Jean-Luc Widlowski, Land Resources Management Unit, Italy
Corrado Mio, Land Resources Management Unit, Italy
Mathias Disney, University College London
Jennifer Adams, University College London
Ioannis Andredakis, Global Security and Crisis Management Unit, Italy
Clement Atzberger, University of Natural Resources and Life Sciences
James Brennan, University College London
Lorenzo Busetto, National Research Council (CNR-IREA), Italy
Michael Chelle, Institut National de la Recherche Agronomique
Guido Ceccherini, Water Resources Unit, Italy
Roberto Colombo, University of Milano-Bicocca
Jean-Francois Cote, Canadian Wood Fibre Centre
Alo Eenmae, Estonian University of Life Sciences
Richard Essery, University of Edinburgh
Jean-Philippe Gastellu-Etchegorry, Universite de Toulouse, UPS-CNES-CNRS-IRD
Nadine Gobron, Land Resources Management Unit, Italy
Eloi Grau, Universite de Toulouse
Vanessa E. Haverd, CSIRO Oceans and Atmosphere FlagshipFollow
Lucie Homolova, University of Zurich
Huaguo Huang, Beijing Forestry University
Linda Hunt, Science Systems and Applications, Inc.
Hideki Kobayashi, Japan Agency for Marine-Earth Science and Technology
Benjamin Koetz, ESRIN D/EOP-SEP
Andres Kuusk, Tartu Observatory
Joel Kuusk, Tartu Observatory
Mait Lang, Tartu Observatory
Philip E. Lewis, University College London
Jennifer L. Lovell, CSIRO Oceans and Atmosphere Flagship
Zbynek Malenovky, University of WollongongFollow
Michele Meroni, Monitoring Agricultural Resources Unit, Italy
Felix Morsdorf, University of Zurich
Matti Mottus, University of Helsinki
Wenge Ni-Meister, Hunter College
Bernard Pinty, Land Resources Management Unit, Italy
Miina Rautiainen, University of Helsinki
Martin Schlerf, Land Resources Management Unit, Italy
Ben Somers, Land Resources Management Unit, Italy
Jan Stuckens, Land Resources Management Unit, Italy
Michel M. Verstraete, South Africa National Space Agency
Wenze Yang, University of Maryland
Feng Zhao, Beijing University of Aeronautics and Astronautics
Terenzio Zenone, University of Antwerp



Publication Details

Widlowski, J., Mio, C., Disney, M., Adams, J., Andredakis, I., Atzberger, C., Brennan, J., Busetto, L., Chelle, M., Ceccherini, G., Colombo, R., Cote, J., Eenmae, A., Essery, R., Gastellu-Etchegorry, J., Gobron, N., Grau, E., Haverd, V., Homolova, L., Huang, H., Hunt, L., Kobayashi, H., Koetz, B., Kuusk, A., Kuusk, J., Lang, M., Lewis, P. E., Lovell, J. L., Malenovky, Z., Meroni, M., Morsdorf, F., Mottus, M., Ni-Meister, W., Pinty, B., Rautiainen, M., Schlerf, M., Somers, B., Stuckens, J., Verstraete, M. M., Yang, W., Zhao, F. & Zenone, T. (2015). The fourth phase of the radiative transfermodel intercomparison (RAMI) exercise: actual canopy scenarios and conformity testing. Remote Sensing of Environment: an interdisciplinary journal, 169 418-437.


The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RT model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals.



Link to publisher version (DOI)