Exploiting Frequency-Domain Information of GNSS Reflectometry for Sea Surface Wind Speed Retrieval

Abstract

Global navigation satellite system reflectometry (GNSS-R) Delay-Doppler map measures the sea surface roughness, which has recently been applied to retrieve sea surface wind speed. However, current studies on GNSS-R wind speed retrieval only use the spatial domain of the delay-Doppler map without considering the variations patterns in the map, which is regarded as frequency domain information of the map. In this study, we propose a joint frequency-spatial-domain wind speed retrieval network (FSNet) based on reflectivity data provided by the Cyclone Global Navigation Satellite System (CyGNSS) mission. We construct a matchup dataset between the CyGNSS satellite data and ECMWF model data from January 1, 2018, to December 31, 2019. The wind speed range is 0–25 m/s. Using the proposed FSNet, frequency and spatial features are simultaneously extracted. The frequency domain feature supplements the spatial-domain information of the mid and high-level features in the neural network. Rather than directly concatenating the frequency-domain features with the spatial-domain features, we designed a feature fusion module to fuse frequency and spatial features for wind speed retrieval adaptively. Experiments show that our FSNet wind speed retrieval has a root mean square error (RMSE) of 1.63 m/s for a wind range of 0-25 m/s. This accuracy is 25.4% better than the operational algorithm provided by the CyGNSS Level 2 wind speed product. For a higher wind range of 16-25m/s, FSNet performed even better, improving the RMSE by 31%.