIRO, H. S. K.
700 1 $aXAVIER, P. A. M.
700 1 $aBHERING, S. B.
700 1 $aPEREIRA, N. R.
700 1 $aCALDERANO FILHO, B.
773 $tRevista Brasileira de Ciência do Solo$gv. 42, article e0170183, 2018.
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Registro original: |
Embrapa Solos (CNPS) |
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| 1. |  | 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. Mapping soil cation exchange capacity in a semiarid region through predictive models and covariates from remote sensing data. Revista Brasileira de Ciência do Solo, v. 42, article e0170183, 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Solos. |
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