Surface carbon fluxes associated with terrestrial vegetation play a key role in the global carbon cycle. Remote sensing (RS) and land surface models (LSM) have demonstrated to be valuable tools in assessing the gross primary production (GPP). Yet, the seasonal variability of this flux, and timing of the seasonal cycle remain challenging to observe and simulate accurately. Here, the ability of four RS products and two LSM to simulate GPP and its variability was assessed. It was found that the mean seasonal GPP was simulated accurately with RS-based models, but that it failed to capture the seasonal variability and timing of the seasonal cycle. In contrast, the LSMs demonstrated their ability to simulate seasonal anomalies in GPP, but had trouble to simulate the associated phenology.