Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/30313
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dc.contributor.authorBrinkhoff, Jamesen
dc.contributor.authorDunn, Brian Wen
dc.contributor.authorHart, Joshen
dc.contributor.authorDunn, Tinaen
dc.date.accessioned2021-03-30T06:24:11Z-
dc.date.available2021-03-30T06:24:11Z-
dc.date.issued2020-
dc.identifier.citationIGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, p. 4355-4358en
dc.identifier.isbn9781728163741en
dc.identifier.isbn9781728163758en
dc.identifier.issn2153-7003en
dc.identifier.urihttps://hdl.handle.net/1959.11/30313-
dc.description.abstractThis work examines the impact of image acquisition time on rice paddy remote sensing data in Yanco, NSW, Australia. The normalized difference red edge (NDRE) is an important vegetation index for rice paddy N status. Models of midseason N uptake were extracted using physical samples and NDRE data taken at multiple times of day from a UAV with multi-spectral camera. The best prediction error was 15.8 kg/ha. With a model extracted at 13:00 on 1 January, model predictions from 27 December to 11 January, at image times from 11:00 to 16:00, N uptake prediction errors were less than 28.5 kg/ha. Predictions from images acquired at times very different from the one used to extract the model are more erroneous.en
dc.languageenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartofIGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposiumen
dc.titleImpact of UAV Time-of-Flight on Rice Nitrogen Uptake Modelsen
dc.typeConference Publicationen
dc.relation.conferenceIGARSS 2020: 2020 IEEE International Geoscience and Remote Sensing Symposiumen
dc.identifier.doi10.1109/IGARSS39084.2020.9323619en
local.contributor.firstnameJamesen
local.contributor.firstnameBrian Wen
local.contributor.firstnameJoshen
local.contributor.firstnameTinaen
local.subject.for2008070104 Agricultural Spatial Analysis and Modellingen
local.subject.seo2008820402 Riceen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailjbrinkho@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.date.conference26th September - 2nd October, 2020en
local.conference.placeWaikoloa, United States of Americaen
local.publisher.placeDanvers, United States of Americaen
local.format.startpage4355en
local.format.endpage4358en
local.identifier.scopusid85101979942en
local.peerreviewedYesen
local.contributor.lastnameBrinkhoffen
local.contributor.lastnameDunnen
local.contributor.lastnameHarten
local.contributor.lastnameDunnen
dc.identifier.staffune-id:jbrinkhoen
local.profile.orcid0000-0002-0721-2458en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/30313en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleImpact of UAV Time-of-Flight on Rice Nitrogen Uptake Modelsen
local.relation.fundingsourcenoteAgriFutures Australiaen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsIGARSS 2020: 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 26th September - 2nd October 2020en
local.search.authorBrinkhoff, Jamesen
local.search.authorDunn, Brian Wen
local.search.authorHart, Joshen
local.search.authorDunn, Tinaen
local.uneassociationYesen
dc.date.presented2020-09-26-
local.atsiresearchNoen
local.conference.venueVirtual Symposiumen
local.sensitive.culturalNoen
local.year.published2020en
local.year.presented2020en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/5e8d4926-65cc-4c2f-99be-1880ffd435d2en
local.subject.for2020300206 Agricultural spatial analysis and modellingen
local.subject.seo2020260308 Riceen
local.date.start2020-09-26-
local.date.end2020-10-02-
local.relation.worldcathttp://www.worldcat.org/oclc/1240536764en
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School of Science and Technology
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