Spaceborne GNSS-R Wind Speed Retrieval Using Machine Learning Methods

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This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is divided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of individual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future.