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Delineation of shellfish beds through field surveys is time consuming. Remote sensing can help in detecting the location and boundaries of shellfish beds. This can be achieved through the use of aerial photographs and optical satellite sensors during cloud-free and low-tide conditions. Cloud penetrating radar is an alternative, but still requires careful selection of low tide imagery. Manual selection becomes cumbersome for large areas, such as the entire Dutch Wadden Sea. We therefore developed a method that automatically combines dense time series of Sentinel-1 radar imagery into a useful mosaic that allows for effective delineation of shellfish beds. The method consists of temporal compositing many images in the cloud-based Google Earth Engine. We evaluated different combinations of 1) compositing options (average backscatter and various percentiles), 2) temporal periods for compositing, 3) different polarizations, and 4) imagery from ascending versus descending satellite paths. The resulting composite images were visually compared with in-situ records to identify which composite visually best allowed for shellfish bed delineation. Although the average VH-polarized backscatter for one full year provided effective demarcation, an RGB color composite containing three average VH images for four months each provided improved visibility of the shellfish beds. We then quantitatively compared which compositing option was most effective in separating pixels with shellfish beds from those without. For this purpose, the unsupervised classification approach of K-means clustering was applied to the different composite images, and outcomes (i.e. selected classes) were compared with field data derived from the annual in-situ survey. The multi-season image composites (i.e. Jan–Apr, May–Aug, and Sep–Dec) that were made by combining VH image acquisitions of only descending and of combined ascending and descending paths resulted in higher accuracies than other compositing options tested. The producer and user accuracy of the multi-season descending composite were respectively 40% and 70% for the full Dutch Wadden Sea, against 59% and 73% for a smaller subset. These accuracies are similar to what is previously achieved for small study areas with carefully selected radar images acquired around low tide. Moreover, our approach could identify various shellfish beds that were initially missed by the in-situ surveys, but for which later the presence was confirmed (so the actual producer accuracy would increase to about 82%). The composite images can be created on the fly with Google Earth Engine shortly before a field campaign and used as a base for sampling design. • We performed rapid temporal compositing of Sentinel-1 images in Google Earth Engine. • We evaluated different compositing options for the entire Dutch Wadden Sea. • A multi-season VH composite performed best for shellfish bed classification. • Producer and user accuracies reached 82% and 73% respectively, for a smaller subset. • Our approach allows for effective monitoring and already guides field inventories. [ABSTRACT FROM AUTHOR]