It has been shown that mild water deficit in grapevine contributes to wine quality, in terms of especially grape and subsequent wine flavor. Water deficit irrigation and selective harvesting are implemented to optimize quality, but these approaches require rigorous measurement of vine water status. While traditional in-field physiological measurements have made operational implementation onerous, modern small unmanned aerial systems (sUAS) have presented the unique opportunity for rigorous management across vast areas. This study sought to fuse hyperspectral remote sensing, sUAS, and sound multivariate analysis techniques for the purposes of assessing grapevine water status. High-spatial and -spectral resolution hyperspectral data were collected in the visible/near-infrared (VNIR; 400-1000nm) and short-wave infrared (SWIR; 950-2500 nm) spectral regions across three flight days at a commercial vineyard in the Finger Lakes region of upstate New York. A pressure chamber was used to collect traditional field measurements of stem water potential (ψstem) during image acquisition. We completed some preliminary exploration of spectral smoothing, signal-to-noise ratio, and calibration techniques in forging our experimental design. We then correlated our hyperspectral data with a limited stress range (wet growing season) of traditional measurements for ψstem using multiple linear regression (R2 between 0.34 and 0.55) and partial least squares regression (R2 between 0.36 and 0.39). We demonstrated statistically significant trends in our experiment, further qualifying the potential of hyperspectral data, collected via sUAS, for the purposes of grapevine water management. There was indication that the chlorophyll and carotenoid absorption regions in the VNIR, as well as several SWIR water band regions warrant further exploration. This work was limited since we did not have access to experimentally-controlled plots, and future work should ensure a full range of water stress. Ultimately, models will need validation in different vineyards with a full range of plant stress.

Library of Congress Subject Headings

Grapes--Climatic factors--Remote sensing; Moisture--Measurement; Hyperspectral imaging; Drone aircraft

Publication Date


Document Type


Student Type


Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


Jan A. van Aardt

Advisor/Committee Member

Carl Salvaggio

Advisor/Committee Member

Emmett Ientilucci


RIT – Main Campus

Plan Codes