Assessment of Complementary Medium-Resolution Satellite Imageries for Nearshore Bathymetry Estimation

Journal article (2018)

Authors

Ankita Misra Indian Institute of Technology Bombay
Balaji Ramakrishnan Indian Institute of Technology Bombay
Zoran Vojinovic IHE Delft Institute for Water Education
Arjen Luijendijk Deltares, Coastal Engineering - CEG
Roshanka Ranasinghe Deltares, IHE Delft Institute for Water Education, University of Twente
Research Group
Coastal Engineering (CEG) (TU Delft)
More Info
expand_more

Published Date

2018

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Civil Engineering & Geosciences

Department

Hydraulic Engineering

Research Group

Coastal Engineering

Abstract

This paper focuses on utilizing Sentinel 2 MSI datasets to generate satellite-derived bathymetry (SDB) maps at a resolution of 10 m for two temporally varying datasets of the study region of Ameland Inlet, located in The Netherlands, by using support vector regression (SVR) technique. The relative performance of Landsat 8 OLI (30 m) datasets with SVR technique is also assessed to demonstrate the complementary nature of these freely available medium-resolution imageries. Further, the root mean square error and mean absolute error between the retrieved and measured bathymetries are estimated and reported to evaluate the capability of SVR in estimating depths. It is evident that the SDBs thus generated using this machine learning approach provide dependable estimations of depths that can further be utilized for various coastal engineering studies.

Files

Misra2019_Article_AssessmentOf... (pdf)
(pdf | 1.23 Mb)
Unknown license

Download not available