Tree crown architecture, which we define as the 3-D arrangement and orientation of leaves within a tree crown, influences the rates of photosynthesis, evapotranspiration, and spectral reflectance that affect tree and forest responses to climate change. As part of their adaptive and acclimation strategies for responding to environmental variability, trees are likely to differ in their tree crown architectures, but these differences remain poorly described. We use measurements from 11 deciduous forest locations within the National Ecological Observatory Network (NEON) to quantify traits that can define key dimensions of variability in crown architecture. Specifically, we: (1) measure seasonal trends in mean leaf angle (MLA) from tower-based time-lapse photography, (2) quantify traits describing the density and distribution of leaves in tree crowns from NEON Airborne Observation Platform (AOP) LiDAR data, and (3) infer crown functioning from multi-scale data on near-infrared reflectance of vegetation (NIRv), as obtained from phenocams, the NEON AOP imaging spectrometer, and Harmonized Landsat Sentinel-2 (HLS). From these data, we test for trait covariations (e.g., among MLA, the vertical distribution of plant area index, and the seasonal peak of NIRv) that can suggest fundamental tradeoffs governing how each species arranges and orients leaves in their crowns. In describing how these crown architectural traits covary across the diverse tree species and wide environmental gradients within 11 NEON sites, we highlight implications for tree ecophysiology and remote sensing-based studies on the interactions of trees, forests and climate change.