Retrieval of Boreal Forest Heights Using an Improved Random Volume over Ground (RVoG) Model Based on Repeat-Pass Spaceborne Polarimetric SAR Interferometry: The Case Study of Saihanba, China - Yu Mao (MDPI AG, 2021)
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 (<semantics>kz</semantics>) 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 <semantics>kz</semantics> 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.