Conference Agenda

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Phase triplets, is an interferometric technique that explores three distinct (interferometric) SAR acquisitions, acquired at different temporal and/or spatial baseline to each other, in form of three interferograms combined to form a closed phase difference loop. This allows to cancelling out common phase difference contributions between the three interferograms, as imposed for example by propagation differences, and obtain phase differences imposed by certain changes in the (vertical) distribution of scatterers in the scene [1]. While a number of studies have demonstrated the potential of phase triplets to monitor surface and vegetation changes, including soil moisture variations [1],[2], vegetation growth [3] or fluctuations in vegetation water content [2], the physical mechanisms underlying phase non-closure, and its dependence on wave polarization, remain insufficiently understood.

This study aims to advance the understanding of surface and vegetation change processes by investigating the polarimetric dependence of phase triplets. To this end, data from ESA’s BIOMASS mission, acquired during the In Orbit Commissioning (IOC) phase of the mission, are employed. With fully polarimetric data and sufficient penetration into forest volumes, BIOMASS data provide sensitivity to scattering processes throughout the entire canopy, offering a unique opportunity to study the temporal behaviour of forest backscatter under different vegetation and change conditions.

Such capabilities are explored over the Gabonese rainforest, a natural environment characterized by high above-ground biomass, complex canopy structure, and diverse tree species composition. Triplets of temporally proximate P-band acquisitions (3 days intervals) are analysed to assess how polarization (HH, HV, VV, and linear combinations) influences both the magnitude and spatial distribution of phase non-closure. The analysis focuses on zero and small-baseline interferometric baselines to minimize geometric decorrelation, thereby isolating the contributions of volumetric and polarimetric effects. In addition, complementary airborne LiDAR data are used to relate phase non-closure patterns to canopy height and structural heterogeneity.

Results reveal a clear polarization-dependent behaviour: over open areas, HH and VV channels exhibit low and spatially stable closure deviations, whereas in forested regions, HV and cross-polarized combinations display larger and more variable phase non-closure values. These findings suggest that phase non-closure carries valuable information about canopy structure and dielectric heterogeneity. The analysis and discussion is supported by appropriate modelling. Understanding polarimetric information of phase triplets can improve the interpretation of multi-temporal P-band interferometric data for biomass estimation, vegetation dynamics monitoring, and physical model validation in tropical forests.

[1] F. De Zan, A. Parizzi, P. Prats-Iraola and P. López-Dekker, "A SAR Interferometric Model for Soil Moisture," in IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 1, pp. 418-425, Jan. 2014.

[2] F. De Zan, M. Zonno and P. López-Dekker, "Phase Inconsistencies and Multiple Scattering in SAR Interferometry," in IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 12, pp. 6608-6616, Dec. 2015.

[3] Y. Yuan, M. Kleinherenbrink and P. López-Dekker, "On Crop Growth and InSAR Closure Phases," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-12, 2024.

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], monitoring forest degradation [3], and measuring ground deformation [4]. SAR sensors operating at L or P bands use larger-wavelength signals that can penetrate dense vegetal volumes down to the ground, and can be used to retrieve certain geophysical features, such as above-ground biomass [5,6] and ground motion in vegetated areas [7]. However, the robust and accurate estimation of specific descriptors is often hampered by multiple wave-matter interactions occurring either at the ground level (in forest monitoring applications), or within the overlying vegetation volume (for soil motion and dielectric characterization ). These undesired components significantly affect the total SAR response and exhibit highly variable radiometric and polarimetric patterns, influenced by numerous factors, such as acquisition geometry, local topography, soil (and vegetation) humidity and roughness...

SAR tomography constitutes a solution for discriminating echos from a forest canopy and the ground [8] : 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 unrealistically large number of SAR acquisitions may be required to achieve sufficient separation 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 [8]: 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 (or overlying volume) scattering by generalizing the coherent ground filtering principle introduced in [9] to the case of SAR tomography.

This combined processing cancels out the undesired component with an isolation capability that does not depend on the vertical tomographic resolution, while yielding a refined image of the forest 3D reflectivity (or a cleaned ground response). 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] François Jouanne, Lea Pousse-Beltran, Marie-Pierre Doin, Pascale Bascou, Franck Thollard, et al.. Current tectonic deformation of the Sulaiman Range (Pakistan) with InSAR. Geophysical Journal International, 2025, 240 (3), pp.2060-2075.

[5] 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).

[6] 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)

[7] Danielle Lindsay, Roland Burgmann, Kathryn Materna, et al. Nine-Year L-band InSAR Time Series of Tectonic and Non-tectonic Surface Deformation in Northern California. ESS Open Archive . August 29, 2025.

[8] 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).

[9] 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)