LAUSR

Ocean surface winds play an essential role in regulating the Earth’s weather and climate, and the cyclone GNSS (CYGNSS) mission launched in 2016 is designed specially to monitor the ocean wind speed. In this study, an innovative model is developed based on a deep learning method to retrieve the ocean wind speed by making full use of the spatiotemporal information of CYGNSS observations. The proposed model named CNN-LSTM is established based on two modules, i.e., the convolution neural network (CNN) module that extracts the spatial features around the specular point (SP) from a two-dimensional (2-D) matrix of delay-Doppler map (DDM) and the long short-term memory (LSTM) module that extracts the temporal features over a time series. The performance of the ocean wind speed derived from CNN-LSTM is assessed with the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) products. The results show that the wind speed derived from CNN-LSTM reveals an accuracy of 1.34 m/s in terms of root-mean-square error (RMSE) values, showing an improvement of about 36.8%, 14.6%, and 6.3%, when compared to the official retrieval algorithm called minimum variance estimator (MVE), multilayer perceptron (MLP) net, and CNN, respectively, confirming the feasibility and effectiveness of the designed method. Among all the experiments in this study which apply machine learning-based algorithms, the wind speed achieved by CNN-LSTM presents the smallest RMSE value. Furthermore, the error analyses of the wind speed retrieval in spatial and temporal scales are also discussed, which indicates the robust performance of CNN-LSTM model. The results show that the CNN-LSTM model proposed in this study contributes to offering efficient processing of Global Navigation Satellite Systems Reflectometry (GNSS-R) observations and fully exploits the capabilities of high-accurate ocean wind speed retrieval on a global scale.