Conference Agenda

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Launched April 29, 2025, BIOMASS is ESA's seventh Earth Explorer, carrying the first spaceborne P-Band Synthetic Aperture Radar (SAR), fully polarimetric and with across-track interferometric capabilities. Its primary objective is to map biomass and forest height, as well as their changes over time. The onboard antenna is based on a large deployable 12 m reflector with an offset feed array. BIOMASS operates in a Stripmap mode with a swath illuminated by a single antenna beam. Global coverage is obtained by the interleaved acquisition of three complementary swaths. The mission will also feature an experimental tomographic phase during its first year of operations, to provide 3D mapping of forests.

In-Orbit Commissioning (IOC) Phase activities have been mostly dedicated to instrument calibration and validation, to meet required data quality. The peculiarity of the mission is such that presents several challenges. Examples are: the use of a dedicated transponder instead of passive corner reflectors, due to the medium resolution and low expected RCS; difficulty of relying on rainforest as a natural calibration site, due to high penetration into the vegetation; strong ionospheric effects and relevant RFI contamination.

In this presentation we discuss the activities performed by Aresys and Politecnico di Milano in the IOC framework, with perspective on their continuation during the rest of the mission:

- Radiometric stability based on the PSCAL technique [2]

- RFI characterization and mitigation [1]

- Antenna pattern and pointing verification using natural distributed targets

- Noise Equivalent Sigma Nought (NESZ) estimation from very low back-scatter areas

- Noise characterization from transition scenes

Preliminary to these activities, a dedicated assessment of Targets of Opportunity (ToO) was carried out to enable accurate Antenna pattern and pointing verification, as well as NESZ estimation. Different candidates, including deserts, oceans, Antarctica and rainforest were assessed in terms of In/Out Band Power Ratio, radiometric homogeneity, and temporal stability. Amazon rain forest was identified as the most reliable ToO for Antenna pattern and pointing verification due to its high spatial uniformity and stability, while Antarctica and Desert were identified as optimal for NESZ estimation. RFI activities have been mostly dedicated to tune the mitigation algorithm featured by the operational processor, as well as assessing RFI global presence and effects on polarimetry and interferometry. Rx-Only acquisitions over transition areas have been analyzed to characterize the noise level of sea/ice/land scenes. PSCAL activities are discussed in a companion presentation [3].

Results so fairly confirm the robustness of BIOMASS calibration strategies despite the challenges of P-band and the huge potential of the mission to deliver consistent global forest monitoring

References

[1] Franceschi, Niccolo, et al. "Operational RFI mitigation approach in Sentinel-1 IPF." EUSAR 2022; 14th European Conference on Synthetic Aperture Radar. VDE, 2022.

[2] D. D'Aria, A. Ferretti, A. Monti Guarnieri and S. Tebaldini, "SAR Calibration Aided by Permanent Scatterers," in IEEE Transactions on Geoscience and Remote Sensing, April 2010

[3] Manzoni, Marco and Banda, Francesco “Evaluation of BIOMASS Radiometric Stability using Permanent Scatterers: first results from the commissioning phase”, submitted to POLINSAR 2026

This work presents the results of the assessment of the radiometric stability of the BIOMASS mission, a key step in validating the performance of its P-band radar instrument. The evaluation of stability relies on the exploitation of Permanent Scatterers (PSs), which are assumed to be radiometrically stable over time, though their reflectivity varies spatially from scatterer to scatterer. In contrast, any residual radiometric gains are expected to remain spatially constant but vary temporally, making PSs an ideal reference for disentangling temporal instrument variations from scene-dependent effects.

The proposed approach involves a two-stage processing framework. In the first stage, PSs are detected using a stack of radar images, where the reliability of detection increases with the number of images. During the BIOMASS Commissioning Phase, however, the available acquisitions per site were limited to between 7 and 11 images, depending on location. Consequently, the initial part of this study focused on evaluating the probability of false alarms and missed detections as a function of the number of images and the Signal-to-Noise Ratio (SNR) of the PSs. Simulation results demonstrated that, even under these constraints, PSs can be detected with satisfactory reliability.

The second part of the analysis focused on evaluating the performance of the estimator for the residual radiometric gains. Since BIOMASS is the first mission operating at P-band, no prior information was available on the expected density of PSs within a scene. To address this uncertainty, extensive simulations were conducted by varying the number of PSs from as few as 10 to several hundred. The results indicated that the estimator remains robust even with a relatively small number of PSs, provided that the average SNR is sufficiently high to ensure stable estimates.

Following the satellite launch, the algorithm was tested on multiple sites worldwide, encompassing a wide range of topographies, vegetation types, levels of Radio Frequency Interference (RFI), and the presence or absence of urban areas. The analysis revealed that PSs are more abundant at P-band than initially expected, disproving earlier assumptions of their scarcity. The results on radiometric stability consistently confirmed excellent instrument performance, with observed variations remaining below 0.2 dB across all test sites. These findings validate both the robustness of the proposed PS-based method and the outstanding radiometric stability of the BIOMASS sensor.

Will follow soon !

The European Space Agency’s BIOMASS is the first spaceborne SAR sensor operating at P-band since April 2025. Due to the high sensitivity of P-band microwaves to Earth's ionosphere and its spatio-temporal changes, BIOMASS data where expected to reveal prominent ionospheric features more than any previous spaceborne SAR sensors. The total electron content (TEC), its spatial variation, and its vertical profile is characterized by exploring Faraday rotation (FR), azimuth spatial shifts and azimuths sub-band shift estimates from BIOMASS data.

The global ionospheric map (GIM) is generated from the ionospheric delay measurements acquired by selected ground stations from the GNSS satellite signals. It is provided in form of global maps of every second hour with 2.5° latitude and 5° longitude grid. Despite its low resolution, its global coverage and long time series reaching almost 30 years of archive make it one of the fundamental data for the ionospheric analysis.

In this study the TEC levels from GIM are validated using the Earth’s geomagnetic field and the FR measurements of BIOMASS. First the effect of the lower orbit of the sun-synchronous SAR compared to the whole-integrated TEC value from GIM are evaluated in terms of local time of day and seasons (for now summer and autumn only). The spectral analysis demonstrates the power of the ionospheric spatial variations finer than the resolution of GIM.

