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This article in VZJ

  1. Vol. 12 No. 3
    Received: Mar 29, 2012
    Published: July 19, 2013

    * Corresponding author(s): c.montzka@fz-juelich.de


Estimation of Radiative Transfer Parameters from L-Band Passive Microwave Brightness Temperatures Using Advanced Data Assimilation

  1. Carsten Montzka ,
  2. Jennifer P. Grantb,
  3. Hamid Moradkhanic,
  4. Harrie-Jan Hendricks Franssena,
  5. Lutz Weihermüllera,
  6. Matthias Druschd and
  7. Harry Vereeckena
  1. Forschungszentrum Jülich, Agrosphere Institute (IBG 3), Leo-Brandt-Strasse, 52425 Jülich, Germany
    Lund University, Department of Physical Geography and Ecosystem Science, Lund, Sweden
    Portland State University, Department of Civil and Environmental Engineering, Portland, OR
    European Space Agency, ESA-ESTEC, Noordwijk, The Netherlands


The temporal evolution of radiative transfer parameters vegetation opacity and soil surface roughness is estimated by assimilating L-band brightness temperatures. The L-MEB model is used within a particle filter in synthetic applications with increasing complexity. Finally, a real-world experiment is conducted with multiangular SMOS observations covering a SCAN site.

ESA’s Soil Moisture and Ocean Salinity (SMOS) mission has been designed to extend our knowledge of the Earth’s water cycle. Soil Moisture and Ocean Salinity records brightness temperatures at the L-band, which over land are sensitive to soil and vegetation parameters. On the basis of these measurements, soil moisture and vegetation opacity data sets have been derived operationally since 2009 for applications comprising hydrology, numerical weather prediction (NWP), and drought monitoring. We present a method to enhance the knowledge about the temporal evolution of radiative transfer parameters. The radiative transfer model L-Band Microwave Emission of the Biosphere (L-MEB) is used within a data assimilation framework to retrieve vegetation opacity and soil surface roughness. To analyze the ability of the data assimilation approach to track the temporal evolution of these parameters, scenario analyses were performed with increasing complexity. First, the HYDRUS-1D code was used to generate soil moisture and soil temperature time series. On the basis of these data, the L-MEB forward model was run to simulate brightness temperature observations. Finally, the coupled model system HYDRUS-1D and L-MEB were integrated into a data assimilation framework using a particle filter, which is able to update L-MEB states as well as L-MEB parameters. Time invariant and time variable radiative transfer parameters were estimated. Moreover, it was possible to estimate a “bias” term when model simulations show a systematic difference as compared to observations. An application to a USDA-NRCS Soil Climate Analysis Network (SCAN) site showed the good performance of the proposed approach under real conditions.

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Copyright © 2013. Copyright © by the Soil Science Society of America, Inc.