PlanetScope and Landsat 8 Imageries for Bathymetry Mapping

Bathymetry has a great importance in coastal projects. Obtaining proper bathymetric information is necessary for navigation, numerical modeling, and coastal zone management studies. Over the past three decades, a number of measuring protocols have been validated for bathymetry mapping, either by means of echosounding or LIght Detection and Ranging (LIDAR). Although these traditional methods hold a high vertical accuracy, they may have limitations in accessibility for some areas. Remote sensing (RS) techniques can be alternatively utilized for bathymetry extraction and update for such cases. The satellite derived bathymetry (SDB) can be analytically or empirically obtained based on various RS datasets with different spatiotemporal resolution. The current study proposes a methodology to spatially enhance the Landsat-derived bathymetry. Two different satellite images, i.e., Landsat and PlantScope with a spatial resolution of 30 and 3 m respectively have been assessed in bathymetry mapping. The Landsat image resolution has been spatially enhanced to match the Planetscope resolution. The panchromatic band of the Landsat image has been downscaled and used for pan-sharpening the multispectral bands. The bathymetry was empirically estimated from the blue and green spectral bands using the linear model by Lyzenga. The SDB model was calibrated using field measurements of water depths observed by a single beam echosounder. The Bathymetry detection methodology has been applied in an area of the Northern coast of Egypt. The SDB from the PlanetScope, Landsat 8 OLI, and Enhanced Landsat 8 OLI were assessed using error analysis. It was found that the Enhanced Landsat has a comparable result with the PlanetScope. The root mean square error is 0.38 and 0.43 m for PlanetScope and Enhanced Landsat, respectively. The current methodology was also tested by the ratio transform model for SDB and the results revealed the same conclusion as the linear model. Thus, the developed algorithm provides SDB using free Landsat images that is of comparable accuracy to the relatively expensive PlanetScope SDB.