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25 February 2016 Using remote sensing to understand Pinot noir vineyard variability in Ontario.
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The study objective was to determine whether multispectral high spatial resolution airborne imagery could be used to segregate zones in Pinot noir vineyards to target highest quality fruit for premium winemaking. We hypothesized that remotely sensed data would be correlated with vine size and leaf water potential (Ψ), and by extension with yield and berry composition. In 2008-2009, multispectral (blue, green, red, near-infrared) airborne images were acquired from four Ontario Pinot noir vineyards (four dates, 2008; three, 2009), with the final flight date near veraison. A process was developed to extract information from sentinel vine locations, and to calculate normalized difference vegetation index (NDVI). Data were extracted at 1 × 1, 3 × 3, and 5 × 5 pixel re-sampling rates to test for ideal image resolution. A method was developed to mask non-vine pixels to simplify qualitative assessment of images. The 3 × 3 pixel re-sampling provided most useful information. In 2008, 3 × 3 re-sampling NDVI correlated with (r-values; p < 0.0001): berry pH (-0.48), soluble solids (-0.43), vine size (0.46), anthocyanins (-0.65), colour (-0.58), and soil clay and sand content (-0.55, 0.55). In 2009, mean 3 × 3 re-sampling NDVI correlated with (r-values; p < 0.0001): anthocyanins (0.49), soil moisture (-0.89), and soil clay and silt content (-0.75, 0.83). No clear trends in correlations existed between vegetation indices vs. vine size, anthocyanins, phenolics, or soil moisture throughout the growing season in either vintage. Masked images proved effective for viewing spatial trends in airborne images without full data extraction. Qualitative similarities existed between maps of vineyard and grape composition variables vs. maps of extracted data and masked images. Remote sensing may be useful to determine colour or phenolic potential of grapes, in addition to vine water status, yield, and vine size. This study was unique by employing remote sensing in cover-cropped vineyards and thereafter using protocols for excluding spectral reflectance contributed by inter-row vegetation.

David Ledderhof, Ralph Brown, Andrew Reynolds, and Marilyne Jollineau "Using remote sensing to understand Pinot noir vineyard variability in Ontario.," Canadian Journal of Plant Science 96(1), 89-108, (25 February 2016).
Received: 10 April 2015; Accepted: 1 August 2015; Published: 25 February 2016

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