Use of remote sensing to understand the terroir of the Niagara Peninsula. Applications in a Riesling vineyard

摘要

Aim: The purpose of this study was to determine if multispectral high spatial resolution airborne imagery could be used to segregate zones in vineyards to target fruit of highest quality for premium winemaking. We hypothesized that remotely sensed data would correlate with vine size and leaf water potential (ψ), as well as with yield and berry composition.Methods and results: Hypotheses were tested in a 10-ha Riesling vineyard [Thirty Bench Winemakers, Beamsville (Ontario)]. The vineyard was delineated using GPS and 519 vines were geo-referenced. Six sub-blocks were delineated for study. Four were identified based on vine canopy size (low, high) with remote sensing in 2005. Airborne images were collected with a four-band digital camera every 3-4 weeks over 3 seasons (2007-2009). Normalized difference vegetation index (NDVI) values (NDVI-red, green) and greenness ratio were calculated from the images. Single-leaf reflectance spectra were collected to compare vegetation indices (VIs) obtained from ground-based and airborne remote-sensing data. Soil moisture, leaf ψ, yield components, vine size, and fruit composition were also measured. Strong positive correlations were observed between VIs and vine size throughout the growing season. Vines with higher VIs during average to dry years had enhanced fruit maturity (higher °Brix and lower titratable acidity). Berry monoterpenes always had the same relationship with remote sensing variables regardless of weather conditions.Conclusions: Remote sensing images can assist in delineating vineyard zones where fruit will be of different maturity levels, or will have different concentrations of aroma compounds. Those zones could be considered as sub-blocks and processed separately to make wines that reflect those terroir differences. Strongest relationships between remotely sensed VIs and berry composition variables occurred when images were taken around veraison.Significance and impact of the study: Remote sensing may be effective to quantify spatial variation in grape flavour potential within vineyards, in addition to characteristics such as water status, yield, and vine size. This study was unique by employing remote sensing in cover-cropped vineyards and using protocols for excluding spectral reflectance contributed by inter-row vegetation. IntrductionMany studies have shown that fruit composition is impacted by vine vigour, whereby high vigour vines tend to compromise berry composition and quality (Bramley et al., 2011a, b, c; Hall et al., 2002; Johnson et al., 2003). Moreover, high vine vigour and high canopy density will also negatively impact crop size the following growing season, by shading the forming buds and consequently reducing fruitfulness (May et al., 1976; Sanchez and Dokoozlian, 2005). As well, others have shown that soil and vine water status both have a great effect on vine vigour, canopy development and fruit maturity (Hardie and Considine, 1976; Koundouras et al., 1999; Van Leeuwen, 2010; Van Leeuwen et al., 2003, 2004). Thus, it is of utmost importance to define and determine vine vigour accurately. Many techniques exist to estimate vine vigour manually in the field. The two principal methods are measurement of weight of cane prunings produced during the previous growing season (referred herein as vine size) and, the calculation of a leaf area index (Johnson et al., 2003).Unfortunately, both methods are laborious procedures in vineyards. Over the past 12 years, methods have been devised to assess vigour in other crops by remote sensing (Hall et al., 2002; Turner, 2001; White et al., 2001). The technology has been used to assess field crop vigour from airborne images and thereafter employ precision agriculture techniques such as fertilizer application from these data. But the main difference between a vineyard (perennial crop) and an annual field crop (i.e., corn, soybeans, wheat) is that annual crops have complete ground cover (Hall et al., 2003), while vines are planted in separate and discrete rows, making data extraction from airborne images more difficult for use in precision viticulture. The difficulty in many vineyards, particularly those in eastern North America and Europe, is to differentiate vine canopy from the ground cover between the rows. Initially, research studies conducted under non-cover cropped conditions in Australia demonstrated a direct link between the values of vegetation indices (VIs) calculated from data extracted from air- or ground- based leaf reflectance and the vigour of vines (Hall et al., 2003; Lamb et al., 2002; Stamatiadis et al., 2006). Vine vigour has been directly correlated to vine water status and to berry composition, mainly in Australia (Bramley, 2005; Bramley and Hamilton, 2004; Bramley et al., 2011a, b, c) and California (Turner, 2001) vineyards. Precision viticulture techniques have been employed as well in New Zealand Sauvignon blanc vineyards (Trought and Bramley, 2011; Trought et al., 2008), in addition