The quality of grapes in the production of wine is highly influenced by vine water status, where optimal water deficit or selective harvesting can improve berry quality. It is in this context that the rapid advancement in small unmanned aerial system (sUAS) technology and the potential application of real-time, high-spatial resolution hyperspectral imagery for vineyard moisture assessment, have become tractable. This study sought to further sUAS hyperspectral imagery as a tool to model water status in a commercial vineyard in Upstate New York. High-spatial resolution (2.5 cm ground sample distance) hyperspectral data were collected in the visible/near-infrared (VNIR; 400-1000nm) regime on three flight days. A Scholander pressure chamber was used to directly measure the midday stem water potential (Ψ<sub>stem</sub>) within imaged vines at the time of flight. High spatial resolution pixels enabled the targeting of pure (sunlit) vine canopy with vertically trained shoots and significant shadowing. We used the partial least squares-regression (PLS-R) modeling method to correlate our hyperspectral imagery with measured field water status and applied a wavelength band selection scheme to detect important wavelengths. We evaluated spectral smoothing and band reduction approaches, given signal-to-noise ratio (SNR) concerns. Our regression results indicated that unsmoothed curves, with the range of wavelength bands from 450- 1000 nm, provided the highest model performance with R<sup>2</sup> = 0.68 for cross-validation. Future work will include hyperspectral flight data in the short-wave infrared (SWIR; 1000-2500 nm) regime that were also collected. Ultimately, models will need validation in different vineyards with a full range of plant stress.