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 |
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