Using imaging spectroscopy to detect variation in terrestrial ecosystem productivity across a water-stressed landscape
Abstract
A central challenge to understanding how climate anomalies, such as drought and heatwaves, impact the terrestrial carbon cycle, is quantification and scaling of spatial and temporal variation in ecosystem gross primary productivity (GPP). Existing empirical and model-based satellite broadband spectra-based products have been shown to miss critical variation in GPP. We evaluate the potential of high spectral resolution (10 nm) shortwave (400–2,500 nm) imagery to better detect spatial and temporal variations in GPP across a range of ecosystems, including forests, grassland-savannas, wetlands, and shrublands in a water-stressed region. Estimates of GPP from eddy covariance observations were compared against airborne hyperspectral imagery, collected across California during the 2013–2014 HyspIRI airborne preparatory campaign. Observations from 19 flux towers across 23 flight campaigns (102 total image-flux tower pairs) showed GPP to be strongly correlated to a suite of spectral wavelengths and band ratios associated with foliar physiology and chemistry. A partial least squares regression (PLSR) modeling approach was then used to predict GPP with higher validation accuracy (adjusted R 2 = 0.71) and low bias (0.04) compared to existing broadband approaches (e.g., adjusted R 2 = 0.68 and bias = -5.71 with the Sims et al. model). Significant wavelengths contributing to the PLSR include those previously shown to coincide with Rubisco (wavelengthsmore »
- Authors:
-
[4];
- ICF Inc., Fairfax, VA (United States); Univ. of Wisconsin, Madison, WI (United States). Dept. of Forest and Wildlife Ecology and Dept. of Atmospheric and Oceanic Sciences
- Univ. of Wisconsin, Madison, WI (United States). Dept. of Atmospheric and Oceanic Sciences
- Univ. of Wisconsin, Madison, WI (United States). Dept. of Forest and Wildlife Ecology; Univ. of Florida, Gainesville, FL (United States). Dept. of Agricultural and Biological Engineering
- Brookhaven National Lab. (BNL), Upton, NY (United States). Environment and Climate Sciences Dept.
- Univ. of California, Irvine, CA (United States). Dept. of Earth System Science
- Univ. of California, Berkeley, CA (United States). Dept. of Environmental Science, Policy, and Mangement
- San Diego State Univ., San Diego, CA (United States). Dept. of Biology; Univ. of Exeter (United Kingdom). Dept. of Geography
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Atmospheric, Earth and Energy Division
- Univ. of Wisconsin, Madison, WI (United States). Dept. of Forest and Wildlife Ecology
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23). Climate and Environmental Sciences Division; National Aeronautic and Space Administration (NASA); USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1464103
- Alternate Identifier(s):
- OSTI ID: 1466134
- Report Number(s):
- BNL-207950-2018-JAAM; LLNL-JRNL-736478
Journal ID: ISSN 1051-0761
- Grant/Contract Number:
- SC0012704; NNX12AQ28G; AC52-07NA27344
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Ecological Applications
- Additional Journal Information:
- Journal Volume: 28; Journal Issue: 5; Journal ID: ISSN 1051-0761
- Publisher:
- Ecological Society of America
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; 47 OTHER INSTRUMENTATION; eddy covariance; gross primary productivity; hyperspectral imagery; hysplRI; imaging spectroscopy; partial least squares regression; Environmental sciences; Agriculture and botany; Geosciences
Citation Formats
DuBois, Sean, Desai, Ankur R., Singh, Aditya, Serbin, Shawn P., Goulden, Michael L., Baldocchi, Dennis D., Ma, Siyan, Oechel, Walter C., Wharton, Sonia, Kruger, Eric L., and Townsend, Philip A. Using imaging spectroscopy to detect variation in terrestrial ecosystem productivity across a water-stressed landscape. United States: N. p., 2018.
Web. doi:10.1002/eap.1733.
DuBois, Sean, Desai, Ankur R., Singh, Aditya, Serbin, Shawn P., Goulden, Michael L., Baldocchi, Dennis D., Ma, Siyan, Oechel, Walter C., Wharton, Sonia, Kruger, Eric L., & Townsend, Philip A. Using imaging spectroscopy to detect variation in terrestrial ecosystem productivity across a water-stressed landscape. United States. doi:10.1002/eap.1733.
DuBois, Sean, Desai, Ankur R., Singh, Aditya, Serbin, Shawn P., Goulden, Michael L., Baldocchi, Dennis D., Ma, Siyan, Oechel, Walter C., Wharton, Sonia, Kruger, Eric L., and Townsend, Philip A. Wed .
"Using imaging spectroscopy to detect variation in terrestrial ecosystem productivity across a water-stressed landscape". United States. doi:10.1002/eap.1733. https://www.osti.gov/servlets/purl/1464103.
@article{osti_1464103,
title = {Using imaging spectroscopy to detect variation in terrestrial ecosystem productivity across a water-stressed landscape},
author = {DuBois, Sean and Desai, Ankur R. and Singh, Aditya and Serbin, Shawn P. and Goulden, Michael L. and Baldocchi, Dennis D. and Ma, Siyan and Oechel, Walter C. and Wharton, Sonia and Kruger, Eric L. and Townsend, Philip A.},
abstractNote = {A central challenge to understanding how climate anomalies, such as drought and heatwaves, impact the terrestrial carbon cycle, is quantification and scaling of spatial and temporal variation in ecosystem gross primary productivity (GPP). Existing empirical and model-based satellite broadband spectra-based products have been shown to miss critical variation in GPP. We evaluate the potential of high spectral resolution (10 nm) shortwave (400–2,500 nm) imagery to better detect spatial and temporal variations in GPP across a range of ecosystems, including forests, grassland-savannas, wetlands, and shrublands in a water-stressed region. Estimates of GPP from eddy covariance observations were compared against airborne hyperspectral imagery, collected across California during the 2013–2014 HyspIRI airborne preparatory campaign. Observations from 19 flux towers across 23 flight campaigns (102 total image-flux tower pairs) showed GPP to be strongly correlated to a suite of spectral wavelengths and band ratios associated with foliar physiology and chemistry. A partial least squares regression (PLSR) modeling approach was then used to predict GPP with higher validation accuracy (adjusted R2 = 0.71) and low bias (0.04) compared to existing broadband approaches (e.g., adjusted R2 = 0.68 and bias = -5.71 with the Sims et al. model). Significant wavelengths contributing to the PLSR include those previously shown to coincide with Rubisco (wavelengths 1,680, 1,740, and 2,290 nm) and Vcmax (wavelengths 1,680, 1,722, 1,732, 1,760, and 2,300 nm). These results provide strong evidence that advances in satellite spectral resolution offer significant promise for improved satellite-based monitoring of GPP variability across a diverse range of terrestrial ecosystems.},
doi = {10.1002/eap.1733},
journal = {Ecological Applications},
number = 5,
volume = 28,
place = {United States},
year = {2018},
month = {5}
}
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