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https://doi.org/10.5194/bg-2020-360
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2020-360
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  04 Nov 2020

04 Nov 2020

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This preprint is currently under review for the journal BG.

Retrieval and validation of forest background reflectivity from daily MODIS bidirectional reflectance distribution function (BRDF) data across European forests

Jan Pisek1, Angela Erb2, Lauri Korhonen3, Tobias Biermann4, Arnaud Carrara5, Edoardo Cremonese6, Matthias Cuntz7, Silvano Fares8, Giacomo Gerosa9, Thomas Grünwald10, Niklas Hase11, Michal Heliasz4, Andreas Ibrom12, Alexander Knohl13, Johannes Kobler14, Bart Kruijt15, Holger Lange16, Leena Leppänen17, Jean-Marc Limousin18, Francisco Ramon Lopez Serrano19, Denis Loustau20, Petr Lukeš21, Lars Lundin22, Riccardo Marzuoli9, Meelis Mölder4, Leonardo Montagnani23, Johan Neirynck24, Matthias Peichl25, Corinna Rebmann11, Eva Rubio19, Margarida Santos-Reis26, Crystal Schaaf2, Marius Schmidt27, Guillaume Simioni28, Kamel Soudani29, and Caroline Vincke30 Jan Pisek et al.
  • 1Tartu Observatory, University of Tartu, Observatooriumi 1, Tõravere, 61602, Tartumaa, Estonia
  • 2University of Massachusetts Boston, Boston, Boston, USA
  • 3University of Eastern Finland, Joensuu, Finland
  • 4Lund University, Lund, Sweden
  • 5Fundacion CEAM, Paterna, Spain
  • 6ARPA Valle d'Aosta, Saint Christophe, Italy
  • 7Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy, France
  • 8CNR-National Research Council, Rome, Italy
  • 9Università Cattolica del Sacro Cuore, Brescia, Italy
  • 10Technische Universität Dresden, Dresden, Germany
  • 11Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
  • 12Technical University of Denmark, Kongens Lyngby, Denmark
  • 13University of Göttingen, Göttingen, Germany
  • 14Umweltbundesamt, Vienna, Austria
  • 15Wageningen University & Research, Wageningen, Netherlands
  • 16Norwegian Institute of Bioeconomy Research, Ås, Norway
  • 17Finnish Meteorological Institute, Space and Earth Observation Centre, Sodankylä, Finland
  • 18CEFE Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier, Montpellier, France
  • 19IER-ETSIAM, Universidad de Castilla-La Mancha, Albacete, Spain
  • 20INRAe, Bordeaux, France
  • 21Global Change Research Institute, Academy of Sciences of the Czech Republic, Brno, Czech Republic
  • 22Swedish University of Agricultural Sciences, Uppsala, Sweden
  • 23Free University of Bolzano, Bolzano, Italy, and 23b Forest Services, Autonomous Province of Bolzano, Bolzano, Italy
  • 24INBO, Geraardsbergen, Belgium
  • 25Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden
  • 26cE3c – Centre for Ecology, Evolution and Environmental Changes, Lisbon, Portugal
  • 27Forschungszentrum Juelich, Juelich, Germany
  • 28INRAe - URFM, Avignon, France
  • 29Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolution, 91405, Orsay, France
  • 30Université Catholique de Louvain, Louvain-la-Neuve, Belgium

Abstract. Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory) with remote sensing. Separating under and overstory signals would enable more accurate modeling of forest carbon and energy fluxes. We retrieved values of normalized difference vegetation index (NDVI) of forest understory with multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 meter daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were hence tested across diverse forest conditions and phenological phases during the growing season, to examine its applicability on ecosystems other than boreal forests. Here we report the method can deliver good retrievals especially over different forest types with open canopies (low foliage cover). The performance of the method was found limited over forests with closed canopies (high foliage cover), where the signal from understory gets much attenuated. The spatial heterogeneity of individual field sites as well as the limitations and documented quality of the MODIS BRDF product are shown to be important for correct assessment and validation of the retrievals obtained with remote sensing.

Jan Pisek et al.

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Short summary
Understory vegetation is the most diverse, least understood component of forests worldwide. Understory communities are important as drivers of overstory succession, nutrient cycling. Multi-angle remote sensing enables us now to describe surface properties by means that are not possible using mono-angle data. Evaluated over an extensive set of forest ecosystem experimental sites in Europe, here reported method can deliver good retrievals especially over different forest types with open canopies.
Understory vegetation is the most diverse, least understood component of forests worldwide....
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