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@ARTICLE{Qu:890910,
      author       = {Qu, Yuquan and Zhu, Zhongli and Montzka, Carsten and Chai,
                      Linna and Liu, Shaomin and Ge, Yong and Liu, Jin and Lu,
                      Zheng and He, Xinlei and Zheng, Jie and Han, Tian},
      title        = {{I}nter-comparison of several soil moisture downscaling
                      methods over the {Q}inghai-{T}ibet {P}lateau, {C}hina},
      journal      = {Journal of hydrology},
      volume       = {592},
      issn         = {0022-1694},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2021-01241},
      pages        = {125616},
      year         = {2021},
      abstract     = {Microwave remote sensing is able to retrieve soil moisture
                      (SM) at an adequate level of accuracy. However, these
                      microwave remotely sensed SM products usually have a spatial
                      resolution of tens of kilometers which cannot satisfy the
                      requirements of fine to medium scale applications such as
                      agricultural irrigation and local water resource management.
                      Several SM downscaling methods have been proposed to solve
                      this mismatch by downscaling the coarse-scale SM to
                      fine-scale (several kilometers or hundreds of meters).
                      Although studies have been conducted over different climatic
                      zones and from different data sets with good results, there
                      is still a lack of a comprehensive comparison and evaluation
                      between them to guide the production of high-resolution and
                      high-accuracy SM data. Therefore, in this study we compared
                      several SM downscaling methods (from 0.25° to 0.01°) based
                      on polynormal fitting, physical model, machine learning and
                      geostatistics over the Qinghai-Tibet plateau where there is
                      a wide range of climate conditions from four aspects, that
                      is, comparison with the original microwave product,
                      comparison with in situ measurements, inter-comparison based
                      on three-cornered hat (TCH) method, and a spatial
                      feasibility analysis. The comparison results show that the
                      method based on a physical model, in this case the
                      Disaggregation based on Physical And Theoretical scale
                      Change (DisPATCh) method, has the highest ability on
                      preserving the coarse-scale feature of original microwave SM
                      product, while to some extent, this ability could be a
                      disadvantage for improving the accuracy of the downscaling
                      results. In addition, soil evaporation efficiency (SEE)
                      alone is not sufficient to represent SM spatial patterns
                      over complex land surface. Geostatistics based area-to-area
                      regression Kriging (ATARK) introduces the highest
                      uncertainty caused by the overcorrection during the residual
                      interpolation process while this process can also improve
                      correlation (R) and correct the bias as well as provide more
                      feasible spatial patterns and details. Two machine learning
                      methods, the random forest (RF) and Gaussian process
                      regression (GPR) show high stability on all comparison
                      results but provide smoother spatial patterns. The
                      multivariate statistical regression (MSR) method performs
                      worst due to the fact that its simple linear regression
                      model could not meet the requirement of SM fitting on
                      complicated land surface. Moreover, all five downscaling
                      methods show a declining accuracy after downscaling. This
                      phenomenon may be caused by the spatial mismatch on
                      fine-scale. In addition, this could also be caused by the
                      tendency that downscaled results will usually provide more
                      spatial details from downscaling predictors, while they
                      cannot capture the temporal changes of the microwave SM
                      product well. In general, this phenomenon tends to be more
                      significant over heterogeneous land surface. All in all,
                      five widely used soil moisture downscaling methods were
                      compared based on a comprehensive comparison scheme to add
                      to the body of knowledge in applicability of downcaling
                      methods under different weather conditions.},
      cin          = {IBG-3},
      ddc          = {690},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {217 - Für eine nachhaltige Bio-Ökonomie – von
                      Ressourcen zu Produkten (POF4-217)},
      pid          = {G:(DE-HGF)POF4-217},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000639844900018},
      doi          = {10.1016/j.jhydrol.2020.125616},
      url          = {https://juser.fz-juelich.de/record/890910},
}