Work

Enhanced Sub-Canopy Topography Retrieval Method Using Dual-Baseline TanDEM-X InSAR Data

Year: 2026

Type: article

Abstract: Accurately measuring topography in forested areas using single-baseline InSAR data is challenging due to volume scattering effects and a limited number of observations. To address these limitations, we propose a new dual-baseline InSAR framework for retrieving sub-canopy topography utilizing two bistatic TanDEM-X InSAR observations. Our approach adopts the Random Volume over Ground (RVoG) model to estimate two spatially varying terms: ground-to-volume amplitude ratio and forest canopy height. Using these parameters we then estimate the phase center height (PCH), representing the elevation difference between the conventional InSAR-based digital elevation model (DEM, a numerical representation of terrain elevations) and the true ground surface. By subtracting the estimated PCH from the conventional InSAR DEM provided by each baseline, our method produces two individual sub-canopy topography products. Further refinement is achieved through a fusion process that integrates the sub-canopy topography maps derived from different baselines, enhancing the accuracy of the output. To validate the proposed framework, we conducted experiments in Sweden and Spain, across diverse forest types, climates, and terrain conditions. The accuracy of the estimated sub-canopy topography was assessed against airborne lidar data. At the Krycklan site in Sweden, our method achieved a root-mean-square error (RMSE) of 2.04 m, demonstrating a performance improvement of 60% and 70% over conventional InSAR, depending on the baselines used. In Spain, the Vegaquemada and Covaleda sites yielded RMSEs of 3.44 m and 4.70 m, respectively, surpassing conventional InSAR DEMs for forested regions as well as simple single-baseline approaches which leverage space-borne LiDAR to fit model parameters, though with some influence from terrain slope. This approach has the potential to support largescale sub-canopy topography with InSAR data.

Source: IEEE Transactions on Geoscience and Remote Sensing

DOI: 10.1109/tgrs.2026.3663263