Evolving ocean monitoring With GNSS-R: promises in surface wind speed and prospects for rain detection

After developing a wind speed retrieval algorithm, derived winds from measurements of UK TechDemoSat-1 (TDS-1), from May 2015 to July 2017, are compared to wind products of Advanced Scatterometer showing a reliable performance, especially during rain events. However, a rain signature in GNSS-R observations, a decrease in the value of the bistatic radar cross section at low winds, is demonstrated, which can potentially enable the technique to detect precipitation over oceans induced by low-to-moderate winds. This phenomenon is investigated and finally characterized as the rain splash effect altering the ocean surface roughness. To improve the quality of derived winds, a machine learning technique is implemented for the wind speed inversion as a geophysical model function. The trained feedforward neural network shows a significant improvement of 17% in the wind speed RMSE compared to the LS approach. In the end, one can conclude that space-borne ocean monitoring is evolving existing products with a potential for novel geophysical applications