Description
This contribution was developed in the frame of the Hidden Image of Thawing (HIT) Permafrost Project, which is a project supported by the Helmholtz Imaging Platform. The HIT Permafrost project combines the knowledge and the project-dedicated datasets of two Helmholtz institutes, namely the Alfred Wegener Institute (AWI) and the German Aerospace Center (DLR). Its goal is to retrieve parameters of a common permafrost super-site by analyzing DLR’s multi-modal airborne Synthetic Aperture Radar (SAR) dataset in combination with AWI’s Lidar products on the same region. This poster will show the results of our common study, which were published recently as [1].
SAR remote sensing is an established approach for observing Earth processes. The combination of different types of SAR acquisitions in polarimetric, interferometric, and polarimetric-interferometric frameworks is well studied for retrieving parameters of certain landscape features, such as forests and glaciers. These frameworks have only been rarely applied to permafrost regions, characterized by particular dielectric and structural properties, in particular frozen ground. Here, we investigate the effect of permafrost characteristics on the different SAR imaging modes. This study performs an analysis of the SAR data retrieved during an airborne campaign conducted by the DLR in the Canadian low Arctic, more specifically at Trail Valley Creek (Northwest Territories). Established polarimetric SAR, SAR interferometry, and polarimetric SAR interferometry techniques are applied on the region of interest. For each of these techniques, results are analyzed in several dimensions: SAR frequency band (X-, C-, and L-band), season (summer and winter) and vegetation class. Winter and summer observables are compared, the influence of vegetation type is assessed, and differences between results obtained at different radar frequencies are shown. These results are a step toward the retrieval of soil and vegetation parameters in permafrost tundra environments using multimodal SAR techniques.
[1] P. Saporta, A. Alonso-González, J. Hammar, I. Grünberg, J. Boike and I. Hajnsek, "Observing Seasonal Variabilities of a Permafrost Landscape With PolSAR, InSAR and Pol-InSAR," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 10733-10748, 2025, doi: 10.1109/JSTARS.2025.3551422