Despite the fact that BIOMASS data are limited in local time (6:00 and 18:00), this study provides a first global validation of TEC against non-GNSS measurement.

Early analysis of BIOMASS interferometric data acquired during the commissioning phase at Boreal and Antarctic latitudes revealed the presence of strong ionospheric disturbances that cause severe coherence losses. This phenomenon was investigated by studying the variation of the interferometric phase with respect to the squint angle. Results are consistent with the assumption of a complex vertical structure of the ionospheric phase, characterized by fast azimuth variations occurring at Km or sub-Km scale at multiple layers between the ground and the satellite orbit. To the best of our knowledge this phenomenon has never been considered in Synthetic Aperture Radar literature. This paper is therefore intended to present and discuss the results of our investigation on suitable correction methods needed to guarantee the generation of accurate geophysical products from BIOMASS interferometric data. The starting point of our analysis is a multi-layered model of the ionospheric phase as observed by varying the Radar Line of Sight (LoS) across different squint angles. The model is analyzed from a theoretical perspective to discuss the sensitivity of BIOMASS to ionospheric layering. Next, an inversion procedure is presented to estimate the ionospheric phase at an arbitrary number of layers directly from the complex coherence. The third element of our research consists in the formulation of correction methods to restore data quality given the estimated single- or multi-layer ionospheric phase. All aspects of the research are consistently supported by analysis of BIOMASS interferometric data acquired at different latitudes and affected by mild to very strong ionospheric disturbances. Results demonstrate that the methodologies presented in this paper are capable of compensating for the observed ionospheric effects in all cases analyzed so far

With BIOMASS, it was desired to exploit the capability of low-frequency

Synthetic Aperture Radar (SAR) to penetrate into volumetric targets such as

dense forest, snow and ice. With its long wavelength (approx 69 cm) and quad-pol

operation, it has proven to, for example, detect and separate targets below tree canopies (e.g., the ground and tree trunks) while maintaining the interferometric

coherence, facilitating the implementation of interferometric techniques such as

SAR tomography.

One of the main challenges associated with low-frequency radar is the distortion introduced by the ionosphere, which is seen in both single acquisitions and

interferometric pairs. The effects can be grouped mainly into two categories:

Faraday rotation affecting the polarimetry, and phase errors that introduce

defocusing of single images, and azimuth shifts (misregistration) and residual phase screens between interferometric pairs. Both types of errors are proportional to the ionospheric free electron content and the wavelength. Small-scale ionospheric irregularities introduce high-frequency Faraday rotation and phase

errors, usually referred to as scintillation. It has been proven that it is possible,

for example, to use the polarimetry to estimate Faraday rotation and correct

for phase errors [1] (this approach has proven to be effective at high latitudes).

However, the Faraday rotation sensitivity to small irregularities decreases

rapidly towards mid- and low-latitudes. Then, the problem can be addressed

by examining the phase signatures directly. Techniques such as autofocus help

correct the impact in single images. In interferometric pairs or stacks, the

differential ionospheric phase screen can be resolved by exploiting the induced

coregistration errors in azimuth. The two mentioned approaches are based on

cross-correlation between image pairs, for which the contrast and content of the

scenes highly compromise the performance.

In this work, we exploit the fact that the phase error is common to all

polarimetric channels to add redundancy to the observations. This allows us

to reduce the uncertainty in retrieving the ionospheric distortion map when

a least-mean squares inversion scheme is implemented. Similarly, we also use

the multiple passes in the interferometric stacks to attempt to improve the

performance of both the single-pass and interferometric corrections, as it was

proposed in [2]. Besides regaining coherence and reducing interferometric phase

errors, accurate scintillation maps will be valuable for studies that require high-

resolution ionospheric imaging.

References

[1] Kim, J. S., Papathanassiou, K. P., Scheiber, R., and Quegan, S. (2015).

Correcting distortion of polarimetric SAR data induced by ionospheric scin-

tillation, IEEE Transactions on Geoscience and Remote Sensing, 53(12),

6319-6335.

[2] F. Betancourt-Payan, M. Rodriguez-Cassola, P. Prats-Iraola and G. Krieger,

Towards an Interferometric Autofocus for the Estimation of Ionospheric Sig-

natures in Biomass, EUSAR 2024; 15th European Conference on Synthetic

Aperture Radar, Munich, Germany, 2024, pp. 1227-1231

Monitoring the 3D structure and biomass of tropical forests, particularly the Amazon, is essential for global carbon accounting and climate modelling, yet remains a principal challenge for remote sensing. Current spaceborne systems, such as the C-band (5.6 cm) radar on Sentinel-1, are hampered by rapid temporal decorrelation; the short wavelengths scatter from the dynamic canopy surface (leaves and small branches), rendering interferometric analysis ineffective over dense vegetation.

Here we show that the BIOMASS mission's P-band (69 cm) SAR overcomes this fundamental limitation, providing transformative coherence over the Amazon rainforest. We observed a mean interferometric coherence of 0.85 (3-day repeat) for BIOMASS, a 3-fold improvement over Sentinel-1's 0.28 (12-day repeat).

Most strikingly, 93.5% of P-band pixels maintain coherence above the 0.7 threshold required for robust interferometric applications, compared to only 0.8% for C-band. This 117-fold increase in viable data stems from P-band's ability to penetrate the volatile upper canopy and interact coherently with the stable structural framework of the forest—the trunks and ground.

This unprecedented coherence stability unlocks capabilities previously impossible over tropical forests, including the retrieval of forest height via Polarimetric InSAR and the mapping of 3D forest structure through SAR tomography. As the only P-band SAR in space, BIOMASS provides an irreplaceable dataset, filling a critical observational gap for climate science and the implementation of international climate agreements.

The authors acknowledge the support in part from the Center National d'Etudes Spatiales/Terre, Ocean, Surfaces Continentales, Atmosphere (CNES/TOSCA - projects GEDITOMO3D, BayesTomo, BIOMALT, and TS4Biomass). We thank ESA for providing BIOMASS data through the CAL/VAL activities in the project GEDI-TOMO-PP0104473 and AQUABIO_PP0106230.

