Spectroscopy provides a rapid and accurate means to retrieve foliar traits.
The traditional approaches used to measure many leaf functional traits, including the amount of leaf mass per unit area (LMA) are destructive, laborious, time consuming, and expensive. On the other hand, a novel spectroscopy approach, which uses measurements of the light reflected by leaves, can be used as an alternative to rapidly and nondestructively infer these foliar traits across plants growing from the high Arctic to the tropics.
Earth system models (ESMs) require detailed information on the structural and functional properties of leaves across global biomes to simulate vegetation responses to global change and inform policy decisions. Traditional approaches used to characterize plant properties that are key inputs for ESMs are slow and limited to small geographic areas. However, remote sensing approaches that this research enables can be used to remotely measure these traits over large areas and through time.
LMA is a key plant trait used in ecological research and climate modeling. This trait reflects fundamental tradeoffs in resource investments to leaf photosynthesis, longevity or robustness, and structure. Characterizing the within and across biome spatial and temporal variabilities in LMA has been a long-standing goal of ecological research and is an essential component for advancing ESMs. In this study, researchers from Brookhaven National Laboratory explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 grams (g) per m2. They used leaves collected from a wide range of locations encompassing broad and needleleaf species and upper and lower canopy (i.e., sun and shade) growth environments. They demonstrated the ability to rapidly estimate LMA using only leaf reflectance data with high accuracy and low error. This finding highlights the fact that the leaf economics spectrum is mirrored by a corresponding variation in leaf optical properties, paving the way for this technology to predict the diversity of LMA, and potentially a range of other leaf traits, in ecosystems across global biomes.
U.S. Department of Energy Office of Science, Office of Biological and Environmental Research
Earth and Environmental Systems Sciences Division (SC-33.1)
Environmental System Science
Shawn P. Serbin
Brookhaven National Laboratory
This work and associated field data collection campaigns were supported by the Next-Generation Ecosystem Experiments projects (NGEE–Arctic and NGEE–Tropics), which are funded by the Terrestrial Ecosystem Science program of the Office of Biological and Environmental Research (BER) within the U.S. Department of Energy (DOE) Office of Science; the National Aeronautics and Space Administration (NASA) Earth and Space Sciences Fellowship (NNX08AV07H) to SPS; NASA Forest Functional Types (NNX12AQ28G) and HyspIRI grants (NNX12AQ28G) and National Science Foundation Macrosystems Biology grant (1638720) to PAT and ELK; and a U.S. Department of Agriculture McIntire-Stennis grant (WIS01809) to PAT and ELK.
Serbin, S. P., J. Wu, K. S. Ely, E. L. Kruger, P. A. Townsend, R. Meng, B. T. Wolfe, A. Chlus, Z. Wang, and A. Rogers. “From the Arctic to the Tropics: Multibiome prediction of leaf mass per area using leaf reflectance.” New Phytologist 224(4), 1557–68 (2019). [DOI:10.1111/nph.16123].
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