Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region were tested for modelling grapevine water status estimated by the predawn leaf water potential (Ψ_pd). The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation between VIs and Ψ_pd. A linear regression was defined using a parameterization dataset. The correlation analysis between Ψ_pd and the VIs computed with the standard formulation showed relatively poor results, with values for squared Pearson correlation coefficient (r²) smaller than 0.67. However, the results of r² highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs). The optimal Visible Atmospherically Resistant Index (VARI) and Normalized Difference Greenness Vegetation Index (NDGI) showed the higher r² and stability index results. The equations obtained through the regression between measured Ψ_pd (Ψ_{pd_obs}) and optimal VARI and between Ψ_{pd_obs} and optimal NDGI when using the parameterization dataset were adopted for predicting Ψ_pd using a testing dataset. The comparison of Ψ_{pd_obs} with Ψ_pd predicted based on VARI led to R² = 0.79 and a regression coefficient b = 0.96. Similar R² was achieved for the prediction based on NDGI, but b was smaller (b = 0.93). Results obtained allow the future use of optimal VARI and NDGI for estimating Ψ_pd, supporting vineyards irrigation management.

Non-destructive, accurate, user-friendly and low-cost approaches to determining crop leaf area (LA) are a key tool in many agronomic and physiological studies, as well as in current agricultural management. Although there are models that estimate cut rose LA in the literature, they are generally designed for a specific stage of the crop cycle, usually harvest. This study aimed to estimate the LA of cut “Red Naomi” rose stems in several phenological phases using morphological descriptors and allometric measurements derived from image processing. A statistical model was developed based on the “multiple stepwise regression” technique and considered the stem height, the number of stem leaves, and the stage of the flower bud. The model, based on 26 stems (232 leaves) collected at different developmental stages, explained 95% of the LA variance (R² = 0.95, n = 26, p < 0.0001). The mean relative difference between the observed and the estimated LA was 8.2%. The methodology had a high accuracy and precision in the estimation of LA during crop development. It can save time, effort, and resources in determining cut rose stem LA, enhancing its application in research and production contexts.