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@ARTICLE{Qu:861589,
      author       = {Qu, Yuquan and Zhu, Zhongli and Chai, Linna and Liu,
                      Shaomin and Montzka, Carsten and Liu, Jin and Yang, Xiaofan
                      and Lu, Zheng and Jin, Rui and Li, Xiang and Guo, Zhixia and
                      Zheng, Jie},
      title        = {{R}ebuilding a {M}icrowave {S}oil {M}oisture {P}roduct
                      {U}sing {R}andom {F}orest {A}dopting {AMSR}-{E}/{AMSR}2
                      {B}rightness {T}emperature and {SMAP} over the
                      {Q}inghai–{T}ibet {P}lateau, {C}hina},
      journal      = {Remote sensing},
      volume       = {11},
      number       = {6},
      issn         = {2072-4292},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2019-02039},
      pages        = {683},
      year         = {2019},
      abstract     = {Time series of soil moisture (SM) data in the
                      Qinghai–Tibet plateau (QTP) covering a period longer than
                      one decade are important for understanding the dynamics of
                      land surface–atmosphere feedbacks in the global climate
                      system. However, most existing SM products have a relatively
                      short time series or show low performance over the
                      challenging terrain of the QTP. In order to improve the
                      spaceborne monitoring in this area, this study presents a
                      random forest (RF) method to rebuild a high-accuracy SM
                      product over the QTP from 19 June 2002 to 31 March 2015 by
                      adopting the advanced microwave scanning radiometer for
                      earth observing system (AMSR-E), and the advanced microwave
                      scanning radiometer 2 (AMSR2), and tracking brightness
                      temperatures with latitude and longitude using the
                      International Geosphere–Biospheres Programme (IGBP)
                      classification data, the digital elevation model (DEM) and
                      the day of the year (DOY) as spatial predictors. Brightness
                      temperature products (from frequencies 10.7 GHz, 18.7 GHz
                      and 36.5 GHz) of AMSR2 were used to train the random forest
                      model on two years of Soil Moisture Active Passive (SMAP) SM
                      data. The simulated SM values were compared with third year
                      SMAP data and in situ stations. The results show that the RF
                      model has high reliability as compared to SMAP, with a high
                      correlation (R = 0.95) and low values of root mean square
                      error (RMSE = 0.03 m3/m3) and mean absolute percent error
                      (MAPE = $19\%).$ Moreover, the random forest soil moisture
                      (RFSM) results agree well with the data from five in situ
                      networks, with mean values of R = 0.75, RMSE = 0.06 m3/m3,
                      and bias = −0.03 m3/m3 over the whole year and R = 0.70,
                      RMSE = 0.07 m3/m3, and bias = −0.05 m3/m3 during the
                      unfrozen seasons. In order to test its performance
                      throughout the whole region of QTP, the three-cornered hat
                      (TCH) method based on removing common signals from
                      observations and then calculating the uncertainties is
                      applied. The results indicate that RFSM has the smallest
                      relative error in $56\%$ of the region, and it performs best
                      relative to the Japan Aerospace Exploration Agency (JAXA),
                      Global Land Data Assimilation System (GLDAS), and European
                      Space Agency’s Climate Change Initiative (ESA CCI)
                      project. The spatial distribution shows that RFSM has a
                      similar spatial trend as GLDAS and ESA CCI, but RFSM
                      exhibits a more distinct spatial distribution and responds
                      to precipitation more effectively than GLDAS and ESA CCI.
                      Moreover, a trend analysis shows that the temporal variation
                      of RFSM agrees well with precipitation and LST (land surface
                      temperature), with a dry trend in most regions of QTP and a
                      wet trend in few north, southeast and southwest regions of
                      QTP. In conclusion, a spatiotemporally continuous SM product
                      with a high accuracy over the QTP was obtained.},
      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:000465615300060},
      doi          = {10.3390/rs11060683},
      url          = {https://juser.fz-juelich.de/record/861589},
}