skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy

Abstract

To date, the utility of ecosystem and Earth system models (EESMs) has been limited by poor spatial and temporal representation of critical input parameters. For example, EESMs often rely on leaf-scale or literature-derived estimates for a key determinant of canopy photosynthesis, the maximum velocity of RuBP carboxylation (Vcmax, μmol m –2 s –1). Our recent work (Ainsworth et al., 2014; Serbin et al., 2012) showed that reflectance spectroscopy could be used to estimate Vcmax at the leaf level. Here, we present evidence that imaging spectroscopy data can be used to simultaneously predict Vcmax and its sensitivity to temperature (E V) at the canopy scale. In 2013 and 2014, high-altitude Airborne Visible/Infrared Imaging Spectroscopy (AVIRIS) imagery and contemporaneous ground-based assessments of canopy structure and leaf photosynthesis were acquired across an array of monospecific agroecosystems in central and southern California, USA. A partial least-squares regression (PLSR) modeling approach was employed to characterize the pixel-level variation in canopy V cmax (at a standardized canopy temperature of 30 °C) and E V, based on visible and shortwave infrared AVIRIS spectra (414–2447 nm). Our approach yielded parsimonious models with strong predictive capability for Vcmax (at 30 °C) and E V (R 2 of withheld datamore » = 0.94 and 0.92, respectively), both of which varied substantially in the field (≥ 1.7 fold) across the sampled crop types. The models were applied to additional AVIRIS imagery to generate maps of V cmax and E V, as well as their uncertainties, for agricultural landscapes in California. The spatial patterns exhibited in the maps were consistent with our in-situ observations. As a result, these findings highlight the considerable promise of airborne and, by implication, space-borne imaging spectroscopy, such as the proposed HyspIRI mission, to map spatial and temporal variation in key drivers of photosynthetic metabolism in terrestrial vegetation.« less

Authors:
 [1];  [2];  [2];  [2];  [2];  [2];  [2];  [2]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States)
  2. Univ. of Wisconsin-Madison, Madison, WI (United States)
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1228836
Report Number(s):
BNL-108474-2015-JA
Journal ID: ISSN 0034-4257; R&D Project: 21087/21088; YN0100000
Grant/Contract Number:
SC00112704
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
Journal Volume: 167; Journal Issue: C; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; activation energy; AVIRIS; imaging spectroscopy; canopy optical reflectance; photosynthesis; temperature; Vcmax; photosynthetic metabolism

Citation Formats

Serbin, Shawn P., Singh, Aditya, Desai, Ankur R., Dubois, Sean G., Jablonski, Andrew D., Kingdon, Clayton C., Kruger, Eric L., and Townsend, Philip A.. Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. United States: N. p., 2015. Web. doi:10.1016/j.rse.2015.05.024.
Serbin, Shawn P., Singh, Aditya, Desai, Ankur R., Dubois, Sean G., Jablonski, Andrew D., Kingdon, Clayton C., Kruger, Eric L., & Townsend, Philip A.. Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. United States. doi:10.1016/j.rse.2015.05.024.
Serbin, Shawn P., Singh, Aditya, Desai, Ankur R., Dubois, Sean G., Jablonski, Andrew D., Kingdon, Clayton C., Kruger, Eric L., and Townsend, Philip A.. Thu . "Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy". United States. doi:10.1016/j.rse.2015.05.024. https://www.osti.gov/servlets/purl/1228836.
@article{osti_1228836,
title = {Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy},
author = {Serbin, Shawn P. and Singh, Aditya and Desai, Ankur R. and Dubois, Sean G. and Jablonski, Andrew D. and Kingdon, Clayton C. and Kruger, Eric L. and Townsend, Philip A.},
abstractNote = {To date, the utility of ecosystem and Earth system models (EESMs) has been limited by poor spatial and temporal representation of critical input parameters. For example, EESMs often rely on leaf-scale or literature-derived estimates for a key determinant of canopy photosynthesis, the maximum velocity of RuBP carboxylation (Vcmax, μmol m–2 s–1). Our recent work (Ainsworth et al., 2014; Serbin et al., 2012) showed that reflectance spectroscopy could be used to estimate Vcmax at the leaf level. Here, we present evidence that imaging spectroscopy data can be used to simultaneously predict Vcmax and its sensitivity to temperature (EV) at the canopy scale. In 2013 and 2014, high-altitude Airborne Visible/Infrared Imaging Spectroscopy (AVIRIS) imagery and contemporaneous ground-based assessments of canopy structure and leaf photosynthesis were acquired across an array of monospecific agroecosystems in central and southern California, USA. A partial least-squares regression (PLSR) modeling approach was employed to characterize the pixel-level variation in canopy Vcmax (at a standardized canopy temperature of 30 °C) and EV, based on visible and shortwave infrared AVIRIS spectra (414–2447 nm). Our approach yielded parsimonious models with strong predictive capability for Vcmax (at 30 °C) and EV (R2 of withheld data = 0.94 and 0.92, respectively), both of which varied substantially in the field (≥ 1.7 fold) across the sampled crop types. The models were applied to additional AVIRIS imagery to generate maps of Vcmax and EV, as well as their uncertainties, for agricultural landscapes in California. The spatial patterns exhibited in the maps were consistent with our in-situ observations. As a result, these findings highlight the considerable promise of airborne and, by implication, space-borne imaging spectroscopy, such as the proposed HyspIRI mission, to map spatial and temporal variation in key drivers of photosynthetic metabolism in terrestrial vegetation.},
doi = {10.1016/j.rse.2015.05.024},
journal = {Remote Sensing of Environment},
number = C,
volume = 167,
place = {United States},
year = {Thu Jun 11 00:00:00 EDT 2015},
month = {Thu Jun 11 00:00:00 EDT 2015}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 18works
Citation information provided by
Web of Science

