Remote sensing of vegetation phenology is an important method with which to monitor terrestrial responses to climate change, but most approaches include signals from multiple forcings, such as mixed phenological signals from multiple biomes, urbanization, political changes, shifts in agricultural practices, and disturbances. Consequently, it is difficult to extract a clear signal from the usually assumed forcing: climate change. Here, using global 8 km 1982 to 1999 Normalized Difference Vegetation Index (NDVI) data and an eight-element monthly climatology, we identified pixels whose wavelet power spectrum was consistently dominated by annual cycles and then created phenologically and climatically self-similar clusters, which we term phenoregions. We then ranked and screened each phenoregion as a function of landcover homogeneity and consistency, evidence of human impacts, and political diversity. Remaining phenoregions represented areas with a minimized probability of non-climatic forcings and form elemental units for long-term phenological monitoring.