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<p>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.</p> <p>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.</p>

<p>GNSS Reflectometry (GNSS-R) has emerged as a novel remote sensing technique for monitoring geophysical parameters. GNSS signals reflected from the Earth’s surface are tracked and measured by low-mass receivers onboard small satellites, providing abundant information about the target with higher sampling frequency and special coverages. The main observable of GNSS-R is Delay-Doppler Maps (DDMs), which map signal power at a range of delay and Doppler frequency shifts. The conventional retrieval algorithms rely on the parametric regression approaches inverting observables derived from the DDMs to the ocean wind speed products. Thus, GNSS-R has become a new technique for ocean wind retrieval and hurricane monitoring. <br />With the large datasets of cost-effective GNSS-R measurements available, the AI4GNSSR project (Artificial Intelligence for GNSS Reflectometry: Novel Remote Sensing of Ocean and Atmosphere) was proposed to implement Artificial Intelligence for characterizing geophysical parameters and investigating new applications and approaches for the GNSS-R technique. In this study, A global ocean wind speed dataset is created by processing the observables of NASA’s Cyclone GNSS (CyGNSS) mission. The primary implementations of AI algorithms have shown great potential in improving the quality of the existing wind speed products. The deep learning model based on convolutional layers and fully connected layers processes the input CyGNSS measurements and directly extracts features from bistatic radar cross section (BRCS) DDMs. This model achieves an overall RMSE of 1.31 m/s compared with the ERA5 reanalysis data on an unseen dataset and leads to an improvement of 28% in comparison to the operational retrieval algorithm.<br />Moreover, we found that data fusion with ancillary precipitation data is able to correct the rain effects, especially for high wind speed. For wind speeds larger than 16 m/s, our data fusion model outperforms the operational retrieval algorithm by 40%. For further validation of the model performance under extreme weather conditions, a case study of Hurricane Laura in August 2020 will be presented and discussed after a brief introduction to our models.</p>