Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests

Abstract

Understanding the temporal patterns of leaf traits is critical in determining the seasonality and magnitude of terrestrial carbon and water fluxes. However, robust and efficient ways to monitor the temporal dynamics of leaf traits are lacking. Here we assessed the potential of using leaf spectroscopy to predict leaf traits across their entire life cycle, forest sites, and light environments (sunlit vs. shaded) using a weekly sampled dataset across the entire growing season at two temperate deciduous forests. The dataset includes field measured leaf-level directional-hemispherical reflectance/transmittance together with seven important leaf traits [total chlorophyll (chlorophyll a and b), carotenoids, mass-based nitrogen concentration (Nmass), mass-based carbon concentration (Cmass), and leaf mass per area (LMA)]. All leaf properties, including leaf traits and spectra, varied significantly throughout the growing season, and displayed trait-specific temporal patterns. We used a Partial Least Square Regression (PLSR) analysis to estimate leaf traits from spectra, and found a significant capability of PLSR to capture the variability across time, sites, and light environment of all leaf traits investigated (R2=0.6~0.8 for temporal variability; R2=0.3~0.7 for cross-site variability; R2=0.4~0.8 for variability from light environments). We also tested alternative field sampling designs and found that for most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate characterization of the leaf trait seasonal patterns. Increasing the sampling frequency improved in the estimation of Nmass, Cmass and LMA comparing with foliar pigments. Our results, based on the comprehensive analysis of spectra-trait relationships across time, sites and light environments, highlight the capacity and potential limitations to use leaf spectra to estimate leaf traits with strong seasonal variability, as an alternative to time-consuming traditional wet lab approaches.