Submitted as: methods for assessment of models 12 Oct 2020

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

Review status: 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.

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for authors/topical editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Alexey N. Shiklomanov et al.

Data sets

Code and data repository (Open Science Framework) Alexey N. Shiklomanov

Alexey N. Shiklomanov et al.


Total article views: 451 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
347 97 7 451 7 10
  • HTML: 347
  • PDF: 97
  • XML: 7
  • Total: 451
  • BibTeX: 7
  • EndNote: 10
Views and downloads (calculated since 12 Oct 2020)
Cumulative views and downloads (calculated since 12 Oct 2020)

Viewed (geographical distribution)

Total article views: 365 (including HTML, PDF, and XML) Thereof 364 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
Latest update: 27 Jan 2021
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.