Conference Agenda

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The NASA-ISRO Synthetic Aperture Radar (NISAR) Mission launched successfully on July 30, 2025, and will begin distributing science data at the end of its commissioning phase, ramping up to full science operations – collecting all land and ice-covered surfaces every 12 days from ascending and descending orbit vantage points. These data will be freely and openly distributed from the NASA Alaska Satellite Facility within days of acquisition. NISAR observations are capable of addressing fundamental and applied research topics spanning disciplines that include ecosystems science, cryosphere science, geodesy, solid earth science, hydrology, disaster response, and resource management. This talk will provide an overview of the mission, including its science and technology innovation, and dive into its status with focus on data and uniqueness of this first-of-its-kind L and S band mission. Particular emphasis will be placed on NISAR’s contributions to the polarimetric and polarimetric-interferometric SAR community, with examples from early science demonstrations that showcase initial research applications.

The Advanced Land Observing Satellite-4 (ALOS-4) was launched by the Japan Aerospace Exploration Agency (JAXA) on July 1, 2024. ALOS-4 carries the Phased Array L-band Synthetic Aperture Radar-3 (PALSAR-3) instrument, extending Japan’s more than 30-year heritage of L-band SAR missions that began with JERS-1 SAR, followed by ALOS PALSAR and ALOS-2 PALSAR-2.

Similar to ALOS-2, ALOS-4 operates in a 12 a.m./p.m. sun-synchronous orbit with a 14-day repeat cycle. A major advancement of PALSAR-3 over its predecessors is its enhanced wide-swath polarimetric capability, which enables quad-polarisation observations across a 100 km swath. This improvement allows for regional, gap-free coverage within only two observation cycles—compared to five cycles required by PALSAR-2.

Under the ALOS-4 Basic Observation Scenario, global quad-polarisation coverage is planned on an annual basis, with higher-frequency fully polarimetric acquisitions over selected regional and local target areas. The Earth Observation Research Center (EORC) of JAXA oversees research coordination, application development, and the generation of research datasets for both ALOS-2 and ALOS-4 missions. Amongst others, EORC coordinates the ALOS Kyoto & Carbon Initiative, which comprises a dedicated activity for polarimetric research using ALOS-2 and ALOS-4.

Through this presentation, we provide an overview of the ALOS-4 PALSAR-3 mission, with emphasis on its polarimetric capabilities, including the plans for polarimetric dataset generation and distribution by JAXA EORC.

We also touch upon the potential role of ALOS-4 in the era of other current and near-future L-band SAR missions, such as NISAR and ROSE-L, and how these missions can complement each other through cross-agency mission coordination.

This contribution presents an ongoing effort to modernize selected components of the PolSARpro toolbox by re-implementing its core algorithms in Python. The work follows recommendations from PolInSAR 2021 and aims to provide a more accessible environment for both research and education while preserving numerical consistency with the original C implementation. The overarching goal is to offer a clean and sustainable foundation for future algorithmic development, teaching, and reproducible experimentation.

Python was chosen as the new language for the PolSARPro routines because it is strongly adopted in the remote sensing and open-source communities The new design relies on Xarray with a Dask backend to overcome two major limitations of NumPy: the absence of metadata and the lack of native parallel execution. This framework enables explicit polarimetric data structures (S, C3, T3, T4 matrices, etc.) whose elements are accessed through intuitive labels such as S.vh or C3.m11. Lazy evaluation plays a central role by delaying actual computations until explicitly requested, which helps limit memory pressure, allows the construction of complex workflows without allocating intermediate arrays, and provides opportunities for Dask to fuse tasks and optimize execution. Combined with Dask’s automatic blockwise scheduling, this setup enables parallel processing without writing explicit tiled or multithreaded code.

Input data adhere to the NetCDF-BEAM format used in SNAP. A dedicated loader interprets the structural metadata and constructs a PolSARpro-compatible dataset, automatically identifying the polarimetric type. If the source dataset has been processed with Range–Doppler terrain correction in SNAP, geolocation information is preserved throughout the workflow, allowing decomposition outputs to be exported as GeoTIFFs and viewed directly in GIS software.

