DEPTH ESTIMATION OF SHALLOW WATER USING MULTISPECTRAL SATELLITE IMAGERY SENTINEL-2A

Abstract

Shallow water bathymetry estimation from remote sensing data has been increasing widespread, as an alternative to traditional bathymetry measurement that has disturbed by technical and logistic problem. Deriving bathymetry data from Sentinel 2A images, at visible wavelength (blue, green and red) 10 meter spatial resolution was carried out around the waters of the Kemujan Island Karimunjawa National Park Central Java. Amount of 1280 points data are used as training data sets and 854 points data as test data set produced from sounding. Dark Object Substraction (DOS) has been to correct atmospherically the Sentinel-2A images. Several algorithm has been applied to derive bathymetry data, including: linear transform, ratio transform and support vector machine (SVM). The highest correlation between depth prediction and observe resulted from SVM algorithm with a coefficient of determination (R2) 0.71 (training data) and 0.56 (test data). The assessment of the accuracy of the three methods using RMSE and MAE values, the SVM algorithm has the smallest value (< 1 m). This indicates that the SVM algorithm has a high accuracy compared to the other two methods. The bathymetry map derived from Sentinel 2A imagery cannot be used as a reference for navigation.