GNSS Reflectometry: Novel Remote Sensing of the Earth

The exploitation of Global Navigation Satellite System (GNSS) signals after reflection from the Earth’s surface, called GNSS Reflectometry (GNSS-R), is a novel technique for the remote sensing of a variety of geophysical parameters. The demonstration mission, UK TechDemoSat-1 (TDS-1) launched in 2014, carried a spaceborne GNSS-R receiver showing the capabilities of the technique to monitor ocean, land, and the cryosphere. The GNSS-R small satellites need to carry only the low-cost, low-mass, and low-power receivers, which leads to the cost-effective development of multi-satellite constellations. This along with the capability of GNSS-R receivers to track multiple reflected GNSS signals at the same time offer an unprecedented sampling rate and potentially knowledge of the Earth system and the climate beyond those derived from conventional sensors. NASA Cyclone GNSS (CYGNSS), launched in late 2016, is one of the operational constellations with eight microsatellites tracking up to four GPS reflected signals. More missions are in orbit, e.g., constellations launched by Spire (commercial) and Chinese FENGYUN-3E mission. GNSS-R satellites with different objectives will be launched, e.g., ESA Passive REflecTomeTry and dosimetry (PRETTY) CubeSat and ESA HydroGNSS, whose data will be available in 2022 and 2024 respectively.

In this presentation, an overview of selected recent GNSS-R studies at the German Research Centre for Geosciences GFZ will be given. After a brief history of the technique, the principles of the measurements and important GNSS-R missions will be introduced. The ocean surface and wind speed monitoring, from the first products of TDS-1 to recent deep learning-based CYGNSS wind speeds with an RMSE and 1.4 m/s, will be presented. Rain splash alters ocean surface, based on which, rain over calm ocean can be detected. Experiments for precipitation monitoring are carried out. The capabilities of GNSS-R for inland water body detection, land surface, and aridity monitoring in forests is studied.