Abstract 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.