EGU22-5202, updated on 02 Jan 2024
EGU General Assembly 2022
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Assessment of gravity field models derived from Sentinel GPS data

Thomas Grombein, Martin Lasser, Daniel Arnold, Ulrich Meyer, and Adrian Jäggi
Thomas Grombein et al.
  • University of Bern, Astronomical Institute, Bern, Switzerland (

Besides gravity field information derived from ultra-precise inter-satellite ranging of dedicated missions like GRACE and GRACE-FO, the analysis of GPS tracking data collected by various Low Earth orbiting (LEO) satellites can provide alternative and mostly uninterrupted time series of large-scale time-variable gravity field signals. For this purpose, the GPS data may be used to derive kinematic LEO orbit positions that can subsequently be utilized as pseudo-observations for gravity field recovery.

In this study, we focus on the use of the GPS data obtained by the Copernicus Sentinel-1, -2, and -3 missions. Each of these missions consists of a constellation of two LEO satellites operating on sun-synchronous orbits with inclinations of about 98° and at different altitudes ranging from about 700 to 800 km. Besides mission-specific instruments, the Sentinel satellites are equipped with high-quality dual-frequency GPS receivers providing a data sampling rate of 10s (Sentinel-1, -2) or 1s (Sentinel-3). At the Astronomical Institute of the University of Bern (AIUB), GPS-based precise orbit determination is routinely performed for the Sentinel satellites. We make use of the kinematic LEO orbit positions to perform gravity field recovery with the Celestial Mechanics approach. In the presentation, we will provide details on the quality and sensitivity of Sentinel-based gravity field models and analyze their contribution to a combined gravity field time series derived from Swarm and GRACE-FO GPS data.

How to cite: Grombein, T., Lasser, M., Arnold, D., Meyer, U., and Jäggi, A.: Assessment of gravity field models derived from Sentinel GPS data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5202,, 2022.


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