A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data

Detalhes bibliográficos
Autor(a) principal: Chaves, Michel Eustáquio Dantas
Data de Publicação: 2018
Outros Autores: Alves, Marcelo de Carvalho, Oliveira, Marcelo Silva de, Sáfadi, Thelma
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/34535
Resumo: Advances in satellite imagery and remote sensing have enabled the acquisition of spatial data at several different resolutions. Geographic information systems (GIS) and geostatistics can be used to link geographic data from different sources. This article discusses the need to improve soybean crop detection and yield prediction by linking census data, GIS, remote sensing, and geostatistics. The proposed approach combines Brazilian Institute of Geography and Statistics (IBGE) census data with an eight-day enhanced vegetation index (EVI) time series derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data to monitor soybean areas and yields in Mato Grosso State, Brazil. In situ data from farms were used to validate the obtained results. Binomial areal kriging was used to generate maps of soybean occurrence over the years, and Gaussian areal kriging was used to predict soybean crop yield census data inside detected soybean areas, which had a downscaling effect on the results. The global accuracy and the Kappa index for the soybean crop detection were 92.1% and 0.84%, respectively. The yield prediction presented 95.09% accuracy considering the standard deviation and probable error. Soybean crop detection and yield monitoring can be improved by this approach.
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spelling A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing dataGeographic information systemsDownscalingSoybean crop monitoringRemote sensingSistemas de informação geográficaMonitoramento da safra de sojaSensoriamento remotoAdvances in satellite imagery and remote sensing have enabled the acquisition of spatial data at several different resolutions. Geographic information systems (GIS) and geostatistics can be used to link geographic data from different sources. This article discusses the need to improve soybean crop detection and yield prediction by linking census data, GIS, remote sensing, and geostatistics. The proposed approach combines Brazilian Institute of Geography and Statistics (IBGE) census data with an eight-day enhanced vegetation index (EVI) time series derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data to monitor soybean areas and yields in Mato Grosso State, Brazil. In situ data from farms were used to validate the obtained results. Binomial areal kriging was used to generate maps of soybean occurrence over the years, and Gaussian areal kriging was used to predict soybean crop yield census data inside detected soybean areas, which had a downscaling effect on the results. The global accuracy and the Kappa index for the soybean crop detection were 92.1% and 0.84%, respectively. The yield prediction presented 95.09% accuracy considering the standard deviation and probable error. Soybean crop detection and yield monitoring can be improved by this approach.MDPI2019-06-03T13:17:54Z2019-06-03T13:17:54Z2018info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfCHAVES, M. E. D. et al. A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data. Remote Sensing, [S. l.], v. 10, n. 5, p. 1-29, 2018.http://repositorio.ufla.br/jspui/handle/1/34535Remote Sensingreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessChaves, Michel Eustáquio DantasAlves, Marcelo de CarvalhoOliveira, Marcelo Silva deSáfadi, Thelmaeng2019-06-03T13:17:55Zoai:localhost:1/34535Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2019-06-03T13:17:55Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data
title A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data
spellingShingle A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data
Chaves, Michel Eustáquio Dantas
Geographic information systems
Downscaling
Soybean crop monitoring
Remote sensing
Sistemas de informação geográfica
Monitoramento da safra de soja
Sensoriamento remoto
title_short A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data
title_full A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data
title_fullStr A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data
title_full_unstemmed A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data
title_sort A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data
author Chaves, Michel Eustáquio Dantas
author_facet Chaves, Michel Eustáquio Dantas
Alves, Marcelo de Carvalho
Oliveira, Marcelo Silva de
Sáfadi, Thelma
author_role author
author2 Alves, Marcelo de Carvalho
Oliveira, Marcelo Silva de
Sáfadi, Thelma
author2_role author
author
author
dc.contributor.author.fl_str_mv Chaves, Michel Eustáquio Dantas
Alves, Marcelo de Carvalho
Oliveira, Marcelo Silva de
Sáfadi, Thelma
dc.subject.por.fl_str_mv Geographic information systems
Downscaling
Soybean crop monitoring
Remote sensing
Sistemas de informação geográfica
Monitoramento da safra de soja
Sensoriamento remoto
topic Geographic information systems
Downscaling
Soybean crop monitoring
Remote sensing
Sistemas de informação geográfica
Monitoramento da safra de soja
Sensoriamento remoto
description Advances in satellite imagery and remote sensing have enabled the acquisition of spatial data at several different resolutions. Geographic information systems (GIS) and geostatistics can be used to link geographic data from different sources. This article discusses the need to improve soybean crop detection and yield prediction by linking census data, GIS, remote sensing, and geostatistics. The proposed approach combines Brazilian Institute of Geography and Statistics (IBGE) census data with an eight-day enhanced vegetation index (EVI) time series derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data to monitor soybean areas and yields in Mato Grosso State, Brazil. In situ data from farms were used to validate the obtained results. Binomial areal kriging was used to generate maps of soybean occurrence over the years, and Gaussian areal kriging was used to predict soybean crop yield census data inside detected soybean areas, which had a downscaling effect on the results. The global accuracy and the Kappa index for the soybean crop detection were 92.1% and 0.84%, respectively. The yield prediction presented 95.09% accuracy considering the standard deviation and probable error. Soybean crop detection and yield monitoring can be improved by this approach.
publishDate 2018
dc.date.none.fl_str_mv 2018
2019-06-03T13:17:54Z
2019-06-03T13:17:54Z
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 CHAVES, M. E. D. et al. A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data. Remote Sensing, [S. l.], v. 10, n. 5, p. 1-29, 2018.
http://repositorio.ufla.br/jspui/handle/1/34535
identifier_str_mv CHAVES, M. E. D. et al. A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data. Remote Sensing, [S. l.], v. 10, n. 5, p. 1-29, 2018.
url http://repositorio.ufla.br/jspui/handle/1/34535
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Remote Sensing
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
_version_ 1815439078545424384

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