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In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO _2 and CH _4 fluxes across the YK Delta of south-west AK. We found that waterbody size and contour were strong predictors for aquatic CO _2 emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (