Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/29171
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dc.contributor.authorRahman, Muhammad Moshiuren
dc.contributor.authorRobson, Andrewen
dc.date.accessioned2020-07-31T05:42:58Z-
dc.date.available2020-07-31T05:42:58Z-
dc.date.issued2020-04-21-
dc.identifier.citationRemote Sensing, 12(8), p. 1-15en
dc.identifier.issn2072-4292en
dc.identifier.urihttps://hdl.handle.net/1959.11/29171-
dc.description.abstractEarly prediction of sugarcane crop yield at the commercial block level (unit area of a single crop of the same variety, ratoon or planting date) offers significant benefit to growers, consultants, millers, policy makers, crop insurance companies and researchers. This current study explored a remote sensing based approach for predicting sugarcane yield at the block level by further developing a regionally specific Landsat time series model and including individual crop sowing (or previous seasons' harvest) date. For the Bundaberg growing region of Australia this extends over a five months period, from July to November. For this analysis, the sugarcane blocks were clustered into 10 groups based on their specific planting or ratoon commencement date within the specified five months period. These clustered or groups of blocks were named 'bins'. Cloud free (<20%) satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors were acquired over the cane growing region in Bundaberg (area of 32,983 ha), from the growing season starting in July 2014, with the average green normalised difference vegetation index (GNDVI) derived for each block. The number of images acquired for each season was defined by the number of cloud free acquisitions. Using the Simple Linear Machine Learning (ML) algorithm, the extracted Landsat derived GNDVI values for each of the blocks were converted to Sentinel GNDVI. The average GNDVI of each 'bin' was plotted and a quadratic model was fitted through the time series to identify the peak growth stage defined as the maximum GNDVI value. The model derived maximum GNDVI values for each of the bins were then regressed against the average actual yield (t·ha<sup>-1</sup>) achieved for the respective bin over the five growing years, producing strong correlations (R<sup>2</sup> = 0.92 to 0.99). The quadratic curves developed for the different bins were shifted according to the specific planting or ratoon date of an individual block allowing for the peak GNDVI value of the block to be calculated, regressed against the actual block yield (t·ha<sup>-1</sup>) and the prediction of yield to be made. To validate the accuracies of the 10 time series algorithms representing each of the 10 bins, 592 individual blocks were selected from the Bundaberg region during the 2019 harvest season. The crops were clustered into the appropriate bins with the respective algorithm applied. From a Sentinel image acquired on the 5 May 2019, the prediction accuracies were encouraging (R<sup>2</sup> = 0.87 and RMSE = 11.33 (t·ha<sup>-1</sup>)) when compared to actual harvested yield, as reported by the mill. The results presented in this paper demonstrate significant progress in the accurate prediction of sugarcane yield at the individual sugarcane block level using a remote sensing, time-series based approach.en
dc.languageenen
dc.publisherMDPI AGen
dc.relation.ispartofRemote Sensingen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleIntegrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Levelen
dc.typeJournal Articleen
dc.identifier.doi10.3390/rs12081313en
dcterms.accessRightsUNE Greenen
local.contributor.firstnameMuhammad Moshiuren
local.contributor.firstnameAndrewen
local.subject.for2008070104 Agricultural Spatial Analysis and Modellingen
local.subject.for2008080106 Image Processingen
local.subject.for2008070301 Agro-ecosystem Function and Predictionen
local.subject.seo2008820603 Sugar Cane (Cut for Crushing)en
local.subject.seo2008820304 Sugaren
local.subject.seo2008810106 Logisticsen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailmrahma37@une.edu.auen
local.profile.emailarobson7@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeSwitzerlanden
local.identifier.runningnumber1313en
local.format.startpage1en
local.format.endpage15en
local.identifier.scopusid85084642257en
local.peerreviewedYesen
local.identifier.volume12en
local.identifier.issue8en
local.access.fulltextYesen
local.contributor.lastnameRahmanen
local.contributor.lastnameRobsonen
dc.identifier.staffune-id:mrahma37en
dc.identifier.staffune-id:arobson7en
local.profile.orcid0000-0001-6430-0588en
local.profile.orcid0000-0001-5762-8980en
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/29171en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleIntegrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Levelen
local.relation.fundingsourcenoteSugar Research Australia (grant number AR008)en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorRahman, Muhammad Moshiuren
local.search.authorRobson, Andrewen
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/6d55c6f3-0c05-4871-b962-266019ec9239en
local.istranslatedNoen
local.uneassociationYesen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.identifier.wosid000534628800086en
local.year.available2020en
local.year.published2020en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/6d55c6f3-0c05-4871-b962-266019ec9239en
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/6d55c6f3-0c05-4871-b962-266019ec9239en
local.subject.for2020300206 Agricultural spatial analysis and modellingen
local.subject.for2020460306 Image processingen
local.subject.for2020300402 Agro-ecosystem function and predictionen
local.subject.seo2020260403 Sugar cane (cut for crushing)en
local.subject.seo2020260607 Sugaren
local.subject.seo2020140107 Logisticsen
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School of Science and Technology
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