Coastal bathymetry is the most essential tool for marine planning, monitoring and management, modelling, nautical navigation and scientific studies of marine environments. The techniques have been developed over the last decade to derive bathymetry using remote sensing technology with efficiently and low costly. Log linear bathymetric inversion model and non-linear bathymetric inversion model provides two empirical approaches for deriving bathymetry from multispectral satellite imagery, which have been refined and widely applied. This paper compares these two approaches by means of a geographical error analysis for the site Kankesanturai using WorldView-2 satellite imagery. In order to calibrate both models; Single Beam Echo Sounding (SBES) data in this study area were used as reference points. Corrections for atmospheric and sun-glint effects are applied prior to the water depth algorithm. The algorithm was tuned and both models were calibrated by performing the necessary algorithm with available single beam echo sounding data in the study area. The coefficients of standard R2 is estimated as 0.846 for log-linear and 0.692 for non-linear model. Log linear model performs better than the non-linear model. The model residuals were mapped and the spatial auto-correlation was calculated based on the bathymetric estimation model. A spatial error model was constructed to generate more reliable estimates of bathymetry by calculating the spatial autocorrelation of model error and integrating this into an improved regression model. Finally, the spatial error model improved the bathymetric estimates of R2 up to 0.854 for log-linear and 0.704 non-linear model respectively. The Root Mean Square Error (RMSE) was calculated for the different depth ranges and also for all reference points. The overall accuracy for the log linear and the non-linear inversion model after the geographical error analysis is estimated as ±1.532 m and ±2.089 m for this study. The spatial error model improved bathymetric estimates than those derived from a conventional log-linear and non-linear technique although these methods perform very similar estimates overall.
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