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@ARTICLE{Major:1047697,
      author       = {Major, David and Horváth, Zsolt and Kröber, Felix and
                      Augustin, Hannah and Sudmanns, Martin and Ševčík, Petr
                      and Baraldi, Andrea and Berg, Astrid and Cornel, Daniel and
                      Tiede, Dirk},
      title        = {{A} holistic approach for multi-spectral {S}entinel-2
                      super-resolution and spectral evaluation},
      journal      = {International journal of remote sensing},
      volume       = {46},
      number       = {20},
      issn         = {0143-1161},
      address      = {London},
      publisher    = {Taylor $\&$ Francis},
      reportid     = {FZJ-2025-04463},
      pages        = {7437 - 7464},
      year         = {2025},
      abstract     = {Images provided by the European Copernicus Sentinel-2
                      satellites are valuable and easily accessible sources of
                      remote sensing data for tasks across various fields. These
                      data have a high spectral and temporal resolution, but a
                      rather low spatial resolution, limiting their applicability
                      for many tasks. In agricultural tasks, such as crop
                      monitoring of small land parcels, the use of these data for
                      fine-scale analysis is contingent upon the enhancement of
                      spatial resolution while maintaining spectral fidelity. In
                      this work, we propose a comprehensive single-image
                      super-resolution reconstruction workflow that ensures both
                      properties and is divided into two parts. First, a deep
                      learning-based super-resolution reconstruction approach is
                      applied to improve the spatial resolution of multi-spectral
                      Sentinel-2 images to 2.5 m. For this purpose, a novel
                      method is applied to achieve super-resolution of multiple
                      spectral bands where associated real-word reference data is
                      only partially available. It learns to increase the spatial
                      resolution while preserving spectral accuracy of 10 m
                      bands using high-resolution data from an auxiliary satellite
                      with spectral correspondence, and 20 m bands without
                      reference data using synthetic Sentinel-2 pairs. Second, the
                      suitability of the method to subsequent agricultural tasks
                      is evaluated by measuring the discrepancy between the
                      super-resolved and reference data through a novel spectral
                      knowledge-based validation method. This method leverages
                      mappings of reflectances to spectral categories that enable
                      assessing the spectral fidelity of super-resolved outputs,
                      which is complementary to existing image quality assessment
                      metrics, but with greater depth. The promising spectral
                      validation results suggest that our super-resolution
                      reconstruction pipeline has a great potential for
                      agricultural applications.},
      cin          = {IBG-2},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1080/01431161.2025.2549132},
      url          = {https://juser.fz-juelich.de/record/1047697},
}