Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana
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 resear...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Horticulturae |
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| Online Access: | https://www.mdpi.com/2311-7524/8/1/11 |
| _version_ | 1797493569593802752 |
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| author | Benjamin Adjah Torgbor Muhammad Moshiur Rahman Andrew Robson James Brinkhoff Azeem Khan |
| author_facet | Benjamin Adjah Torgbor Muhammad Moshiur Rahman Andrew Robson James Brinkhoff Azeem Khan |
| author_sort | Benjamin Adjah Torgbor |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:21:53Z |
| format | Article |
| id | doaj.art-633e572a8a354a708a4ec827de2aea9f |
| institution | Directory Open Access Journal |
| issn | 2311-7524 |
| language | English |
| last_indexed | 2024-03-10T01:21:53Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj.art-633e572a8a354a708a4ec827de2aea9f2023-11-23T13:57:09ZengMDPI AGHorticulturae2311-75242021-12-01811110.3390/horticulturae8010011Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in GhanaBenjamin Adjah Torgbor0Muhammad Moshiur Rahman1Andrew Robson2James Brinkhoff3Azeem Khan4Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, AustraliaApplied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, AustraliaApplied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, AustraliaApplied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, AustraliaApplied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, AustraliaIn 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.https://www.mdpi.com/2311-7524/8/1/11mango (<i>Mangifera indica</i>)phenologyremote sensingSentinel-2 MSIvegetation indices (VIs)Enhanced Vegetation Index (EVI) |
| spellingShingle | Benjamin Adjah Torgbor Muhammad Moshiur Rahman Andrew Robson James Brinkhoff Azeem Khan Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana Horticulturae mango (<i>Mangifera indica</i>) phenology remote sensing Sentinel-2 MSI vegetation indices (VIs) Enhanced Vegetation Index (EVI) |
| title | Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana |
| title_full | Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana |
| title_fullStr | Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana |
| title_full_unstemmed | Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana |
| title_short | Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana |
| title_sort | assessing the potential of sentinel 2 derived vegetation indices to retrieve phenological stages of mango in ghana |
| topic | mango (<i>Mangifera indica</i>) phenology remote sensing Sentinel-2 MSI vegetation indices (VIs) Enhanced Vegetation Index (EVI) |
| url | https://www.mdpi.com/2311-7524/8/1/11 |
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