Mankind is more and more confronted with the consequences of the climate change such as the recent obvious increase of natural disasters like typhoons and flash floods. The crucial task of the Earth Science with high social relevance is therefore to provide a better understanding of these processes endangering the planet. Important for this are observation, modelling and prediction of the manifold and complex Earth system.

The exploitation of Global Navigation Satellite Systems signals for Remote Sensing (GNSS-RS) provides an innovative, cost-effective, and versatile tool for Earth system observation. The GNSS-RS includes reflectometry, radio occultation, and ground-based sounding techniques that can be used to monitor the atmosphere, ionosphere, cryosphere, oceans, and land with high spatiotemporal resolution on a global scale. The number of GNSS receivers for Remote Sensing in regional and global ground networks, as well as aboard small satellites and CubeSat constellations, is rapidly increasing. These numerous sensors currently generate a huge amount of observation data, which have to be analyzed and effectively used in geophysical models to characterize the Earth system and its evolution like never before. This task can only be optimally solved by a synergetic combination of GNSS-RS with innovative, advanced and interdisciplinary Data Science (DS) tools.

The Data Science methods are all the mathematical processes, algorithms, and systems to optimally extract the required geophysical information from the GNSS data. They specifically include (but are not limited to): statistical analysis, data fusion, homogenization, assimilation, and machine learning. Recent large investments in Data Science activities both in academia (e.g., the European Commission’s Program “Destination Earth”) and industry, encourage and facilitate the development and application of DS tools for GNSS-RS.

The German Research Centre for Geosciences GFZ in Potsdam, Germany, has been actively together with the international community, driving forward the GNSS-RS techniques. Integrating Data Science was an important part of these activities. Therefore, GFZ will bring together for the first time the GNSS Remote Sensing and Data Science experts to foster these innovative and interdisciplinary developments in Earth Science at one of the most historical geophysical research institutes.

We welcome abstracts in the core areas of

  • GNSS Remote Sensing data fusion with other sensors/techniques,
  • Data assimilation into Earth system models,
  • Machine learning based retrieval algorithms and knowledge extraction from the data,
  • Machine learning based Earth system modeling and forecasts using GNSS data,
  • Long-term data homogenization and processing for climate studies,
  • and those still related to the objectives of the meeting but not listed above.




Scientific Organizing Committee

  • Milad Asgarimehr (GFZ, Germany)
  • Dieter Bilitza (GMU, US)
  • Claudia Borries (DLR, Germany)
  • Adriano Camps (UPC, Spain)
  • Guergana Guerova (Sofia University, Bulgaria)
  • Mainul Hoque (DLR, Germany)
  • Jonathan Jones (MetOffice, UK)
  • Manuel Martin-Neira (ESA, the Netherlands)
  • Dallas Masters (Muon Space, US)
  • Ryan McGranaghan (Astra Space, US)
  • Roland Potthast (DWD, Germany)
  • Witold Rohm (UP Wroclaw, Poland)
  • Ludger Scherliess (Utah State University, US)
  • Michael Schmidt (TUM, Germany)
  • Yuri Shprits (GFZ, Germany)
  • Benedikt Soja (ETH Zürich, Switzerland)
  • Frederik Tilmann (GFZ, Germany)
  • Jens Wickert (GFZ, Germany)
  • Karina Wilgan (GFZ, Germany)
  • Liangpei Zhang (Wuhan University, China)
  • Xiaoxiang Zhu (DLR, Germany)

Local Organizing Committee

  • Milad Asgarimehr
  • Karina Wilgan
  • Jens Wickert
  • Katrin Gundrum
  • Galina Dick
  • Kirstin Winkler
  • Yuri Shprits
  • Frederik Tilmann
  • Tianqi Xiao




  • Abstract submission: 14 March 2022
  • Notification of acceptance: 1 May 2022
  • Preliminary programme: 1 May 2022
  • Registration: 1 April 2022 – early bird, 15 May 2022 – regular