Comparison of bathymetry estimated from multibeam echosounder and optical data

Antonino Maltese; Valeria Lo Presti; Giovanni Andrea Nocera; Attilio Sulli

doi:10.1117/12.3038063

20 November 2024 Comparison of bathymetry estimated from multibeam echosounder and optical data

Antonino Maltese, Valeria Lo Presti, Giovanni Andrea Nocera, Attilio Sulli

Proceedings Volume 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI; 1319105 (2024) https://doi.org/10.1117/12.3038063
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

Accurate bathymetry estimation is crucial for various marine and coastal applications, including resource management and environmental protection. This study presents a comparison of bathymetry estimated using optical satellite imagery, on sandy bottom, versus multibeam bathymetric data. The study area was the Gulf of Sciacca, on the southern coast of Sicily Island (Italy). High-resolution PlanetScope SuperDove satellite images were processed using a simplified version of the original radiative transfer equation from Jain and Miller (1977) was used to estimate water depth. The model was calibrated and then validated using multibeam data. Multibeam data were acquired using a Reason 8125 multibeam echosounder. Bathymetry was estimated from optical data from the shoreline down to ⁓25m water depth; while approaching this depth, the reflectance assumed an asymptotic value. The results show a strong correlation between the depths estimated from satellite images and those obtained from multibeam data, with a root mean square error lower than 11% with a confidence interval of 95%. However, some discrepancies were observed in areas in mixed pixels or reflectance surface effects.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Antonino Maltese, Valeria Lo Presti, Giovanni Andrea Nocera, and Attilio Sulli "Comparison of bathymetry estimated from multibeam echosounder and optical data", Proc. SPIE 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI, 1319105 (20 November 2024); https://doi.org/10.1117/12.3038063

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