A new procedure is proposed for estimating actual basal crop coefficients from vegetation indices (K_{cb VI}) considering a density coefficient (K_d) and a crop coefficient for bare soil. K_d is computed using the fraction of ground cover by vegetation (f_{c VI}), which is also estimated from vegetation indices derived from remote sensing. A combined approach for estimating actual crop coefficients from vegetation indices (K_{c VI}) is also proposed by integrating the K_{cb VI} with the soil evaporation coefficient (K_e) derived from the soil water balance model SIMDualKc. Results for maize, barley and an olive orchard have shown that the approaches for estimating both f_{c VI} and K_{cb VI} compared well with results obtained using the SIMDualKc model after calibration with ground observation data. For the crops studied, the correlation coefficients relative to comparing the actual K_{cb VI} and K_{c VI} with actual K_cb and K_c obtained with SIMDualKc were larger than 0.73 and 0.71, respectively. The corresponding regression coefficients were close to 1.0. The methodology herein presented and discussed allowed for obtaining information for the whole crop season, including periods when vegetation cover is incomplete, as the initial and development stages. Results show that the proposed methods are adequate for supporting irrigation management.

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.