A Multibaseline PolInSAR Forest Height Inversion Model Based on Fourier–Legendre Polynomials

Abstract

In this letter, we propose a forest height inversion model based on three-order Fourier-Legendre (FL) polynomials from the multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) data. The proposed model expresses the vertical structure of the volume layer as three-order FL polynomials. Meanwhile, the forest height is treated as an unknown parameter, rather than a priori information, as adopted in polarization coherence tomography technology. On the other hand, to be more realistic, the proposed model uses multipolarization PolInSAR data and considers that the synthetic aperture radar (SAR) signals in different polarizations describe the forest vertical structure in different ways so that we can obtain a more comprehensive forest vertical structure. Airborne P-band PolInSAR data acquired over the boreal and tropical forest areas were selected for testing the forest height inversion method. The results show that, compared to random volume over ground (RVoG) model-based inversion, the accuracy of the proposed model is improved by 28.20% and 17.30%, respectively, for the boreal and tropical forest scenes.