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84 WINES&VINES February 2017 GRAPEGROWING WINE EAST poorly drained Beverly/Toledo soils (St. David's Bench). This array of soil types pro- vided a significant range of water- holding capacities that impact vine water status. Vineyard blocks were GPS-delineated to determine shape using a Trimble hand-held GPS tracker equipped with Ter- raSync software. Between 80 and 100 sentinel "data" vines were identified in a ≈ 8-meter x 8-meter grid within each vineyard and geolocated by GPS. Field measure- ments and berry samples were taken from these vines. Soil moisture and leaf water potential (ψ): Vineyard soil mois- ture (SM) was measured by time domain reflectometry using the Field Scout TDR 300 Soil Moisture Meter. The volumetric water con- tent mode was used. Measure- ments were obtained over a 20 cm depth for all sentinel vines at three times during the growing season: berry set, lag phase and véraison. Vine water status was measured using midday leaf ψ with a pres- sure bomb from Soil Moisture Equipment using a pressurized supply of nitrogen gas. Measure- ments were conducted only at the designated leaf ψ vines (≈20 per vineyard), on the same days as SM measurements. Leaf ψ was deter- mined on mature, undamaged/ non-diseased leaves fully exposed to sun for between 1,000 and 1,400 hours. Yield components, vine size, berry analysis: Harvest dates were at the discretion of vineyard managers. Fruit from each sentinel vine was hand- harvested; clusters were counted, and fruit was weighed using a portable field scale. Cane prun- ings were weighed to determine vine size in all vineyards. A 100- berry sample was taken from each sentinel vine at harvest and frozen at -25° C. Each sample was weighed to determine mean berry weight and placed in a bea- ker in a water bath at 80° C for one hour to dissolve precipitated tartrates. All viticultural data collection, basic berry composition analysis including Brix, pH and titratable acidity (TA) and monoterpene analysis (Riesling) were con- ducted as in Willwerth et al., 28 and berry composition analysis for Cabernet Franc and Pinot Noir CAVE SPRING CABERNET FRANC Individual values on maps from 2015 represent highest and lowest values for the respective color zones. Individual points on maps represent the sentinel vines. A SHORT DICTIONARY Geolocation: A technique used to determine the geographic location of an object on the earth's surface; i.e., the process of obtaining spatial information for individual objects, mainly from geographical location (latitude and longitude). Geospatial technologies: Sometimes referred to as geomat- ics, geospatial technologies include a range of information tools and techniques used to acquire, analyze, manage, store and/ or visualize different types of location-based data. Commonly used geospatial technologies include global positioning systems (GPS), geographic information systems (GIS) and remote sensing. Ground truthing: In remote sensing, ground truthing refers to information collected at the study site location. It may also refer to a process in which pixels on a satellite image are compared to site locations (at the present time) to verify the contents of the pixels within an image. It can involve acquiring the geographic coordi- nates of site locations with GPS technology and comparing those to the coordinates of the image pixels under investigation. Inverse distance weighting: A type of deterministic method for multivariate interpolation with a known scattered set of points. The predicted values of a given variable to unknown (unsampled) points are calculated with the inverse function of the distance from the unsampled points to the known (sampled) points. k-means clustering: A method of vector quantization popular for cluster analysis in multidimensional data mining. k-means clustering aims to partition n observations into k clusters (classes) in which each observation belongs to the cluster with the nearest Euclidean distance. Moran's I: Moran's I is a common index of spatial autocorrelation developed by P.A.P. Moran. Spatial autocorrelation is character- ized by a correlation in a signal among nearby locations in space. Spatial autocorrelation is more complex than one-dimensional autocorrelation because spatial correlation is multi-dimensional (i.e., two or three dimensions) and multi-directional. Normalized difference vegetation index (NDVI): A numeric index, ranging in value from -1 to +1, that is calculated by taking a ratio of spectral reflectance measurements acquired by devices operating in the red (R) and near-infrared (NIR) portions of the electromagnetic spectrum. NDVI is used to assess vegetation condition including plant vigor and is calculated using the following equation: NIR - R / NIR + R. Given that healthy vegetation strongly reflects NIR energy and absorbs most of the red energy incident upon it, the NDVI value for healthy vegetation is often close to +1. On the other hand, water-, disease- or pest-stressed (vegetation) canopies will reflect more red energy, resulting in a lower NDVI value (close to 0); negative NDVI values are seldom found in objects of agricultural relevance. Proximal sensing: A form of remote-sensing technology whereby a sensing device is positioned proximal (close to) the object of inter- est. Information is acquired by ground-sensing devices recording electromagnetic energy reflected or emitted from targets of interest. Spatial resolution: Commonly refers to the size of the smallest discrete object that can be clearly differentiated and identified in an image of a scene, and is a function of the number of picture elements or "pixels" in the image and the physical extent of the complete scene (e.g., the surface area captured by a remote sensing image). The term "pixel size" is frequently used to pro- vide an indication of potential spatial resolution. NDVI Vine Size (kg) Leaf ψ (MPa) 0.80-0.81 0.83-0.84 Color (OD 520) Anthocyanins (mg/L) Phenols (mg/L) 8.9-19 23-28 0.5-0.9 1.2-1.6 -1.1- -1.2 -0.90- -0.93 420-910 1,100-1,200 1,000- 1,700 2,100-2,500