For many of its inhabitants, the underwater soundscape is a rich source of information that may be crucial for their survival. Moreover, in shallow coastal waters where visibility is poor, the importance of sound is emphasized. Yet coastal waters are also rich in anthropogenic sounds which may disturb the ecosystem. Passive Acoustics Monitoring (PAM) is a flexible, non-invasive and cost-effective solution to acquire information at habitat or community level. Studying the acoustic scene of a habitat in a global, holistic way is known as soundscape analysis. However, there are currently no standardized methods to characterize and understand marine soundscapes in an automated way. Here we propose a methodology for clustering underwater soundscapes and linking the obtained categories to environmental parameters in space and time. This is done using explainable artificial intelligence. The methodology is applied to a PAM dataset collected in the Belgian Part of the North Sea. The obtained categories focus on background sound which include all combinations of sounds that occur under certain conditions at specific places. With this information the marine acoustic scene and its change over space and time can be mapped for the whole area of interest.