Satellite Derived Bathymetry using Adaptive-Geographically Weighted Regression Model

umgd20-v039-i05-coverMarine Geodesy, published online 07 October 2016

By Poliyapram Vinayaraj, Venkatesh Raghavan, and Shinji Masumoto

“The common practice adopted in previous attempts on Satellite Derived Bathymetry (SDB) has been to calibrate a single set of coefficients using global regression model. In this study we propose an Adaptive Geographically Weighted Regression (A-GWR) model that takes into account local factors in determining the regression coefficients. A-GWR model is examined as an effective solution for addressing heterogeneity and could provide better water depth estimates in near-shore region. The study has been carried out for a 30┬ákm stretch and covers 160 km2 of a complex near-shore coastal region of Puerto Rico, Northeastern Caribbean Sea. Medium resolution (Landsat-8) and high resolution (RapidEye) images were used to estimate water depth. Results demonstrate that the A-GWR model performs well in estimating bathymetry for shallow water depths (1 to 20m), showing the correlation coefficient (R) of 0.98 and 0.99, determination coefficient (R2) of 0.95 and 0.99 and Root Mean Square Error (RMSE) of 1.14m and 0.4m) for Landsat-8 and RapidEye respectively. The data processing workflow has been entirely implemented in an Open Source GIS environment and can be easily adopted in other areas.”