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@ARTICLE{Sivaprasad:1041302,
      author       = {Sivaprasad, Visakh and Rahmati, Mehdi and Springer, Anne
                      and Vereecken, Harry and Montzka, Carsten},
      title        = {{D}evelopment of {C}ontinuous {AMSR}-{E}/2 {S}oil
                      {M}oisture {T}ime {S}eries by {H}ybrid {D}eep {L}earning
                      {M}odel ({C}onv{LSTM}2{D} and {C}onv2{D}) and {T}ransfer
                      {L}earning for {R}eanalyses},
      journal      = {IEEE journal of selected topics in applied earth
                      observations and remote sensing},
      volume       = {0},
      issn         = {1939-1404},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-02209},
      pages        = {1 - 16},
      year         = {2025},
      abstract     = {Surface soil Moisture (SSM) is a crucial climate variable
                      of the Earth system that regulates water and energy
                      exchanges between the land and atmosphere, directly
                      influencing hydrological, biogeochemical, and energy cycles.
                      However, satellite-derived SSM, particularly from the
                      Advanced Microwave Scanning Radiometer AMSR-E/2, is limited
                      by radio frequency interference (RFI), vegetation effects,
                      frozen ground, and significant spatial and temporal data
                      gaps. By excluding data points affected by these problems,
                      we are able to train an unaffected system and fill the gaps
                      with high accuracy predictions. We developed a sophisticated
                      deep learning ConvLSTM model, that combines Convolutional
                      Long ShortTerm Memory (ConvLSTM2D) layers and Convolutional
                      Neural Network (Conv2D) layers. The model initially enhances
                      AMSR-2 SSM values across time and space using Advanced
                      SCATterometer (ASCAT) SSM as input. The ConvLSTM model,
                      trained to enhance AMSR-2 SSM, is then fine-tuned by using
                      the transfer learning technique to enhance AMSR-E data. The
                      enhanced AMSR-2 data is used as a target to guide the
                      enhancement of AMSR-E. This approach ensures that gaps in
                      AMSR-E data are filled, while aligning the characteristics
                      with the more consistent AMSR-2 SSM, resulting in a seamless
                      AMSR-E/2 dataset from 2003 to 2023. Unlike previous studies
                      incorporating additional datasets like precipitation,
                      temperature, and Digital Elevation Models, our approach
                      avoids these to prevent redundancy and potential
                      inaccuracies when generating land surface reanalyses based
                      on data assimilation, since such data are already integrated
                      into the land surface model. The ConvLSTM model achieved a
                      lower RMSE of 0.07 for AMSR-2 prediction and 0.04 for AMSR-E
                      via transfer learning demonstrating significant gap-filling
                      accuracy. The enhanced SSM demonstrated a $26\%$ improvement
                      in the correlation with in-situ SSM measurements, while
                      maintaining accuracy and consistency in spatial and temporal
                      patterns.},
      cin          = {IBG-3},
      ddc          = {520},
      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:001480466600003},
      doi          = {10.1109/JSTARS.2025.3557956},
      url          = {https://juser.fz-juelich.de/record/1041302},
}