The UK’s TechDemoSat-1 (TDS-1), launched 2014, has demonstrated the use of global positioning system (GPS) signals for monitoring ocean winds and sea ice. Here it is shown, for the first time, that Galileo and BeiDou signals detected by TDS-1 show similar promise. TDS-1 made seven raw data collections, recovering returns from Galileo and BeiDou, between November 2015 and March 2019. The retrieved open ocean delay Doppler maps (DDMs) are similar to those from GPS. Over sea ice, the Galileo DDMs show a distinctive triple peak. Analysis, adapted from that for GPS DDMs, gives Galileo’s signal-to-noise ratio (SNR), which is found to be inversely sensitive to wind speed, as for GPS. A Galileo track transiting from open ocean to sea ice shows a strong instantaneous SNR response. These results demonstrate the potential of future spaceborne constellations of GNSS-R (global navigation satellite system–reflectometry) instruments for exploiting signals from multiple systems: GPS, Galileo, and BeiDou.

Global navigation satellite system reflectometry (GNSS-R) has found many applications in the field of Earth observation including ocean wind-speed detection, ice altimetry, soil moisture monitoring, and more. The main focus of GNSS-R research to date has been on forward-scattered reflections, but theoretical work has proposed a backscattering regime and associated new application opportunities, including marine target detection. This article discusses the methods and results of processing the U.K. TechDemoSat-1 raw data collections in a backscattering regime for the first time, with initial results from sea ice datasets presented. The research has also identified a key problem with the backscatter method-for certain geometries the power from the specular point (forward scattered) may contaminate the data. The theory behind this and a method for predicting such occurrences is also discussed.

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GNSS Reflectometry (GNSS-R), a method of remote sensing using the reflections from satellite navigation systems, was initially envisaged for ocean wind speed sensing. In recent times there has been significant interest in the use of GNSS-R for sensing land parameters such as soil moisture, which has been identified as an Essential Climate Variable (ECV). Monitoring objectives for ECVs set by the Global Climate Observing System (GCOS) organisation include a reduction in data gaps from spaceborne sources. GNSS-R can be implemented on small, relatively cheap platforms and can enable the launch of constellations, thus reducing such data gaps in these important datasets. However in order to realise operational land sensing with GNSS-R, adaptations are required to existing instrumentation. Spaceborne GNSS-R requires the reflection points to be predicted in advance, and for land sensing this means the effect of topography must be considered. This paper presents an algorithm for on-board prediction of reflection points over the land, allowing generation of DDMs on-board as well as compression and calibration. The algorithm is tested using real satellite data from TechDemoSat-1 in a software receiver with on-board constraints being considered. Three different resolutions of Digital Elevation Model are compared. The algorithm is shown to perform better against the operational requirements of sensing land parameters than existing methods and is ready to proceed to flight testing.