Methane fluxes on an active flood plain situated in the Siberian Lena River Delta were studied applying the eddy covariance method. During the growing season, the observed fluxes exhibited a great deal of temporal variability, which was largely the result of the pronounced spatial variability of soil and vegetation characteristics within the footprint. Explaining this variability was based on three data-driven modelling approaches: the automatically operating algorithms stepwise regression as well as neural network, and a mechanistic model, which utilised exponential relationships between the methane flux and both flux drivers soil temperature and friction velocity. A substantial improvement in model performance was achieved by applying footprint information in the form of relative contributions of three vegetation classes to the flux signal. This aspect indicates that the vegetation served as an integrated proxy for flux drivers, whose characteristics permanently varied according to the shifting source area. The neural network performed best in explaining the variability of the observed methane fluxes. However, validating the models’ generalisability revealed that the mechanistic model provided the most predictive power suggesting that this model best captured the causality between the methane flux and its drivers. After integrating the gap-filled time series, all models yielded footprint budgets that were similar in magnitude. These budgets, however, lacked representativity due to the sensor location bias, i.e. their strong dependence on tower location, measurement height and wind field conditions. Thus, an unbiased budget of the total area of the flood plain was estimated utilising the mechanistic model. Initially, a downscaling procedure partitioned the observed flux with a seasonal mean of 0.012 μmol m^-2 s^-1 into three individual vegetation class fluxes accounting for shrubs (0.0004 μmol m^-2 s^-1), sedges (0.052 μmol m^-2 s^-1) and intermediate vegetation (0.018 μmol m^-2 s^-1). These decomposed fluxes in turn formed the basis – in conjunction with a classified high-resolution orthomosaic of the flood plain – for the vegetation class area-weighted upscaling. Alternatively, the straightforward upscaling of the footprint budgets (without the preceding downscaling) yielded budgets that underestimated the methane source strength of the flood plain by roughly 42 %. Hence, the application of fine-scale information on surface characteristics is crucial for both modelling methane flux dynamics and adequately estimating budgets of heterogeneous ecosystems being abundant in the tundra biome.