Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 9, issue 6
Biogeosciences, 9, 2195–2201, 2012
https://doi.org/10.5194/bg-9-2195-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 9, 2195–2201, 2012
https://doi.org/10.5194/bg-9-2195-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Technical note 18 Jun 2012

Technical note | 18 Jun 2012

Technical Note: Calibration and validation of geophysical observation models

M. S. Salama1,2, R. Van der Velde1, H. J. van der Woerd3, J. C. Kromkamp2, C. J. M. Philippart4, A. T. Joseph5, P. E. O'Neill5, R. H. Lang6, T. Gish7, P. J. Werdell8,9, and Z. Su1 M. S. Salama et al.
  • 1Department of Water Resources, ITC, University of Twente, The Netherlands
  • 2Royal Netherlands Institute for Sea Research (NIOZ), Yerseke, The Netherlands
  • 3Institute for Environmental Studies (IVM), VU University, The Netherlands
  • 4Royal Netherlands Institute for Sea Research (NIOZ), The Netherlands
  • 5Hydrological Sciences Branch/614.3, Hydrospheric and Biospheric Sciences Laboratory, NASA/GSFC, USA
  • 6Dept. of Electrical and Computer Engineering, George Washington University, USA
  • 7Hydrology and Remote Sensing Lab, USDA-ARS, USA
  • 8NASA Goddard Space Flight Center, Ocean Ecology Branch Code 614.2, USA
  • 9Science Systems and Applications, Inc., Lanham, USA

Abstract. We present a method to calibrate and validate observational models that interrelate remotely sensed energy fluxes to geophysical variables of land and water surfaces. Coincident sets of remote sensing observation of visible and microwave radiations and geophysical data are assembled and subdivided into calibration (Cal) and validation (Val) data sets. Each Cal/Val pair is used to derive the coefficients (from the Cal set) and the accuracy (from the Val set) of the observation model. Combining the results from all Cal/Val pairs provides probability distributions of the model coefficients and model errors. The method is generic and demonstrated using comprehensive matchup sets from two very different disciplines: soil moisture and water quality. The results demonstrate that the method provides robust model coefficients and quantitative measure of the model uncertainty. This approach can be adopted for the calibration/validation of satellite products of land and water surfaces, and the resulting uncertainty can be used as input to data assimilation schemes.

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