Methane (CH4) emissions were measured at the Wilma H. Schiermeier Olentangy River Wetland Research Park (ORWRP) over three summers and two winters using an eddy covariance system. We used an empirical model to determine the main environmental drivers of methane emissions. Methane emissions covary strongly with water vapor fluxes, CO2 fluxes, and soil temperature. We adjust our models to account for the heterogeneous environment of the wetland by including the flux footprint distribution among different microsites as a predictive variable in the methane model. We used a forward linear stepwise model in combination with an Akaike information criteria-based model selection process and neural network modeling to determine which environmental variables are most effective in modeling methane emissions in our site. Different models and environmental variables best represented methane fluxes in the winter and summer and also during the day or night within each season. We parameterized an optimal empirical model for methane emissions from the ORWRP that is used for gap filling of site-level methane fluxes over 2 years. Some of the most effective variables for modeling methane were carbon, water vapor, and heat fluxes, all of which typically have the same data gaps as the time series of methane flux. In order to determine if these variables were useful for modeling methane despite the additional gap-filling error, we determined through an error propagation experiment that eddy covariance gap-filling models for methane may be best developed by including other gap-filled fluxes as predictors, despite the high level of shared gaps and subsequent gap-fill error propagation.