Save / Share:
  • A simple method based on a mass-conservation principle is presented for estimating the aerodynamic characteristics of forest and tall vegetation canopies. The method uses semi-empirical modifications of the profiles in the transition layer, eliminating the need for measured wind data extending into the logarithmic regime. Also, various schemes are presented for determining the transition-layer depth z[sub *] in terms of the particular physical characteristics of the canopy. 10 refs., 3 figs.
  • A semianalytical method based on a mass conservation principle is presented for describing the transition-layer profiles of mean wind speed and momentum diffusivity and for estimating the aerodynamic characteristics of forest and tall vegetation canopies. This method incorporates density and vertical structure of the canopy and assumes that the transition-layer mean wind speed profile can be expressed in polynomial form having second-order osculation. It is also suggested that canopy structure has a major influence on the transition-layer mean wind speed and momentum diffusivity profile. The proposed methodology may help in simulating airflow for use in large-scale models of plant-atmosphere exchanges.more » 21 refs., 5 figs.« less
  • Measurements of the microwave brightness temperature (TB) with the Pushbroom Microwave Radiometer (PBMR) over the Walnut Gulch Experiment Watershed were made on selected days during the MONSOON 90 field campaign. The PBMR is an L-band instrument (21-cm wavelength) that can provide estimates of near-surface soil moisture over a variety of surfaces. Aircraft observations in the visible and near-infrared wavelengths collected on selected days also were used to compute a vegetation index. Continuous micrometeorological measurements and daily soil moisture samples were obtained at eight locations during experimental period. Two sites were instrumented with time domain reflectometry probes to monitor the soilmore » moisture profile. The fraction of available energy used for evapotranspiration was computed by taking the ratio of latent heat flux (LE) to the sum of net radiation (Rn) and soil heat flux (G). This ratio is commonly called the evaporative fraction (EF) and normally varies between 0 and 1 under daytime convective conditions with minimal advection. A wide range of environmental conditions existed during the field campaign, resulting in average EF values for the study area varying from 0.4 to 0.8 and values of TB ranging from 220 to 280 K. Comparison between measured TB and EF for the eight locations showed an inverse relationship. Other days were included in the analysis by estimating TB with the soil moisture data. Because transpiration from the vegetation is more strongly coupled to root zone soil moisture, significant scatter in this relationship existed at high values of TB or dry near-surface soil moisture conditions.« less
  • The capability to automatically detect vegetation changes using multitemporal remotely sensed image data is of upmost importance to many global-change research projects. A procedure to automatically map vegetation changes within arid and semi-arid regions of the southwestern United States is presented. Multitemporal Landsat Multispectral Scanner (MSS) images were the primary data source, but some preliminary work was also done using same-date Visible-Infrared Spin-Scan Radiometer (VISSR) data for comparison with the MSS results. The change-detection procedure includes multitemporal image calibration using a hybrid method that we developed for the project; the hybrid calibration allows a radiometric calibration to be applied tomore » historical data by using field-radiance information rather than a modeling procedure. The results indicate that a calibrated visible band is more sensitive than the widely used Normalized Difference Vegetation Index (NDVI) in detecting vegetation changes in the arid and semi-arid environments of the southwestern United States. Changes were detected in the desert environment, where the vegetation density is relatively low, with both Landsat MSS and GOES VISSR images. Some changes detected by the automatic procedure were confirmed in the field during two of the Landsat overpasses. The changes corresponded mostly to the blooming of ephemeral or annual vegetation.« less
  • The authors propose a method to estimate sea surface nitrate (N) from space using satellite measurements of sea surface temperature (SST) and chlorophyll a (chl a). The procedure relies on empirical relationships between shipboard measurements of N and its predictor variables, temperature (T) and chl a in surface and near surface waters. Although N appears to be controlled primarily by T, the addition of the biological variable chl a helps improve N prediction by reducing local and regional differences in the character of the temperature-nitrate (T-N) relationship. In the present study, the authors have applied these empirical algorithms to SSTmore » and chl a data from the Ocean Color and Temperature Scanner (OCTS) on board the Advanced Earth Observation Satellite (ADEOS). The results clearly suggest that measurements of SST and chl a now possible by modern-day ocean satellites could be exploited usefully to extend the resolution of shipboard N measurements over large spatial and temporal scales. Systematic errors in estimates of N that could result from errors in satellite estimates of SST and chl a are examined through sensitivity analyses.« less