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Vineyards are sampled on multiple occasions during the growing season for a range of purposes, particularly to assess fruit maturation. The objective of this work was to determine if satellite normalized difference vegetation index (NDVI) vineyard images could be used to compute optimal spatially explicit sampling protocols for determining fruit maturation and quality, and minimize the number of locations physically sampled in a vineyard. An algorithm was designed to process Landsat images to locate three consecutive pixels that best represent the three quantile means representing the left tail, center, and right tail of the NDVI pixel population of a vineyard block. This new method (NDVI3) was compared to a commonly used method (CM8) and random sampling (R20) in 13 and 16 vineyard blocks in 2016 and 2017, respectively, in the Central Valley of California. Both NDVI3 and CM8 were highly correlated with R20 in pairwise comparisons of soluble sugars, pH, titratable acidity, and total anthocyanins. Kolmogorov-Smirnov tests indicated that NDVI pixels sampled via the NDVI3 method generally better represented the block population than pixels selected by CM8 or R20. Analysis of 24 blocks over a 3-year period indicated that sampling solutions were temporally stable. [ABSTRACT FROM AUTHOR]