Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes

[EN] Turfgrass phenotyping is a potential tool in different grass program breeding. The traditional methods for turfgrass drought phenotyping in field are time-consuming and labor-intensive. However, remote sensing techniques emerge as effective, rapid and easy approaches to optimize turfgrass selec...

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Autores: Yousfi, Salima, Marin, José, Parra, Lorena, Mauri, Pedro V., Lloret, Jaime|||0000-0002-0862-0533
Tipo de documento: artigo
Data de publicação:2022
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositório:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglês
OAI Identifier:oai:riunet.upv.es:10251/200349
Acesso em linha:https://riunet.upv.es/handle/10251/200349
Access Level:Acceso aberto
Palavra-chave:Remote sensing
NDVI
RGB images
Canopy temperature
Water deficit
Turfgrass
INGENIERÍA TELEMÁTICA
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spelling Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimesYousfi, SalimaMarin, JoséParra, LorenaMauri, Pedro V.Lloret, Jaime|||0000-0002-0862-0533Remote sensingNDVIRGB imagesCanopy temperatureWater deficitTurfgrassINGENIERÍA TELEMÁTICA[EN] Turfgrass phenotyping is a potential tool in different grass program breeding. The traditional methods for turfgrass drought phenotyping in field are time-consuming and labor-intensive. However, remote sensing techniques emerge as effective, rapid and easy approaches to optimize turfgrass selection under water stress. Remote sensing approaches are considerate as important strategies to select species of turfgrass tolerable to drought allowing green space sustainability and environment protection in regions with water limitation. Here we evaluated differences between six mixtures of C-3-C-4 turfgrass grown under two water regimes (limited and high irrigation). The performance of turf species was achieved using the green area (GA) vegetation index calculated from RGB (red green, blue) images obtained by ground camera and drone imagery, the normalized difference vegetation index (NDVI), the plant canopy temperature (CT) and soil moisture content (SM). Both vegetation (GA and NDVI) and water status (CT and SM) indices presented a significant difference in turfgrass growth under the two water regimes. Differences among turfgrass species were detected under limited and high irrigation using the vegetation indices. Both NDVI and GA allowed clear separation between drought-tolerant and susceptible turf grass, as well as the identification of the mixtures with a rapid green regeneration after a period of limited irrigation. Moreover, the canopy temperature also discriminated between turfgrass species but only under limited irrigation, while soil moisture values did not differentiate between species. Furthermore, the regression and conceptual model using remote sensing parameters revealed the most adequate criteria to detect turfgrass variability under each growing condition. This study also highlights the usefulness of green area vegetation index derived from drone imagery. GA obtained by drone images in this study explained turfgrass variability better than that derived from ground RGB images or the NDVI.Projects GO-PDR18-XEROCESPED funded by the European Agricultural Fund for Rural Development (EAFRD) and IMIDRA and the AREA VERDE-MG projects are acknowledged.ElsevierDepartamento de ComunicacionesEscuela Politécnica Superior de GandiaComunidad de MadridEuropean Agricultural Fund for Rural DevelopmentRepositorio Institucional de la Universitat Politècnica de València Riunet20222022-05-31journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/200349reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengCaja de Ahorros del Mediterráneo https://doi.org/10.13039/100012818 PDR18-XEROCESPEDopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2003492026-06-13T07:49:27Z
dc.title.none.fl_str_mv Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes
title Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes
spellingShingle Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes
Yousfi, Salima
Remote sensing
NDVI
RGB images
Canopy temperature
Water deficit
Turfgrass
INGENIERÍA TELEMÁTICA
title_short Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes
title_full Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes
title_fullStr Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes
title_full_unstemmed Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes
title_sort Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes
dc.creator.none.fl_str_mv Yousfi, Salima
Marin, José
Parra, Lorena
Mauri, Pedro V.
Lloret, Jaime|||0000-0002-0862-0533
author Yousfi, Salima
author_facet Yousfi, Salima
Marin, José
Parra, Lorena
Mauri, Pedro V.
Lloret, Jaime|||0000-0002-0862-0533
author_role author
author2 Marin, José
Parra, Lorena
Mauri, Pedro V.
Lloret, Jaime|||0000-0002-0862-0533
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Comunicaciones
Escuela Politécnica Superior de Gandia
Comunidad de Madrid
European Agricultural Fund for Rural Development
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Remote sensing
NDVI
RGB images
Canopy temperature
Water deficit
Turfgrass
INGENIERÍA TELEMÁTICA
topic Remote sensing
NDVI
RGB images
Canopy temperature
Water deficit
Turfgrass
INGENIERÍA TELEMÁTICA
description [EN] Turfgrass phenotyping is a potential tool in different grass program breeding. The traditional methods for turfgrass drought phenotyping in field are time-consuming and labor-intensive. However, remote sensing techniques emerge as effective, rapid and easy approaches to optimize turfgrass selection under water stress. Remote sensing approaches are considerate as important strategies to select species of turfgrass tolerable to drought allowing green space sustainability and environment protection in regions with water limitation. Here we evaluated differences between six mixtures of C-3-C-4 turfgrass grown under two water regimes (limited and high irrigation). The performance of turf species was achieved using the green area (GA) vegetation index calculated from RGB (red green, blue) images obtained by ground camera and drone imagery, the normalized difference vegetation index (NDVI), the plant canopy temperature (CT) and soil moisture content (SM). Both vegetation (GA and NDVI) and water status (CT and SM) indices presented a significant difference in turfgrass growth under the two water regimes. Differences among turfgrass species were detected under limited and high irrigation using the vegetation indices. Both NDVI and GA allowed clear separation between drought-tolerant and susceptible turf grass, as well as the identification of the mixtures with a rapid green regeneration after a period of limited irrigation. Moreover, the canopy temperature also discriminated between turfgrass species but only under limited irrigation, while soil moisture values did not differentiate between species. Furthermore, the regression and conceptual model using remote sensing parameters revealed the most adequate criteria to detect turfgrass variability under each growing condition. This study also highlights the usefulness of green area vegetation index derived from drone imagery. GA obtained by drone images in this study explained turfgrass variability better than that derived from ground RGB images or the NDVI.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-05-31
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/200349
url https://riunet.upv.es/handle/10251/200349
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Caja de Ahorros del Mediterráneo https://doi.org/10.13039/100012818 PDR18-XEROCESPED
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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