We investigated P-band SAR interferometry using early interferometric acquisitions of the BIOMASS mission obtained through our Open Cal/Val project. In an initial assessment of the data, we checked the orbit data, noise equivalent sigma zero, spatial resolution, range and azimuth spectra, the presence of radio frequency interference (RFI) and the co-registration accuracy of the STA product. Special care was then given to the identification and mitigation of ionospheric effects. Both split-beam interferograms and azimuth offset refinement fields determined in the co-registration of interferometric image pairs clearly indicated the presence of ionospheric effects. Gradients in the ionospheric path delay cause azimuth positional offsets. Not considering these in the co-registration results in a significant reduction of the interferometric coherence. In our processing we considered the orbit and topography based geometry as well as an offset field refinement determined using cross-correlation based matching techniques. For the correction of the ionospheric path delay phase, we investigated using a split-spectrum technique [1]. Alternatively, the low frequency part of the ionospheric, tropospheric and residual orbital phase was estimated using spatial filtering techniques.

Both the backscatter and the coherence very clearly separate forest and open water areas in the Amazonas area. For short spatial (< 50m) and temporal (3 days) intervals the coherence levels over the tropical forest were generally very high. Over open water very low coherence levels were observed. This is expected, but it also serves as a confirmation of a “clean signal” without significant azimuth ambiguities. In some of the forest areas we observed reduced coherence levels, possibly related to precipitation. This reduction was the smallest at HH polarization, larger at VV polarization and even larger at cross polarization. Our interpretation is that the precipitation slightly changed the direct backscattering as well as the propagation parameters in the vegetation cover. Localized backscatter and phase variations observed might relate to changes in the water level below the canopy.

Hopefully, we will soon be able to also investigate pairs with longer spatial baselines to investigate the dependence of the coherence on the baseline and forest parameters. Furthermore, we look forward to study the P-band coherence in other geographic and thematic areas.

We will present the processing methods applied and discuss the results obtained.

[1] U. Wegmüller, C. Werner, O. Frey, and C. Magnard, “Estimation and Compensation of the Ionospheric Path Delay Phase in PALSAR-3 and NISAR-L Interferograms,” Atmosphere, vol. 15, no. 6, p. 632, May 2024, doi: 10.3390/atmos15060632.

Phase triplets are an interferometric observable derived by summing the interferometric phases around closed loops formed by three SAR acquisitions [1]. In the absence of perturbing effects, the closure sum should be zero. Deviations from this condition, referred to as phase non-closure, are primarily attributed to multi-looking effects, which occur when two or more scattering populations with distinct phase behaviours coexist within a single resolution cell [2]. Previous studies have demonstrated the potential of phase triplets to monitor temporal surface and vegetation changes, including soil moisture variations [1][2], vegetation growth [3], and fluctuations in vegetation water content [2]. However, the physical mechanisms underlying phase non-closure, and in particular its dependence on wave polarization, remain insufficiently understood.

This study aims to advance our understanding of surface and vegetation mechanisms along time by investigating the polarimetric dependence of phase non-closure. To this end, early data from the ESA BIOMASS P-band SAR mission, acquired during the commissioning phase, are employed. With its fully polarimetric capabilities and long wavelength (70 cm), BIOMASS provides sensitivity to scattering processes throughout the entire canopy, from top to bottom, offering a unique opportunity to study the temporal behaviour of forest backscatter under dense vegetation 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 influences both the magnitude and spatial distribution of phase non-closure. The analysis focuses on small-baseline interferometric pairs to minimize geometric decorrelation, thereby isolating the contributions of volumetric and polarimetric effects. In addition, complementary airborne LiDAR data are used to discriminate forested from non-forested areas and 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. Understanding its polarimetric sensitivity 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.

The closure phase, constructed by a circular summation of three interferometric phases, each obtained from multilooking a SAR interferogram, consists of a geophysical component and phase noise, often exhibits non-zero values. These non-conservative closure phases challenge the validity of the implicit phase consistency assumption in SAR interferometry. This assumption relies on a geometric interpretation of the interferometric phase, where the expected values of the three interferometric phases are redundant, given that their sum, the closure phase, is equal to zero. Quantifying the statistical significance of non-zero closure phases across different land cover types and examining their correlation with geophysical variations are essential for assessing their effect on interferometric phases and, consequently, improving the accuracy of InSAR applications, such as deformation analysis.

We conducted an extensive spatiotemporal statistical analysis using Sentinel-1 acquisitions over the Iberian Peninsula, a region encompasses diverse land cover types and spans multiple climate zones. The results indicate that the non-zero closure phases are statistically significant. Our case study over agricultural fields revealed a clear geophysical signature strongly associated with vegetation phenology[1]. Two primary mechanisms have been proposed to explain the observed signature: variations in dielectric properties and the line-of-sight motion induced by vegetation growth. This C-band study has highlighted the potential of using closure phase observations to detect variations in vegetation water content. However, Sentinel-1 observations are limited in their ability to exploit the physical processes underlying the closure phase signals. The temporal sparsity of orbital passes and the lack of vertical resolution of contributing scatterers restrict the extent to which closure phases can be investigated and exploited.

Analyzing closure phase observations derived from Biomass P-band tomographic data will enhance the understanding of closure phase signatures, as these observations provide different temporal baselines, greater penetration depth, and the ability to resolve the vertical distribution of scatterers. This study aims to statistically quantify P-band closure phase signatures across diverse land cover types, including bare soil, croplands, forests, and glaciers, using multilooked interferograms. We begin by estimating the standard deviation of the closure phases under the null hypothesis that they originate solely from phase noise, followed by an assessment of the statistical significance of geophysical closure phases across sub-regions categorized by different land cover and climate conditions. Subsequently, we evaluate the mean values and percentiles of closure phases to investigate their spatiotemporal variability. When possible, we compare these results with Sentinel-1, depending on data availability and acquisition location.

