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@ARTICLE{Lievens:811707,
      author       = {Lievens, H. and De Lannoy, G. J. M. and Al Bitar, A. and
                      Drusch, M. and Dumedah, G. and Hendricks-Franssen,
                      Harrie-Jan and Kerr, Y. H. and Tomer, S. K. and Martens, B.
                      and Merlin, O. and Pan, M. and Roundy, J. K. and Vereecken,
                      H. and Walker, J. P. and Wood, E. F. and Verhoest, N. E. C.
                      and Pauwels, V. R. N.},
      title        = {{A}ssimilation of {SMOS} soil moisture and brightness
                      temperature products into a land surface model},
      journal      = {Remote sensing of environment},
      volume       = {180},
      issn         = {0034-4257},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2016-04091},
      pages        = {292 - 304},
      year         = {2016},
      abstract     = {The Soil Moisture and Ocean Salinity (SMOS) mission has the
                      potential to improve the predictive skill of land surface
                      models through the assimilation of its observations. Several
                      alternate products can be distinguished: the observed
                      brightness temperature (TB) data at coarse scale, indirect
                      estimates of soil moisture (SM) through the inversion of the
                      coarse-scale TB observations, and fine-scale soil moisture
                      through the a priori downscaling of coarse-scale soil
                      moisture. The SMOS TB products include observations over a
                      large range of incidence angles at both H- and
                      V-polarizations, which allows the merit of assimilating the
                      full set of multi-angular/polarization observations, as
                      opposed to specific sub-sets of observations, to be
                      assessed. This study investigates the performance of various
                      observation scenarios with respect to soil moisture and
                      streamflow predictions in the Murray Darling Basin. The
                      observations are assimilated into the Variable Infiltration
                      Capacity (VIC) model, coupled to the Community Microwave
                      Emission Modeling (CMEM) platform, using the Ensemble Kalman
                      filter. The assimilation of these various observation
                      products is assessed under similar realistic assimilation
                      settings, without optimization, and validated by comparison
                      of the modeled soil moisture and streamflow to in situ
                      measurements across the basin. The best results are achieved
                      from assimilation of the coarse-scale SM observations. The
                      reduced improvement using downscaled SM is probably due to a
                      lower number of observations, as a result of cloud cover
                      effects on the downscaling method. The assimilation of TB
                      was found to be a promising alternative, which led to
                      improvements in soil moisture prediction approaching those
                      of the coarse-scale SM assimilation},
      cin          = {IBG-3},
      ddc          = {050},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000376801000023},
      doi          = {10.1016/j.rse.2015.10.033},
      url          = {https://juser.fz-juelich.de/record/811707},
}