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@ARTICLE{Montzka:820871,
      author       = {Montzka, Carsten and Jagdhuber, Thomas and Horn, Ralf and
                      Bogena, Heye and Hajnsek, Irena and Reigber, Andreas and
                      Vereecken, Harry},
      title        = {{I}nvestigation of {SMAP} {F}usion {A}lgorithms {W}ith
                      {A}irborne {A}ctive and {P}assive {L}-{B}and {M}icrowave
                      {R}emote {S}ensing},
      journal      = {IEEE transactions on geoscience and remote sensing},
      volume       = {54},
      number       = {7},
      issn         = {1558-0644},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2016-06135},
      pages        = {3878 - 3889},
      year         = {2016},
      abstract     = {The objective of the NASA Soil Moisture Active Passive
                      (SMAP) mission is to provide global measurements of soil
                      moisture and freeze/thaw states. SMAP integrates L-band
                      radar and radiometer instruments as a single observation
                      system combining the respective strengths of active and
                      passive remote sensing for enhanced soil moisture mapping.
                      Airborne instruments are a key part of the SMAP validation
                      program. Here, we present an airborne campaign in the Rur
                      catchment, Germany, in which the passive L-band system
                      Polarimetric L-band Multi-beam Radiometer and the active
                      L-band system F-SAR of DLR were flown simultaneously on six
                      dates in 2013. The flights covered the full heterogeneity of
                      the area under investigation, i.e., the main land cover
                      types and all experimental monitoring sites. Here, we used
                      the obtained data sets as a test bed for the analysis of
                      three active-passive fusion techniques: 1) estimation of
                      soil moisture by passive sensor data and subsequent
                      disaggregation by active sensor backscatter data; 2)
                      disaggregation of passive microwave brightness temperature
                      by active microwave backscatter and subsequent inversion to
                      soil moisture; and 3) fusion of two single-source soil
                      moisture products from radar and radiometer. Results
                      indicate that the regression parameters β are dependent on
                      the radar vegetation index. The best performance was
                      obtained by the fusion of radiometer brightness temperatures
                      and radar backscatter, which was able to reach the same
                      accuracy as single-source coarse-scale radiometer soil
                      moisture retrieval but on a higher spatial resolution.},
      cin          = {IBG-3},
      ddc          = {550},
      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:000377478400012},
      doi          = {10.1109/TGRS.2016.2529659},
      url          = {https://juser.fz-juelich.de/record/820871},
}