Articles | Volume 14, issue 5
https://doi.org/10.5194/gmd-14-2603-2021
https://doi.org/10.5194/gmd-14-2603-2021
Methods for assessment of models
 | 
12 May 2021
Methods for assessment of models |  | 12 May 2021

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

Alexey N. Shiklomanov, Michael C. Dietze, Istem Fer, Toni Viskari, and Shawn P. Serbin

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Cited articles

Asner, G. P.: Biophysical and Biochemical Sources of Variability in Canopy Reflectance, Remote Sens. Environ., 64, 234–253, https://doi.org/10.1016/S0034-4257(98)00014-5, 1998. a
Baker, I. T., Prihodko, L., Denning, A. S., Goulden, M., Miller, S., and da Rocha, H. R.: Seasonal Drought Stress in the Amazon: Reconciling Models and Observations, J. Geophys. Res.-Biogeo., 113, G00B01, https://doi.org/10.1029/2007JG000644, 2008. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Bonan, G. B.: Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests, Science, 320, 1444–1449, https://doi.org/10.1126/science.1155121, 2008. a, b
Combal, B., Baret, F., Weiss, M., Trubuil, A., Macé, D., Pragnère, A., Myneni, R., Knyazikhin, Y., and Wang, L.: Retrieval of Canopy Biophysical Variables from Bidirectional Reflectance: Using Prior Information to Solve the Ill-Posed Inverse Problem, Remote Sens. Environ., 84, 1–15, https://doi.org/10.1016/S0034-4257(02)00035-4, 2003. a, b
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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 models 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 northeastern US.