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Retrogressive thaw slumps (RTS) are a form of abrupt permafrost thaw that can rapidly mobilize ancient frozen soil carbon, magnifying the permafrost carbon feedback. However, the magnitude of this effect is uncertain, largely due to limited information about the distribution and extent of RTS across the circumpolar region. Although deep learning methods such as Convolutional Neural Networks (CNN) have shown the ability to map RTS from high-resolution satellite imagery (≤10 m), challenges remain in deploying these models across large areas. Imagery selection and procurement remain one of the largest challenges to upscaling RTS mapping projects, as the user must balance cost with resolution and sensor quality. In this study, we compared the performance of three satellite imagery sources that differed in terms of sensor quality and cost in predicting RTS using a Unet3+ CNN model and identified RTS characteristics that impact detectability. Maxar WorldView imagery was the most expensive option, with a ground sample distance of 1.85 m in the multispectral bands (downloaded at 4 m resolution). Planet Labs PlanetScope imagery was a less expensive option with a ground sample distance of approximately 3.0–4.2 m (downloaded at 3 m resolution). Although PlanetScope imagery was downloaded at a higher resolution than WorldView, the radiometric footprint is around 10–12 m, resulting in less crisp imagery. Finally, Sentinel-2 imagery is freely available and has a 10 m resolution. We used 756 RTS polygons from seven sites across Arctic Canada and Siberia in model training and 63 RTS polygons in model testing. The mean IoU of the validation and testing data sets were 0.69 and 0.75 for the WorldView model, 0.70 and 0.71 for the PlanetScope model, and 0.66 and 0.68 for the Sentinel-2 model, respectively. The IoU of the RTS class was nonlinearly related to the RTS Area, showing a strong positive correlation that attenuated as the RTS Area increased. The models were better able to predict RTS that appeared bright on a dark background and were less able to predict RTS that had higher plant cover, indicating that bare ground was a primary way the models detected RTS. Additionally, the models performed less well in wet areas or areas with patchy ground cover. These results indicate that all imagery sources tested here were able to predict larger RTS, but higher-quality imagery allows more accurate detection of smaller RTS. [ABSTRACT FROM AUTHOR]

Climate change is causing an intensification in tundra fires across the Arctic, including the unprecedented 2015 fires in the Yukon‐Kuskokwim (YK) Delta. The YK Delta contains extensive surface waters (∼33% cover) and significant quantities of organic carbon, much of which is stored in vulnerable permafrost. Inland aquatic ecosystems act as hot‐spots for landscape CO2 and CH4 emissions and likely represent a significant component of the Arctic carbon balance, yet aquatic fluxes of CO2 and CH4 are also some of the most uncertain. We measured dissolved CH4 and CO2 concentrations (n = 364), in surface waters from different types of waterbodies during summers from 2016 to 2019. We used Sentinel‐2 multispectral imagery to classify landcover types and area burned in contributing watersheds. We develop a model using machine learning to assess how waterbody properties (size, shape, and landscape properties), environmental conditions (O2, temperature), and surface water chemistry (dissolved organic carbon composition, nutrient concentrations) help predict in situ observations of CH4 and CO2 concentrations across deltaic waterbodies. CO2 concentrations were negatively related to waterbody size and positively related to waterbody edge effects. CH4 concentrations were primarily related to organic matter quantity and composition. Waterbodies in burned watersheds appeared to be less carbon limited and had longer soil water residence times than in unburned watersheds. Our results illustrate the importance of small lakes for regional carbon emissions and demonstrate the need for a mechanistic understanding of the drivers of greenhouse gasses in small waterbodies. Key Points: Waterbody size and shape have strong nonlinear effects on CO2, with the highest concentrations in small, complex, waterbodiesDissolved organic carbon quantity and composition are the most important drivers of CH4 concentrations in waterbodiesWildfires increase the sensitivity of waterbody CO2 and CH4 concentrations to degraded permafrost in upstream watersheds [ABSTRACT FROM AUTHOR]

Rapid climate warming at northern high latitudes is driving geomorphic changes across the permafrost zone. In the Yamal and Gydan peninsulas in western Siberia, subterranean accumulation of methane beneath or within ice-rich permafrost can create mounds at the land surface. Once over-pressurized by methane, these mounds can explode and eject frozen ground, forming a gas emission crater (GEC). While GECs pose a hazard to human populations and infrastructure, only a small number have been identified in the Yamal and Gydan peninsulas, where the regional distribution and frequency of GECs and other types of land surface change are relatively unconstrained. To understand the distribution of landscape change within 327,000 km2 of the Yamal-Gydan region, we developed a semi-automated multivariate change detection algorithm using satellite-derived surface reflectance, elevation, and water extent in the Google Earth Engine cloud computing platform. We found that 5% of the landscape changed from 1984 to 2017. The algorithm detected all seven GECs reported in the scientific literature and three new GEC-like features, and further revealed that retrogressive thaw slumps were more abundant than GECs. Our methodology can be refined to detect and better understand diverse types of land surface change and potentially mitigate risks across the northern permafrost zone. [ABSTRACT FROM AUTHOR]