Leaf water potential (Ψl) in vineyards and orchards is a well-known indicator of plant water status and stress and it is commonly used by growers to make immediate crop and water management decisions. However, Ψl measurement via the direct method presents challenges as it is labor and time intensive and represents leaf-level conditions for only a small sampling of the vineyard or orchard block. Models are existed for vegetation water status prediction by using optical and thermal images. Considering this, a small unmanned aerial system (sUAS) can potentially collect those data and help to build a predictive model at a high resolution. In this study, we identify relationships and trends of vineyard Ψl and sUAS imagery at different times of the day and throughout the growing season in California. This study examines aerial and ground measurements collected by the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) program over a period of eight years across California to build multivariable models for real time water status prediction. This preliminary analysis looks at spatial and temporal trends using a stepwise regression between leaf-level Ψl measurements and sUAS optical, thermal, and elevation data to identify potential predictors of Ψl, thus enabling mapping of Ψl at the scale of individual grapevines across the block. Such predictive models could be used to map the spatial variability in Ψl across multiple blocks during the growing season and at critical phenological stages in real time and improve the targeting of irrigation applications for vineyards and other perennial crops.