We explored the possibilities of a Bayesian-based data assimilation algorithm to improve the wetland CH₄ flux estimates by a dynamic vegetation model. By assimilating CH₄ observations from 14 wetland sites, we calibrated model parameters and estimated large-scale annual emissions from northern wetlands. Our findings indicate that this approach leads to more reliable estimates of CH₄ dynamics, which will improve our understanding of the climate change feedback from wetland CH₄ emissions.