Using remote sensing to predict grape phenolics and colour at harvest in a Cabernet Sauvignon vineyard: Timing observations against vine phenology and optimising image resolution

Authors

  • D.W. LAMB,

    Corresponding author
    1. National Wine and Grape Industry Centre, Charles Sturt University, Locked Bag 588, Wagga Wagga NSW 2678
    2. presently: School of Biological, Biomedical and Molecular Sciences, University of New England, Armidale NSW 2351
    3. Cooperative Research Centre for Viticulture, PO Box 154, Glen Osmond SA 5064 Australia
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  • M.M. WEEDON,

    1. National Wine and Grape Industry Centre, Charles Sturt University, Locked Bag 588, Wagga Wagga NSW 2678
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  • R.G.V. BRAMLEY

    1. CSIRO Land and Water, PMB 2, Glen Osmond SA 5064 Australia
    2. Cooperative Research Centre for Viticulture, PO Box 154, Glen Osmond SA 5064 Australia
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facsimile: +61 2 6773 3268, email: dlamb@pobox.une.edu.au

Abstract

Optical remote sensing can provide a synoptic view of grapevine photosynthetically-active biomass over entire vineyards both rapidly and cost-effectively. Such output offers viticulturists and winemakers a management tool of enormous potential with red grape varieties, especially if canopy architecture (defined in this way) can be linked to production of phenolics and colour in ripe grapes. Accordingly, this paper describes such associations for a Cabernet Sauvignon vineyard in Australia's cool-climate Coonawarra region. A link is established between physical descriptors of grapevine canopies (derived from remotely-sensed images), and subsequent measurements of grape phenolics and colour. High-resolution images were acquired on three occasions during each of two consecutive growing seasons and post-processed to a range of on-ground resolutions. The strength of correlation between those images and berry properties (both total phenolics, and colour levels at harvest), varied according to spatial resolution and vine phenology at the time of imaging. An image resolution corresponding approximately to row spacing resulted in the strongest correlations between berry constituents and image-based data on all occasions. Referenced to grapevine phenology, correlations were initially weak (insignificant) at bud-burst, reached maximum strength at veraison, then diminished somewhat as grapes ripened. Prospects for applying such remotely-sensed imagery (at an appropriate resolution and timing), to predict berry phenolics and colour at harvest, are discussed.

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