Evapotranspiration represents a major influence on atmospheric behaviour; however, the Soil, Vegetation, and Snow (SVS) Land Surface Model, which is currently under development at Environment and Climate Change Canada (ECCC), has been shown to underestimate total evapotranspiration rates and overestimate the control of soil moisture dynamics on evapotranspiration, in turn causing inaccuracies in model projections. The exact reasons for this are complex, stemming from a combination of a simplified representation of soil moisture and vegetation dynamics. For example, leaf area index (LAI) presents a major control on evapotranspiration, however LSMs often use simplified representations of annual LAI cycles, such as generalized monthly averages of LAI based on vegetation type with no inter-year variation. In order to improve upon this, a variety of methods have been developed to assimilate LAI observations. This study seeks to explore whether assimilating LAI will improve the representation of evapotranspiration within SVS. SVS was run in point form at the University of Guelph’s lysimeter site at the Elora Research Station. Assimilated LAI values were derived from monthly averages of LAI collected from hyperspectral drone data during the summers of 2021-2023. SVS estimates of evapotranspiration while then be compared to lysimeter observations from the study site. Based on similar studies, the expected results are that assimilating LAI will improve model estimates of evapotranspiration by bringing the model more in line with reality.