Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , |
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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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 |
|
_version_ |
1808129554468831232 |