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

Serbin, Shawn P.; Singh, Aditya; Desai, Ankur R.; Dubois, Sean G.; Jablonski, Andrew D.; Kingdon, Clayton C.; Kruger, Eric L.; Townsend, Philip A.

doi:10.1016/j.rse.2015.05.024

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

Journal Article · Thu Jun 11 00:00:00 EDT 2015 · Remote Sensing of Environment

DOI:https://doi.org/10.1016/j.rse.2015.05.024· OSTI ID:1228836

Serbin, Shawn P. ^[1]; Singh, Aditya ^[2]; Desai, Ankur R. ^[2]; Dubois, Sean G. ^[2]; Jablonski, Andrew D. ^[2]; Kingdon, Clayton C. ^[2]; Kruger, Eric L. ^[2]; Townsend, Philip A. ^[2]

Brookhaven National Lab. (BNL), Upton, NY (United States)
Univ. of Wisconsin-Madison, Madison, WI (United States)

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 (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 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.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: SC00112704

OSTI ID:: 1228836

Report Number(s):: BNL-108474-2015-JA; R&D Project: 21087/21088; YN0100000

Journal Information:: Remote Sensing of Environment, Vol. 167, Issue C; ISSN 0034-4257

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 119 works

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54 ENVIRONMENTAL SCIENCES
activation energy
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temperature
Vcmax
photosynthetic metabolism