Object-based analysis of grapevine canopy relationships with winegrape composition and yield in two contrasting vineyards using multitemporal high spatial resolution optical remote sensing

Predictive mapping of within-vineyard winegrape yield, quality, and ripeness, using high spatial resolution optical remote sensing, relies upon relationships between image-derived canopy vigour metrics and fruit composition and yield components. Regular image acquisition of two contrasting vineyard sites enabled a temporal analysis of variation in these relationships. An image processing algorithm was developed to segment vineyard imagery into single grapevine objects. Various remote-sensing vegetation indices, calculated for each grapevine object, revealed that indices sensitive to high vegetation densities performed significantly better at predicting fruit composition and yield elements than the commonly used normalized difference vegetation index. The strength and direction of correlations between canopy vigour and season-end fruit descriptors varied by phenological stage and vineyard type. The ability of optical remote sensing to successfully map within-vineyard winegrape composition and yield may vary depending upon vineyard characteristics, management, and temporal variability in overall vineyard production.