Nearshore Bathymetry Estimation Through Dual-Time Phase Satellite Imagery in the Absence of In-Situ Data

Accurate bathymetric information is an important foundation for marine resource development and nearshore ecological protection. Existing empirical algorithms can estimate water depth from high resolution images with the aid of ground truth bathymetry data. However, empirical models are not applicable in areas without measured bathymetric data. To overcome the difficulty of estimating bathymetry in areas without measured data, we developed a dual-time phase bispectrum optimization algorithm (DPBOA) that combines the empirical data for estimating bathymetry in Case-I water using the blue-green bands of dual-temporal multispectral imagery. The hyperspectral optimization process exemplar is first transformed into a dual-band model applicable to multi-band images, and the model is then applied separately to the same locations of different time-phase images, using least squares optimization to derive all unknown model parameters. The depth was estimated by the dual-band model after the unknown parameters were determined. To assess the performance of the algorithm, bathymetric estimation was performed using dual-time images from Sentinel-2, Gaofen-2, Gaofen-6, and Worldview-2 in Ganquan Island and Qilianyu Island, respectively. Validation was performed with the data from airborne LiDAR bathymetry (ALB) and ICESat-2. The results indicate that for the Ganquan Islands, the coefficient of determination ( R 2 ) was 0.92, and the root mean square deviation (RMSE) was 1.26 m; for the Qilianyu Islands, the R 2 was 0.94 and the RMSE was 1.00 m. The developed model obtains better results compared with the Lyzenga empirical model and the dual-band bathymetry estimation algorithm in terms of bathymetry estimation without measured data. Additionally, the impacts of the time interval between images on bathymetry estimation are analyzed.