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@ARTICLE{Lorenz:857643,
      author       = {Lorenz, C. and Montzka, C. and Jagdhuber, T. and Laux, P.
                      and Kunstmann, H.},
      title        = {{L}ong-term and high-resolution global time series of
                      brightness temperature from {C}opula-based fusion of {SMAP}
                      {E}nhanced and {SMOS} data},
      journal      = {Remote sensing},
      volume       = {10},
      number       = {11},
      issn         = {2072-4292},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2018-06621},
      pages        = {1842},
      year         = {2018},
      abstract     = {Long and consistent soil moisture time series at adequate
                      spatial resolution are key to foster the application of soil
                      moisture observations and remotely-sensed products in
                      climate and numerical weather prediction models. The two
                      L-band soil moisture satellite missions SMAP (Soil Moisture
                      Active Passive) and SMOS (Soil Moisture and Ocean Salinity)
                      are able to provide soil moisture estimates on global scales
                      and in kilometer accuracy. However, the SMOS data record has
                      an appropriate length of 7.5 years since late 2009, but with
                      a coarse resolution of ∼25 km only. In contrast, a
                      spatially-enhanced SMAP product is available at a higher
                      resolution of 9 km, but for a shorter time period (since
                      March 2015 only). Being the fundamental observable from
                      passive microwave sensors, reliable brightness temperatures
                      (Tbs) are a mandatory precondition for satellite-based soil
                      moisture products. We therefore develop, evaluate and apply
                      a copula-based data fusion approach for combining SMAP
                      Enhanced $(SMAP_E)$ and SMOS brightness Temperature (Tb)
                      data. The approach exploits both linear and non-linear
                      dependencies between the two satellite-based Tb products and
                      allows one to generate conditional $SMAP_E-like$ random
                      samples during the pre-SMAP period. Our resulting global
                      Copula-combined $SMOS-SMAP_E$ (CoSMOP) Tbs are statistically
                      consistent with $SMAP_E$ brightness temperatures, have a
                      spatial resolution of 9 km and cover the period from 2010 to
                      2018. A comparison with Service Soil Climate Analysis
                      Network (SCAN)-sites over the Contiguous United States
                      (CONUS) domain shows that the approach successfully reduces
                      the average RMSE of the original SMOS data by $15\%.$ At
                      certain locations, improvements of $40\%$ and more can be
                      observed. Moreover, the median NSE can be enhanced from zero
                      to almost 0.5. Hence, CoSMOP, which will be made freely
                      available to the public, provides a first step towards a
                      global, long-term, high-resolution and multi-sensor
                      brightness temperature product, and thereby, also soil
                      moisture},
      cin          = {IBG-3},
      ddc          = {620},
      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:000451733800173},
      doi          = {10.3390/rs10111842},
      url          = {https://juser.fz-juelich.de/record/857643},
}