Clearing systematic range biases from SLR observations: assessment and outlook

Satellite Laser Ranging (SLR) has become an invaluable core technique in numerous geodetic applications. SLR measurements to passive spherical satellites essentially contribute to the determination of geocenter coordinates and global scale in the International Terrestrial Reference Frame (ITRF) realizations. In addition, SLR measurements to active satellites in Low Earth Orbit (LEO) are up to now mostly used for independent validation of orbit solutions, usually derived by microwave tracking techniques based on Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) or Global Navigation Satellite Systems (GNSS). This allows for the analysis of systematic orbit errors (e.g., originating from poorly known non-gravitational perturbations or sensor offsets) not only in the radial direction (key to satellite altimetry missions), but in three dimensions.

 

It has recently been shown, in Saquet et al. (2023), that the analysis of SLR data to a constellation of active LEO satellites with fixed microwave-derived orbit solutions is a promising approach to exhibit SLR range biases independently from well-known correlation issues and less prone to geographically correlated orbit errors. Clearing systematic range biases from SLR observations is also an essential step to meeting stringent future requirements such as for ESA’s GENESIS mission or enabling the calibration of altimeter range biases for the next generation of altimetry missions.

 

In this paper, we use geodetic sphere (e.g., LAGEOS-1/2) measurement SLR residuals to assess the quality of the station range biases derived from satellite altimetry compared to ILRS-based range bias values. Systematic differences between the two independent approaches are analyzed, especially for well-performing laser stations with diverse detector types (e.g., single/multi-photon). Finally, relying on multiple years of observations, the contribution of tropospheric delay modeling errors to these biases is estimated.