Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information

Detalhes bibliográficos
Autor(a) principal: Teixeira, Daniel D.B. [UNESP]
Data de Publicação: 2017
Outros Autores: Marques, José [UNESP], Siqueira, Diego S. [UNESP], Vasconcelos, Vinicius, Carvalho, Osmar A., Martins, Éder S., Pereira, Gener T. [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.geoderma.2017.06.001
http://hdl.handle.net/11449/169843
Resumo: There is a great global demand for detailed soil property description; therefore, an ideal site-specific sampling has become indispensable to meet this demand. This study aimed to assess the implications of incorporating geological, geomorphological, and pedological information in reducing the required sampling density for magnetic susceptibility (MS), clay content (CC), and base saturation (BS) characterizations. The study area is located in Guatapará-SP (Brazil) and has 870 ha. A total of 371 samples were collected at a depth of 0–0.25 m for assessing magnetic susceptibility (MS), clay content, and base saturation (BS). A density of one sample was considered every 2.6, 3, 4, 5, 6, 7, 8, 9, 11, and 14 ha. The incorporation of secondary information in a geostatistical framework was performed by means of simple kriging with varying local means. Accuracy assessment of the spatial estimates at each sampling density, with and without incorporating secondary information, was performed by using external validation. For MS, geology and geomorphology information were responsible for about 45% and 44% spatial continuity, respectively. As for CC, these results were higher, being of 54% (geology) and 53% (geomorphology). Conversely, no spatial variability was detected for these properties by using pedological information. For BS, there was no relationship between secondary information and its spatial continuity. Incorporating geological and geomorphological information to MS data enabled a reduction in the number of samples required of 37% and 44%, respectively, in order to represent its spatial pattern. Likewise, this information provides a 35% reduction in the required sampling density for CC. However, secondary information was no helpful in decreasing sampling density for BS. In brief, incorporating pre-existing information can ensure a high quality of estimates and decrease the number of samples required for a detailed description for both MS and CC. Estimates of spatial patterns with geological and geomorphological information for modeling of soil properties might have a greater potential of use for environmental model composition.
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spelling Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary informationGeologyGeomorphologyGeostatisticsSampling densitySimple kriging with varying local meanSoil classThere is a great global demand for detailed soil property description; therefore, an ideal site-specific sampling has become indispensable to meet this demand. This study aimed to assess the implications of incorporating geological, geomorphological, and pedological information in reducing the required sampling density for magnetic susceptibility (MS), clay content (CC), and base saturation (BS) characterizations. The study area is located in Guatapará-SP (Brazil) and has 870 ha. A total of 371 samples were collected at a depth of 0–0.25 m for assessing magnetic susceptibility (MS), clay content, and base saturation (BS). A density of one sample was considered every 2.6, 3, 4, 5, 6, 7, 8, 9, 11, and 14 ha. The incorporation of secondary information in a geostatistical framework was performed by means of simple kriging with varying local means. Accuracy assessment of the spatial estimates at each sampling density, with and without incorporating secondary information, was performed by using external validation. For MS, geology and geomorphology information were responsible for about 45% and 44% spatial continuity, respectively. As for CC, these results were higher, being of 54% (geology) and 53% (geomorphology). Conversely, no spatial variability was detected for these properties by using pedological information. For BS, there was no relationship between secondary information and its spatial continuity. Incorporating geological and geomorphological information to MS data enabled a reduction in the number of samples required of 37% and 44%, respectively, in order to represent its spatial pattern. Likewise, this information provides a 35% reduction in the required sampling density for CC. However, secondary information was no helpful in decreasing sampling density for BS. In brief, incorporating pre-existing information can ensure a high quality of estimates and decrease the number of samples required for a detailed description for both MS and CC. Estimates of spatial patterns with geological and geomorphological information for modeling of soil properties might have a greater potential of use for environmental model composition.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Exact Sciences State University of São Paulo (UNESP) Research Group CSME — Soil Characterization for Specific ManagementDepartment of Soils and Fertilizers State University of São Paulo (UNESP) Research Group CSME — Soil Characterization for Specific ManagementDepartment of Geography University of Brasília (UNB) LSIE — Laboratory of Spatial Information SystemsEmbrapa CerradosDepartment of Ecology University of Brasília (UNB) LIE - Laboratory of Spatial Information SystemsDepartment of Exact Sciences State University of São Paulo (UNESP) Research Group CSME — Soil Characterization for Specific ManagementDepartment of Soils and Fertilizers State University of São Paulo (UNESP) Research Group CSME — Soil Characterization for Specific ManagementFAPESP: 2013/25118-4Universidade Estadual Paulista (Unesp)LSIE — Laboratory of Spatial Information SystemsEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)LIE - Laboratory of Spatial Information SystemsTeixeira, Daniel D.B. [UNESP]Marques, José [UNESP]Siqueira, Diego S. [UNESP]Vasconcelos, ViniciusCarvalho, Osmar A.Martins, Éder S.Pereira, Gener T. [UNESP]2018-12-11T16:47:50Z2018-12-11T16:47:50Z2017-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article208-218application/pdfhttp://dx.doi.org/10.1016/j.geoderma.2017.06.001Geoderma, v. 305, p. 208-218.0016-7061http://hdl.handle.net/11449/16984310.1016/j.geoderma.2017.06.0012-s2.0-850208319142-s2.0-85020831914.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengGeoderma1,717info:eu-repo/semantics/openAccess2024-01-24T06:27:54Zoai:repositorio.unesp.br:11449/169843Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:48:55.512238Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information
title Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information
spellingShingle Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information
Teixeira, Daniel D.B. [UNESP]
Geology
Geomorphology
Geostatistics
Sampling density
Simple kriging with varying local mean
Soil class
title_short Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information
title_full Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information
title_fullStr Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information
title_full_unstemmed Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information
title_sort Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information
author Teixeira, Daniel D.B. [UNESP]
author_facet Teixeira, Daniel D.B. [UNESP]
Marques, José [UNESP]
Siqueira, Diego S. [UNESP]
Vasconcelos, Vinicius
Carvalho, Osmar A.