The software is fully open-source under the Apache-2.0 license and hosted on GitHub, with extensive documentation on Read the Docs. The site includes installation instructions, reproducible tutorials linked to executable notebooks, an API reference, and a concise theory section. The package is distributed through conda, and availability on conda-forge is planned to simplify deployment and enable integration into ESA’s MAAP platform. A small ALOS-1 dataset over San Francisco will be released to support hands-on practice.

The current release provides Boxcar and Refined Lee filters, H/A/Alpha, Freeman decompositions, the Yamaguchi three- and four-component decompositions as well as the PWF (Polarimetric Whitening Filter). Additional decompositions, classification tools, and further polarimetric modules are planned. Systematic validation tests ensure continued numerical alignment with the original PolSARpro algorithms.

Although Polarimetric SAR (PolSAR), Polarimetric Interferometric SAR (Pol-InSAR), and multi-frequency SAR systems hold significant scientific and operational potential, their research and application remain relatively underdeveloped. This limitation is to a large extent attributable to the scarcity of consistent, long-term time series of polarimetric data for research and development, as well as the limited availability of (near-)coincident, multi-sensor, multi-frequency datasets required for interoperability studies.

The potential coordinating role of the Committee on Earth Observation Satellites (CEOS)—serving as a conduit between the SAR research community and space agencies operating SAR missions—was highlighted during the ESA PolInSAR Workshop held in Toulouse, France, in June 2023. Following subsequent discussions between the ESA PolInSAR representatives and the CEOS Land Surface Imaging Virtual Constellation (LSI-VC), a dedicated LSI-VC PolSAR activity was initiated in 2024.

The LSI-VC PolSAR activity aims to facilitate coordination among relevant space agencies operating SAR missions with polarimetric capabilities, promoting the systematic acquisition of polarimetric and multi-frequency SAR observations over a defined set of CEOS PolSAR reference sites. Data tasking and acquisitions are conducted on a best-effort basis.

A PolSAR Science Team has been formed, and approximately 25 global reference sites have been identified to represent diverse ecosystems and land cover types. Ten CEOS member agencies have been engaged, with the potential to contribute polarimetric SAR data across all operational radar frequency bands available from space—P, L, S, C, and X. Data acquisitions began in late 2025 and are scheduled to continue through 2026.

Collaboration with the CEOS System Engineering Office (SEO) additionally provides the PolSAR Science Team access to a cloud-based environment for SAR and PolSAR data analysis via the CEOS Analytics Lab (CAL). The CAL platform has been extended to include a suite of SAR analytical tools and software packages, including SNAP and PolSARpro.

In the longer term, and subject to the data policies of the individual SAR missions, the compiled datasets are foreseen to be made available to a wider scientific community engaged in polarimetric and tomographic SAR research. This initiative aims to advance the development of polarimetric and multi-frequency SAR science and applications and to lower the entry bar for new user groups.

Soil moisture (SM) plays a critical role in hydrological phenomena, impacting life, groundwater availability, and agricultural activities. SM controls water infiltration and runoff after precipitation, determining water availability to surface vegetation and also affecting erosion and flood event occurrence and strength. Satellite remote sensing can provide synoptic and cost-effective SM monitoring capabilities, enhancing our understanding of environmental and societal challenges, such as agriculture to water resources management. In this framework, microwave remote sensing, and particularly Synthetic Aperture Radar (SAR), plays a key role thanks to its fine spatial resolution, which enables SM estimation at the scale of probing wavelength.

SAR SM estimation can be challenging due to the presence of speckle noise. In addition, the inherently ill-posed nature of the retrieval problem adds more complexity, as SAR measurements are jointly affected by SM and nuisance parameters, both coming from the radar system and from the probed scene (e.g., soil roughness, vegetation, thermal noise). This demands for tailored and robust decoupling methods, whose design is crucial for an accurate estimation of the SM from SAR imagery.

