% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Bayat:907822,
      author       = {Bayat, Bagher and Montzka, Carsten and Graf, Alexander and
                      Giuliani, Gregory and Santoro, Mattia and Vereecken, Harry},
      title        = {{O}ne decade (2011–2020) of {E}uropean agricultural water
                      stress monitoring by {MSG}-{SEVIRI}: workflow implementation
                      on the {V}irtual {E}arth {L}aboratory ({VL}ab) platform},
      journal      = {International journal of digital earth},
      volume       = {15},
      number       = {1},
      issn         = {1753-8947},
      address      = {London [u.a.]},
      publisher    = {Taylor $\&$ Francis},
      reportid     = {FZJ-2022-02233},
      pages        = {730 - 747},
      year         = {2022},
      abstract     = {Cloud computing facilities can provide crucial computing
                      support for processing the time series of satellite data and
                      exploiting their spatio-temporal information content.
                      However, dedicated efforts are still required to develop
                      workflows, executable on cloud-based platforms, for
                      ingesting the satellite data, performing the targeted
                      processes, and generating the desired products. In this
                      study, an operational workflow is proposed, based on
                      monthly Evaporative Stress Index (ESI) anomaly, and
                      implemented in cloud-based online Virtual Earth Laboratory
                      (VLab) platform, as a demonstration, to monitor European
                      agricultural water stress. To this end, daily time-series of
                      actual and reference evapotranspiration (ETa and ET0), from
                      the Spinning Enhanced Visible and Infrared Imager (SEVIRI)
                      sensor, were used to execute the proposed workflow
                      successfully on VLab. The execution of the workflow
                      resulted in obtaining one decade (2011–2020) of European
                      monthly agricultural water stress maps at 0.04˚ spatial
                      resolution and corresponding stress reports for each
                      country. To support open science, all the workflow outputs
                      are stored in GeoServer, documented in GeoNetwork, and made
                      available through MapStore. This enables creating a
                      dashboard for better visualization of the results for
                      end-users. The results from this study demonstrate the
                      capability of VLab platform for water stress detection from
                      time series of SEVIRI-ET data.},
      cin          = {IBG-3},
      ddc          = {910},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / ERA-PLANET - The European network for observing
                      our changing planet (689443)},
      pid          = {G:(DE-HGF)POF4-2173 / G:(EU-Grant)689443},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000783506400001},
      doi          = {10.1080/17538947.2022.2061617},
      url          = {https://juser.fz-juelich.de/record/907822},
}