EGU2020-6674, updated on 12 Jun 2020
https://doi.org/10.5194/egusphere-egu2020-6674
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Evaluation of understory LAI estimation methodologies over forest ecosystem ICOS sites across Europe

Jan-Peter George1, Jan Pisek1, and the Tobias Biermann (2), Arnaud Carrara (3), Edoardo Cremonese (4), Matthias Cuntz (5), Silvano Fares (6), Giacomo Gerosa (7), Thomas Grünwald (8), Niklas Hase (9), Michal Heliasz (2), Andreas Ibrom (10), Alexander Knohl (11), Bart Kruijt (12), Hikdeki Kobaya*
Jan-Peter George and Jan Pisek and the Tobias Biermann (2), Arnaud Carrara (3), Edoardo Cremonese (4), Matthias Cuntz (5), Silvano Fares (6), Giacomo Gerosa (7), Thomas Grünwald (8), Niklas Hase (9), Michal Heliasz (2), Andreas Ibrom (10), Alexander Knohl (11), Bart Kruijt (12), Hikdeki Kobaya
  • 1University of Tartu, Tartu Observatory, Science and Technology, Estonia (jan.peter.george@gmail.com)
  • *A full list of authors appears at the end of the abstract

Leaf area index (i.e. one-half the total green leaf area per unit of horizontal ground surface area) is a crucial parameter in carbon balancing and modeling. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as in ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. The aim of this study is to compare currently available understory LAI assessment methodologies over a diverse set of greenhouse gas measurement sites distributed along a wide latitudinal and elevational gradient across Europe. This will help to quantify  the fraction of the canopy LAI which is represented by understory, since this is still the major source of uncertainty in global LAI products derived from remote sensing data. For this, we took ground photos as well as in-situ reflectance measurements of the understory vegetation at 30 ICOS (Integration Carbon Observation System) sites distributed across 10 countries in Europe. The data were analyzed by means of three conceptually different methods for LAI estimation and comprised purely empirical (fractional cover), semi-empirical (in-situ NDVI linked to the radiative transfer model FLiES), and purely deterministic (Four-scale geometrical optical model) approaches. Finally, our results are compared with global forest understory LAI maps derived from remote sensing data at 1 km resolution (Liu et al. 2017). While we found some agreement among the three methods (e.g. Pearson-correlation between empirical and semi-empirical = 0.63), we also identified sources that are particularly prone to error inclusion such as inaccurate assessment of fractional cover from ground photos. Relationships between understory LAI and long-term climate variables were weak and suggested that understory LAI at the ICOS sites is probably more strongly determined by microclimatic conditions.

Liu Y. et al. (2017): Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data. Biogeosciences 14: 1093-1110.

Tobias Biermann (2), Arnaud Carrara (3), Edoardo Cremonese (4), Matthias Cuntz (5), Silvano Fares (6), Giacomo Gerosa (7), Thomas Grünwald (8), Niklas Hase (9), Michal Heliasz (2), Andreas Ibrom (10), Alexander Knohl (11), Bart Kruijt (12), Hikdeki Kobaya:

(1) Tartu Observatory, University of Tartu, Estonia, (2) Lund University, Lund, Sweden, (3) Fundacion CEAM, Paterna, Spain, (4) ARPA Valle d'Aosta, Saint Christophe, Italy, (5) Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy, France, (6) CNR-National Research Council, Rome, Italy, (7) Università Cattolica del Sacro Cuore, Brescia, Italy, (8) Technische Universität Dresden, Dresden, Germany, (9) Helmholtz Centre for Environmental Research GmbH - UFZ, Leipzig, Germany, (10) Technical University of Denmark, Kongens Lyngby, Denmark, (11) University of Goettingen, Göttingen, Germany, (12) Wageningen University & Research, Wageningen, Netherlands, (13) JAMSTEC, Yokohama, Japan, (14) NIBIO, Ås, Norway, (15) CEFE CNRS UMR 5175, Montpellier, France, (16) INRA, Bordeaux, France, (17) Global Change Research Institute, Academy of Sciences of the Czech Republic, Brno, Czech Republic, (18) Università Cattolica del Sacro Cuore, Brescia, Italy, (19) Free University of Bolzano, Bolzano, Italy, (20) INBO, Geraardsbergen, Belgium, (21) Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden, (22) Forschungszentrum Juelich, Juelich, Germany, (23) IER-ETSIAM, Universidad de Castilla-La Mancha, Albacete, Spain, (24) Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolution, 91405, Orsay, France, (25) Université Catholique de Louvain,Louvain-la-Neuve, Belgium, (26) WSL, Birmensdorf, Switzerland

How to cite: George, J.-P. and Pisek, J. and the Tobias Biermann (2), Arnaud Carrara (3), Edoardo Cremonese (4), Matthias Cuntz (5), Silvano Fares (6), Giacomo Gerosa (7), Thomas Grünwald (8), Niklas Hase (9), Michal Heliasz (2), Andreas Ibrom (10), Alexander Knohl (11), Bart Kruijt (12), Hikdeki Kobaya: Evaluation of understory LAI estimation methodologies over forest ecosystem ICOS sites across Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6674, https://doi.org/10.5194/egusphere-egu2020-6674, 2020

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