Novel Spectroscopy Approach Provides a Rapid and Accurate Means to Retrieve Foliar Traits in Plants

The Science

The traditional approaches used to measure many leaf functional traits, including the amount of leaf mass per unit area (leaf mass per area, LMA) are destructive, laborious, time consuming, and expensive. Researchers developed a novel spectroscopy approach, which utilizes measurements of light reflected by leaves, which can be used as an alternative to rapidly and non-destructively infer foliar traits across plants growing from the high Arctic to the tropics.

The Impact

Earth system models (ESMs) require detailed information on the structural and functional properties of leaves across global biomes in order to simulate vegetation responses to global change and inform policy decisions. Traditional approaches used to characterize plant properties which are key inputs for ESMs are slow and limited to small geographic areas. On the other hand, remote sensing approaches that this research enables can be used to remotely measure these traits over large areas and through time.

Summary

Leaf mass per area (LMA) is a key plant trait used in ecological research and climate modeling. LMA reflects fundamental tradeoffs between in resource investments to leaf photosynthesis, longevity or robustness, and structure. Characterizing the within and across biome spatial and standing goal of ecological research and is an essential component for advancing Earth system models (ESMs). In this study, researchers explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m^-2. Researchers 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. These findings highlight the fact that the leaf economics spectrum is mirrored by corresponding variation in leaf optical properties, which paves the way for this technology to predict the diversity of LMA, and potentially a range of other leaf traits, in ecosystems across global biomes.

Principal Investigator

Shawn Serbin
Brookhaven National Laboratory
[email protected]

Program Manager

Daniel Stover
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
[email protected]

Funding

This work and associated field data collection campaigns were supported by the Next-Generation Ecosystem Experiments (NGEE Arctic and NGEE Tropics), projects that are supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science, by NASA Earth and Space Sciences Fellowship (NNX08AV07H) provided to SPS, Forest Functional Types (NNX12AQ28G) and HyspIRI grants (NNX12AQ28G), NSF Macrosystems Biology grant (1638720) to PAT and ELK, as well as a USDA McIntire-Stennis grant (WIS01809) to PAT and ELK.

References