Leaf Area Index (LAI) is a key indicator of canopy development and productivity in sustainable viticulture. However, accurately estimating LAI in vineyards remains challenging due to complex canopy architectures, which often lead radiative transfer models (RTMs), such as PROSAIL, to underestimate LAI. This study proposes a novel hybrid approach tailored to vertically trained vineyards that uses a PROSAIL-based spectral LAI baseline with LiDAR-derived structural information through a data-driven correction strategy. The study was conducted during the 2023 season in a Mediterranean vineyard comprising three grapevine varieties. A structurally weighted index (LLIw) was derived using multiple linear regression to assign global, phenology- and variety-specific weights accounting for the relative importance of structural parameters across growth stages. The PROSAIL-derived LAI and LLIw were subsequently integrated using Support Vector Machine regression. The hybrid model showed strong agreement with ground-measured LAI (R² = 0. 88) and clearly outperformed PROSAIL alone (R² = 0. 42). The hybrid model captured spatio-temporal and varietal differences in canopy development, correcting seasonal underestimations and revealing structural dynamics not detected by spectral models alone. By integrating spectral and structural data, this hybrid approach provides physiologically informed, temporally consistent LAI estimates, supporting adaptive, variety-specific vineyard monitoring.
Sandonís-Pozo et al. (Sun,) studied this question.