Spaceborne polarimetric synthetic aperture radar interferometry (PolInSAR) has the potential to deal with large-scale forest height inversion. However, the inversion is influenced by strong temporal decorrelation interference resulting from a large temporal baseline. Additionally, the forest canopy induces phase errors, while the smaller vertical wavenumber (kz) enhances the sensitivity of the inversion to temporal decorrelation, which limits the efficiency in forest height inversion. This research is based on the random volume over ground (RVoG) model and follows the assumptions of the three-stage inversion method, to quantify the impact of repeat-pass spaceborne PolInSAR temporal decorrelation on the relative error of retrieval height, and develop a semi-empirical improved inversion model, using ground data to eliminate the interference of coherence and phase error caused by temporal decorrelation. Forest height inversion for temperate forest in northern China was conducted using repeat-pass spaceborne L-band ALOS2 PALSAR data, and was further verified using ground measurement data. The correction of temporal decorrelation using the improved model provided robust inversion for mixed conifer-broad forest height retrieval as it addressed the over-sensitivity to temporal decorrelation resulting from the inappropriate kz value. The method performed height inversion using interferometric data with temporal baselines ranging from 14 to 70 days and vertical wavenumbers ranging from 0.015 to 0.021 rad/m. The R2 and RMSE reached 0.8126 and 2.3125 m, respectively.

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Forest density affects the inversion of forest height by influencing the penetration and attenuation of synthetic aperture radar (SAR) signals. Traditional forest height inversion methods often fail in low-density forest areas. Based on L-band single-baseline polarimetric SAR interferometry (PolInSAR) simulation data and the BioSAR 2008 data, we proposed a forest height optimization model at the stand scale suitable for various forest densities. This optimization model took into account shortcomings of the three-stage inversion method by employing height errors to represent the mean penetration depth and SINC inversion method. The relationships between forest density and extinction coefficient, penetration depth, phase, and magnitude were also discussed. In the simulated data, the inversion height established by the optimization method was 17.35 m, while the RMSE value was 3.01 meters when the forest density was 100 stems/ha. This addressed the drawbacks of the conventional techniques including failing at low forest density. In the real data, the maximum RMSE of the optimization method was 2.17 m as the stand density increased from 628.66 stems/ha to 1330.54 stems/ha, showing the effectiveness and robustness of the optimization model in overcoming the influence of stand density on the inversion process in realistic scenarios. This study overcame the stand density restriction on L-band single baseline PolInSAR data for forest height estimation and offered a reference for algorithm selection and optimization. The technique is expected to be extended from the stand scale to a larger area for forest ecosystem monitoring and management.