Self-Attention-Guided Multiindicator Retrieval for Ocean Surface Wind Field With Multimodal Data Augmentation and Fusion

The deployment of global navigation satellite system reflectometry (GNSS-R) emerges as a compelling approach for the extraction of ocean surface wind field, primarily due to its exceptional cost-effectiveness, all-weather robustness, and excellent spatiotemporal coverage. Despite these advantages, the insufficient use of various data and the lack of ability to perform multiindicator retrieval limit the performance of existing methods in practical ocean wind field retrieval. To overcome these limitations, this article introduces a novel self-attention-guided ocean surface wind field multiindicator retrieval algorithm based on multimodal observation data augmentation and fusion. Initially, data generation modules are employed to complement high-quality observation data that are not fully provided by GNSS-R system. Subsequently, the multiscale data fusion encoder (MDFE) is implemented to extract and fuse multiple data features of different scales to enhance the utilization ability of data and improve the accuracy of wind field retrieval. Finally, the self-attention multiindicator predictor (SAMIP) is put to use for optimizing the feature attention strategy, achieving accurate retrieval of ocean surface wind speed and direction simultaneously. The proposed method provides a novel solution for the comprehensive utilization of various data products in the GNSS-R system, simultaneously achieves synchronous retrieval of multiple wind field indicators, which are wind speed and direction. In the context of recent advancements in algorithmic development, the proposed algorithm exhibits a notable enhancement in the precision of wind speed and direction retrieval. Benchmarking against ERA5 wind field data, the proposed algorithm achieves a root-mean-square error (RMSE) of 1.23 m/s in wind speed retrieval, demonstrating at least a 9% accuracy improvement compared to five state-of-the-art algorithms from recent years. Furthermore, the RMSE in wind direction retrieval stands at 20.7°, surpassing comparison algorithms by achieving a reduction of over 8% in error. Collectively, these metrics robustly validate the efficacy of the proposed algorithm.