Papaver rhoeas L. mapping with cokriging using UAV imagery

Accurately mapping the spatial distribution of weeds within a field is a first step towards effective Site-specific Weed Management. The main objective of this study was to investigate if the multivariate geostatistical method of cokriging (COK) can be used to improve the accuracy of Papaver rhoeas...

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Autores: Jurado-Expósito, Montserrat, Castro, Ana Isabel de, Torres-Sánchez, Jorge, Jiménez-Brenes, Francisco Manuel, López Granados, Francisca
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/206699
Acceso en línea:http://hdl.handle.net/10261/206699
Access Level:acceso abierto
Palabra clave:Ancillary variables
Corn poppy
Geostatistics
Kriging
Precision agriculture
Cross-semivariogram
SSWM
Weeds
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spelling Papaver rhoeas L. mapping with cokriging using UAV imageryJurado-Expósito, MontserratCastro, Ana Isabel deTorres-Sánchez, JorgeJiménez-Brenes, Francisco ManuelLópez Granados, FranciscaAncillary variablesCorn poppyGeostatisticsKrigingPrecision agricultureCross-semivariogramSSWMWeedsAccurately mapping the spatial distribution of weeds within a field is a first step towards effective Site-specific Weed Management. The main objective of this study was to investigate if the multivariate geostatistical method of cokriging (COK) can be used to improve the accuracy of Papaver rhoeas L. infestations maps in winter wheat fields using high-resolution UAV imagery as ancillary information. The primary variable was obtained by intensive grid weed density field samplings and the secondary variables were derived from the UAV imagery taken the same day as the weed field samplings (e.g. wavebands and derivative products, such as band ratios and vegetation indexes). Univariate Ordinary Kriging (OK) and multivariate cokriging (COK) interpolation methods were used and compared for Papaver density mapping. The performances of the different methods were assessed by cross-validation. The results indicated that COK outperformed OK in the spatial interpolation of Papaver density. COK reduced the prediction errors and enhanced the accuracy of Papaver estimates maps. The best performances were obtained when COK was performed with the UAV-secondary variables that yielded the highest correlation with Papaver density and produced the strongest spatial cross-semivariograms. On average, the COK with UAV-derived ancillary variables improved the accuracy of mapping Papaver density by 11 to 21% compared with OK. The results suggest the great potential of high-resolution UAV imagery as a source of ancillary information to improve the accuracy of spatial mapping of sparsely sampled target variables using COK.This research was financed by the AGL2014-52465-C4-4-R and AGL2017-83325-C4-4-R MINECO (Spanish Ministry of Economy and Competition, FEDER Funds). Research of AI. de Castro was financed by Juan de la Cierva (MINECO) program.Peer reviewedSpringer NatureMinisterio de Economía y Competitividad (España)Ministerio de Ciencia, Innovación y Universidades (España)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202019info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/206699reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2014-52465-C4-4-Rinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/AGL2017-83325-C4-4-RAGL2017-83325-C4-4-R/AEI/10.13039/501100011033https://doi.org/10.1007/s11119-019-09635-zSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2066992026-05-22T06:33:51Z
dc.title.none.fl_str_mv Papaver rhoeas L. mapping with cokriging using UAV imagery
title Papaver rhoeas L. mapping with cokriging using UAV imagery
spellingShingle Papaver rhoeas L. mapping with cokriging using UAV imagery
Jurado-Expósito, Montserrat
Ancillary variables
Corn poppy
Geostatistics
Kriging
Precision agriculture
Cross-semivariogram
SSWM
Weeds
title_short Papaver rhoeas L. mapping with cokriging using UAV imagery
title_full Papaver rhoeas L. mapping with cokriging using UAV imagery
title_fullStr Papaver rhoeas L. mapping with cokriging using UAV imagery
title_full_unstemmed Papaver rhoeas L. mapping with cokriging using UAV imagery
title_sort Papaver rhoeas L. mapping with cokriging using UAV imagery
dc.creator.none.fl_str_mv Jurado-Expósito, Montserrat
Castro, Ana Isabel de
Torres-Sánchez, Jorge
Jiménez-Brenes, Francisco Manuel
López Granados, Francisca
author Jurado-Expósito, Montserrat
author_facet Jurado-Expósito, Montserrat
Castro, Ana Isabel de
Torres-Sánchez, Jorge
Jiménez-Brenes, Francisco Manuel
López Granados, Francisca
author_role author
author2 Castro, Ana Isabel de
Torres-Sánchez, Jorge
Jiménez-Brenes, Francisco Manuel
López Granados, Francisca
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
Ministerio de Ciencia, Innovación y Universidades (España)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Ancillary variables
Corn poppy
Geostatistics
Kriging
Precision agriculture
Cross-semivariogram
SSWM
Weeds
topic Ancillary variables
Corn poppy
Geostatistics
Kriging
Precision agriculture
Cross-semivariogram
SSWM
Weeds
description Accurately mapping the spatial distribution of weeds within a field is a first step towards effective Site-specific Weed Management. The main objective of this study was to investigate if the multivariate geostatistical method of cokriging (COK) can be used to improve the accuracy of Papaver rhoeas L. infestations maps in winter wheat fields using high-resolution UAV imagery as ancillary information. The primary variable was obtained by intensive grid weed density field samplings and the secondary variables were derived from the UAV imagery taken the same day as the weed field samplings (e.g. wavebands and derivative products, such as band ratios and vegetation indexes). Univariate Ordinary Kriging (OK) and multivariate cokriging (COK) interpolation methods were used and compared for Papaver density mapping. The performances of the different methods were assessed by cross-validation. The results indicated that COK outperformed OK in the spatial interpolation of Papaver density. COK reduced the prediction errors and enhanced the accuracy of Papaver estimates maps. The best performances were obtained when COK was performed with the UAV-secondary variables that yielded the highest correlation with Papaver density and produced the strongest spatial cross-semivariograms. On average, the COK with UAV-derived ancillary variables improved the accuracy of mapping Papaver density by 11 to 21% compared with OK. The results suggest the great potential of high-resolution UAV imagery as a source of ancillary information to improve the accuracy of spatial mapping of sparsely sampled target variables using COK.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/206699
url http://hdl.handle.net/10261/206699
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2014-52465-C4-4-R
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/AGL2017-83325-C4-4-R
AGL2017-83325-C4-4-R/AEI/10.13039/501100011033
https://doi.org/10.1007/s11119-019-09635-z
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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