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

Understanding the temporal patterns of leaf traits is critical in determining the seasonality and magnitude of terrestrial carbon, water, and energy fluxes. However, we lack robust and efficient ways to monitor the temporal dynamics of leaf traits. Here we assessed the potential of leaf spectroscopy to predict and monitor leaf traits across their entire life cycle at different forest sites and light environments (sunlit vs. shaded) using a weekly sampled dataset across the entire growing season at two temperate deciduous forests. In addition, 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 (N mass), mass-based carbon concentration (C mass), and leaf mass per area (LMA)]. All leaf traits varied significantly throughout the growing season, and displayed trait-specific temporal patterns. We used a Partial Least Square Regression (PLSR) modeling approach to estimate leaf traits from spectra, and found that PLSR was able to capture the variability across time, sites, and light environments of all leaf traits investigated (R 2 = 0.6–0.8 for temporal variability; R 2 = 0.3–0.7 for cross-site variability; R 2 = 0.4–0.8 for variability from light environments). We also tested alternative field samplingmore » designs and found that for most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate characterization of the seasonal patterns. Compared with the estimation of foliar pigments, the performance of N mass, C mass and LMA PLSR models improved more significantly with sampling frequency. Our results demonstrate that leaf spectra-trait relationships vary with time, and thus tracking the seasonality of leaf traits requires statistical models calibrated with data sampled throughout the growing season. In conclusion, our results have broad implications for future research that use vegetation spectra to infer leaf traits at different growing stages.« less
Authors:
ORCiD logo [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6] ;  [3]
  1. Brown Univ., Providence, RI (United States). Department of Earth, Environment, and Planetary Sciences; Marine Biological Laboratory, Woods Hole, MA (United States). The Ecosystems Center
  2. Marine Biological Laboratory, Woods Hole, MA (United States). The Ecosystems Center
  3. Brown Univ., Providence, RI (United States). Department of Earth, Environment, and Planetary Sciences
  4. Univ. of Arizona, Tucson, AZ (United States). Department of Ecology and Evolutionary Biology; Brookhaven National Lab. (BNL), Upton, NY (United States). Environmental & Climate Sciences Department
  5. The Ohio State University, Wooster, OH (United States). School of Environment and Natural Resources, Ohio Agricultural and Research Development Center
  6. Brookhaven National Lab. (BNL), Upton, NY (United States). Environmental & Climate Sciences Department
Publication Date:
Report Number(s):
BNL-112192-2016-JA
Journal ID: ISSN 0034-4257; R&D Project: 2016-BNL-EE630EECA-Budg; KP1701000
Grant/Contract Number:
SC00112704; SC0006951
Type:
Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
Journal Volume: 179; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Phenology; Leaf physiology; Foliar chemistry; Carbon cycle; Chlorophyll; Carotenoids; Nitrogen; Leaf mass per area; Partial least square regression (PLSR); Sun and shaded leaves
OSTI Identifier:
1336038
Alternate Identifier(s):
OSTI ID: 1359826