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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-324
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-324
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: methods for assessment of models 12 Oct 2020

Submitted as: methods for assessment of models | 12 Oct 2020

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This preprint is currently under review for the journal GMD.

Cutting out the middleman: Calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance

Alexey N. Shiklomanov1, Michael C. Dietze2, Istem Fer3, Toni Viskari3, and Shawn P. Serbin4 Alexey N. Shiklomanov et al.
  • 1NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 2Department of Earth and Environment, Boston University, Boston, MA, USA
  • 3Finnish Meteorological Institute, Helsinki, Finland
  • 4Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA

Abstract. Ecosystem models are often calibrated and/or validated against derived remote sensing data products, such as MODIS leaf area index. However, these data products are generally based on their own models, whose assumptions may not be compatible with those of the ecosystem model in question, and whose uncertainties are usually not well quantified. Here, we develop an alternative approach whereby we modify an ecosystem model to predict full-range, high spectral resolution surface reflectance, which can then be compared directly against airborne and satellite data. Specifically, we coupled the two-stream representation of canopy radiative transfer in the Ecosystem Demography model (ED2) with a leaf radiative transfer model (PROSPECT 5) and a simple soil reflectance model. We then calibrated this model against reflectance observations from the NASA Airborne VIsible/InfraRed Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration successfully constrained the posterior distributions of model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. The calibrated model was able to accurately reproduce surface reflectance and leaf area index for sites with highly varied forest composition and structure, using a single common set of parameters across all sites. We conclude that having dynamic vegetation models directly predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.

Alexey N. Shiklomanov et al.

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Alexey N. Shiklomanov et al.

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Code and data repository (Open Science Framework) Alexey N. Shiklomanov https://doi.org/10.17605/OSF.IO/B6UMF

Alexey N. Shiklomanov et al.

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Latest update: 23 Oct 2020
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Short summary
Airborne and satellite images are a great resource for calibrating and evaluating computer models of ecosystems. Typically, researchers derive ecosystem properties from these images and then compare model against these derived properties. Here, we present an alternative approach where we modify a model to predict what the satellite would see more directly. We then show how this approach can be used to calibrate model parameters using airborne data from forest sites in the northeast US.
Airborne and satellite images are a great resource for calibrating and evaluating computer...
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