Conference Agenda

Spaceborne SAR systems represent a powerful mean for monitoring forest on a global scale. In particular, time-series data provided by the Sentinel 1 C-band SAR mission, have been widely used for detecting deforestation [1,2], or measuring forest degradation [3]. SAR sensors operating at L or P bands use larger-wavelength signals that can penetrate dense forest canopies to the ground, and can be used to retrieve certain geophysical forest features, such as above-ground biomass [4,5]. However, the robust and accurate estimation of internal forest descriptors is usually hampered by multiple wave-matter interactions that occur at the ground level and contribute significantly to the total SAR response. These undesired components exhibit highly variable radiometric and polarimetric patterns, influenced by numerous factors, such as acquisition geometry, local topography, soil humidity and roughness...

SAR tomography constitutes a solution for discriminating echos from a forest canopy and the ground [6] : multiple coherent signals acquired from slightly offset trajectories are focused in 3D, providing access to the reflectivity of forest components located at different elevations. Nevertheless, vertical separation is, in general, not perfect, and an unrealistic number of SAR acquisitions may be required to achieve a sufficient level of isolation between the imaged responses of the ground and the overlying volume. Another approach consists in canceling out responses originating from the ground level by coherently combining a pair of SAR images [7]: the intensity of the resulting image is a non-linear function of the above-ground reflectivity of the scene, and depends on multiple, often unknown, factors, such as acquisition geometry, local topography, forest structure…

This paper proposes generating 3D reflectivity maps that are insensitive to ground scattering by generalizing the coherent ground filtering principle introduced in [7] to the case of SAR tomography.

This combined processing cancels out the undesired component with a level of isolation that does not depend on the vertical tomographic resolution, while yielding a refined image of the forest 3D reflectivity. The method is based on an unconstrained optimization problem whose analytical solution may be applied to non-parametric tomographic focusing, e.g. Beamformer or Capon’s method, of single- or multi-look SAR data. The techniques can handle polarimetric SAR data and deliver optimal or full-rank ground-notched 3D polarimetric information.

The performance of the approach is assessed through a thorough comparison with the aforementioned methods, using measurements from ESA’s airbone SAR campaigns and early BIOMASS data.

[1] Reiche, J., Verhoeven, R., Verbesselt, J. et al. Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts. Remote Sensing, 10, 777 (2018).

[2] Bottani, M., Ferro-Famil, L., Doblas, J. et al. Novel unsupervised Bayesian method for Near Real-Time forest loss detection using Sentinel-1 SAR time series: Assessment over sampled deforestation events in Amazonia and the Cerrado. Remote Sensing of Environment, 331, 115037 (2025).

[3] Dupuis, C., Fayolle, A., Bastin, J.F. et al. Monitoring selective logging intensities in central Africa with sentinel-1: A canopy disturbance experiment. Remote Sensing of Environment, 298, 113828 (2023).

[4] Le Toan, T., Quegan, S., Davidson, M. W. J. et al. The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle. RSE, 115(11), 2850-2860. (2011).

[5] Bouvet, A., Mermoz, S., Le Toan, et. al. An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR. Remote sensing of environment, 206, 156-173. (2018)

[6] Aghababaei, H., Ferraioli, G., Ferro-Famil, L. et. al.. Forest SAR tomography: Principles and applications. IEEE geoscience and remote sensing magazine, 8(2), 30-45 (2020).

[7] Mariotti d’Alessandro, M. Tebaldini, S., Quegan et. al. Interferometric ground cancellation for above ground biomass estimation. IEEE Transactions on Geoscience and Remote Sensing, 58(9), 6410-6419. (2020)