Airborne P- and L-band SAR Tomography for Forest Vertical Structure Mapping Using Only Four Images

Synthetic aperture radar (SAR) tomography (TomoSAR) technology effectively provides precise three-dimensional (3D) forest vertical structure information. However, conventional TomoSAR methods require abundant acquisitions for accurate 3D reconstruction, which is time-consuming and low-efficiency for forest vertical structure mapping. To address these limitations, this paper proposes a novel micro-stack TomoSAR imaging approach utilizing four images, referred to as the double iterative adaptive residual approach (DIARA). The DIARA innovatively combines iterative inner-outer adaptive spectral estimation and residual optimization to enhance both processing efficiency and accuracy. For validation purposes, both simulated and airborne TomoSAR experiments are analyzed at P- and L-band. The P-band results from the BorTomoSAR campaign indicate that the DIARA acquires higher accuracy for estimating forest height than the conventional methods, i.e., improving R² from 0.443 to 0.628, mean absolute error (MAE) from 0.779 m to 0.625 m, mean absolute percentage error (MAPE) from 4.629% to 3.680%, and root mean square error (RMSE) from 0.941 m to 0.771 m. Additionally, the performance is further validated by the TropiSAR P-band campaign, which confirms the robustness of the proposed DIARA in high-canopy, complex forest environments. Furthermore, the L-band results from HaiTomoSAR campaign demonstrate that the DIARA method successfully detects the weak ground scatterers beneath dense forest canopies, which validates its super-resolution capability in vertical structure reconstruction.