Many cities, including Toronto, have pledged to reduce their greenhouse gas emissions. However, in order to accurately monitor their progress, it is important to estimate emissions using observation-based approaches. These methods, however, often do not account for fluxes from vegetation in and around the cities. Our research aims to use remote sensing and vegetation models to estimate biogenic fluxes of CO₂ in the city of Toronto. To do this we use Solar-Induced Fluorescence (SIF), a by-product of photosynthesis which can be detected from satellite instruments, and two vegetation models: the SIF for Modelling Urban biogenic Fluxes (SMUrF) model and the Urban Vegetation Photosynthesis and Respiration Model (UrbanVPRM). We have made several updates to each model to optimize their use over the Toronto area. We validate the results from these models against ground-based measurements of biogenic CO₂ fluxes in Southern Ontario and compare the results from each of the models over the city of Toronto. The results from this research will help to constrain both urban biogenic and anthropogenic fluxes of CO₂.