Pléiades Neo-Derived Bathymetry in Coastal Temperate Waters: The Case Study of Bay of Saint-Malo

Abstract

Satellite-derived bathymetry is increasingly attracting stakeholders’ attention tasked with remote and/or shallow depths, given its affordability compared to airborne lidar and waterborne sonar surveys. The 6-band 1.2 m Pléiades Neo (PNEO) multispectral imagery has not yet been evaluated for such a purpose. The contribution of the novel PNEO bands to the depth retrieval was assessed over unclear coastal seawaters (0.2 m−1 of vertical light attenuation in the bay of Saint-Malo, France). The relevance of the radiometric level was also tested: top-of-atmosphere (TOA) digital number (DN), TOA radiance, TOA reflectance, bottom-of-atmosphere (BOA) maritime-modeled reflectance, and BOA tropospheric-modeled reflectance. The lidar response, ranging from 0 to 20 m depth, was stratified by 90 random samples per bathymetric slice of 1 m. The model was based on an easy-to-transfer neural network (one hidden layer and three neurons). The best predictions, reaching R2test of 0.81, were equally obtained for the full PNEO dataset at TOA DN, radiance, and reflectance. For both BOA full-dataset products, the results were slightly less satisfactory: R2test of 0.75 (maritime) and 0.76 (tropospheric).

Keywords

• very high resolution
• satellite
• neural networks