From the Arctic to the tropics: multi-biome prediction of leaf mass per area using leaf reflectance
Abstract
Summary 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. We 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 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad‐ and needleleaf 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 ( R 2 = 0.89; root mean square error ( RMSE ) = 15.45 g m −2 ). Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way formore »
- Authors:
-
[1];
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States); Univ. of Hong Kong (China)
- Univ. of Wisconsin, Madison, WI (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States); Huazhong Agricultural Univ., Wuhan, Hubei (China)
- Smithsonian Tropical Research Inst., Apartado (Panama); Louisiana State Univ., Baton Rouge, LA (United States)
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (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. doi: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 = {Summary 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. We 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 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad‐ and needleleaf 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 ( R 2 = 0.89; root mean square error ( RMSE ) = 15.45 g m −2 ). Our finding highlights the fact that the leaf economics 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 = {Fri Aug 16 00:00:00 EDT 2019},
month = {Fri Aug 16 00:00:00 EDT 2019}
}
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