Satellite-derived coastal bathymetry over a broad range of geography and water clarity

Abstract

<p>Monitoring the complex seafloor morphology that drives the functioning of shallow coastal ecosystems is vital for assessing marine activities such as navigation, research, fisheries, aquaculture, tourism, and design or monitoring applications. Satellite-derived bathymetry (SDB) can support these activities, particularly when using data from a platform, like the Sentinel-2A/B twin mission of the Copernicus programme, which provides routine and repetitive image acquisition at 10 m spatial resolution. As a result, SDB can expand on the temporal and spatial scope of more conventional mapping methods, such as lidar or multi-beam. However, SDB still has limitations when applied to the turbid, but optically shallow, nearshore regions that encompass large areas of the world’s coasts. Turbid water produces false shoaling in the imagery, constraining SDB for its routine application. In this study, we integrate water quality information from the Sentinel-2A/B satellites with a multi-temporal compositing method to demonstrate a potential for comprehensively operational bathymetric mapping over a range of environments. The automated compositing method diminishes the turbidity impact in addition to inferring the maximum detectable depth and removing optically deep-water areas. It also returns turbidity data to indicate areas that may still have residual shoaling bias. Examining a wide range of conditions along the Caribbean and eastern coast of the U.S. shows detailed bathymetry as deep as 30 m at 10 m spatial resolution with median errors <1 m when compared to high-resolution lidar surveys. These results demonstrate that the model adopted can provide useful bathymetry in areas that do not have consistently clear water and can be extended across multiple geographic regions and optical conditions for local, regional, and national scales. Because minimal in-situ information is required, this computationally-efficient technique has the potential for automated implementation, allowing rapid and repeated application in more environments than most existing methods. Certainly, SDB can enhance our knowledge of Earth’s nearshore regions generating a crucial dataset for bathymetry maps to understand hazards and impacts produced by climate change in vulnerable coasts.</p>