Interior wetlands supported by snowmelt, such as those in the arid and semi-arid Intermountain American West, serve critical functions for the wild and human communities of dryland regions, and sequester carbon in quantities that could meaningfully contribute to climate mitigation. Evaluations of individual reaches have shown higher carbon stocks in floodplains than neighboring uplands, but there are currently no maps that estimate the overall carbon storage benefits of mesic ecosystems in the American West, and mapping them is challenging due to their small size and dispersion across the landscape. While existing models of biomass and soil organic carbon (SOC) at regional-and-greater extents are well suited to represent larger, more homogenous upland areas, the spatial resolution of these models are too coarse to capture many small interior wetlands. To fill this gap, I have developed a mapped product that highlights the carbon stock contributions of mesic ecosystems in the American West. Underlying this product is a LiDAR-multispectral data fusion model constrained to wetland vegetation extent that predicts canopy height at a 10-meter spatial resolution with lower-than-typical error metrics for models of this kind. By leveraging these height predictions and spectral differences in phenology, as well as publicly available data on height/biomass relationships, I can estimate carbon at every pixel according to vegetation type and size. The final product is a landscape carbon map that overlays our high-resolution wetland carbon map onto coarser existing products and highlights the contribution of mesic areas relative to their extent, allowing for broader understanding of the spatial distribution of natural carbon stores in dryland regions. Preliminary results indicate a highly disproportionate amount of carbon stock found in mesic ecosystems, but substantial uncertainties remain, particularly with SOC estimates, and efforts that improve our ability to predict floodplain SOC heterogeneity will be key to future improvements in locally-specific estimates.