This study investigates the performance of soil moisture estimation over bare soils under a multistatic radar geometry leveraging two data augmentation strategies: polarimetric diversity, which consists of processing both co- and cross-polarized measurements at L-band; frequency diversity, which exploits L- and C-band co-polarized measurements. A novel approach is proposed, it consists in two parts: i) numerical prediction of

the normalized radar cross-section using first- and second-order small-slope approximation solutions of the scattering, adopting an anisotropic description of the soil surface; 2) Cramer-Rao lower bound (CRLB) to evaluate, from a statistical viewpoint,

the sensitivity of the normalized radar cross-section – predicted according to the above-mentioned data generation strategies – to the soil moisture under the presence of nuisance parameters, e.g., soil row orientations and small scale soil roughness. The

investigation demonstrates that both strategies offer low values of the CRLB, smaller than 5%, with optimal positions of the receivers identified across multiple regions, particularly along the track with respect to the master radar.

Polarimetric Synthetic Aperture Radar (PolSAR) is highly effective for characterizing scattering targets, offering rich information about their physical and structural properties. Over the years, numerous decomposition techniques—both data-driven and model-based—have been developed to interpret PolSAR measurements and separate the contributions of different scattering mechanisms. Examples include model-based approaches such as Freeman–Durden and Yamaguchi decompositions.

However, a key limitation of most model based approaches lies in how the scattering components are extracted. The mathematical operations involved often fail to preserve the positive semi-definite (PSD) nature of the resulting covariance or coherency matrices. Since physical scattering matrices must remain PSD to ensure valid power, this issue can lead to non-physical or unstable results. To address this, various correction techniques could be applied as in Arii ANNED. Nevertheless, in some instances such adjustments may distort the original data or reduce interpretability, highlighting the need for decomposition strategies that naturally maintain the physical and statistical integrity of the polarimetric information.

In this study, we investigate and compare different strategies for decomposing a partial target into two distinct components: a primary (focus) partial target and a residual, physically feasible partial target. We call this 1F+R. Please note the Focus target we extract is partial and can have any entropy. Its covariance matrix is not forced to have rank 1. Specifically, we compared different extraction methods based on simple subtraction, inner product, Rayleigh quotient optimisations (RQO), vectorisation of the covariance matrix and notch filtering, and tensor fields decompositions. We checked these extraction methods evaluating which one preserves the total power and maintains the PSD property across both the Focus and Residual partial targets. Preserving these characteristics is crucial for ensuring that the decomposition remains meaningful and that each component accurately represents its corresponding scattering mechanism without introducing artifacts.

To test the mentioned methodologies, we performed extensive Monte Carlo simulations to test its accuracy under controlled conditions. Furthermore, we applied the same analysis to real quad-polarimetric datasets acquired from ALOS-2 and (soon to) BIOMASS missions. These experiments allow us to assess the algorithm’s performance on real-world data characterized by different target types and scattering conditions. The results confirm that not all extractions techniques preserve PSD and polarimetric characteristics and we come with final suggested procedures including stages like RQO or tensor field analysis to be considered.

Quad-pol (QP) SAR systems acquire comprehensive polarimetric information by transmitting two and receiving four polarizations. However, they require higher pulse repetition rates (PRF) and more complex hardware, resulting in reduced swath width. Compact-pol (CP) systems, a specialized dual-pol mode, offer wider coverage with lower PRF requirements and have proven effective for land cover mapping, crop classification, soil moisture estimation, vegetation characterization, and ship detection.

Two main approaches exist for utilizing CP SAR data: extracting polarimetric features through decompositions, and reconstructing the 3×3 QP covariance matrix from the 2×2 CP covariance matrix. This work focuses on the latter approach, enabling application of QP processing techniques to reconstructed data.

Most reconstruction methods rely on Souyris' reflection symmetry assumption to relate CP and QP matrix elements, iteratively determining cross-polarization intensity. However, this assumption is valid primarily for volume-dominated scatterers and often fails for other scattering mechanisms.

We propose a deep learning method that reconstructs QP data from CP data without assuming reflection symmetry. The network uses real and imaginary components of the complex covariance matrix to directly map CP to QP data. The model was trained and evaluated on multiple PolSAR datasets to demonstrate performance across diverse scenarios.

Performance was assessed using Hotelling Lawley Trace (HLT), Determinant Ratio Test (DRT), and Wishart Distance for qualitative evaluation, alongside Mean Square Error (MSE), Mean Absolute Error (MAE), and Coherence Index (COI) for quantitative analysis. Results were compared against Souyris' and Nord's classical reconstruction methods.

