Estimating biophysical forest features from optical reflectance is a key approach for enhancing large-scale forest management. The present study estimates Leaf Area Index (LAI), leaf chlorophyll content (Cab), Leaf Mass per Area (LMA) and Equivalent Water Thickness (EWT) in different Eucalyptus sp. plantations using Sentinel-2 images and multiple linear regression models applicable to various Eucalyptus species and genotypes. This study investigates how canopy vertical heterogeneity, sun–sensor geometry, genotype, and related foliage traits influence the accuracy of biophysical variable retrieval. The key findings show that using a weighted average that gives greater importance to lower canopy leaves improved the accuracy of estimating Cab, LMA and EWT by approximately 35%, 22% and 28%, respectively, compared to relying solely on the upper canopy layer. These results indicate that the sensor's reflectance is substantially influenced by contributions from lower canopy layers. Additionally, incorporating sun–sensor geometry information alongside vegetation indices increased the accuracy of empirical model estimates by 13% for LAI and 41% for LMA. Furthermore, including either genotype information or related biophysical variables further improved model accuracy compared to other tested models, with gains of up to 21% for LAI, 11% for Cab, 44% for LMA and 4% for EWT.