Mustard John F.

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Mustard
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John F.
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Subject: Phenology × Subject: Leaf physiology ×

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  • Preprint
    Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests
    ( 2015-08) Yang, Xi ; Tang, Jianwu ; Mustard, John F. ; Wu, Jin ; Zhao, Kaiguang ; Serbin, Shawn ; Lee, Jung-Eun
    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.
  • Article
    Beyond leaf color : comparing camera-based phenological metrics with leaf biochemical, biophysical, and spectral properties throughout the growing season of a temperate deciduous forest
    (John Wiley & Sons, 2014-03-31) Yang, Xi ; Tang, Jianwu ; Mustard, John F.
    Plant phenology, a sensitive indicator of climate change, influences vegetation-atmosphere interactions by changing the carbon and water cycles from local to global scales. Camera-based phenological observations of the color changes of the vegetation canopy throughout the growing season have become popular in recent years. However, the linkages between camera phenological metrics and leaf biochemical, biophysical, and spectral properties are elusive. We measured key leaf properties including chlorophyll concentration and leaf reflectance on a weekly basis from June to November 2011 in a white oak forest on the island of Martha's Vineyard, Massachusetts, USA. Concurrently, we used a digital camera to automatically acquire daily pictures of the tree canopies. We found that there was a mismatch between the camera-based phenological metric for the canopy greenness (green chromatic coordinate, gcc) and the total chlorophyll and carotenoids concentration and leaf mass per area during late spring/early summer. The seasonal peak of gcc is approximately 20 days earlier than the peak of the total chlorophyll concentration. During the fall, both canopy and leaf redness were significantly correlated with the vegetation index for anthocyanin concentration, opening a new window to quantify vegetation senescence remotely. Satellite- and camera-based vegetation indices agreed well, suggesting that camera-based observations can be used as the ground validation for satellites. Using the high-temporal resolution dataset of leaf biochemical, biophysical, and spectral properties, our results show the strengths and potential uncertainties to use canopy color as the proxy of ecosystem functioning.