Spatial and temporal information on plant functional traits are lacking in ecology, which limits our understanding of how plant communities and ecosystems are changing. This problem is acute in remote tropical regions, where information on plant functional traits is difficult to ascertain. We used Carnegie Airborne Observatory visible-to-shortwave infrared (VSWIR) imaging spectroscopy with light detection and ranging (LiDAR) to assess the foliar traits of Amazonian and Andean tropical forest canopies. We calibrated and validated the retrieval of 15 canopy foliar chemicals and leaf mass per area (LMA) across a network of 79 1-hectare field plots using a new VSWIR-LiDAR fusion approach designed to accommodate the enormous scale mismatch between field and remote sensing studies. The results indicate that sparse and highly variable field sampling can be integrated with VSWIR-LiDAR data to yield demonstrably accurate estimates of canopy foliar chemical traits. This new airborne approach addresses the inherent limitations and sampling biases associated with field-based studies of forest functional traits, particularly in structurally and floristically complex tropical canopies.