Canopy structural and leaf photosynthesis parameterizations such as maximum carboxylation capacity (V cmax ), slope of the Ball-Berry stomatal conductance model (BB slope ) and leaf area index (LAI) are crucial for modeling the plant physiological processes and canopy radiative transfer. These parameters are large sources of uncertainty in predictions of carbon and water fluxes. In this study, we develop an optimal inversion framework to use the Soil Canopy Observation Photochemistry and Energy fluxes (SCOPE) model for estimating V cmax , BB slope and LAI by constraining observations of coupled carbon and energy fluxes from eddy covariance towers. We adapted SCOPE to follow the biochemical implementation of the Community Land Model and applied a moving window Bayesian non-linear inversion framework using SCOPE to invert the ecosystem parameters V cmax , BB slope and LAI that best match flux-tower observations of Gross Primary Productivity (GPP) and Latent Energy (LE) fluxes. We applied this inversion framework to plant species having both the C 3 and C 4 photosynthetic pathways across three different ecosystems. Our results demonstrate the applicability of the approach in terms of capturing the seasonal variability and posterior error reduction (40–90 %) of key ecosystem parameters. The optimized parameters capture the diurnal and seasonal variability in the GPP and LE fluxes well when compared to flux tower observations (0.95 > R 2 > 0.79). This study thus demonstrates the feasibility of parameter inversions using SCOPE, which can be easily adapted to incorporate additional data sources such as spectrally resolved reflectance and solar induced chlorophyll fluorescence.