Deploying aircraft to acquire snapshot observations of CH4 emissions from landfills once or repeatedly can be costly and logistically complex. Satellites address some concern related to survey frequency and possibly costs but are affected by spatial resolution, higher detection limits, cloud cover, and the accuracy of emissions priors if the data are used in an inversion framework to estimate an annual emissions rate (e.g., . As such, there is value in calculating landfill CH4 emissions using models, similar to the calculations used by operators in reporting to national GHGRPs. Currently, models used to estimate CH4 emissions from landfills have known issues, such as overestimating gas capture system efficiencies and soil oxidation rates , and lack of accounting for fugitive emissions from construction or other infrastructure . The FOD method may perform better than the CEA method for estimating emissions, but landfill CH4 generation can diverge from FOD principles -generation and thus emissions rates can be higher and sustained over longer periods . This underscores that measurement and modeling are complimentary efforts in accurately constraining estimates of total landfill CH4 emissions. Empirical data should inform gas collection system efficiency coefficients in models, and measurements of fugitives should be integrated into models. More nuanced models for landfills do exist and should be used broadly, such as the California Landfill Methane Inventory Model (CALMIM), which was developed to better account for variability in soil cover oxidation rates depending on the cover type, weather, and climate . Addressing these issues may narrow the mismatch between estimates reported across the studies, enhance the credibility of estimates of CH4 emissions from landfills, and better inform targeted abatement and mitigation efforts.