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Title: Potential of Time-Series Sentinel 2 Data for Monitoring Avocado Crop Phenology
Contributor(s): Rahman, Muhammad Moshiur  (author)orcid ; Robson, Andrew  (author)orcid ; Brinkhoff, James  (author)orcid 
Publication Date: 2022-11-24
Open Access: Yes
DOI: 10.3390/rs14235942
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Abstract: The ability to accurately and systematically monitor avocado crop phenology offers significant benefits for the optimization of farm management activities, improvement of crop productivity, yield estimation, and evaluation crops' resilience to extreme weather conditions and future climate change. In this study, Sentinel-2-derived enhanced vegetation indices (EVIs) from 2017 to 2021 were used to retrieve canopy reflectance information that coincided with crop phenological stages, such as flowering (F), vegetative growth (V), fruit maturity (M), and harvest (H), in commercial avocado orchards in Bundaberg, Queensland and Renmark, South Australia. Tukey's honestly significant difference (Tukey-HSD) test after one-way analysis of variance (ANOVA) with EVI metrics (EVImean and EVIslope) showed statistically significant differences between the four phenological stages. From a Pearson correlation analysis, a distinctive seasonal trend of EVIs was observed (R = 0.68 to 0.95 for Bundaberg and R = 0.8 to 0.96 for Renmark) in all 5 years, with the peak EVIs being observed at the M stage and the trough being observed at the F stage. However, a Tukey-HSD test showed significant variability in mean EVI values between seasons for both the Bundaberg and Renmark farms. The variability of the mean EVIs between the two farms was also evident with a p-value < 0.001. This novel study highlights the applicability of remote sensing for the monitoring of avocado phenological stages retrospectively and near-real time. This information not only supports the 'benchmarking' of seasonal orchard performance to identify potential impacts of seasonal weather variation and pest and disease incursions, but when seasonal growth profiles are aligned with the corresponding annual production, it can also be used to develop phenology-based yield prediction models.
Publication Type: Journal Article
Source of Publication: Remote Sensing, 14(23), p. 1-18
Publisher: MDPI AG
Place of Publication: Switzerland
ISSN: 2072-4292
Fields of Research (FoR) 2020: 300206 Agricultural spatial analysis and modelling
300802 Horticultural crop growth and development
300207 Agricultural systems analysis and modelling
Socio-Economic Objective (SEO) 2020: 260502 Avocado
280101 Expanding knowledge in the agricultural, food and veterinary sciences
280111 Expanding knowledge in the environmental sciences
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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