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)

ESA’s BIOMASS, was launched on 29th of April 2025 to capture interferometric polarimetric and tomographic information, for the first time enabling the P-band spaceborne Pol-InSAR forest height inversion [1]. The spatial baseline distribution of BIOMASS is optimized for tropical forest height retrieval and tomographic profile reconstruction, while the satellite’s short revisit time of three days provides a unique opportunity to obtain high-quality forest height maps [2].

It is well known that forest height correlates with cross-track interferometric coherence [2]. However, obtaining high-quality P-band interferograms remains challenging due to ionospheric propagation effects, correct accounting of temporal decorrelation, dielectric change due to mainly rain events and correct approximation of vertical reflectivity profile [3-4]. This paper addresses these challenges step by step, leading to the implementation and validation of a forest height inversion algorithm. The first results demonstrate the immense potential of BIOMASS in delivering high-quality data and accurate forest height measurements.

The different model-based strategies of forest height from provided BIOMASS data will be discussed: single-, dual- and three-baseline forest height inversion scenarios. The single-baseline inversion approach is straightforward in interpretation and computationally efficient; however, it requires prior knowledge of the ground phase and assumptions regarding temporal decorrelation. In contrast, multi-baseline inversions (dual- and three-baseline) enable a more balanced estimation framework but may lead to ambiguous solutions and increased uncertainty in result interpretation [3].

This study will provide a full analysis of forest height inversion potential with BIOMASS, using its full dataset and addressing the inversion in physics-aware model-based framework. The primary focus is on assessing performance across the rainforests of the Amazon, Central Africa, and Southeast Asia. Results will be closely linked to the mission’s commissioning phase and critically validated using GEDI’s LiDAR-derived RH98 forest height data, ensuring the verification of BIOMASS-derived height products.

REFERENCES

[1] S. Quegan et al., The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space. Remote Sensing of Environment, 227, 44-60, 2019

[2] S. R. Cloude and K. P. Papathanassiou, "Polarimetric SAR interferometry," in IEEE Transactions on Geoscience and Remote Sensing, vol. 36, no. 5, pp. 1551-1565, Sept. 1998, doi: 10.1109/36.718859.

[3] R. Guliaev, J. Su Kim, M. Pardini and K. P. Papathanassiou, "On the Use of Tomographically Derived Reflectivity Profiles for Pol-InSAR Forest Height Inversion in the Context of the BIOMASS Mission," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-12, 2024,

[4] M. Pardini, M. Tello, V. Cazcarra-Bes, K. P. Papathanassiou and I. Hajnsek, "L- and P-Band 3-D SAR Reflectivity Profiles Versus Lidar Waveforms: The AfriSAR Case," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 10, pp. 3386-3401, Oct. 2018, doi: 10.1109/JSTARS.2018.2847033

Monitoring forest structure is a key objective of the recently launched ESA’s BIOMASS mission [1]. It operates at P-band (70 cm wavelength) to retrieve forest height, biomass, and disturbance at global scale. The long wavelength provides sensitivity to the full canopy depth, offering unique opportunities to characterize forest scattering mechanisms from canopy to ground. This study presents first analyses of forest structure mapping using early BIOMASS P-band fully polarimetric and interferometric data.

Natural forest scenes are complex radar scattering environments, where surface, dihedral, and volume contributions coexist within a single resolution cell. While polarimetric SAR (PolSAR) techniques can reveal scattering diversity, they cannot resolve its vertical distribution. Conversely, interferometric SAR (InSAR) provides height sensitivity but is influenced by temporal and environmental decorrelation. The combination of both by means of Pol-InSAR [2] techniques offers the potential to jointly exploit scattering diversity and height information for structural mapping.

To this end, this work proposes to first separate the fully polarimetric response into simpler scattering contributions by means of interferometry, i.e., ground and volume [3]. The ground response is then modelled as a mixture of elementary surface (Bragg) and dihedral (Fresnel) mechanisms, simplifying the interpretation of their polarimetric signatures [4]. Preliminary results using fully polarimetric COM-1 acquisitions already demonstrate the excellent radiometric and polarimetric quality of the BIOMASS data. The scattering entropy can be employed as a measure of polarimetric coherence and spatial variability. The proposed decomposition effectively distinguishes forest areas with open and closed canopies, highlighting the mission’s sensitivity to structural diversity even prior to full calibration.

