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°.
Highlights
Wind speed and wind direction are important basic climate variables
Results of Wind Direction on the best parameters obtained by grid search cross-validation, the support vector machine (SVM) model
Wind Speeds φ1 of and φ2Set canResults betterfor reflect the geometric features of Delay Doppler Map (DDM) asymmetry, which is related to the wind direction, so the
Summary
Wind speed and wind direction are important basic climate variables. As a passive and non-contact remote sensing method, the Global Navigation Satellite System Reflectometry (GNSS-R) technique uses the reflected signal of the navigation satellite L-band signal as the remote sensing source. In 2016, Park et al used a Normalized Bistatic Radar Cross Section (NBRCS) to study the effect of wind direction on GNSS-R application sea surface specular scattering [24]. According to the above research results, it is difficult to establish a sea surface wind direction retrieval model, especially in the case of a large space and time span. After our research about the sea surface wind speed inversion model of the CYGNSS sea surface data based on Machine Learning [32], this paper studies the sea surface wind direction retrieval model of space-borne GNSS-R based on SVM. The results show that the SVM method proposed in this paper can effectively retrieve the sea surface wind direction
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