Introduction
Permafrost, which refers to soil or rock that remains frozen for at least two consecutive years, currently covers about 11% of Earth’s land surface. It is typically found in high-latitude regions such as Arctic Alaska, where winter freezing exceeds summer thawing. Above the permafrost lies the supra-permafrost zone, which includes the seasonally thawed active layer and, in some cases, an unfrozen talik layer. The active layer can range from 10 cm to over 3 m in thickness and has shown signs of deepening in response to widespread warming trends.
This deepening is significant because Arctic permafrost holds an estimated 1700 petagrams of organic carbon—more than twice the amount in the atmosphere. As the active layer thickens and permafrost thaws, stored carbon is released into the atmosphere as CO₂ and CH₄, enhancing the greenhouse effect and further accelerating warming—a feedback known as the permafrost carbon feedback (PCF).
Monitoring ALT
Monitoring the active layer thickness (ALT) and soil moisture profiles is crucial for understanding permafrost stability. While ground-based methods (e.g., metal rods, thaw tubes, and ground-coupled GPR) provide accurate data, they are limited in spatial coverage and are labor-intensive. Airborne and satellite remote sensing can offer broader coverage but are restricted by coarse spatial resolution and shallow penetration capabilities. UAV-based radar systems bridge this gap by enabling localized, high-resolution ALT measurements over broader areas. Due to their proximity to the ground, UAV systems achieve higher sensitivity and signal-to-noise ratio, allowing for deeper ALT sensing. However, they introduce additional challenges in signal processing and electromagnetic modeling compared to ground-coupled systems.
UAV-based GPR for Permafrost Sensing
We deployed a UAV-based GPR during a field campaign conducted in August 2024 over permafrost in the Arctic Alaska. The initial results highlighted the need for more advanced processing to improve radargram interpretability. Our ongoing research focuses on developing an advanced processing framework consisting of three main components: (1) a distortion correction module to compensate for artifacts caused by system non-idealities, (2) a clutter suppression module to reduce direct-path and surface reflections, and (3) a range profiling module that employs various techniques, including super-resolution methods, to generate the radargram.
