Physics-based two-layer polarimetric interferometric models provide a well-established framework for forest parameter retrieval. Yet, under Copernicus Sentinel-1 C-band repeat-pass acquisition constraints, the empirical behavior and characteristics of the Random Motion over Ground (RMoG) [1] model parameters remain largely unexplored. To gain insight into the behavior of the key model parameters—including the ground-to-volume scattering ratio, volumetric attenuation, vegetation motion decorrelation, and forest height—we assemble a large, globally distributed benchmark dataset by combining Sentinel-1 dual-polarimetric, single-baseline interferometric coherence observations with independent forest height measurements from GEDI and ICESat-2. The dataset spans boreal, temperate, and tropical forest biomes, providing a comprehensive basis for the large-scale analysis of the RMoG model parameters under real acquisition conditions. The analysis reveals distinct empirical behaviors among the RMoG parameters. Vegetation motion decorrelation exhibits a strong and consistent relationship with interferometric coherence and forest height across all investigated forest biomes. In contrast, volumetric attenuation and the ground-to-volume scattering ratio exhibit high variability and broad distributions across the coherence range. These findings motivate a simplified RMoG inversion through the direct parameterization of vegetation motion decorrelation, reducing the inversion dimensionality while maintaining a physically based estimation framework. Furthermore, the assembled SAR–LiDAR dataset motivates the investigation of machine learning as a means of compensating for the limitations of the physical model, yielding further improvements in forest height estimation. Experimental results demonstrate that, despite the challenging Sentinel-1 C-band repeat-pass acquisition conditions, the physics-based RMoG inversion yet preserves the dominant spatial variability of forest height and remains suitable for discriminating broad forest height classes.
Assessing the Two-Layer Forest Scattering Model Using Sentinel-1 Dual-Polarimetric InSAR Observations
Aghababaei, Hossein;Yang, Wenyu;Vitale, Sergio;Ferraioli, Giampaolo;Schirinzi, Gilda;
2026-01-01
Abstract
Physics-based two-layer polarimetric interferometric models provide a well-established framework for forest parameter retrieval. Yet, under Copernicus Sentinel-1 C-band repeat-pass acquisition constraints, the empirical behavior and characteristics of the Random Motion over Ground (RMoG) [1] model parameters remain largely unexplored. To gain insight into the behavior of the key model parameters—including the ground-to-volume scattering ratio, volumetric attenuation, vegetation motion decorrelation, and forest height—we assemble a large, globally distributed benchmark dataset by combining Sentinel-1 dual-polarimetric, single-baseline interferometric coherence observations with independent forest height measurements from GEDI and ICESat-2. The dataset spans boreal, temperate, and tropical forest biomes, providing a comprehensive basis for the large-scale analysis of the RMoG model parameters under real acquisition conditions. The analysis reveals distinct empirical behaviors among the RMoG parameters. Vegetation motion decorrelation exhibits a strong and consistent relationship with interferometric coherence and forest height across all investigated forest biomes. In contrast, volumetric attenuation and the ground-to-volume scattering ratio exhibit high variability and broad distributions across the coherence range. These findings motivate a simplified RMoG inversion through the direct parameterization of vegetation motion decorrelation, reducing the inversion dimensionality while maintaining a physically based estimation framework. Furthermore, the assembled SAR–LiDAR dataset motivates the investigation of machine learning as a means of compensating for the limitations of the physical model, yielding further improvements in forest height estimation. Experimental results demonstrate that, despite the challenging Sentinel-1 C-band repeat-pass acquisition conditions, the physics-based RMoG inversion yet preserves the dominant spatial variability of forest height and remains suitable for discriminating broad forest height classes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
