Combining Multi-Dimensional SAR Parameters to Improve RVoG Model for Coniferous Forest Height Inversion Using ALOS-2 Data

This paper considers extinction coefficient changes with height caused by the inhomogeneous distribution of scatterers in heterogeneous forests and uses InSAR phase center histogram Gaussian function to fit normalized curve so as reflect vertical structure forest. Combining polarization decomposition based on physical model PolInSAR parameter inversion method, ground volume coherence matrices can be separated characteristics interference diversity. By combining new abovementioned parameters, semi-empirical improved RVoG used both quantify effects temporal decorrelation errors avoid small wavenumbers large baseline spaceborne data. The provided robust for coniferous forest enhanced estimation structure. study addressed influence differences coefficient, though sparse vegetation areas could not accurately estimated, oversensitivity inappropriate wavenumbers. According this method we L-band ALOS-2 PALSAR data Saihanba Hebei Province acquired 2020 purpose inversion, a range 14–70 days wavenumber 0.01–0.03 rad/m. results are further validated using sample data, R2 reaching 0.67.