The morphological and biochemical properties of plant canopies are strong predictors of photosynthetic capacity and nutrient cycling. Remote sensing research at the leaf and canopy scales has demonstrated the ability to characterize the biochemical status of vegetation canopies using reflectance spectroscopy, including at the leaf level and canopy level from air‐ and spaceborne imaging spectrometers. We developed a set of accurate and precise spectroscopic calibrations for the determination of leaf chemistry (contents of nitrogen, carbon, and fiber constituents), morphology (leaf mass per area, M_area), and isotopic composition (δ¹⁵N) of temperate and boreal tree species using spectra of dried and ground leaf material. The data set consisted of leaves from both broadleaf and needle‐leaf conifer species and displayed a wide range in values, determined with standard analytical approaches: 0.7–4.4% for nitrogen (N_mass), 42–54% for carbon (C_mass), 17–58% for fiber (acid‐digestible fiber, ADF), 7–44% for lignin (acid‐digestible lignin, ADL), 3–31% for cellulose, 17–265 g/m² for M_area, and −9.4‰ to 0.8‰ for δ¹⁵N. The calibrations were developed using a partial least‐squares regression (PLSR) modeling approach combined with a novel uncertainty analysis. Our PLSR models yielded model calibration (independent validation shown in parentheses) R² and the root mean square error (RMSE) values, respectively, of 0.98 (0.97) and 0.10% (0.13%) for N_mass, R² = 0.77 (0.73) and RMSE = 0.88% (0.95%) for C_mass, R² = 0.89 (0.84) and RMSE = 2.8% (3.4%) for ADF, R² = 0.77 (0.69) and RMSE = 2.4% (3.9%) for ADL, R² = 0.77 (0.72) and RMSE = 1.4% (1.9%) for leaf cellulose, R² = 0.62 (0.60) and RMSE = 0.91‰ (1.5‰) for δ¹⁵N, and R² = 0.88 (0.87) with RMSE = 17.2 g/m² (22.8 g/m²) for M_area. This study demonstrates the potential for rapid and accurate estimation of key foliar traits of forest canopies that are important for ecological research and modeling activities, with a single calibration equation valid over a wide range of northern temperate and boreal species and leaf physiognomies. The results provide the basis to characterize important variability between and within species, and across ecological gradients using a rapid, cost‐effective, easily replicated method.

Researchers from a number of disciplines have long sought the ability to estimate the functional attributes of plant canopies, such as photosynthetic capacity, using remotely sensed data. To date, however, this goal has not been fully realized. In this study, fresh-leaf reflectance spectroscopy (λ=450–2500 nm) and a partial least-squares regression (PLSR) analysis were used to estimate key determinants of photosynthetic capacity—namely the maximum rates of RuBP carboxylation (V_cmax) and regeneration (J_max)—measured with standard gas exchange techniques on leaves of trembling aspen and eastern cottonwood trees. The trees were grown across an array of glasshouse temperature regimes. The PLSR models yielded accurate and precise estimates of V_cmax and J_max within and across species and glasshouse temperatures. These predictions were developed using unique contributions from different spectral regions. Most of the wavelengths selected were correlated with known absorption features related to leaf water content, nitrogen concentration, internal structure, and/or photosynthetic enzymes. In a field application of our PLSR models, spectral reflectance data effectively captured the short-term temperature sensitivities of V_cmax and J_max in aspen foliage. These findings highlight a promising strategy for developing remote sensing methods to characterize dynamic, environmentally sensitive aspects of canopy photosynthetic metabolism at broad scales.

A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS‐Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (M_area), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ¹⁵N) as well as pixel‐wise uncertainties in their estimates based on error propagation in the analytical methods. BothM_areaand %N PLSR models had aR²> 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted withR²> 0.65 and carbon and cellulose withR²> 0.45. AlthoughR²of %C and cellulose were lower thanM_areaand %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ¹⁵N was the lowest (R²= 0.48, RMSE = 0.95‰), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first‐of‐its‐kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub‐boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non‐discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.