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

Ankita Misra, Balaji Ramakrishnan*, Zoran Vojinovic, Arjen Luijendijk, Roshanka Ranasinghe

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)537-540
Number of pages4
JournalJournal of the Indian Society of Remote Sensing
Issue number3
Publication statusPublished - 7 Mar 2019


  • Coastal bathymetry
  • Landsat 8 OLI
  • Nonlinear
  • Sentinel 2 MSI
  • Support vector regression
  • n/a OA procedure


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