Spectral measurements have been used to assess leaf biochemical and biophysical properties for decades and are commonly employed in field monitoring in both aerial and ground based platforms. These measurements can rapidly provide data that can be linked to plant species, properties, and health. To date, leaf models that use these spectral measurements have focused on the biochemical properties of plants (water, chlorophyll, carotenoid, or anthocyanin content) but less work has been published on modelling the biophysical properties (surface roughness, trichomes, surface waxes). These biophysical features are not only valuable in identifying plant species, health and physiological response, but can also mask the biochemical effects resulting in lower accuracy when non-standard biophysical attributes are present. To expand the applications of biochemical leaf models, modelling approaches should account for spectral and spatial variations in reflectance due to surface phenotypes. In this work, two approaches are used to integrate biophysical modelling with the current models. First, the leaf biochemical model PROSPECT-D is modified to account for leaf hairiness (pubescence) and surface waxes. This is done by recalibrating the model using plants that express hairs, glossy waxes, and glaucous waxes. The second approach uses a newly developed surface phenotype identification model in series with the PROSEPCT-D model. The results of modelling (of both biochemical and biophysical properties) are then compared among the two approaches and the traditional PROSPECT-D model to highlight the importance and effectiveness of surface phenotype modelling and identification.