Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/38117
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dc.contributor.authorTorgbor, Benjamin Adjahen
dc.contributor.authorRahman, Muhammad Moshiuren
dc.contributor.authorRobson, Andrewen
dc.contributor.authorBrinkhoff, Jamesen
dc.contributor.authorKhan, Azeemen
dc.date.accessioned2022-01-31T22:07:20Z-
dc.date.available2022-01-31T22:07:20Z-
dc.date.issued2022-
dc.identifier.citationHorticulturae, 8(1), p. 1-17en
dc.identifier.issn2311-7524en
dc.identifier.urihttps://hdl.handle.net/1959.11/38117-
dc.description.abstract<p>In 2020, mango (<i>Mangifera indica</i>) exports contributed over 40 million tons, worth around US$20 billion, to the global economy. Only 10% of this contribution was made from African countries including Ghana, largely due to lower investment in the sector and general paucity of research into the mango value chain, especially production, quality and volume. Considering the global economic importance of mango coupled with the gap in the use of the remote sensing technology in the sector, this study tested the hypothesis that phenological stages of mango can be retrieved from Sentinel-2 (S2) derived time series vegetation indices (VIs) data. The study was conducted on four mango farms in the Yilo Krobo Municipal Area of Ghana. Seasonal (temporal) growth curves using four VIs (NDVI, GNDVI, EVI and SAVI) for the period from 2017 to 2020 were derived for each of the selected orchards and then aligned with five known phenology stages: Flowering/Fruitset (F/FS), Fruit Development (FRD), Maturity/Harvesting (M/H), Flushing (FLU) and Dormancy (D). The significance of the variation "within" and "between" farms obtained from the VI metrics of the S2 data were tested using single-factor and two-factor analysis of variance (ANOVA). Furthermore, to identify which specific variable pairs (phenology stages) were significantly different, a Tukey honest significant difference (HSD) post-hoc test was conducted, following the results of the ANOVA. Whilst it was possible to differentiate the phenological stages using all the four VIs, EVI was found to be the best related with <i>p</i> < 0.05 for most of the studied farms. A distinct annual trend was identified with a peak in June/July and troughs in December/January. The derivation of remote sensing based 'time series' growth profiles for commercial mango orchards supports the 'benchmarking' of annual and seasonal orchard performance and therefore offers a near 'real time' technology for identifying significant variations resulting from pest and disease incursions and the potential impacts of seasonal weather variations.</p>en
dc.languageenen
dc.publisherMDPI AGen
dc.relation.ispartofHorticulturaeen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleAssessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghanaen
dc.typeJournal Articleen
dc.identifier.doi10.3390/horticulturae8010011en
dcterms.accessRightsUNE Greenen
local.contributor.firstnameBenjamin Adjahen
local.contributor.firstnameMuhammad Moshiuren
local.contributor.firstnameAndrewen
local.contributor.firstnameJamesen
local.contributor.firstnameAzeemen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailbtorgbor@myune.edu.auen
local.profile.emailmrahma37@une.edu.auen
local.profile.emailarobson7@une.edu.auen
local.profile.emailjbrinkho@une.edu.auen
local.profile.emailmkhan64@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeSwitzerlanden
local.identifier.runningnumber11en
local.format.startpage1en
local.format.endpage17en
local.identifier.scopusid85123713020en
local.peerreviewedYesen
local.identifier.volume8en
local.identifier.issue1en
local.access.fulltextYesen
local.contributor.lastnameTorgboren
local.contributor.lastnameRahmanen
local.contributor.lastnameRobsonen
local.contributor.lastnameBrinkhoffen
local.contributor.lastnameKhanen
dc.identifier.staffune-id:btorgboren
dc.identifier.staffune-id:mrahma37en
dc.identifier.staffune-id:arobson7en
dc.identifier.staffune-id:jbrinkhoen
dc.identifier.staffune-id:mkhan64en
local.profile.orcid0000-0002-9017-6821en
local.profile.orcid0000-0001-6430-0588en
local.profile.orcid0000-0001-5762-8980en
local.profile.orcid0000-0002-0721-2458en
local.profile.orcid0000-0001-8932-4578en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/38117en
local.date.onlineversion2021-12-22-
dc.identifier.academiclevelStudenten
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleAssessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghanaen
local.relation.fundingsourcenoteRemote Sensing scholarship granted by the Applied Agricultural Remote Sensing Centre (AARSC) of the University of New England, Australia.en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorTorgbor, Benjamin Adjahen
local.search.authorRahman, Muhammad Moshiuren
local.search.authorRobson, Andrewen
local.search.authorBrinkhoff, Jamesen
local.search.authorKhan, Azeemen
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/574abc96-dc47-4722-849d-b67e97bb4a22en
local.uneassociationYesen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.identifier.wosid000747275200001en
local.year.available2021-
local.year.published2022-
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/574abc96-dc47-4722-849d-b67e97bb4a22en
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/574abc96-dc47-4722-849d-b67e97bb4a22en
local.subject.for2020300207 Agricultural systems analysis and modellingen
local.subject.for2020300802 Horticultural crop growth and developmenten
local.subject.seo2020260513 Stone fruit (excl. avocado)en
local.subject.seo2020260516 Tropical fruiten
local.codeupdate.date2022-02-09T16:49:35.506en
local.codeupdate.epersonmrahma37@une.edu.auen
local.codeupdate.finalisedtrueen
local.original.for2020300802 Horticultural crop growth and developmenten
local.original.for2020300207 Agricultural systems analysis and modellingen
local.original.for2020300206 Agricultural spatial analysis and modellingen
local.original.seo2020260516 Tropical fruiten
local.original.seo2020260513 Stone fruit (excl. avocado)en
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School of Science and Technology
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