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The present study adopts a multi-sensor approach that utilizes optical remote sensing and Synthetic Aperture Radar (SAR) data complimentarily for coastal bathymetry estimation. The depths between 0–15 m are derived from Landsat 8 OLI imageries, by using the machine learning approach of Support Vector Regression (SVR); and high accuracies with RMSEs ranging between 0.17 m−0.95 m are obtained for the chosen study regions. Further, ALOS PALSAR images are used to obtain depths beyond 15 m, by applying the Wave Based Approach (WBA) which is based on the wave transformation and linear dispersion principles. The estimated depths are compared with the bathymetry obtained from admiralty charts or GEBCO and RMSEs between 0.83–1.34m are observed. Finally, the outputs of the SVR and WBA are combined to derive depths between 0–50 m at 75 m spatial resolution. The resultant depth maps agree with those obtained from GEBCO, with an additional advantage of higher accuracy in the near-shore region.

Synergistic utilization of optical and microwave satellite data for coastal bathymetry estimation