These results contribute to the ESA’s BioTomEx project and support the development of BIOMASS-derived forest disturbance products. Data from the GABON-X campaign that will take place in November 2025 will help consolidating proposed Pol-InSAR approaches for forest structure monitoring by comparison with BIOMASS data.

[1] S. Quegan et al., “The European Space Agency BIOMASS mission: Measuring Forest above-ground biomass from space,” Rem. Sens. Environment, vol. 227, pp. 44-60, June 2019.

[2] K. P. Papathanassiou and S. R. Cloude, “Single-baseline polarimetric SAR interferometry,” IEEE Trans. Geosci. and Remote Sens., vol. 39, no. 11, pp. 2352-2363, Nov. 2001.

[3] Alonso-González, A., Papathanassiou, K. “Polarimetric Change Analysis in Forest using PolInSAR Ground and Volume separation techniques,” EUSAR 2021; 13th European Conference on Synthetic Aperture Radar, pp. 1-6. VDE, 2021.

[4] A. Freeman and S. L. Durden, “A three-component scattering model for polarimetric SAR data,” IEEE Trans. Geosci. and Remote Sens., vol. 36, no. 3, pp. 963-973, May 1998.

ESA BIOMASS mission, operating in orbit since April 2025, is designed to provide near global estimates of forest biomass, structure and change. In this study, we present a first evaluation of the ESA BIOMASS mission Level-1B In-Orbit Commissioning (IOC) data over Brazilian forests covering a wide range of biomass, from dense tropical forests to savannas. We compared BIOMASS amplitude data with independent reference data from the Brazilian National Forest Inventory (NFI) and INPE Airborne Laser Scanning (ALS) data. Furthermore, we superimposed BIOMASS data with a secondary forest age product, to determine if there was a relationship between BIOMASS signal and forest age. Although the L1B IOC data are not yet radiometrically or polarimetrically calibrated, the results show promising relationships between the BIOMASS amplitude and key forest structural parameters (aboveground biomass and canopy height) as well as forest age.

As expected, the strongest correlations with forest structure were observed at cross-polarizations (HV and VH). Moreover, the cross-polarized P-band signal exhibits a strong relationship with the estimated age of secondary forests. Finally, the uncalibrated BIOMASS data shows stronger correlations with forest structure and age than the L-band SAR data, from ALOS-2 PALSAR-2, highlighting an enhanced sensitivity of the long wavelength P-band signal with high biomass forests (>300 t/ha).

It is to expect that radiometric and polarimetric calibration as well as terrain normalization of the BIOMASS data will further improve the correlations with forest structural parameters. In addition, a significant improvement in sensitivity to high biomass forests is anticipated from the BIOMASS ground-notched data, which exclude ground contribution from the P-band signal. In summary, this study shows promising results from the early ESA BIOMASS L1B IOC amplitude data to advance global forest mapping from space and meet mission’s scientific requirements.

This work was conducted under ESA BIOMASS DISC as part of the BIOMASS In-Orbit Commissioning Programme.

Satellite imagery acquired in the last decade have substantially improved the knowledge of the spatial distribution of aboveground biomass (AGB) worldwide. Wall-to-wall maps of AGB impact downstream activities related to climate and vegetation modelling, national inventories and policy making. The start of operations of the BIOMASS satellite provides a novel data stream that in principle shall further improve the estimation of AGB from space. At the early stage of the mission, data from the Cal/Val phase are relevant to understand the contribution of BIOMASS to the quantification of AGB from space. Here, we intend to relate the Cal/Val datasets released by ESA to the set of predictors used by the Climate Change Initiative (CCI) Biomass retrieval algorithm, i.e., multi-temporal Sentinel-1 and ALOS-2 PALSAR-2 radar backscatter images and canopy height from the ICESat-2 LiDAR mission. Our scope is to assess similarities and differences between these data streams and understand the signatures the BIOMASS data. Ultimately, our goal is to assess how BIOMASS data can contribute to large-scale estimates of AGB within the context of the CCI Biomass framework. This work relies on the database of satellite observations gathered in CCI Biomass, which is updated regularly with the newest observations. A second objective of our study is to relate the BIOMASS data to CCI Biomass estimates of AGB. This is a purely explorative approach aimed at flagging potential caveats of the CCI Biomass dataset (e.g., due to lack of sensitivity of the C- and L-band predictors to biomass) and the BIOMASS data (e.g., due to ionosphere, geolocation accuracy, pre-processing etc.). This work is undertaken as part of ESA’s CCI Biomass project and EU’s NextGenCarbon project.