The use of polarimetric Synthetic Aperture Radar (PolSAR) has the capability to improve detection and bio-physical parameters extraction in many remote sensing applications compared to the use of a single polarisation channel. The scattering matrix or the scattering vector allow to analyse the polarimetric information of targets. Unfortunately, when dealing with distributed targets, speckle introduces a statistical variation on the observed polarimetric behaviour and we need to use statistical tools. A typical solution is to extract the second order statistics building a covariance matrix [1].

The covariance matrix is formed by performing an averaged outer product of the scattering vector with itself which imposes constraints on the power distribution in the polarimetric space (i.e. the shape of the surface drawn by varying the projection vector in the matrix quadratic form). The surface is forced to be an ellipsoid with 3 main axes (in quad-pol after reciprocity and monostatic sensor assumptions).

In the past we showed how indiscriminate averaging can produce a loss of information when we are in a situation of not fully developed speckle (e.g. the distribution of the covariance matrix is not Wishart). We proposed an alternative way to decompose the partial target into a sum of low entropy components which does not require indiscriminate pre-averaging. This allows us to extract more than 3 scattering mechanisms and it does not force orthogonality among them. This is performed doing a search in the tensor field based on angular distances. We also demonstrated its usefulness by using both Monte Carlo simulations and real quad-pol ALOS-2 and RADARSAT-2 data. The simulations also show that the new methodology is able to identify the low entropy components even if the composing scattering mechanisms are not orthogonal to each other.

In this study, we introduce two main innovations. First, we extend the extraction methodology by using a modified K-means clustering algorithm that operates using angular distances rather than traditional Euclidean metrics. This adaptation allows for a faster convergence and more accurate grouping of scattering mechanisms within the polarimetric space. Second, we will apply and evaluate this improved approach using BIOMASS mission data. The BIOMASS dataset provides a valuable test case due to its high sensitivity to vegetation and structural parameters, offering an ideal context to assess the method’s effectiveness in real-world scenarios. For instance, through this application, we aim to determine whether the proposed technique can successfully isolate and separate the surface scattering contribution from other dominant mechanisms present in the data.

[1] Polarisation: Applications in Remote Sensing, Cloude, S. R., Oxford University Press, Oxford, UK, 2009 "

Unlike surface, double-bounce, or volume scattering, helical scattering conveys the target's structural chirality and phase asymmetry, which can only be captured through full-polarimetric SAR systems. The study investigates helical scattering using full polarimetric C-band data from ISRO's EOS-04 satellite over general land cover types over Shadnagar, Telangana state, India to understand spatial distribution of different scattering mechanisms. ISRO’s Earth Observing Satellite (EOS-04) launched in 2022 is a follow-on Radar Imaging Satellite (RISAT-1) intended to serve operational users with an objective to provide high-quality quantitative data with extended swath coverage and spatial resolutions. EOS-04 is equipped with on-board Hybrid polarimetric architecture and Full polarimetry apart from conventional single/dual polarization to support land and ocean applications.

Methodology: The primary objective was to examine the traceability of the helical component within full-pol data, specifically to identified helical scattering directly from the covariance (COV) matrix instead of performing conventional polarimetric decompositions. The scattering mechanism viz surface (POdd), double-bounce (PEven), volume (PVol), and helical (PHelix) scattering powers within predefined Areas of Interest to identify regions with potential structural complexity and asymmetric features from Yamaguchi four-component decomposed images. The same AOIs were also applied to the corresponding Level-1C covariance product to ensure spatial consistency and direct comparison between decomposition-derived helical power (PHelix) and covariance-derived cross-polar terms. By mapping PHelix values from decomposed products against HHHV values from covariance products using the same AOIs, can establish direct spatial and numerical correspondence.

