Trackwise Prediction of GNSS-R Delay–Doppler Maps With DDM-PredRNN Network

Abstract

Global navigation satellite system reflectometry (GNSS-R) is an emerging Earth observation method that utilizes reflection signals from navigation satellites for remote sensing of physical parameters, particularly in detecting ocean wind speed. However, challenges, such as low signal-to-noise ratio (SNR) and insufficient gain of the receiving antenna have hindered the acquisition of reliable observation data in many areas. With the high temporal resolution offered by GNSS-R, the delayed-Doppler map (DDM) of radar echo images reveals inherent spatial continuity and temporal correlation across sequential intervals. This article introduces an advanced deep learning framework DDM-PredRNN for predicting DDM in previously unobserved regions. Employing a spatiotemporal long short-term memory network with a PredRNN architecture, this model integrates axial attention to capture and leverage the delay/Doppler features embedded within the DDM, thereby enhancing the extraction of complex spatiotemporal characteristics. Experiments utilizing cyclone GNSS data illustrate that the proposed method can effectively predict unknown DDMs along the GPS motion track within a 20-step timeframe. The results indicate that the root mean square error of the DDM prediction is 1.32 dB, the mean absolute error is 0.82 dB, and the structural similarity is 0.71. This approach not only effectively addresses the gaps in ocean wind observation data due to poor GNSS-R data quality but also fills the trackwise blind zones of GNSS-R. Furthermore, the predicted DDM accurately reflects trends in the NBRCS near specular point, providing a new perspective for GNSS-R retrieval forecasting of ocean wind.