Landscape changes in the terrestrial Arctic occur on different time scales. One can normally distinguish between gradual and abrupt changes. Permafrost top-down thaw, changes in pedological processes and vegetation structure as well as aridification and paludification are gradual changes. In contrast to that, wildfires, thermokarst formation, thermokarst lake drainage, flooding and rapid soil erosion are abrupt and sometimes even discrete change events. A high temporal resolution in observations is required to properly monitor both types of changes. Particularly discrete change events that happen abruptly can be identified and characterized more precisely with better temporal resolution. However, a better understanding of the nature of gradual changes can also be gained since short periods of increased change or stagnation can also be identified with higher temporal resolution. LandTrendr is an algorithm developed by Kennedy et al. (2010) to capture a wide range of forest disturbance and recovery occurrences based on Landsat time series. The algorithm creates cloud-free mosaics from multiple images and extracts the temporal trajectory of spectral data on a pixel-by-pixel basis. Furthermore, it segments the temporal trajectories based on regressions and point-to-point fittings of spectral indices, depicting then both long-term trends and abrupt changes (Kennedy et al., 2010). As optical remote sensing in the Arctic is highly restricted by frequent cloud cover and low illumination angles, the number of useable images per summer season is low. However, combining data of multiple optical systems bypasses this limitation. Combining data from Landsat and the newly launched ESA Copernicus Sentinel-2 mission, enables the use of data from three satellites (L-8, S-2A, S-2B), which shortens the revisit time to less than five days in high latitudes, enhances the likelihood of cloud-free images considerably, and increase the number of images per summer season dramatically. Using both Landsat and Sentinel-2 images will lead to much denser time-series and therefore capturing changes more precisely. In this study in progress, we apply the LandTrendr algorithm to three different areas in North Eastern Siberia: the central Lena Delta, Batagay in the Yana Highlands and Yukechi in central Yakutia. These three sites are located along an approximate longitudinal transect and exemplify the different climatic, geologic, geomorphologic and vegetational conditions of the heterogeneous North Eastern Siberian permafrost landscapes that experience different disturbances. In order to ensure a full coverage of the study sites and to obtain higher temporal resolutions, we create a homogenous time series combining both Landsat and Sentinel-2 data. We anticipate that the LandTrendr algorithm is transferable to Arctic study areas and that the results will benefit from the higher spatial coverage and especially temporal resolution when combining Landsat and Sentinel-2. We hypothesize that we can distinguish between different permafrost disturbances, both gradual and abrupt changes, like thermokarst, erosion and fires, by applying key spectral indices, such as NDVI, NDWI, NBR and tasselled cap indices and by adapting the temporal segmentation parameters to permafrost landscape changes.