Work

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

Year: 2025

Type: article

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.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI: 10.1109/jstars.2025.3555623

Authors: Weichen Sun, Xiaochen Wang, Bing Han, Dongkai Yang

Institutions: Beihang University, Chinese Academy of Sciences, Aerospace Information Research Institute

Language: English

Topic: Ocean Waves and Remote Sensing

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $1,800