Martins, Éder S.
Pereira, Gener T. [UNESP]
author_role author
author2 Marques, José [UNESP]
Siqueira, Diego S. [UNESP]
Vasconcelos, Vinicius
Carvalho, Osmar A.
Martins, Éder S.
Pereira, Gener T. [UNESP]
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
LSIE — Laboratory of Spatial Information Systems
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
LIE - Laboratory of Spatial Information Systems
dc.contributor.author.fl_str_mv Teixeira, Daniel D.B. [UNESP]
Marques, José [UNESP]
Siqueira, Diego S. [UNESP]
Vasconcelos, Vinicius
Carvalho, Osmar A.
Martins, Éder S.
Pereira, Gener T. [UNESP]
dc.subject.por.fl_str_mv Geology
Geomorphology
Geostatistics
Sampling density
Simple kriging with varying local mean
Soil class
topic Geology
Geomorphology
Geostatistics
Sampling density
Simple kriging with varying local mean
Soil class
description There is a great global demand for detailed soil property description; therefore, an ideal site-specific sampling has become indispensable to meet this demand. This study aimed to assess the implications of incorporating geological, geomorphological, and pedological information in reducing the required sampling density for magnetic susceptibility (MS), clay content (CC), and base saturation (BS) characterizations. The study area is located in Guatapará-SP (Brazil) and has 870 ha. A total of 371 samples were collected at a depth of 0–0.25 m for assessing magnetic susceptibility (MS), clay content, and base saturation (BS). A density of one sample was considered every 2.6, 3, 4, 5, 6, 7, 8, 9, 11, and 14 ha. The incorporation of secondary information in a geostatistical framework was performed by means of simple kriging with varying local means. Accuracy assessment of the spatial estimates at each sampling density, with and without incorporating secondary information, was performed by using external validation. For MS, geology and geomorphology information were responsible for about 45% and 44% spatial continuity, respectively. As for CC, these results were higher, being of 54% (geology) and 53% (geomorphology). Conversely, no spatial variability was detected for these properties by using pedological information. For BS, there was no relationship between secondary information and its spatial continuity. Incorporating geological and geomorphological information to MS data enabled a reduction in the number of samples required of 37% and 44%, respectively, in order to represent its spatial pattern. Likewise, this information provides a 35% reduction in the required sampling density for CC. However, secondary information was no helpful in decreasing sampling density for BS. In brief, incorporating pre-existing information can ensure a high quality of estimates and decrease the number of samples required for a detailed description for both MS and CC. Estimates of spatial patterns with geological and geomorphological information for modeling of soil properties might have a greater potential of use for environmental model composition.
publishDate 2017
dc.date.none.fl_str_mv 2017-11-01
2018-12-11T16:47:50Z
2018-12-11T16:47:50Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.geoderma.2017.06.001
Geoderma, v. 305, p. 208-218.
0016-7061
http://hdl.handle.net/11449/169843
10.1016/j.geoderma.2017.06.001
2-s2.0-85020831914
2-s2.0-85020831914.pdf
url http://dx.doi.org/10.1016/j.geoderma.2017.06.001
http://hdl.handle.net/11449/169843
identifier_str_mv Geoderma, v. 305, p. 208-218.
0016-7061
10.1016/j.geoderma.2017.06.001
2-s2.0-85020831914
2-s2.0-85020831914.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Geoderma
1,717
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 208-218
application/pdf
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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