Mechanism-Driven Adaptive Combined Inversion of Forest Height Using P-Band PolInSAR Data

Forest height is a key parameter for quantifying forest biomass and carbon stocks and serves as an important indicator of forest ecosystem health. The successful launch of the European Space Agency’s P-band Biomass satellite, which provides Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data for global high-precision forest height mapping, heralds a new era in global forest carbon monitoring. However, the accuracy of forest height inversion is significantly influenced by scattering mechanisms. This study investigates the impact of dominant scattering mechanisms on forest height inversion accuracy. Four classical algorithms were selected: the polarimetric phase center height estimation method (PPC), the complex coherence phase center differencing algorithm (CCPCD), the coherence amplitude inversion method (CAI), and the hybrid inversion method using both phase and coherence information. The Freeman–Durden three-component decomposition was employed to identify the dominant scattering mechanisms. The results show that (1) at P-band, inversion model performance exhibits strong coupling with scattering mechanisms, and no single algorithm achieves global robustness; (2) the hybrid inversion method using both phase and coherence information performs better in regions dominated by surface and double-bounce scattering, whereas the coherence amplitude inversion method (CAI) yields higher accuracy in volume-scattering-dominated regions; and (3) the adaptive joint inversion strategy based on scattering mechanisms achieved a root mean square error (RMSE) of 4.62 m and a coefficient of determination (R2) of 0.76 at P-band, representing an improvement of approximately 30% over the best single-model performance (RMSE = 6.51 m). This approach overcomes the accuracy limitations of single models in complex global forest scenarios and provides a valuable reference for scientific forest height inversion.