We collected ground-penetrating radar (GPR) and frequency-domain electromagnetic induction (FDEM) profiles in 2011 and 2012 to identify the extent of permafrost relative to surface biomass and solar insolation around Twelvemile Lake near Fort Yukon, Alaska. We compared a Landsat-derived biomass estimate and modeled solar insolation from a digital elevation model to the geophysical measurements. We show correspondence between vegetation type and biomass relative to permafrost extent and seasonal freeze–thaw. Thicker permafrost (≥25 m) was covered by greater biomass, and seasonal thaw depths in these regions were minimal (1 m). Shallow (1–3 m depth) and thin (20–50 cm) newly forming permafrost or frozen layers from the previous winter occurred below northward oriented slopes with thin biomass cover. South-facing slopes exhibited permafrost when there was enough biomass to shield incoming solar energy. We developed an artificial neural network to predict permafrost extent across the broader region by mapping GPR-observed instances of permafrost to FDEM, biomass, and terrain observations with 90.2% accuracy. We identified a strong linear correlation (r = −0.77) between permafrost probability and seasonal thaw depth, indicating that our models may also be used to explore thaw patterns and variability in active layer thickness. This study highlights the combined influence of biomass and terrain on the presence of permafrost and the value of evaluating such parameters via remote sensing to predict permafrost spatial or temporal variability. Incorporating diverse geophysical datasets with in-situ validation into machine learning models demonstrates a useful approach to upscale estimated permafrost extent across large Arctic expanses.

中文翻译：

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探地雷达、电磁感应、地形和植被观测结合机器学习绘制阿拉斯加十二英里湖永久冻土分布图

我们在 2011 年和 2012 年收集了探地雷达 (GPR) 和频域电磁感应 (FDEM) 剖面，以确定阿拉斯加育空堡附近十二英里湖周围相对于地表生物量和太阳日照的永久冻土范围。我们比较了 Landsat 衍生的生物量估计和从数字高程模型到地球物理测量的模拟太阳日照。我们显示了植被类型和生物量与永久冻土范围和季节性冻融之间的对应关系。更厚的永久冻土（≥25 m）被更大的生物量覆盖，这些地区的季节性融化深度最小（1 m）。来自前一个冬天的浅（1-3 m 深）和薄（20-50 cm）新形成的永久冻土层或冰冻层出现在生物量覆盖薄的北向斜坡下方。当有足够的生物质来屏蔽传入的太阳能时，朝南的斜坡会出现永久冻土。我们开发了一个人工神经网络，通过将 GPR 观测到的永久冻土实例映射到 FDEM、生物量和地形观测，以 90.2% 的准确率预测更广泛地区的永久冻土范围。我们发现了很强的线性相关性（r  = -0.77) 在永久冻土概率和季节性融化深度之间，表明我们的模型也可用于探索融化模式和活动层厚度的变化。这项研究强调了生物量和地形对永久冻土存在的综合影响，以及通过遥感评估这些参数以预测永久冻土空间或时间变化的价值。将各种地球物理数据集与原位验证结合到机器学习模型中，展示了一种有用的方法来扩大北极大片地区的估计永久冻土范围。