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@ARTICLE{Liu:904464,
      author       = {Liu, Jin and Chai, Linna and Dong, Jianzhi and Zheng,
                      Donghai and Wigneron, J.-P. and Liu, Shaomin and Zhou, Ji
                      and Xu, Tongren and Yang, Shiqi and Song, Yongze and Qu,
                      Yuquan and Lu, Zheng},
      title        = {{U}ncertainty analysis of eleven multisource soil moisture
                      products in the third pole environment based on the
                      three-corned hat method},
      journal      = {Remote sensing of environment},
      volume       = {255},
      issn         = {0034-4257},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2021-06034},
      pages        = {112225 -},
      year         = {2021},
      note         = {Ein Postprint steht leider nicht zur Verfügung},
      abstract     = {Soil moisture (SM) is a fundamental environmental variable
                      for characterizing climate, land surface and atmosphere. In
                      recent years, several SM products have been developed based
                      on remote sensing (RS), land surface model (LSM) or land
                      data assimilation system (LDAS). However, little knowledge
                      is available in understanding spatial patterns of the
                      uncertainty of different SM products and potential regional
                      drivers over the Qinghai-Tibet Plateau (QTP), a complex
                      environment for accurate SM estimation. This paper
                      investigates the sensitivity of the SM uncertainties based
                      on the three-cornered hat (TCH) method and a generalized
                      additive model (GAM) in terms of underlying surface
                      characteristics (sand fraction, soil organic matter,
                      vegetation, land surface temperature, and topography) and
                      near-ground meteorology (precipitation and air temperature)
                      in the third pole environment over the 2015–2018 period.
                      Eleven SM products are involved in this work, including Soil
                      Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity
                      INRA-CESBIO (SMOS-IC), Japan Aerospace Exploration Agency
                      (JAXA), Land Surface Parameter Model (LPRM), Climate Change
                      Initiative - Active/Combined $(CCI_A/CCI_C),$ Global Land
                      Data Assimilation System (GLDAS), European Centre for
                      Medium-Range Weather Forecasts Interim reanalysis
                      (ERA-Interim), Global Land Evaporation Amsterdam Model
                      product a/b $(GLEAM_a/GLEAM_b),$ and Random Forest Soil
                      Moisture (RFSM). Results show that most of the SM products
                      perform well across QTP, while SMOS-IC is strongly affected
                      by radio-frequency interference in this region, JAXA has a
                      relatively higher noise level over QTP, and LPRM has larger
                      relative uncertainties (RUs) in the southeast of QTP.
                      Nonlinear regression analysis demonstrates that variations
                      of RUs in SMOS-IC and JAXA are driven by topography, while
                      LPRM's are mainly controlled by vegetation. In addition, two
                      groups of opposite (positive/negative) effects from
                      topography and vegetation, topography and precipitation, and
                      precipitation and land surface temperature affect the
                      spatial variations of RUs in $CCI_A,$ RFSM, and ERA-Interim,
                      respectively. Meanwhile, more complex relationships are
                      found between multiple surface factors and RUs of different
                      products. In general, the underlying surface factors explain
                      on average $39.41\%$ and $28.34\%$ of the variations in RS
                      and LSM/LDAS SM RUs, respectively. Comparatively, the
                      near-ground meteorology factors have a slightly larger
                      effect on LSM/LDAS products than that on RS products.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000619233200001},
      doi          = {10.1016/j.rse.2020.112225},
      url          = {https://juser.fz-juelich.de/record/904464},
}