Improving GNSS-R Sea Surface Wind Speed Retrieval from FY-3E Satellite Using Multi-task Learning and Physical Information

Global Navigation Satellite System Reflectometry (GNSS-R) technology has great advantages over traditional satellite remote sensing detection of sea surface wind field in terms of cost and timeliness. It has attracted increasing attention and research from scholars around the world. This paper focuses on the Fengyun-3E (FY-3E) satellite, which carries the GNOS II sensor that can receive GNSS-R signals. We analyze the limitations of the conventional sea surface wind speed retrieval method and the existing deep learning model for this task, and propose a new sea surface wind speed retrieval model for FY-3E satellite based on a multi-task learning (MTL) network framework. The model uses the forecast product of HurricaneWeather Research and Forecasting (HWRF) model as the label, and inputs all the relevant information of Delay-Doppler Map (DDM) in the first-level product into the network for comprehensive learning. We also add wind direction, U wind and V wind physical information as constraints for the model. The model achieves good results in multiple evaluation metrics for retrieving sea surface wind speed. On the test set, the model achieves a Root Mean Square Error (RMSE) of 2.5 and a Mean Absolute Error (MAE) of 1.85. Compared with the second-level wind speed product data released by Fengyun Satellite official website in the same period, which has an RMSE of 3.37 and an MAE of 1.9, our model improves the performance by 52.74% and 8.65% respectively, and obtains a better distribution.