Abstract
Global Navigation Satellite System Reflectometry (GNSS-R) has achieved great success in the field of ocean wind speed sensing. Missions such as UK-DMC-1 and TechDemoSat-1 (TDS-1) proved the viability of the technique using spaceborne receivers and opened the door for NASA's CYGNSS mission, as well as providing valuable data in their own right. Both TDS-1 and CYGNSS data have been of great importance to the scientific community, with a wide range of applications being explored. GNSS-R offers many benefits over traditional methods of remote sensing, as in the conventional form it requires only a simple payload compatible with low SWaP (size, weight and power) platforms. Small satellites offer flexibility, lower costs and the potential for improved coverage compared with existing systems.
Since the inception of the method the possible use cases of GNSS-R have expanded greatly to include land parameters such as soil moisture and vegetation biomass, but these have not yet had a dedicated spaceborne GNSS-R mission. This research focuses on the potential for remote sensing of these variables offered by GNSS-R and the necessary adaptations to instrumentation required to enable an operational space service. Several land variables which reflectometry is sensitive to have been designated as Essential Climate Variables, indicating that measurement of these variables is critical for modelling and monitoring climate change.
The primary challenge for land sensing with GNSS-R is the effect of topography on the prediction of the reflection points. Existing algorithms were not designed to tackle this, and this research proposes a new algorithm, TARPP, which takes topography into account. This algorithm has been tested on the ground in a software receiver and has also been uploaded to a spacecraft in orbit, DoT-1, for flight testing. The results of this testing show that the new algorithm is more successful than existing methods at capturing the peak power of a DDM within the window, which is a key step on the path to an operational land sensing mission. Results show that proposed operational limits can be met greater than 55% of the time, with an algorithm simple enough to be uploaded to any small satellite. This result is most likely a significant under-estimation of the performance due to difficulties in detecting weak peak pixels (common in land datasets) in the test methodology.
Another contribution is the exploration of new modes of GNSS-R, notably a backscattering mode. This includes experiments with new use cases using raw TDS-1 data and the introduction of a backscatter reflection point prediction tool. A new phenomenon termed ""Specular Point Intrusions"", which will have implications for the implementation of the method in future instrumentation, is identified and a tool for prediction of occurrences of it is introduced.