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Title: Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana
Contributor(s): Torgbor, Benjamin Adjah (author)orcid ; Rahman, Muhammad Moshiur  (author)orcid ; Robson, Andrew  (author)orcid ; Brinkhoff, James  (author)orcid ; Khan, Azeem  (author)orcid 
Publication Date: 2022
Early Online Version: 2021-12-22
Open Access: Yes
DOI: 10.3390/horticulturae8010011
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In 2020, mango (Mangifera indica) 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 p < 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.

Publication Type: Journal Article
Source of Publication: Horticulturae, 8(1), p. 1-17
Publisher: MDPI AG
Place of Publication: Switzerland
ISSN: 2311-7524
Fields of Research (FoR) 2020: 300207 Agricultural systems analysis and modelling
300802 Horticultural crop growth and development
Socio-Economic Objective (SEO) 2020: 260513 Stone fruit (excl. avocado)
260516 Tropical fruit
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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