Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Abrupt permafrost disturbances, such as retrogressive thaw slumps (RTS), expand by several meters each year and lead to an increased soil organic carbon release. We applied the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to Northeast Siberia (8.1 × 10^6km^2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.61). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very high-resolution RapidEye and PlanetScope imagery. The data set presents the results of the first automated detection and assessment of RTS and their temporal dynamics at large-scale for 2001–2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across Northeast Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331% compared to 2000 (2000: 20,158 ha, 2001-2019: 66,699 ha). Contrary to this, focus sites show spatio-temporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The detected increase in RTS dynamics suggests advancing permafrost thaw and underlines the importance of assessing abrupt permafrost disturbances with high spatial and temporal resolution at large-scales. This consistenly obtained disturbance product will help to parametrise regional and global climate change models.
