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Summary Concurrent measurement of multiple foliar traits to assess the full range of trade‐offs among and within taxa and across broad environmental gradients is limited. Leaf spectroscopy can quantify a wide range of foliar functional traits, enabling assessment of interrelationships among traits and with the environment.We analyzed leaf trait measurements from 32 sites along the wide eco‐climatic gradient encompassed by the US National Ecological Observatory Network (NEON). We explored the relationships among 14 foliar traits of 1103 individuals across and within species, and with environmental factors.Across all species pooled, the relationships between leaf economic traits (leaf mass per area, nitrogen) and traits indicative of defense and stress tolerance (phenolics, nonstructural carbohydrates) were weak, but became strong within certain species. Elevation, mean annual temperature and precipitation weakly predicted trait variation across species, although some traits exhibited species‐specific significant relationships with environmental factors.Foliar functional traits vary idiosyncratically and species express diverse combinations of leaf traits to achieve fitness. Leaf spectroscopy offers an effective approach to quantify intra‐species trait variation and covariation, and potentially could be used to improve the characterization of vegetation in Earth system models. 

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models. 

Summary Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long‐standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking.We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m⁻². Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad‐ and needleleaf species, and upper‐ and lower‐canopy (i.e. sun and shade) growth environments.Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R² = 0.89; root mean square error (RMSE) = 15.45 g m⁻²).Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.