Spaceborne L-band bistatic interferometric synthetic aperture radar (InSAR) is an advanced remote sensing technology used for forest height inversion. It enables the detection of forest vertical structures and avoids the effects of temporal decorrelation. However, forest height inversion using single-polarization L-band bistatic InSAR in mountainous areas still faces several challenges. First, multi-parameter scattering models cannot be directly resolved using single-polarization InSAR observations. Second, ground scattering in L-band InSAR significantly affects the accuracy of forest height inversion. Moreover, mountainous terrain alters the interaction between InSAR signals and forest scatterers, further increasing the inversion uncertainty. To address such challenges, this paper proposes a frequency-domain information enhancement adaptive volume coherence optimization (Ada-VolOpt) method using single-polarization LuTan-1 bistatic InSAR data. The proposed method expands the observation space of InSAR through time-frequency analysis. Subsequently, based on frequency-domain information enhancement and the random volume over ground (RVoG) model, an adaptive volume coherence optimization method is proposed to overcome the adverse effects of significant ground scattering on forest height inversion in mountainous areas. Finally, forest height inversion is performed using a slope-adaptive scattering model. The effectiveness of the proposed method was validated across three test sites in China. A total area of 63.11 thousand km2 (6.31 million hectares) was used to test the proposed method, resulting in reliable forest height products. The forest height is estimated with an accuracy of 5.04 m for tropical forests, 3.41 m for mixed forests, and 2.44 m for boreal forests, respectively. Compared to the method that ignores ground contributions, the proposed method improves the accuracy by 8%, 10%, and 33%, respectively. This study provides a comprehensive benchmark evaluation of the performance of large-scale forest height inversion using LuTan-1 bistatic InSAR data.
Wan et al. (Tue,) studied this question.