Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/54683
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dc.contributor.authorBrinkhoff, Jamesen
dc.date.accessioned2023-05-07T22:34:24Z-
dc.date.available2023-05-07T22:34:24Z-
dc.date.issued2022-09-28-
dc.identifier.citationIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, p. 5854-5857en
dc.identifier.isbn9781665427920en
dc.identifier.isbn9781665427913en
dc.identifier.isbn9781665427937en
dc.identifier.urihttps://hdl.handle.net/1959.11/54683-
dc.description.abstract<p>Regional maps of rice fields provided early in each growing season facilitate production estimates, planning around harvest logistics, marketing and targeted agronomic recommendations. This work develops maps of all irrigated rice fields in New South Wales, Australia. Classification models were trained on reference maps from the 2019 and 2020 harvest seasons. Model predictions were tested against a reference rice map from the 2021 harvest season, covering 60,000 km <sup>2</sup> . The random forest algorithm was used, with features from aggregated time-series of Sentinel-2 imagery. A sequence of maps were generated at intervals of 15 days, from early to late in the growing season, with accuracy assessed at each time. The maps achieved 95% overall accuracy against point samples at 16 January 2021 ( ≈80 days after sowing). Pixel-based F1-scores against the reference map were above 80% for the 1, 16 and 31 January classified maps.</p>en
dc.languageenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartofIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposiumen
dc.relation.ispartofseriesIEEE International Geoscience and Remote Sensing Symposium proceedingsen
dc.titleEarly-Season Industry-Wide Rice Maps Using Sentinel-2 Time Seriesen
dc.typeConference Publicationen
dc.relation.conferenceIGARSS 2022: 2022 IEEE International Geoscience and Remote Sensing Symposiumen
dc.identifier.doi10.1109/IGARSS46834.2022.9883755en
local.contributor.firstnameJamesen
dc.contributor.corporateAgrifutures Australiaen
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.conference17th - 22nd July, 2022en
local.conference.placeKuala Lumpur, Malaysiaen
local.publisher.placePiscataway, United States of Americaen
local.format.startpage5854en
local.format.endpage5857en
local.series.issn2153-7003en
local.series.issn2153-6996en
local.peerreviewedYesen
local.contributor.lastnameBrinkhoffen
dc.identifier.staffune-id:jbrinkhoen
local.profile.orcid0000-0002-0721-2458en
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/54683en
dc.identifier.academiclevelAcademicen
local.title.maintitleEarly-Season Industry-Wide Rice Maps Using Sentinel-2 Time Seriesen
local.relation.fundingsourcenoteAgriFutures project PRO-013078, "Realtime remote sensing based monitoring for the rice industry".en
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsIGARSS 2022: 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17th - 22nd July, 2022en
local.search.authorBrinkhoff, Jamesen
local.uneassociationYesen
dc.date.presented2022-07-
local.atsiresearchNoen
local.conference.venueKuala Lumpur Convention Centre (KLCC)en
local.sensitive.culturalNoen
local.year.published2022en
local.year.presented2022en
local.subject.for2020300206 Agricultural spatial analysis and modellingen
local.subject.for2020401304 Photogrammetry and remote sensingen
local.subject.seo2020260308 Riceen
local.date.start2022-07-17-
local.date.end2022-07-22-
local.profile.affiliationtypeUNE Affiliationen
Appears in Collections:Conference Publication
School of Science and Technology
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