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@ARTICLE{Rtzer:187158,
      author       = {Rötzer, K. and Montzka, C. and Vereecken, H.},
      title        = {{S}patio-temporal variability of global soil moisture
                      products},
      journal      = {Journal of hydrology},
      volume       = {522},
      issn         = {0022-1694},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2015-00832},
      pages        = {187 - 202},
      year         = {2015},
      abstract     = {Being an important variable for various applications, for
                      example hydrological and weather prediction models or data
                      assimilation, a large range of global soil moisture products
                      from different sources, such as modeling or active and
                      passive microwave remote sensing, are available. The diverse
                      measurement and estimation methods can lead to differences
                      in the characteristics of the products. This study
                      investigates the spatial and temporal behavior of three
                      different products: (i) the Soil Moisture and Ocean Salinity
                      (SMOS) Level 2 product, retrieved with a physically based
                      approach from passive microwave remote sensing brightness
                      temperatures, (ii) the MetOp-A Advanced Scatterometer
                      (ASCAT) product retrieved with a change detection method
                      from radar remote sensing backscattering coefficients, and
                      (iii) the ERA Interim product from a weather forecast model
                      reanalysis. Results show overall similar patterns of spatial
                      soil moisture, but high deviations in absolute values. A
                      ranking of mean relative differences demonstrates that ASCAT
                      and ERA Interim products show most similar spatial soil
                      moisture patterns, while ERA and SMOS products show least
                      similarities. For selected regions in different climate
                      classes, time series of the ASCAT product generally show
                      higher variability of soil moisture than SMOS, and
                      especially than ERA products. The relationship of spatial
                      mean and variance is, especially during wet periods,
                      influenced by sensor and retrieval characteristics in the
                      SMOS product, while it is determined to a larger degree by
                      the precipitation patterns of the respective regions in the
                      ASCAT and ERA products. The decomposition of spatial
                      variance into temporal variant and invariant components
                      exhibits high dependence on the retrieval methods of the
                      respective products. The change detection retrieval method
                      causes higher influence of temporal variant factors (e.g.
                      precipitation, evaporation) on the ASCAT product, while SMOS
                      and ERA products are stronger determined by temporal
                      invariant factors (e.g. topography, soil characteristics).
                      The investigation of the effect of changing scales on
                      spatial variance in three different areas indicates that the
                      variance does not vary with increasing support scale.
                      Increasing extent scales from 250 to 3000 km raise spatial
                      variance of all products and all study areas according to a
                      power law, which is varying seasonally. ERA shows a
                      consistent scaling behavior with a constant power scale
                      factor and similar intercepts across all study regions. In
                      general, the investigated products show overall different
                      spatial and temporal statistics which are induced by their
                      different estimation methods and which are important to be
                      aware of for the selection of a product for application and
                      for their up- or downscaling.},
      cin          = {IBG-3},
      ddc          = {690},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / 255 - Terrestrial Systems: From Observation to
                      Prediction (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255 / G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000350920200016},
      doi          = {10.1016/j.jhydrol.2014.12.038},
      url          = {https://juser.fz-juelich.de/record/187158},
}