Our statistical analysis results obtained from multi-sensor observations will enhance the interpretation of interferometric phase signals and provide valuable insights for explicitly accounting for closure phases in SAR stack processing. These findings will also support the development of novel techniques for bio-geophysical parameter retrieval, complementing traditional radar observables such as backscatter and interferometric coherence.

References

[1] Yan Yuan, Marcel Kleinherenbrink, and Paco López-Dekker. On crop growth and insar closure phases. IEEE Transactions on Geoscience and Remote Sensing, 2024.

BIOMASS is collecting for the first time quad-pol tomographic SAR (TomoSAR) data from space for the reconstruction of 3D radar reflectivity at P-band, providing the unique opportunity to characterize 3D forest structure at global scale. The information content in terms of forest structure of BIOMASS 3D reflectivity reconstructions is determined by two critical aspects. The first one is the low range resolution caused by the 6 MHz bandwidth, which in turn limits the achievable TomoSAR resolution. The second one is the 3-day repeat-pass implementation which induces temporal decorrelation e.g. from either scatterer movements inside the resolution cells due to wind or dielectric changes from one satellite pass to the next. Larger values of temporal decorrelation can decrease the vertical separation capability and the radiometric accuracy of the reconstruction.

The objective of this work is to evaluate BIOMASS TomoSAR reconstructions to obtain a first direct understanding of their potentials for forest structure mapping. Preliminary conclusions are expected on the impact of the reduced vertical resolution and the robustness to reflectivity variations induced by dielectric changes from pass to pass. Acquisitions in the commissioning phase and in the first tomographic cycles are used over a variety of forest sites with different structural characteristic and subject to different levels of dielectric changes in time. If possible, besides lidar data, first results from the BIOMASS validation campaign AfriSAR 3 / GABONX 2025 in Gabon will support the analysis. In November 2025, DLR’s airborne platform is acquiring interferometric / TomoSAR measurements in correspondence of a few BIOMASS passes over the La Lope National Park, providing a unique opportunity for comparing BIOMASS TomoSAR reconstructions with high-resolution temporal decorrelation-free ones.

BIOMASS [1] is ESA’s seventh Earth Explorer, successfully launched on April 29, 2025. The first P-band Synthetic Aperture Radar (SAR) in space offers unprecedented scientific opportunities, thanks to the long wavelength, full polarimetric capabilities and orbits specifically designed for repeat pass interferometry (InSAR) and tomography (TomoSAR). Its primary objective is mapping forest biomass and height and their changes over time. Secondary objectives are targeted at studying ionosphere, deserts and ice, as well as retrieving Digital Terrain Model (DTM) under vegetation [1].

Activities from launch to date have been dedicated mainly to In-Orbit Commissioning (IOC) phase. Main focus has been on instrument and system calibration, though several other activities have been carried out in the framework of ongoing projects (e.g., BIOTOMEX). In the latter two phases of IOC (COM4/5) data have been acquired in tomographic configuration, i.e., seven acquisitions with baseline spacing corresponding to 15% of the critical baseline, with a theoretical TomoSAR vertical resolution of about 23 m at the Equator, offering a preliminary opportunity to assess InSAR and TomoSAR performance. An additional opportunity is offered by COM2, with drifting orbits designed to characterize antenna pattern over BIOMASS transponder, which allow TomoSAR at higher latitudes with performance comparable to that of COM 4/5 at the Equator. After IOC the mission transitions to TOM phase, with full tomographic coverage in three different swaths.

In this contribution we present first BIOMASS TomoSAR results obtained so far, with preliminary assessment on IOC acquisitions both at equatorial and higher latitudes.

References

[1] Quegan, Shaun, et al. "The European Space Agency BIOMASS mission: Measuring Forest above-ground biomass from space." Remote Sensing of Environment (2019)

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.

The ESA BIOMASS mission, successfully launched on 29 April 2025, is the first spaceborne P-band synthetic aperture radar (SAR) dedicated to continental-scale mapping of forest biomass, height, and disturbance. The first acquired datasets demonstrate excellent radiometric and interferometric quality, confirming the instrument’s unprecedented sensitivity to forest above-ground biomass density (AGBD) and vertical structure.

In this contribution, we present an intercomparison of AGBD maps over the Amazon forest, generated using an updated variant of the global BIOMASS AGBD mapping algorithm. As the primary AGBD predictors, we use three distinct datasets acquired during the commissioning phase:

(i) Level-1B backscatter coefficients,

(ii) Level-2A ground-cancelled backscatter, and

(iii) interferometric phase height at P-band.

As an independent reference, we employ X-band phase-centre height-based AGBD estimates, previously shown to exhibit a sensitivity to biomass comparable to that of airborne lidar.

We evaluate how the inclusion of Level-2A ground-cancellation processing and P-band interferometric phase height enhances the sensitivity of BIOMASS observables to AGBD and improves retrieval robustness relative to conventional backscatter-only approaches. Furthermore, we assess the accuracy and stability of biomass estimates derived directly from the P-band phase-centre height.

These preliminary results provide one of the first demonstrations of the BIOMASS mission’s capability for large-scale AGBD estimation, paving the way for robust, continental-scale biomass mapping and monitoring once the mission enters its operational phase in 2026.

This contribution describes the principles and implementation of a Level 3 (L3) product processor for ESA’s BIOMASS mission. This mission aims at reducing the uncertainty in the worldwide spatial distribution and dynamics of forest biomass, and will achieve this objective using a P-band SAR, providing global maps of forest biomass stocks, forest disturbance and growth [1].

In its dual-baseline interferometric operating phase, the BIOMASS mission will provide at each Global Cycle (GC), i.e. approximately every 7 months, a set of world-wide Level 2b (L2b) products consisting of maps of the Above Ground Biomass (AGB), Forest Height (FH) and Forest Disturbance (FD).

The L2b AGB and FH estimation processes being led independently, and at rather local spatial and temporal scales, it is very likely that output maps show some variability, related to the intrinsic uncertainty of L2b estimators, but also to potential exceptionally unfavorable factors, such as severe meteorological conditions (rain, wind), problematic propagation effects, or non-optimal baseline configuration.

