Ecosystems at high latitudes are under increasing stress from climate change. To understand changes in carbon fluxes, in situ measurements from eddy covariance networks are needed. However, there are large spatiotemporal gaps in the high-latitude eddy covariance network. Here we used the relative extrapolation error index in machine learning-based upscaled gross primary production as a measure of network representativeness and as the basis for a network optimization. We show that the relative extrapolation error index has steadily decreased from 2001 to 2020, suggesting diminishing upscaling errors. In experiments where we limit site activity by either setting a maximum duration or by ending measurements at a fixed time those errors increase significantly, in some cases setting the network status back more than a decade. Our experiments also show that with equal site activity across different theoretical network setups, a more spread out design with shorter-term measurements functions better in terms of larger-scale representativeness than a network with fewer long-term towers. We developed a method to select optimized site additions for a network extension, which blends an objective modeling approach with expert knowledge. Using a case study in the Canadian Arctic we show several optimization scenarios and compare these to a random site selection among reasonable choices. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations.