Wetlands are considered to be the largest emitters of biogenic methane, yet they represent the highest source of uncertainty in global methane emission estimates in Earth System Models (ESMs). This uncertainty is partially attributed to the small-scale spatial and temporal heterogeneity of biogeochemical and hydrological processes driving methane production, oxidation, and transport. Due to their (10s’ of Km), ESMs do not explicitly simulate within-wetland variability of ecosystem conditions and biogeochemical processes. In addition, these variabilities are usually under-represented in coarse spatial- and temporal-resolution remote sensing images. In this study, we apply the Energy Exascale Earth System Model (E3SM) Land Model (ELM), where we developed a separate wetland land-unit. Representing wetland land-units allows the model to simulate multiple eco-hydrological patches (i.e., different vegetation communities) within a wetland at the sub-grid level, with distinct ecological, microbial, and hydrological parameters representing each patch type. The patch cover distribution is input to ELM using global, high-resolution, (30m 30m). We use seasonal time-series of HLS-derived NDVI, which provide distinct seasonal temporal “fingerprints” to classify HLS pixels to specific patch types and infer the corresponding plant cover distribution within the wetland. Regular Eddy-Covariance, chamber flux, and pore-water concentrations from two study-sites in Louisiana were used to validate our model. Our results show correspondence between observed and modelled carbon and methane fluxes and soil methane concentrations after optimizing vegetation photosynthetic rates, respiration rates, and methane production and oxidation parameters using a Bayesian approach (BOA, Bayesian Optimization for Anything). Our findings also show a , thus emphasizing the role of wetland sub-grid representation within-wetland patch distribution in reducing model uncertainty.