Geological remote sensing is an essential tool in mineral exploration, employing satellite, airborne, and drone-based imagery to identify and analyse potential mineral deposits from a distance. This technique allows geologists to survey vast and often inaccessible areas, identifying critical geological features indicative of minerals without needing immediate physical sampling. Remote sensing geology is possible in areas with “good exposure”, which typically refers to arid and semi-arid areas. Surface cover is often considered to be invariant (or only changing on a geological timescale), which holds for rocks and minerals and, to a lesser degree, for soils. What of course does change over time is the acquisition environment, driven by seasonal change and the weather. Mapping at regional scale however requires an image collection acquired over a longer time span, and possibly including temperate and cultivated regions. For small-scale studies, data acquired at a single moment seem to suffice, but results still differ from image to image.Sensors with a continuous multi-temporal operation (e.g. Landsat 8 OLI and Sentinel-2 MSI) enable to monitor land surface processes over time, but also allow to choose an optimal moment of seasonal acquisition. Handling the vast amount of data from ESA and NASA’s earth observation programmes has led to the development of cloud-based processing environments. This presentation will shed a light on the use of Google Earth Engine for mapping surface dynamics and studying the influence of time on geological remote sensing results.