Salt marshes offer a plethora of ecosystem services such as biodiversity support, ocean–climate change regulation, ornithology recreo tourism or plant gathering by hand. They undergo significant worldwide losses due to their conversion into crop fields and to their spatial compression between rising sea levels and armored shorelines. Their management requires multiscale spatiotemporal analysis to detect interrelated patterns and processes. This research innovatively studies continuous salt marsh mapping, based on normalized difference vegetation index (NDVI) ranges, across three spatial and two temporal scales. Sentinel-2 (10 m), Dove (3 m), and unmanned airborne vehicle (UAV) (0.03 m) imagery were used to progressively refine spatial resolutions over dynamic areas (extending from hundreds, tens, and a couple of km2, respectively). NDVI ranges from Sentinel-2 and Dove imagery were monitored with a lag of 5 and 4 years, respectively. Contrary to spaceborne imagery, UAV imagery lacked a near-infrared (NIR) band. The UAV NIR band was thus modelled (R2NIR = 0.98) using a three-layered neutral network (NN) prediction based on red, green, and blue reflectance imagery, itself calibrated/validated/tested by Dove imagery bands (R2red = 0.88, R2green = 0.84, and R2blue = 0.90). The 100-fold increase in pixel size allowed to detect the decimeter-scale objects of salt marshes and tidal flats. The multiscale NDVI ranges were associated with microphytobenthos and topographically low, medium, and high salt marsh vegetation, including the opportunistic Elymus genus. The combination of the NDVI values derived from the Sentinel-2, Dove, and UAV imagery enabled to survey a region while detecting subtle features of salt marshes, providing an updated toolbox for managers.