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Title: From the Arctic to the tropics: multi-biome prediction of leaf mass per area using leaf reflectance

Abstract

Leaf-mass-per-area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. In this work, we explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 g m-2 to 393 g m-2. Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needle-leaf species, and upper and lower canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age, and canopy position, from diverse biomes. Our model captures LMA variability with high accuracy and low error (R2=0.89; RMSE=15.45 g m-2). Lastly, our finding highlights that the leaf economic spectrum is mirrored by the leaf optical spectrum paving the way for this technology to predict the diversity of LMA inmore » ecosystems across global biomes.« less

Authors:
ORCiD logo [1];  [2];  [1];  [3];  [3];  [4];  [5];  [3];  [3];  [1]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States); Univ. of Hong Kong (China)
  3. Univ. of Wisconsin, Madison, WI (United States)
  4. Brookhaven National Lab. (BNL), Upton, NY (United States); Huazhong Agricultural Univ., Wuhan, Hubei (China)
  5. Smithsonian Tropical Research Inst., Apartado (Panama); Louisiana State Univ., Baton Rouge, LA (United States)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER); Laboratory-Directed Research and Development (LDRD)
OSTI Identifier:
1557102
Alternate Identifier(s):
OSTI ID: 1562332
Report Number(s):
BNL-211956-2019-JAAM
Journal ID: ISSN 0028-646X
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
New Phytologist
Additional Journal Information:
Journal Volume: 224; Journal Issue: 4; Journal ID: ISSN 0028-646X
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; spectroscopy; remote sensing; plant traits; leaf mass area; specific leaf area; partial least squares regression (PLSR)

Citation Formats

Serbin, Shawn P., Wu, Jin, Ely, Kim S., Kruger, Eric L., Townsend, Philip A., Meng, Ran, Wolfe, Brett T., Chlus, Adam, Wang, Zhihui, and Rogers, Alistair. From the Arctic to the tropics: multi-biome prediction of leaf mass per area using leaf reflectance. United States: N. p., 2019. Web. https://doi.org/10.1111/nph.16123.
Serbin, Shawn P., Wu, Jin, Ely, Kim S., Kruger, Eric L., Townsend, Philip A., Meng, Ran, Wolfe, Brett T., Chlus, Adam, Wang, Zhihui, & Rogers, Alistair. From the Arctic to the tropics: multi-biome prediction of leaf mass per area using leaf reflectance. United States. https://doi.org/10.1111/nph.16123
Serbin, Shawn P., Wu, Jin, Ely, Kim S., Kruger, Eric L., Townsend, Philip A., Meng, Ran, Wolfe, Brett T., Chlus, Adam, Wang, Zhihui, and Rogers, Alistair. Fri . "From the Arctic to the tropics: multi-biome prediction of leaf mass per area using leaf reflectance". United States. https://doi.org/10.1111/nph.16123. https://www.osti.gov/servlets/purl/1557102.
@article{osti_1557102,
title = {From the Arctic to the tropics: multi-biome prediction of leaf mass per area using leaf reflectance},
author = {Serbin, Shawn P. and Wu, Jin and Ely, Kim S. and Kruger, Eric L. and Townsend, Philip A. and Meng, Ran and Wolfe, Brett T. and Chlus, Adam and Wang, Zhihui and Rogers, Alistair},
abstractNote = {Leaf-mass-per-area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. In this work, we explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 g m-2 to 393 g m-2. Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needle-leaf species, and upper and lower canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age, and canopy position, from diverse biomes. Our model captures LMA variability with high accuracy and low error (R2=0.89; RMSE=15.45 g m-2). Lastly, our finding highlights that the leaf economic spectrum is mirrored by the leaf optical spectrum paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.},
doi = {10.1111/nph.16123},
journal = {New Phytologist},
number = 4,
volume = 224,
place = {United States},
year = {2019},
month = {8}
}

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