Multiscale assessment of ground, aerial and satellite spectral data for monitoring wheat grain nitrogen content

Segarra, Joel; Rezzouk, Fatima Zahra; Aparicio, Nieves; González Torralba, Jon; Aranjuelo Michelena, Iker; Gracia-Romero, Adrian; Araus, José Luis; Kefauver, Shawn C.

Publication:
Multiscale assessment of ground, aerial and satellite spectral data for monitoring wheat grain nitrogen content

dc.contributor.author	Segarra, Joel
dc.contributor.author	Rezzouk, Fatima Zahra
dc.contributor.author	Aparicio, Nieves
dc.contributor.author	González Torralba, Jon
dc.contributor.author	Aranjuelo Michelena, Iker
dc.contributor.author	Gracia-Romero, Adrian
dc.contributor.author	Araus, José Luis
dc.contributor.author	Kefauver, Shawn C.
dc.contributor.department	Ciencias	es_ES
dc.contributor.department	Zientziak	eu
dc.contributor.funder	Gobierno de Navarra / Nafarroako Unibertsitate Publikoa	es
dc.date.accessioned	2023-02-22T18:16:45Z
dc.date.available	2023-02-22T18:16:45Z
dc.date.issued	2022
dc.date.updated	2023-02-22T13:09:08Z
dc.description.abstract	Wheat grain quality characteristics have experienced increasing attention as a central factor affecting wheat end-use products quality and human health. Nonetheless, in the last decades a reduction in grain quality has been observed. Therefore, it is central to develop efficient quality-related phenotyping tools. In this sense, one of the most relevant wheat features related to grain quality traits is grain nitrogen content, which is directly linked to grain protein content and monitorable with remote sensing approaches. Moreover, the relation between nitrogen fertilization and grain nitrogen content (protein) plays a central role in the sustainability of agriculture. Both aiming to develop efficient phenotyping tools using remote sensing instruments and to advance towards a field-level efficient and sustainable monitoring of grain nitrogen status, this paper studies the efficacy of various sensors, multispectral and visible red–greenblue (RGB), at different scales, ground and unmanned aerial vehicle (UAV), and phenological stages (anthesis and grain filling) to estimate grain nitrogen content. Linear models were calculated using vegetation indices at each sensing level, sensor type and phenological stage. Furthermore, this study explores the up-scalability of the best performing model to satellite level Sentinel-2 equivalent data. We found that models built at the phenological stage of anthesis with UAV-level multispectral cameras using red-edge bands outperformed grain nitrogen content estimation (R2 = 0.42, RMSE = 0.18%) in comparison with those models built with RGB imagery at ground and aerial level, as well as with those built with widely used ground-level multispectral sensors. We also demonstrated the possibility to use UAV-built multispectral linear models at the satellite scale to determine grain nitrogen content effectively (R2 = 0.40, RMSE = 0.29%) at actual wheat fields.	en
dc.description.sponsorship	This study was supported by the projects PID2019-106650RB-C21 (Ministerio de Ciencia e Innovación, MICINN, Spain) and 0011-1365-2018-000213/0011-1365-2018-000150 (Government of Navarre, Spain). J.S. is recipient of a FPI doctoral fellowship (Grant: PRE2020-091907) from MICINN, Spain. J.L.A. acknowledges support from ICREA Academia, Generalitat de Catalunya, Spain. S.C.K. is supported by the Ramon y Cajal RYC-2019-027818-I research fellowship from MICINN, Spain. The processing of satellite images was supported by the European Cooperation in Science and Technology Action CA17134 SENSECO.	en
dc.format.mimetype	application/pdf	en
dc.format.mimetype	application/zip	en
dc.identifier.citation	Segarra, J., Rezzouk, F. Z., Aparicio, N., González-Torralba, J., Aranjuelo, I., Gracia-Romero, A., Araus, J. L., & Kefauver, S. C. (2022). Multiscale assessment of ground, aerial and satellite spectral data for monitoring wheat grain nitrogen content. Information Processing in Agriculture, S2214317322000506. https://doi.org/10.1016/j.inpa.2022.05.004	en
dc.identifier.doi	10.1016/j.inpa.2022.05.004
dc.identifier.issn	2214-3173
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/44785
dc.language.iso	eng	en
dc.publisher	Elsevier	en
dc.relation.ispartof	Information Processing in Agriculture (2022)	en
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-106650RB-C21/ES/	en
dc.relation.projectID	info:eu-repo/grantAgreement/MICINN//RYC-2019-027818-I	en
dc.relation.publisherversion	https://doi.org/10.1016/j.inpa.2022.05.004
dc.rights	© 2022 China Agricultural University. This is an open access article under the CC BY license.	en
dc.rights.accessRights	Acceso abierto / Sarbide irekia	es
dc.rights.accessRights	info:eu-repo/semantics/openAccess	en
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Grain nitrogen content	en
dc.subject	Phenotyping	en
dc.subject	Remote sensing	en
dc.subject	Sentinel-2	en
dc.subject	Wheat	en
dc.title	Multiscale assessment of ground, aerial and satellite spectral data for monitoring wheat grain nitrogen content	en
dc.type	Artículo / Artikulua	es
dc.type	info:eu-repo/semantics/article	en
dc.type.version	Versión publicada / Argitaratu den bertsioa	es
dc.type.version	info:eu-repo/semantics/publishedVersion	en
dspace.entity.type	Publication
relation.isAuthorOfPublication	9dac2ff4-da19-4822-8e16-0f5500962edd
relation.isAuthorOfPublication	b8dd84ae-83ed-4e3f-873e-b0023505b3df
relation.isAuthorOfPublication.latestForDiscovery	9dac2ff4-da19-4822-8e16-0f5500962edd