The Challenges of GNSS-R Remote Sensing and Implications for Routine Utilization

Within the past decade, GNSS reflectometers (GNSS-R), such as TechdemoSAT-1 and CyGNSS, have been used to process specular reflections from GNSS transmitters (e.g. GPS constellation). Geophysical parameters, such as soil moisture and ocean surface wind speed, have been successfully inferred from GNSS-R data and provided to the scientific community. There are two major strengths for such passive sensors, namely the relatively low manufacturing cost and potential high temporal revisit rate as seen with the CyGNSS constellation. However, such a GNSS-R technology comes with its unique set of drawbacks: the transmit power originates from GNSS transmitters for which transmit gain characteristics are mostly unknown and are subject to change without notice. Accurate knowledge of this parameter is critical in estimating the normalized bistatic radar cross section (NBRCS), from which geophysical parameters are inferred. Second, the use of multiple GNSS-R sensors within a given constellation requires carefully inter-calibrated satellites. This is quite difficult to achieve as experienced with the CyGNSS mission, which supposedly used eight identical sensors. The Ocean Surface Winds Team (OSWT) at NOAA-NESDIS-STAR has recently acquired access to the commercially available Spire GNSS-R data between January and July 2024 as part of a NOAA Commercial Weather Data Pilot project. Specifically, the OSWT has assessed the quality of the NBRCS from four Spire GNSS-R satellites, processing ocean surface specular reflections from three GNSS systems (GPS, Beidou, and Galileo). Subsequently, a 25 km global ocean surface wind speed product has been derived using an adapted version of the NOAA CyGNSS wind retrieval processor. In this presentation, we will report our instrument calibration findings, and specifically assess the presence of intersatellite NBRCS miscalibrations. The methods used to minimize their impact on the retrieved wind will also be presented. Additionally, the NOAA Spire wind product and Spire derived wind product performances will be assessed, respectively, against both passive and active sensors (e.g. ASCAT, AMSR-2, SMAP, CyGNSS), as well as numerical weather prediction models such as ECMWF, and HWRF. This analysis will illustrate the various instrument calibration obstacles commonly faced when deriving ocean wind speed from GNSS-R data. It will also discuss the usability of such a product for the weather forecasting community.