Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data

Abstract

In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°.

Global Sea Surface Height Measurement From CYGNSS Based on Machine Learning

“…[17] combined principal component analysis (PCA), support vector regression (SVR), PCA combined SVR (PCA-SVR) method, and the convolutional neural network (CNN) method, respectively, thus constructing a sea surface high wind speed inversion model. Zhang et al [18] analyzed CYGNSS data and used the support vector machine (SVM) method for sea surface wind direction inversion. For soil moisture inversion, Senyurek et al [19] used three widely used ML methods, ANN, random forest (RF), and SVM, for comparative analysis of soil moisture inversion.…”