MIWC: A multi-temporal image weighted composition method for satellite-derived bathymetry in shallow waters

Satellite-derived bathymetry (SDB) is a vital technique for the rapid and cost-effective measurement of shallow underwater terrain. However, it faces challenges of image noise, including clouds, bubble clouds, and sun glint. Consequently, the acquisition of no missing and accurate bathymetric maps is frequently challenging, particularly in cloudy, rainy, and large-scale regions. In this study, we propose a multi-temporal image weighted composition (MIWC) method. This method performs iterative segmentation and inverse distance weighted composition of multi-temporal images based only on the near-infrared (NIR) band information of multispectral images to obtain high-quality composite images. The method was applied to scenarios using Sentinel-2 imagery for bathymetry of four representative areas located in the South China Sea and the Atlantic Ocean. The results show that the root mean square error (RMSE) of bathymetry from the composite images using the log-transformed linear model (LLM) and the log-transformed ratio model (LRM) in the water depth range of 0–20 m are 0.67–1.22 m and 0.71–1.23 m, respectively. The RMSE of the bathymetry decreases with the number of images involved in the composition and tends to be relatively stable when the number of images reaches approximately 16. In addition, the composition images generated by the MIWC method generally exhibit not only superior visual quality, but also significant advantages in terms of bathymetric accuracy and robustness when compared to the best single images as well as the composition images generated by the median composition method and the maximum outlier removal method. The recommended value of the power parameter for inverse distance weighting in the MIWC method was experimentally determined to be 4, which typically does not require complex adjustments, making the method easy to apply or integrate. The MIWC method offers a reliable approach to improve the quality of remote sensing images, ensuring the completeness and accuracy of shallow water bathymetric maps.