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@ARTICLE{Huang:886003,
      author       = {Huang, Jingyi and Desai, Ankur R. and Zhu, Jun and
                      Hartemink, Alfred E. and Stoy, Paul C. and Loheide, Steven
                      P. and Bogena, Heye and Zhang, Yakun and Zhang, Zhou and
                      Arriaga, Francisco},
      title        = {{R}etrieving {H}eterogeneous {S}urface {S}oil {M}oisture at
                      100 m {A}cross the {G}lobe via {F}usion of {R}emote
                      {S}ensing and {L}and {S}urface {P}arameters},
      journal      = {Frontiers in water},
      volume       = {2},
      issn         = {2624-9375},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2020-04225},
      pages        = {578367},
      year         = {2020},
      abstract     = {Successful monitoring of soil moisture dynamics at high
                      spatio-temporal resolutions globally is hampered by the
                      heterogeneity of soil hydraulic properties in space and
                      complex interactions between water and the environmental
                      variables that control it. Current soil moisture monitoring
                      schemes via in situ station networks are sparsely
                      distributed while remote sensing satellite soil moisture
                      maps have a very coarse spatial resolution. In this study,
                      an empirical surface soil moisture (SSM) model was
                      established via fusion of in situ continental and regional
                      scale soil moisture networks, remote sensing data (SMAP and
                      Sentinel-1) and high-resolution land surface parameters
                      (e.g., soil texture, terrain) using a quantile random forest
                      (QRF) algorithm. The model had a spatial resolution of 100 m
                      and performed well under cultivated, herbaceous, forest, and
                      shrub soils (overall R2 = 0.524, RMSE = 0.07 m3 m−3). It
                      has a relatively good transferability at the regional scale
                      among different soil moisture networks (mean RMSE =
                      0.08–0.10 m3 m−3). The global model was applied to map
                      SSM dynamics at 30–100 m across a field-scale soil
                      moisture network (TERENO-Wüstebach) and an 80-ha cultivated
                      cropland in Wisconsin, USA. Without the use of local
                      training data, the model was able to delineate the
                      variations in SSM at the field scale but contained large
                      bias. With the addition of $10\%$ local training datasets
                      (“spiking”), the bias of the model was significantly
                      reduced. The QRF model was relatively insensitive to the
                      resolution of Sentinel-1 data but was affected by the
                      resolution and accuracy of soil maps. It was concluded that
                      the empirical model has the potential to be applied
                      elsewhere across the globe to map SSM at the regional to
                      field scales for research and applications. Future research
                      is required to improve the performance of the model by
                      incorporating more field-scale soil moisture sensor networks
                      and assimilation with process-based models.},
      cin          = {IBG-3},
      ddc          = {333.7},
      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:000659431100001},
      doi          = {10.3389/frwa.2020.578367},
      url          = {https://juser.fz-juelich.de/record/886003},
}