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<p>Understanding the sources and sinks of methane (CH<span class="inline-formula">₄</span>) is critical to both predicting and mitigating future climate change. There are large uncertainties in the global budget of atmospheric CH<span class="inline-formula">₄</span>, but natural emissions are estimated to be of a similar magnitude to anthropogenic emissions. To understand CH<span class="inline-formula">₄</span> flux from biogenic sources in the United States (US) of America, a multi-scale CH<span class="inline-formula">₄</span> observation network focused on CH<span class="inline-formula">₄</span> flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and land cover. To determine the gaps in physical infrastructure for developing this network, we need to understand the landscape representativeness of the current infrastructure. We focus here on eddy covariance (EC) flux towers because they are essential for a bottom-up framework that bridges the gap between point-based chamber measurements and airborne or satellite platforms that inform policy decisions and global climate agreements. Using dissimilarity, multidimensional scaling, and cluster analysis, the US was divided into 10 clusters distributed across temperature and precipitation gradients. We evaluated dissimilarity within each cluster for research sites with active CH<span class="inline-formula">₄</span> EC towers to identify gaps in existing infrastructure that limit our ability to constrain the contribution of US biogenic CH<span class="inline-formula">₄</span> emissions to the global budget. Through our analysis using climate, land cover, and location variables, we identified priority areas for research infrastructure to provide a more complete understanding of the CH<span class="inline-formula">₄</span> flux potential of ecosystem types across the US. Clusters corresponding to Alaska and the Rocky Mountains, which are inherently difficult to capture, are the most poorly represented, and all clusters require a greater representation of vegetation types.</p>