The emergence of precision agriculture technologies and an increasing demand for higher quality grape products has led to a growing interest in the practice of precision viticulture; monitoring and managing spatial variations in productivity-related variables within single vineyards. Potentially, one of the most powerful tools in precision viticulture is the use of remote sensing through its ability to rapidly provide a synoptic view of grapevine shape, size and vigour over entire vineyards. Its potential for improving viticultural practice is evident by the relationships that are known to exist between these canopy descriptors and grape quality and yield. This paper introduces the reader to remote sensing and reviews its recent, and potential, applications in viticulture.

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.