High-resolution inventory and classification of retrogressive thaw slumps in West Siberia

Abstract. Permafrost thaw disrupts ecosystems, hydrology, and biogeochemical cycles, reinforcing climate change through a positive permafrost-carbon feedback loop. Thaw can be gradual, deepening the active layer, or abrupt, triggering thermokarst, thermo-erosion, or thermodenudation. Retrogressive thaw slumps (RTSs) are a key manifestation of abrupt permafrost thaw. Yet, their distribution, scale, and environmental controls in the West Siberian Arctic remain poorly understood, further complicated by their rapid evolution. This study presents an extensive update of the West Siberian RTS inventory through manual mapping using high-resolution, multi-source, multi-year recent (2016–2023) satellite basemaps (ESRI, Google Earth, and Yandex Maps). We developed an RTS classification capturing key environmental parameters, including morphology, spatial organization, terrain position, and associated relief-forming concurrent processes. The dataset comprises 6168 classified RTS landforms, integrating newly mapped sites with previously reported occurrences to provide a comprehensive view of a 445 226 km2 region covering the Yamal, Gydan, and Tazovsky peninsulas. The collected data underwent manual filtering and verification, leveraging local field experience and observations from key sites to reduce uncertainty and minimize false positives. Accuracy analysis, performed by comparing the dataset with various field datasets collected across the peninsulas, confirmed high accuracy (>90 %) for RTS identification. The dataset likely underestimated the distribution of small RTSs due to the resolution limitations of remote sensing data, hence generally providing a conservative estimate. This dataset serves as a valuable resource for diverse research fields, including ecology, biogeochemistry, geomorphology, climatology, permafrost science, and natural hazard assessment. Additionally, it provides a crucial reference dataset for machine learning applications, enhancing upcoming remote sensing classification and predictive modeling approaches. The dataset is available from Nesterova et al. (2025; https://doi.org/10.1594/PANGAEA.974406).