## 1 Introduction  
As emphasized in the 2021 IPCC report, methane is one of the most potent greenhouse gases, and by itself, it accounts for 25% of the overall global warming [1]. Wetlands are the largest natural emitters of methane and are estimated to account for 20-40% of global methane emissions [2], while constituting the highest source of uncertainty in the global methane budget [3]. The challenges in modeling methane budgets in wetlands are to a large degree due to the small-scale temporal and spatial heterogeneity of wetland structure and associated methane flux rates [4]. The ecological, hydrological, and biogeochemicalconditions that determine methane production and oxidation vary strongly among different ecological patch-types within a wetland. Methane transport occurs through three pathways: diffusion through soil sediments and water columns, ebullition (bubbling) from the soil, and transport through plant aerenchyma [5]. All have different characteristic rates as functions of the vegetation type [6; 7]. Earth system models (ESMs) represent a common tool for estimating and projecting local and global greenhouse gas emissions [8]. However, such models function at a coarse scale (10s of km), which is larger than that of the typical coastal wetland scale (100s of m), and as a result, ESMs do not explicitly represent the different within-wetland patches (e.g., open water, submerged vegetation, emergent vegetation, etc.). Because the flux rates in different patches can vary by orders of magnitude and because methane fluxes can be dominated by hot spots of emissions, such a mean, \"whole-wetland\" representation often leads to the underestimation of methane flux variability [9]. They also cannot resolve long-term (interannual) changes in fluxes due to hydrological changes that lead to changes in the vegetation composition within the wetland [10]. Currently, efforts involving the ELM--the land surface model of the E3 SM (Energy Exascale Earth System Model)--are underway to expand the model representations of wetlands [2] to be more conceptually similar to heterogeneously vegetated ecosystems by resolving the simultaneous contributions from multiple, different, sub-grid-scale functional type patches representing different eco-hydroological patches within a wetland. This advanced \"within-wetland patch-type\" approach allows for resolving different physical conditions (water elevation, soil column temperature) as well as biogeochemical processes (rates of methanogenesis, methane oxidation, methane transport) for each wetland patch-type. For example, cattails tend to grow in waters of intermediate depth and could have higher aerenchyma transport rates than those of deeper open water patches (which include no plants, and thus no aerenchyma transport), and mud flats could have higher methane oxidation rates and stronger temperature dependences for methane production than floating vegetation patches. However, for this advancement to improve the global estimates of methane emissions, modeling efforts must be able to incorporate global, high-resolution, observation-based characterizations of the within-wetland distributions of patch-types. Such observations are challenging due to the large spatial extent of wetlands globally, combined with the small scale of individual wetlands and high spatial resolution (single-10s of m) of the variation in patch-types, posing a difficulty in identifying the boundaries of eco-hydroological patches within wetlands [11]. Furthermore, global climate change and the consequent changes to sea levels (affecting coastal wetlands) and precipitation regimes (affecting all wetlands) lead to changes in wetland water elevations, and drive further, rapid (months-decades) changes in patch-type identity and distribution within wetlands [12], resulting in important variations in the total methane fluxes [13; 14; 15].