Preprints
https://doi.org/10.5194/gmd-2021-93
https://doi.org/10.5194/gmd-2021-93

Submitted as: methods for assessment of models 27 May 2021

Submitted as: methods for assessment of models | 27 May 2021

Review status: this preprint is currently under review for the journal GMD.

Constraining a land cover map with satellite-based aboveground biomass estimates over Africa

Guillaume Marie1, Sebastiaan Luyssaert2, Cecile Dardel3, Thuy Le Toan4, Alexandre Bouvet4, Stéphane Mermoz4, Ludovic Villard4, Vladislav Bastrikov5, and Philippe Peylin1 Guillaume Marie et al.
  • 1Laboratoire des Sciences du Climat et de l’Environnement (LSCE/IPSL), CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
  • 2Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
  • 3Laboratoire Géosciences Environnement, Paul Sabatier University, Toulouse III, Toulouse, France
  • 4Centre d’Etudes Spatiales de la Biosphère (CESBIO), Toulouse, France
  • 5Science Partners, Paris, France

Abstract. Most land surface models can either calculate the vegetation distribution and dynamics internally by making use of biogeographical principles or use vegetation maps to prescribe spatial and temporal changes in vegetation distribution. Irrespective of whether vegetation dynamics are simulated or prescribed, it is not practical to represent vegetation across the globe at the species level because of its daunting diversity. This issue can be circumvented by making use of 5 to 20 plant functional types (PFT) by assuming that all species within a single functional type show identical land–atmosphere interactions irrespective of their geographical location. In this study, we hypothesize that remote-sensing based assessments of above-ground biomass can be used to refine discretizing real-world vegetation in PFT maps. Remotely sensed biomass estimates for Africa were used in a Bayesian framework to estimate the probability density distributions of woody, herbaceous, and bare soil fractions for the 15 land cover classes, according to the UN-LCCS typology, present in Africa. Subsequently, the 2.5 and 97.5 percentile of the probability density distributions were used to create 2.5 % and 97.5 % confidence interval PFT maps. Finally the original and refined PFT maps were used to drive biomass and albedo simulations with the ORCHIDEE model. This study demonstrates that remotely sensed biomass data can be used to better constrain PFT maps. Among the advantages of using remotely sensed biomass data were the reduced dependency on expert knowledge and the ability to report the confident interval of the PFT maps. Applying this approach at the global scale, would increase confidence in the PFT maps underlying assessments of present day biomass stocks.

Guillaume Marie et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-93', Anonymous Referee #1, 17 Sep 2021
    • AC1: 'Reply on RC1', guillaume Marie, 21 Oct 2021
  • RC2: 'Comment on gmd-2021-93', Anonymous Referee #2, 05 Oct 2021
    • AC2: 'Reply on RC2', guillaume Marie, 21 Oct 2021

Guillaume Marie et al.

Guillaume Marie et al.

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Short summary
Most earth system models make use of vegetation maps to initialize a simulation at global scale. Satellite-based biomass map estimates for Africa were used to estimate cover fractions for the 15 land cover classes. This study successfully demonstrates that satellite-based biomass map can be used to better constrain vegetation maps. Applying this approach at the global scale, would increase confidence in assessments of present day biomass stocks.