Evaluation of optical remote sensing-based shallow water bathymetry for recursive mapping

Lydia Sam*, Ganesh Prusty, Nidhi Gahlot

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Water depth estimation using optical remote sensing offers a reliable and efficient means of mapping coastal zones. Here, we aim to find a suitable model for fast and practical bathymetry of an estuary using Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor (LISS-3) images. The study examines three different models; (1) least square regression model, (2) spectral band-ratio method and (3) multi-tidal bathymetry model. The findings are supported with in situ observed depth values and statistical estimates. Although the least square regression model has provided best results with root mean square error (RMSE) of 0.4 m, it requires a large number of observed data points for absolute depth estimation. Spectral band-ratio and multi-tidal model provides results with RMSEs 2.1 and 0.9 m, respectively. The present investigation demonstrates that multi-date imagery exploitation at disparate tide levels is the best estimation technique for recursive shallow water bathymetry where in situ observation is not possible.

Original languageEnglish
Pages (from-to)737-753
Number of pages17
JournalGeocarto International
Issue number7
Early online date13 Mar 2018
Publication statusPublished - 2018


  • estuary
  • IRS LISS-3
  • multi-date imagery
  • optical remote sensing
  • Shallow water bathymetry


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