Articles | Volume 12, issue 10
Biogeosciences, 12, 3089–3108, 2015

Special issue: EUROSPEC – spectral sampling tools for vegetation biophysical...

Biogeosciences, 12, 3089–3108, 2015

Research article 28 May 2015

Research article | 28 May 2015

On the relationship between ecosystem-scale hyperspectral reflectance and CO2 exchange in European mountain grasslands

M. Balzarolo1, L. Vescovo2,3, A. Hammerle4, D. Gianelle2,3, D. Papale5, E. Tomelleri6, and G. Wohlfahrt4,6 M. Balzarolo et al.
  • 1Centre of Excellence PLECO, University of Antwerpen, Wilrjik, Belgium
  • 2Forests and Biogeochemical Cycles Research Group, Sustainable Agro-Ecosystems and Bioresources Department, Research and Innovation Centre – Fondazione Edmund Mach, S. Michele all'Adige (TN), Italy
  • 3FoxLaB Research and Innovation Centre – Fondazione Edmund Mach, S. Michele all'Adige (TN), Italy
  • 4Institute of Ecology, University of Innsbruck, Innsbruck, Austria
  • 5DIBAF, University of Tuscia, Viterbo, Italy
  • 6European Academy of Bolzano, Bolzano, Italy

Abstract. In this paper we explore the skill of hyperspectral reflectance measurements and vegetation indices (VIs) derived from these in estimating carbon dioxide (CO2) fluxes of grasslands. Hyperspectral reflectance data, CO2 fluxes and biophysical parameters were measured at three grassland sites located in European mountain regions using standardized protocols. The relationships between CO2 fluxes, ecophysiological variables, traditional VIs and VIs derived using all two-band combinations of wavelengths available from the whole hyperspectral data space were analysed. We found that VIs derived from hyperspectral data generally explained a large fraction of the variability in the investigated dependent variables but differed in their ability to estimate midday and daily average CO2 fluxes and various derived ecophysiological parameters. Relationships between VIs and CO2 fluxes and ecophysiological parameters were site-specific, likely due to differences in soils, vegetation parameters and environmental conditions. Chlorophyll and water-content-related VIs explained the largest fraction of variability in most of the dependent variables. Band selection based on a combination of a genetic algorithm with random forests (GA–rF) confirmed that it is difficult to select a universal band region suitable across the investigated ecosystems. Our findings have major implications for upscaling terrestrial CO2 fluxes to larger regions and for remote- and proximal-sensing sampling and analysis strategies and call for more cross-site synthesis studies linking ground-based spectral reflectance with ecosystem-scale CO2 fluxes.

Final-revised paper