A Two-Stage Strategy for Retrieving 2-D Ocean Wave Spectra From Chinese Gaofen-3 SAR Wave Mode Products

Abstract

Synthetic Aperture Radar (SAR) is widely used for observing sea surfaces and retrieving two-dimensional wave spectra. However, existing methods for retrieving directional wave spectra from SAR imagettes face challenges due to the complex non-linear SAR-wave imaging relationship and the limitation of first-guess spectra. This study proposes a novel twostage machine learning strategy for retrieving two-dimensional directional wave spectra from Chinese Gaofen-3 SAR wave mode products. We achieve the generation of complete two-dimensional wave spectra and several wave parameters solely from GF-3 SAR data without necessitating any additional inputs. In the first stage, we employ the Energy Attention Conditional Generative Adversarial Network (EA-CGAN) to retrieve the normalized wave spectrum. The generator of the EA-CGAN establishes a non-linear transformation from normalized SAR cross spectra to normalized wave spectra to enhance the capabilities. In the second stage, the XGBoost model retrieves the intensity of the wave spectrum. The EA-CGAN and XGBoost models were trained on an extensive dataset that consists of about 11,000 Gaofen-3 SAR wave mode imagettes and two-dimensional wave spectra from the fifth-generation reanalysis (ERA-5) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results of the evaluation using test samples reveal high consistency between the retrieved wave spectra and ERA-5 wave spectra in terms of spectral similarity, peak period, peak direction, significant wave height, and mean wave periods. Compared to the traditional methods, our approach offers enhanced effectiveness, demonstrating the potential of advanced deep learning in highprecision SAR wave spectrum inversion.