A timely and appropriate level of water deficit is desirable in wine grape production to optimize fruit quality for winemaking. Thus, it is crucial to find a robust and rapid method to assess grapevine water stress in real time. Hyperspectral imaging (HSI) has the potential to detect changes in leaf water status, but the robustness and accuracy are limited in field applications. This study focused on developing ground based approaches for detecting soil and grapevine water status using HSI obtained in diffused lighting conditions. During the 2021 growing season, leaf water potential (ΨL), stomatal conductance (gs) on the selected leaves and volumetric soil moisture (θv) in the root zone were measured as water status indicators. Spectral data from diffused and direct sunlight conditions were obtained to construct models to estimate plant and soil water status indicators. Partial least squares (PLS) regression models were individually developed to estimate ΨL, gs, and θv using spectra obtained from direct/diffused lighting conditions, respectively. The results indicated that the ΨL estimation model using spectral data from diffused lighting performed better than that obtained using direct sunlight, indicated by a higher R2 (0.89 vs. 0.82), a lower RMSE (0.12 vs. 0.15 MPa) and a lower MAE (0.10 vs. 0.11 MPa). The model developed for estimating θv using spectral data under diffused lighting achieved superior performance to the one in direct sunlight in terms of R2, RMSE and MAE (0.90 vs. 0.89 and 1.56 vs. 1.59 %, 1.26 vs. 1.29 %). These results demonstrated that spectral data obtained under diffused lighting can improve model performance by providing a more uniform illumination. Ground based HSI was capable of high-resolution sensing of grapevine water status by estimating ΨL and gs and map variability within canopies.