Abstract. In light of the recent climate warming, monitoring of lake ice in Arctic and sub-Arctic regions is becoming increasingly important. Many shallow arctic lakes and ponds of thermokarst origin freeze to bed in the winter months, maintaining the underlying permafrost in its frozen state. However, as air temperatures rise and precipitation increases, less lakes are expected to develop bedfast ice. In this work, we propose a novel temporal deep learning approach to lake ice regime mapping from synthetic aperture radar (SAR) and employ it to study lake ice dynamics in the Old Crow Flats (OCF), Yukon, Canada over the 1993 to 2021 period. We utilized a combination of Sentinel-1, ERS-1 and 2, and RADARSAT-1 to create an extensive annotated dataset of SAR time-series labeled as either bedfast ice, floating ice, or land, used to train a temporal convolutional neural network (TempCNN). The trained TempCNN, in turn, allowed to automatically map lake ice regimes. The classified maps aligned well with the available field measurements and ice thickness simulations obtained with a thermodynamic lake ice model. Reaching a mean overall classification accuracy of 95 %, the TempCNN was determined to be suitable for automated lake ice regime classification. The fraction of bedfast ice in the OCF increased by 11 % over the 29-year period of analysis. Findings suggest that the OCF lake ice dynamics is dominated by lake drainage events, brought on by thermokarst processes accelerated by climate warming, as well as fluctuations in water level and winter snowfall. Catastrophic drainage, and lowered water levels cause surface water area and lake depth to decrease and lake ice to often transition from floating to bedfast ice, while a reduction in snowfall allows for the growth of thicker ice.

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<p>Lakes and drained lake basins (DLB) are ubiquitous landforms in permafrost lowland regions, covering 50% to 75% of permafrost lowlands in parts of Alaska, Siberia, and Canada. Depending on the time passed since the drainage event, surface characteristics within the DLB such as surface roughness, vegetation, moisture and abundance of ponds may vary. The mosaic of vegetative and geomorphic succession within DLBs and the distinct differences between DLBs and surrounding areas can be discriminated with remote sensing and used to derive a landscape-scale classification. Previously published local and regional studies have demonstrated the importance of DLBs regarding carbon storage, greenhouse gas and nutrient fluxes, hydrology, geomorphology, and habitat availability. To help quantify these processes on a circumpolar scale and improve the representation of Arctic landscapes in large scale models, a circumpolar data set of DLBs distribution and DLB properties is needed.  Due to the inherent temporal characteristics of DLBs, such a data set also has the potential for space-for-time applications regarding landscape models. A pan-Arctic scale effort to map and further the understanding of DLBs in permafrost-regions is the outcome of work conducted within the International Permafrost Association (IPA) Action Group on DLBs, a bottom-up effort led by the scientific community that includes developing a first pan-Arctic drained lake basin data product based on multispectral remote sensing data (Landsat-8). Comprehensive mapping of DLBs areas across the circumpolar permafrost landscape and including field data into this approach will allow for future utilization of these data in pan-Arctic models, aid upscaling efforts and greatly enhance our understanding of DLBs in the context of permafrost landscapes. This will improve quantitative studies on landscape diversity, wildlife habitat, permafrost, hydrology, geotechnical conditions, high-latitude carbon cycling, and landscape vulnerability to climate change.</p>

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