Technical Framework for Shallow-Water Bathymetry With High Reliability and No Missing Data Based on Time-Series Sentinel-2 Images

Shallow-water bathymetry based on multispectral satellite imagery (MSI) is an important technology for depth measurement, but it is difficult to obtain a bathymetric map with high reliability and no missing data because of the ubiquitous image noise. Here, we propose a time-series-based bathymetry framework (TSBF). First, a pixel-level time series is constructed using remote sensing images collected at multiple points in time. Then, a new time-domain denoising method, the maximum outlier removal method, is used to create an optimal image from this time series. Finally, bathymetric inversion is performed using this optimal image to obtain a bathymetric map. Anda Reef and northeastern Jiuzhang Atoll, which have complex noise features, were selected as test cases to validate the proposed framework. Results show that the proposed TSBF can obtain bathymetric maps with high accuracy, reliability, and no missing data, outperforming the conventional bathymetry framework based on a single image.