Most ecosystem models currently represent plants as belonging to discrete “functional types” with fixed parameters, but approaches based on continuously varying plant traits more effectively capture gradual adaptation of ecosystems to environmental change. Recent syntheses of large trait databases have contributed immensely to our understanding of drivers of global plant trait variability at evolutionary time scales. However, major axes of interspecific trait variability, such as the trade-off between leaf nitrogen and mass per unit area (“leaf economics spectrum”), are insufficient to represent variability in other critical aspects of plant function such as water transport. Furthermore, global trait covariance patterns do not necessarily hold at smaller scales such as within plant functional types or biomes, yet these are the scales of the most policy-relevant ecological change. Tracking spatial and temporal variability in plant traits through succession and in response to environmental change is a task uniquely suited for remote sensing due to the required spatial scale and temporal continuity of observations.