The objective of L3 processing is to improve the consistency of L2b maps, by enforcing geophysical constraints through an iterative statistical regularization process. Three kinds of constraints are considered:

- spatial consistency is derived by comparing the spatial statistics of each product with autocorrelation function features computed over a wider neighborhood. Significantly different behaviors are to be penalized in order to guarantee spatially homogeneous estimates over undisturbed areas.

- temporal consistency is evaluated by observing estimates performed for different GCs, and by limiting the positive change rate (gain velocity) of AGB and FH parameters. One may note maximal gain rates are fixed according the considered geographical location and to the observed type of forest. Unlike gains, AGB and FH losses are not constrained, as they can happen in a very abrupt way.

- allometric consistency allows to mutually regularize AGB and FH fields using local, and forest-class specific relationships [2]. Allometric equations, as well as their associated dispersion, are directly estimated from L2b maps, at each GC, for each of the forest class provided by a land-cover map, and at the scale of an L2b tile, i.e. over regions of about 100 km x 100 km at the equator.

The regularization process is implemented under the form a Maximum Likelihood optimization, aiming to determine AGB and FH space-time maps which maximize a compound likelihood function, composed of losses terms related to L2b, spatial, temporal and allometric statistics [3].

A log-normal framework is adopted, which allows to represent this optimization as a very large, but sparse, system of linear equations.

At each new GC, the optimization process is run considering the freshly estimated L2b parameters in addition to the previously regularized fields. As a consequence, L3 estimate maps are expected to change significantly during the lifetime of the mission, with a quality level that increases with time.

The L3 processor delivers, at each GC and for each processed tile, L3 AGB and FH maps together with their level of confidence [4] and descriptors of their temporal evolution, auxiliary information related to the considered land-cover maps, and parameters of the BIOMASS allometric relationships at all dates and for all forest types.

An example of regularization, built from realistic AGB and FH maps estimated over Gabon, is provided and illustrates the capabilities of the processor to actually improve the consistency of L2b product maps.

[1] Quegan, S. et al. “The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space”, Remote Sensing of Environment, Volume 227, 2019, Pages 44-60, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2019.03.032.

[2] Chave, J. et al. “Improved allometric models to estimate the aboveground biomass of tropical trees”, Global change biology, 2014, 20(10), pp.3177-3190.

[3] Tarantola, A. (2005) Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM: Society for Industrial and Applied Mathematics, 342 p. https://doi.org/10.1137/1.9780898717921

[4] Keener, R.W., 2010. Theoretical statistics: Topics for a core course. Springer Science & Business Media.

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The Product Algorithm Laboratory (PAL) for Biomass mission represents a key asset of the European Space Agency’s Multi-Mission Algorithm and Analysis Platform (ESA MAAP), a cloud-based platform that enables the processing and analysis of Earth observation data across multiple missions.

The PAL is built on CGI’s Insula platform, an advanced environment for Earth Observation (EO) data analytics that combines cutting-edge, production-ready technologies to deliver a flexible and interoperable cloud framework. This infrastructure underpins the ESA MAAP environment, currently supporting the Biomass and EarthCARE missions, while ensuring security, scalability, and efficient orchestration of processing services for a seamless and reliable user experience.

The Biomass MAAP enables scientists and developers to experiment with prototype algorithms, process large-scale mission data, and perform advanced analytical workflows in a shared, scalable, and reproducible infrastructure.

The PAL integrates direct access to the Biomass mission products, auxiliary datasets, and related validation data and provides scalable computing resources. It allows researchers to deploy and execute custom processing chains without managing underlying infrastructure. This capability fosters an iterative approach to algorithm refinement, from prototyping to pre-operational qualification.

Within this environment, researchers can perform comparative studies across multiple algorithm versions or missions, conduct sensitivity analyses to assess the impact of input parameters and assumptions, and perform advanced visualizations and analytics to better interpret data and algorithm behavior.

These capabilities promote knowledge exchange, algorithm harmonization and interoperability across missions, supporting the development of consistent and scientifically robust products within the ESA MAAP where tools, datasets, and users interact efficiently to empower EO data analysis.

By combining collaboration and experimentation, the PAL creates a framework that bridges scientific research and operational implementation, accelerating EO algorithm innovation and ensuring readiness for operational exploitation within ESA’s upcoming missions.

Importantly, the Biomass PAL is fully operational and open to researchers and developers, providing direct access to mission data and computing resources within a ready-to-use environment.

The UAVSAR AfriSAR campaigns of 2016 and 2024 represent major milestones in the joint NASA–ESA–DLR effort to advance calibration and validation (Cal/Val) of the NISAR and BIOMASS missions, while laying the foundation for future radar and lidar missions such as NASA’s Surface Topography and Vegetation (STV). These international collaborations demonstrate how coordinated airborne radar and lidar acquisitions can strengthen cross-agency scientific and technical cooperation, fostering new applications of 3D remote sensing for ecosystem, geomorphological , and hydrological research.

The 2016 AfriSAR campaign in Gabon established a benchmark by acquiring extensive multi-baseline PolInSAR and TomoSAR datasets, complemented by airborne and spaceborne lidar. These data enabled groundbreaking studies—over 100 peer-reviewed publications—on tropical forest structure, biomass, and sub-canopy topography. The 2024 AfriSAR-2 campaign, extending coverage across Ghana, Cameroon, Gabon, Democratic Republic of Congo and the Republic of Congo, builds on this success to refine TomoSAR acquisitions and improving retrieval algorithms for L-band (NISAR) and P-band (BIOMASS) measurements across a wider diversity of landscapes. These datasets directly support the ESA BIOMASS Cal/Val project PP0104629.

To meet the growing analytical complexity of these campaigns, new tools such as Kapok (for multi-baseline PolInSAR and TomoSAR analysis) and CAPON (for adaptive tomographic focusing) are being developed to enhance vertical resolution and structural accuracy. These advances help bridge the algorithmic maturity gap between radar and lidar approaches and contribute directly to the scientific and technological objectives of STV, envisioned as a unified mission for global mapping of surface topography and vegetation structure.

