The maximum carboxylation rate (Vc_max) represents the intrinsic capability of leaves to take up CO₂ through photosynthesis. Limited understanding of observed variability in Vc_max is a major source of uncertainty in today’s earth system models simulating global photosynthesis and the global carbon cycle. In recent years, scientists have proposed several ecological theories to derive seasonal- and spatially varying Vc_max, in an attempt to constrain the uncertainty of models. The resulting global Vc_max datasets can generally be categorized into 3 types: nutrient-driven, climate-driven and climate-nutrient-driven. The quality of these Vc_max products has yet to be evaluated. In this study, we aim to evaluate seven different global gridded Vc_max datasets using newly released satellite-derived global leaf chlorophyll content (Chl), based on the hypothesis that the variability of Chl is tightly coupled with Vc_max. Our results show that a climate-driven Vc_max product based on optimality theory has the strongest correlation with Chl for croplands and grasslands, while for forests, the climate-nutrient-driven Vc_max performed slightly better than those driven by climate only. Our study demonstrates a novel approach for testing ecological theories of photosynthetic potential using remote sensing observations, and implies a difference in the mechanisms underlying Vc_max seasonality and heterogeneity between different biomes.