EGU22-8226, updated on 28 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Deep learning in spaceborne GNSS-R: Recent methodologies and atmospheric products

Tianqi Xiao1,2, Milad Asgarimehr1, Caroline Arnold3, Daixin Zhao4, Tobias Weigel3, Lichao Mou4, and Jens Wickert1,2
Tianqi Xiao et al.
  • 1German Research Centre for Geosciences GFZ, Potsdam, Germany
  • 2Technische Universität Berlin, Berlin, Germany
  • 3German Climate Computing Centre DKRZ, Hamburg, Germany
  • 4German Aerospace Center DLR, Oberpfaffenhofen, Germany


The capability of Deep Learning (DL) for operational wind speed retrieval from the measured Delay-Doppler Maps (DDMs) is recently characterized. It is shown that such techniques can lead to a significant improvement in the derived atmospheric data products. A global ocean dataset is developed processing the measurements of NASA Cyclone GNSS (CYGNSS). The model is based on convolutional layers for direct feature extraction from bistatic radar cross-section (BRCS) DDMs and fully connected layers for processing ancillary technical and higher-level input parameters. This model leads to an RMSE of 1.36 m/s and a significant improvement of 28% in comparison to the officially operational retrieval algorithm.

From the theoretical knowledge, several error sources are known, the modeling and correction of which is not easy due to their highly nonlinear interaction with other and the dependent parameters. DL is potentially able to learn such trends and correct the associated biases. For instance, rain splash on the ocean surface and swell waves alter the surface roughness, and consequently, the GNSS scattering patterns, which appear as a considerable bias in GNSS-R wind products. The magnitude of such biases is nonlinearly dependent on several technical and environmental parameters including the reflection geometry, and ocean surface state. After a brief introduction to the known physical mechanisms, we discuss how a DL-based fusion with data on bias-causing parameters, can improve the wind speed predictions.

How to cite: Xiao, T., Asgarimehr, M., Arnold, C., Zhao, D., Weigel, T., Mou, L., and Wickert, J.: Deep learning in spaceborne GNSS-R: Recent methodologies and atmospheric products, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8226,, 2022.


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