Looking ahead, the upcoming TropiSAR 2026 campaign in Peru and Colombia will expand these Cal/Val activities to the Amazon and Chocó-Darien-central America region, providing cross-continental datasets for BIOMASS, NISAR and STV. This effort reinforces the growing NASA–ESA collaboration in algorithm development, data sharing, and applied science. The presentation will survey the datasets acquired during these campaigns, assess current processing and algorithmic capabilities, and outline future development needs—particularly in TomoSAR processing, canopy-ground separation, and hydrologic coupling—to fully realize the potential of radar missions for global forest and water resource monitoring.

Development and use of low frequency (VHF to UHF) imaging radars has increased in recent years, driven by the presence of flagship scientific programs at European level, such as the BIOMASS mission aiming to map forest height and above ground biomass globally, or by various scientific applications requiring solving FOPEN (Foliage Penetration) issues.

More particularly linked to the BIOMASS mission and to support calibration/validation activities in the tomography phase, a new TropiSAR airborne campaign will be conducted by ONERA on 2027. Objective will be to map and characterize the dense tropical forest cover and the underlying surface using tomoSAR mode at low frequency.

SETHI low-frequency SAR sensors are particularly well-adapted for such a mission where a high performance is required for SAR imaging, repeat-pass interferometric and tomographic measurements.

The proposed campaign will fly again over dense French Guyana tropical forest (Paracou, Nouragues and Rochambeau areas) with P-band, L-band and X-band SAR sensors. Possibility to fly simultaneously multi-band SAR sensors is also of main interest for this campaign: We can then compare multi-band results on same area, with same conditions (weather, vegetation state).

This new campaign will benefit our latest developments in softwares to exploit scientific data using PolinSAR and tomography technics to retrieve information on forest height, density and potentially ground topography.

We will expose in this contribution the campaign plan for TropiSAR-2 experiment, including schedule and tracks selection.

* Land vegetation is a large carbon store and represents opportunities to sequester additional carbon. While many Earth Observation missions aim to estimate forest biomass from space, their calibration and validation is critical. Ultimately trust in biomass maps requires accurate ground data. Supporting ground measurements and the people who make them is thus mission-critical for mapping and tracking Earth’s forest carbon. Building on decades of work from the global research community with a strong representation of partners from the Global South, the GEO-TREES initiative funds high quality ground data from a global network of reference sites, and to make these data openly accessible. 

* In this contribution, we report on the progress in community building, data acquisition, processing and delivery at over 40 biomass reference measurement sites. For each biomass reference measurement site, data acquired by the consortium partners includes plot inventory measurements at ≥10 hectares of forest, aerial laser scanning (ALS) coverage over ≥1000 ha of forests, and terrestrial laser scanning of the forest for ≥3 hectares.  

* We intend to provide the following data: (1) a 0.25-ha resolution aboveground biomass density (AGBD, Mg/ha) estimate for each tree inventory subplot, together with a variance estimate; (2) a 0.25-ha resolution canopy height (m) estimate for each tree inventory subplot, together with a variance estimate; (3) a 0.25-ha map of AGBD inferred from ALS and plot data, together with a pixelwise variance estimate; (4) a 0.25-ha map of canopy height inferred from ALS, together with a pixel-wise variance estimate; (5) a 0.25-ha resolution aboveground biomass density (AGBD, Mg/ha) estimate for subplot scanned with TLS, together with a variance estimate; (6) ancillary data for each site. We detail how plot-level and ALS data is processed to account for uncertainty and possible bias, based on open-access pipelines that are both reproducible and that can be used by the broader GEO-TREES community, using the ESA MAAP. When ready, the data will be accessible on the GEO-TREES data portal. 

* We emphasize the importance of involving research scientists associated with the sites in product validation plans. Not only do they provide essential high-quality data, they also offer invaluable insights about the peculiarities of the study sites which a mission validation plan would ignore at its peril. The establishment of GEO-TREES, a coordinated network of validation sites, is crucial for the success of biomass missions. In the future, it could also prove useful for the validation of other Earth observation missions aimed at quantifying forest-related geophysical measurements. 

Accurate estimation of forest carbon stocks is critical for monitoring ecosystem dynamics and assessing climate mitigation strategies. Terrestrial Laser Scanning (TLS) is a powerful tool for quantifying forest structure and aboveground biomass (AGB) with unprecedented detail. TLS point clouds can be used to produce individual-tree metrics (e.g. stem diameter, height) and quantitative structure models (QSM) to estimate total tree volumes. Local allometric models can then be parameterized to predict tree volume and biomass from diameter and height. However, there are still uncertainties in TLS-based biomass estimates, which propagate through successive analytical stages, ultimately affecting carbon stock estimates while scaling them up from tree to plot level. The accurate estimation of carbon stocks is a challenge due to the use of generic and regionally mismatched allometric models, which potentially have high uncertainty (~40%). The uncertainty arises from field measurement errors, structural metrics errors, and statistical model uncertainties. Therefore, the propagation of uncertainty on the biomass estimates travels at different levels.

GEO-TREES is an initiative to provide the calibration and validation data for the satellite biomass products. This study investigates the propagation of TLS measurement errors into carbon stock assessments across a subset of GEO-TREES reference plots (Barro Colorado Island (Panama), Pasoh (Malaysia), Amacayacu (Colombia), and Mpala (kenya)), representative of a range of tropical forest types.

We quantify sources of uncertainty arising from the TLS data acquisition and post-processing (Segmentation of individual trees and QSM-fitting) to the allometric model parametrization and evaluate their cumulative impact on AGB and carbon estimates. Point cloud processing, tree segmentation, and Quantitative Structure Model (QSM) reconstruction can further contribute to structural inaccuracies, particularly in complex or overlapping canopies. Subsequent derivation of individual tree metrics (e.g., stem diameter, height, branch volume) carries both measurement and model-fitting uncertainties. These propagate into the estimation of tree volume and biomass through the use of volumetric or allometric models, respectively. By employing error-propagation frameworks (Monte Carlo propagation), we identify the dominant contributors, such as the QSMs model fitting error (which can systematically overestimate small branches), to total uncertainty under varying forest conditions. The study further proposes a standardized protocol for quantifying uncertainty and collecting data for TLS-based biomass estimation within the GEO-TREES network. Our findings offer critical insights for harmonizing TLS methodologies and enhancing the reliability of ground-based carbon reference data across a wide range of tree sizes for the ESA BIOMASS mission.

