The synthesis of model and observational information using data assimilation can improve our understanding of the terrestrial carbon cycle, a key component of the Earth's climate-carbon system. Here we provide a data assimilation framework for combining observations of solar-induced chlorophyll fluorescence (SIF) and a process-based model to improve estimates of terrestrial carbon uptake, or gross primary production (GPP). We then quantify and assess the constraint SIF provides on the

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... y of global GPP through model process parameters in an error propagation study. By incorporating one year of satellite SIF observations from the GOSAT satellite, we find that the uncertainty in global annual GPP is reduced by 79 %, from ±13.0 Pg C yr^−1 to ±2.8 Pg C yr^−1. This improvement is achieved through strong constraint of leaf growth processes and weak to moderate constraint of physiological parameters. We also find that the inclusion of uncertainty in shortwave down radiation forcing has a net-zero effect on uncertainty in GPP when incorporated in the SIF assimilation framework. This study demonstrates the powerful capacity of SIF to reduce uncertainties in process-based model estimates of GPP and the potential for improving our predictive capability of this uncertain carbon flux.