Quantification of active-layer thickness (ALT) over seasonally frozen terrains is critical to understand the impacts of climate warming on permafrost ecosystems in cold regions. Current large-scale process-based models cannot characterize the heterogeneous response of local landscapes to homogeneous climatic forcing. Here we linked a climate-permafrost model with a machine learning solution to indirectly quantify soil conditions reflected in the edaphic factor using high resolution remote sensor products, and then effectively estimated ALT across space and time down to local scales. Our nine-year field measurements during 2014–2022 and coincident high resolution airborne hyperspectral, lidar, and spaceborne sensor products provided a unique opportunity to test the developed protocol across two permafrost experiment stations in lowland terrains of Interior Alaska. Our developed model could explain over 60% of the variance of the field measured ALT for estimating the shallowest and deepest ALT in 2015 and 2019, suggesting the potential of the designed procedure for projecting local varying terrain response to long-term climate warming scenarios. This work will enhance the National Aeronautics and Space Administration’s Arctic-Boreal Vulnerability Experiment’s mission of combining field, airborne, and spaceborne sensor products to understand the coupling of permafrost ecosystems and climate change.

中文翻译：

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将气候-永久冻土模型与高分辨率遥感器产品相结合，量化局部尺度的活动层厚度

季节性冰冻地形上活动层厚度（ALT）的量化对于了解气候变暖对寒冷地区永久冻土生态系统的影响至关重要。当前基于大规模过程的模型无法表征局部景观对同质气候强迫的异质响应。在这里，我们将气候-永久冻土模型与机器学习解决方案联系起来，使用高分辨率遥感器产品间接量化土壤因子反映的土壤条件，然后有效地估计跨空间和时间的 ALT 到局部尺度。我们在 2014 年至 2022 年期间进行的九年现场测量以及同时使用的高分辨率机载高光谱、激光雷达和星载传感器产品为在阿拉斯加内陆低地地形的两个永久冻土实验站测试开发的协议提供了独特的机会。我们开发的模型可以解释现场测量的 ALT 的 60% 以上的方差，用于估计 2015 年和 2019 年最浅和最深的 ALT，这表明所设计的程序在预测当地不同地形对长期气候变暖情景的响应方面具有潜力。这项工作将增强美国国家航空航天局的北极-北方脆弱性实验的使命，即结合现场、机载和星载传感器产品，以了解永久冻土生态系统与气候变化的耦合。