Conventional field census measurements, such as diameter at breast height (DBH) or height, capture only limited aspects of three-dimensional distributions in forest structure. These measurements are often converted to aboveground biomass (AGB) estimates using allometric models. AGB estimates through allometric models are often considered as ground truth for the calibration and validation of spaceborne remote sensing products. These tree size-to-mass allometric models are mostly built on a selected sample of harvested biomass data, but are then often applied to trees that fall far outside the size or ecosystem range of the model calibration data. This can result in potential errors in downstream AGB products from satellite data.

Three-dimensional measurements from terrestrial laser scanning (TLS) have demonstrated that they can overcome the typical limitations of current allometric models and capture the spatial distribution of forest biomass. TLS measurements are increasingly becoming more routine, and GEO-TREES is an example of an initiative that builds on and complements existing long-term ecological plot networks by integrating TLS, airborne laser scanning, and forest inventory census to support the upscaling of aboveground biomass using satellite remote sensing.

Using 3D TLS data collected over a range of forest ecosystems, we illustrate the potential impact of the current issue of conventional allometric models. In our case study of Wytham Woods (UK), we demonstrated using TLS that its AGB is 1.77 times more than current allometric model estimates. We will present two solutions to this problem: (a) TLS can be used to estimate the volume of an individual tree and the entire stand in 3D directly. These volume estimates can be converted to AGB using wood density values. This approach also offers full traceability of the AGB of each tree; (b) TLS can be used to generate 3D tree models across the full size range of trees, which can then be used to create new allometric models that do not need to be extrapolated out of sample. We will further illustrate this solution by a recently constructed a new allometric model using TLS for Eucalyptus tereticornis, the dominant species at EucFACE, an ecosystem-scale mature forest free-air CO2 enrichment (FACE) experiment in Australia.

In both solutions (direct or indirect through new allometric models), TLS is essentially used to virtually harvest trees. Whereas the first approach can provide a deeper understanding of the AGB of all trees in a forest stand, it requires significantly more time to collect and process the data. The construction of new allometric models using TLS provides a practical way forward to improve estimates of AGB for calibration and validation of spaceborne AGB estimates using satellites such as ESA BIOMASS.

This work is dedicated to a starting Biomass Cal/Val project focused on temporal variability of P-band backscatter. In spite of an important literature on the subject, a comprehensive characterization of temporal variability remains challenging, especially from few observations (as for Biomass mission revisit) and when changes magnitude due to forest growth or loss can be easily confused with vegetation or soil moisture variations.

To address this question, the originality of our approach relies on the use of in-situ radar and flux data, respectively from the TropiScat-2 experiment and the Guyaflux tower located in the Paracou research area (French Guiana). The former consists in multifrequency (P, L and C bands) and quasi continuous (every 15 min) radar acquisitions from the top of the Guyaflux tower (ca 55m high), which enables to mimic satellite based acquisitions but with a much higher revisit.

The Guyaflux tower (labelled by ICOS as GF-Guy) has been generating meteorological and CO2, H2O and energy fluxes data since 2003, in order to determine the drivers of ecosystem CO2 source or sink strengths and evapotranspiration.

The TropiScat-2 instrumentation has been operating since 2018, making possible the development of data-driven and semi-empirical models of the radar backscatter variability with respect to a rather diverse range of meteorological conditions (especially for what concerns the intensity of dry and rainy periods in this tropical environment). These time-series models are mainly dedicated to backscattering intensity and temporal decorrelation, which can both serve to parameterize retrieval methods dedicated to the forest (dry) biomass and height estimation, contributing thereby to Biomass external calibration activities for this type of forest. Several examples of parameterization based on such time-series modeling will be presented, whether with fast and simple analytical formulations or with more complex microwave interaction models such as MIPERS-4D (a fully coherent model based on 3D forest representation).

For what concerns validation, the main objective is to compare the Net Primary Production (NPP) derived from the in-situ flux measurements with the forest biomass change estimates from the L3 products. Several observation periods will be tackled as the products delivery will follow, from which we should be able to gain in spatial resolution and better fit the Guyaflux footprint. This task will be also supported by the cross analysis between temporal variations of BIOMASS higher products (esp. L1) and TropiScat-2 radar data, together with the analysis of meteorological conditions and the closest in-situ forest biomass and height estimates. Given the first product delivery for French Guiana in 2027, our methodology will will be mainly illustrated with Sentinel-1, ALOS Scansar and simulated P-band data.

The ESA BIOMASS mission, recently launched as the first spaceborne P-band synthetic aperture radar (SAR), aims to generate global maps of forest aboveground biomass (AGB) and improve our understanding of forest contributions to the global carbon cycle. Despite its potential, the influence of canopy structural variability on P-band radar backscatter remains insufficiently understood, limiting the physical interpretability of BIOMASS observations.

In this study, we assess the sensitivity of P-band radar backscatter to forest canopy structure using the Michigan Microwave Canopy Scattering (MIMICS) model parameterised with terrestrial laser scanning (TLS) data. Our initial analysis focuses on four savanna woodland plots in Bicuar National Park, Angola, and one tropical rainforest plot in Lopé National Park, Gabon. Individual trees were extracted from the TLS data and reconstructed into quantitative structural models (QSMs) to derive detailed branch-level architectural parameters, which were then used to parameterise MIMICS simulations across all radar polarisations and incidence angles consistent with the BIOMASS observation geometry.

To investigate the relationship between canopy structure and P-band backscatter, we computed integrated metrics capturing fundamental physical properties relevant to electromagnetic interactions. Results indicate that tree size and structural complexity are primary factors of backscatter magnitude, with less structural complex generally producing stronger P-band backscatter. Simulated backscatter from the Gabon rainforest plot was further compared with BIOMASS Level-1 products over a nearby forested region to evaluate model performance and sensitivity.

This modelling framework enables us to explore and quantify the key structural parameters driving variability in the P-band radar response, and to examine potential variations across forest types and sensitivity to environmental factors such as soil moisture. Ultimately, the insights gained from this work will support the development of a physically-informed deep-learning approach aimed at assessing the accuracy and uncertainty of EO-derived biomass estimates from regional to global scales.

