Nearshore topography retrieval based on wave spectrum inverted from Gaofen-3 image

This paper aims to propose a nearshore water depth retrieval method using Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) imagery. By applying a deep learning-based multi-layer perceptron (MLP) approach, one-dimensional wave spectra are inverted from 12 quad-polarized GF-3 images. Then, SAR-derived wave spectrum is divided into wind-wave and swell based on the ocean wave overshoot phenomenon. Based on the wave dispersion relation over gentle slopes, water depth is iteratively derived using both wind-wave and swell spectra. The proposed method is validated in the Yangtze River estuary. Compared with General Bathymetric Chart of the Oceans (GEBCO) and Earth Topography 2022 (ETOPO2022) water depth, it is found that accuracy of water depth using swell spectrum is higher than that using wind-sea spectrum, indicating that the error is within ± 2 m. Furthermore, the error analysis presents that swell-based depth estimates exhibit smaller discrepancies compared to wind-sea results, particularly with significant wave heights (SWH) below 2 m and peak wavelengths exceeding 100 m. These findings demonstrate the impact of wave characteristics on inversion accuracy, revealing that swell spectra provide higher reliability under specific hydrodynamic conditions. Additionally, the results underscore the capability of GF-3 SAR for coastal underwater observation.