STG-DNN: A Spatiotemporal Graph Deep Neural Network for GNSS-R Ocean Wind Speed Retrieval

Ocean surface wind is vital to the Earth’s meteorological system, and their properties can be detected by spaceborne global navigation satellite system reflectometry (GNSS-R) measurements. With the growing number of GNSS-R signal sources, machine learning technology exhibits prominent advantages in wind speed estimation. Currently, the deep learning techniques that establish relationships between GNSS-R measurements and ocean surface wind speeds generally apply grids and sequence structures and lack flexibility and robustness. Additionally, constructing models with individual GNSS-R observations results in the loss of valuable temporal correlation within delay-Doppler maps (DDMs). Therefore, this study proposes a novel spatiotemporal graph-based deep neural network (STG-DNN) for retrieving wind speed, which incorporates a graph module with a transformer module to fully exploit the spatial-temporal dependencies of DDMs. Results demonstrate that the graph module significantly improves both the accuracy and reliability in wind speed retrieval. Meanwhile, the transformer module effectively captures temporal features from various DDMs. Validations with cyclone GNSS (CYGNSS) test data support the superior accuracy of STG-DNN, revealing a correlation coefficient of 0.92 for the wind speeds. The results indicate that the root-mean-square error (RMSE) of STG-DNN for wind speed is 1.27 m/s, representing improvements of approximately 33.2%, 20.6%, and 13.6% over the minimum variance estimator (MVE), convolutional neural network (CNN), and vision graph (VIG) neural networks, respectively. Additionally, a promising agreement is observed between STG-DNN and ERA5 in the spatial distributions of wind speed retrieval, indicating a robust spatial performance in STG-DNN. As for the temporal scale, the daily variations in retrieval accuracy of STG-DNN exhibit smaller fluctuations compared to both CNN and VIG wind data in the test dataset.