Recent studies show that forest degradation partly explain the decline in the forest carbon sink observed by top-down approaches. Tropical forest degradation is estimated to be responsible for 25% of forest carbon emissions, with approximately 20% of tropical forests disturbed by logging activities. In the Brazilian Amazon, CO2 emissions from fires and forest fragmentation reached 88% of gross deforestation emissions (Silva et al., 2021).

Improving knowledge of greenhouse gas emissions from these processes is essential to better understand and seize opportunities to mitigate climate change. However, many aspects of carbon loss associated with forest degradation remain insufficiently understood. Post-perturbation recovery, the impact of recurrent perturbations (especially understory fires and wildfires), and net biomass loss are still subject to active research.

In this paper, we assess the potential of the BIOMASS P-band radar sensor to evaluate the impact of forest degradation on tropical dense forests. In particular, We analyze the long-term impacts of degradation on forest biomass and structure as reflected in P-band backscatter, measuring post-perturbation resilience as a function of perturbation type, recurrence, and edge effects.

We use the extensive Deter system archive over the Brazilian Amazon as reference perturbation data. This dataset, produced by the National Institute for Space Research (INPE), comprises over 300,000 deforestation warnings and 180,000 degradation warnings spanning 2015-2025. This temporal coverage enables a chronosequence approach to build post-perturbation recovery curves for each perturbation type. By leveraging the Deter archive and chronosequence analysis, we aim to characterize recovery trajectories following various perturbations and assess the compounding effects of recurrent disturbances and edge effects on forest biomass. These results are expected to improve carbon emission estimates from forest degradation and enhance understanding of forest resilience in the Brazilian Amazon, with implications for REDD+ monitoring and tropical forest management.

Ongoing Earth observation missions bring unprecedented detail and comprehensiveness for understanding and quantifying the role of terrestrial ecosystem on the global carbon cycle. Yet, limited for longer term analysis given their contemporaneous shorter period in orbit. Legacy missions and datasets are essential to study dynamics and processes at longer time scales. Here, analysing global long-term datasets on vegetation aboveground biomass dynamics, spanning from 1992 to 2019, and atmospheric CO2 measurements, we study (1) the contribution of biomass dynamics to the atmospheric CO2 growth rate and (2) the CO2 fertilization effect on plant biomass.

Adopting a fully data-driven three-box model that simulates carbon dynamics within live vegetation, woody debris and soil organic carbon pools, and considers wildfires and spatio-temporal changes in primary productivity, we are able to explain over 60% of the observed variability in atmospheric CO2 growth rate over the period of 1997-2019 (R = 0.78, p-value < 0.05), with a low RMSE of 1.0 PgC yr-1. Our results show that, globally, lagged effects from heterotrophic pools account for 50% of the variability in atmospheric CO2 growth rate, exceeding three times the direct contribution of transient effects from the live biomass pool. These findings highlight the importance of quantifying tree mortality and cascading carbon release from litter and soils in shaping the terrestrial carbon balance.

We further leverage these Earth observations to isolate the specific contribution of elevated CO2 concentration to the biomass dynamics using both local multiple regression and residual methods. The approach is evaluated across an ensemble of dynamic global vegetation model simulations, showing low errors (RMSE: 0.04 and 0.02) and high correlation (R2: 0.79 and 0.88; p-value < 0.005). Globally, satellite-derived estimates indicate a global increase in AGB of 16.9% [13.9–18.8%] per 100 ppm rise in CO2 concentration. These observation-based estimates are close to those estimated by current land surface models (16.3 ± 5.0 %) but exceed estimates from global Earth system models (12.7 ± 6.5% for CMIP5, 13.2 ± 4.6% for CMIP6), suggesting an underestimation of Earth system models on the contribution of the land ecosystems in dampening anthropogenic CO2 emissions.

Overall, vegetation-atmosphere interactions from annual to decadal time scales show both the strong role of carbon loss processes and legacy dynamics, alongside a modest though larger CO2 fertilization effect on biomass when compared to global models. Ultimately, we contextualize these results on the possible future benefits from integrating BIOMASS, NISAR and the GEDI missions to better quantify and understand processes controlling growth, disturbance and recovery processes in terrestrial ecosystems.

An improved representation of the carbon and water cycle dynamics in terrestrial ecosystems underpins a large uncertainty reduction in modeling Earth system dynamics. The climate sensitivity of ecosystem processes controls land-atmosphere interactions and the overall atmospheric carbon uptake and release dynamics across scales. Local and Earth observations of vegetation dynamics are key for the evaluation of our understanding and support the quantification of process representation in model development. Previous research has shown the importance in undermining equifinality using multi-variate observation constraints, focusing water and carbon fluxes and stocks.

Long-wavelength radar backscatter provides unique insights into the dynamics of plant water and carbon dynamics when compared to optical EO products, as such, embeds the potential for constraining various parameters controlling local climate vegetation responses. In this study, we present an approach for assimilating Earth observation backscatter data in a terrestrial ecosystem model to improve estimates of vegetation parameters turnover rates. Among others, we focus on the information content of L-band ALOS PALSAR data in constraining vegetation dynamics at selected FLUXNET sites, where carbon and water fluxes and stocks are observed. Using a radar observation operator, a standard radiative transfer model, we design a model-data integration experiment to investigate the benefits of multiple backscatter observations versus unique above ground biomass to constrain model parameters.

The experimental setup focuses on the trade-off between information content from backscatter and uncertainties from observation operators versus sparse above ground biomass observations to constrain parameters controlling leaf and wood pool dynamics in vegetation. Current results indicate that the assimilation improves the estimation of aboveground biomass and constraints on turnover rates for both foliage and woody pools. Ultimately, data sparsity and availability exert control on model performance and prior model uncertainty on parameter constraints. Ultimately, this study highlights the potential of L-band backscatter to enhance vegetation carbon cycle modeling, emphasizes the added value of the upcoming ESA BIOMASS mission, and underscores the importance of integrating vegetation water dynamics into carbon models.