Sub-canopy topography estimation based on sub-aperture decomposition and least-squares collocation from LuTan-1 InSAR data

Abstract

LuTan-1 (LT-1) provides unprecedented L-band bistatic interferometric synthetic aperture radar (InSAR) data for terrain mapping. In forested areas, although the L-band exhibits strong penetration capability, the phase center is still located above the bare ground due to forest volume scattering. Furthermore, the bistatic acquisition provides only single-baseline, single-polarization data, leading to an underdetermined issue for existing scattering models in sub-canopy topography inversion. To address these issues, this study proposes a sub-canopy topography estimation framework based on sub-aperture decomposition and the least-squares collocation (LSC) method. The contributions of this study are: 1) assessing the feasibility of sub-aperture decomposition under LT-1’s small azimuth observation angles; 2) using sub-aperture coherence to provide additional observations and address the underdetermination issue of InSAR inversion; and 3) developing an LSC-based method to separate and calibrate LT-1 orbital and scattering model errors, with the latter arising from complex terrain, forest property variations, and model solution. The proposed framework was tested and validated using LT-1 InSAR data acquired over coniferous, evergreen broadleaf, and tropical forests. The estimated sub-canopy topography achieved a root mean square error (RMSE) between 1.22 and 3.85 m, representing an average improvement of over 60% compared to the InSAR DEM and an improvement of over 30% compared to the initial terrain that did not account for scattering model errors. Moreover, the results indicate that the proposed method also exhibits superior performance under varying terrain and forest conditions, further demonstrating its effectiveness and robustness.