Results and Discussion: Statistical Correlation analysis revealed strong positive correlations between PHelix and specific covariance elements. This consistent pattern across all AOIs confirms that the HHHV element is the strongest statistical indicator of asymmetric scattering in the raw COV input. The covariance matrix structure for helical scattering exhibits specific properties: C₁₁ = C₃₃ (co-polarized symmetry), C₂₂ = 2(C₁₁+C₃₃) (cross-polarized dominance exceeding that of volume scattering), and purely imaginary off-diagonal elements C₁₂ and C₂₃ representing ±j phase relationships. The high correlation coefficients confirm that helical scattering power originates directly from the imaginary cross-polarized covariance terms (r approximately +0.79 to +0.82) that capture depolarization and phase quadrature relationships and negative correlations between PHelix and co-polarized intensity channels (HHHH)mean [r ≈ -0.32] and (VVVV)mean[r ≈ -0.29] confirms poor correlation. The imaginary components Im(C₁₂) and Im(C₂₃), which represent 90° phase shifts characteristic of circular polarization, serve as the mathematical precursors to helical scattering. Moreover, the calculated helical fraction (fc) also quantified the relative contribution of asymmetric scattering across different land covers supporting multiple physical mechanisms generating helical scattering at C-band frequencies. The core finding of this preliminary research is the definitive proof that PHelix is traceable from the covariance matrix.

With advancements in radar remote sensing technology, observation platforms have evolved from satellites to unmanned aerial vehicles (UAVs) and ground-based systems. While satellite radar sensors provide high resolution and wide area coverage, their acquisition geometries and relatively long revisit cycles make it difficult to monitor steep terrain or rapidly changing surfaces. Ground-based radar systems offer a flexible alternative, allowing us to control when and how we observe these dynamic environments. In particular, when full-polarimetric data are acquired from ground-based radar systems, their very high temporal resolution enables daily or even hourly observations, providing significant advantages for monitoring dynamic environments such as diurnal changes in rice paddy canopy structure. However, the accuracy of polarimetric measurements might be degraded by systematic distortions such as channel imbalance and crosstalk. While these issues are well addressed in spaceborne systems, ground-based polarimetric radars require calibration because their antennas are independently deployed. In this study, we conducted polarimetric calibration of full-polarimetric interferometric data acquired by the Gamma Portable Radar Interferometer-II (GPRI-II), a ground-based Ku-band real-aperture radar. To ensure accurate calibration, three trihedral corner reflectors with 50 cm edge lengths were deployed at a distance of 200 meters from the radar. One reflector was used to derive the calibration parameters, while the remaining two were used to validate the results. The calibration process comprised azimuth beam squint correction, azimuth phase ramp removal, and polarimetric distortion calibration. Beam squint, caused by azimuth rotation-induced phase center shifts, was compensated by estimating squint angles from the GPRI-II equipment’s rotation speed and waveguide frequency. Azimuth-phase distortion, resulting from an offset between the mechanical rotation center and the antenna phase center, was modeled and removed using matched filtering. The calibration coefficients, including amplitude and phase imbalances, were derived from covariance matrices around the calibration reflector and applied to the full-polarimetric dataset. After calibration, co-polarized channels exhibited enhanced symmetry and phase consistency, while cross-polarized channels were effectively suppressed. The total phase offset between the co-polarized channels decreased from 27 ° to –1 °, indicating that their phase difference was almost completely corrected, which improved coherence and ensured more accurate polarimetric measurements. Cross-polarization isolation improved from approximately 33 dB to 34 dB, confirming minimal residual crosstalk. The calibration parameters also produced consistent improvements at the validation reflectors, while weaker responses observed at one site were attributed to clutter and minor misalignment. The results demonstrate that accurate polarimetric calibration of ground-based interferometric radar data can be effectively achieved using corner reflectors. The calibrated GPRI-II polarimetric data provide a reliable foundation for quantitative polarimetric analysis and can be further extended to studies on vegetation canopy structures and scattering properties.

Introduction: The Lunar South Pole is characterised by extreme topography creating many Permanently Shadowed Regions (PSRs) which remain beyond the reach of optical imagery, making it difficult to fully explore the surface. The Indian Space Research Organisation’s (ISRO) Chandrayaan-2 mission carries a Dual Frequency SAR (DFSAR) with the capabilities to generate Fully Polarimetric L-band and S-band products, with L-band data being particularly advantageous due to the expected penetration depth of up to 5m in dry, low loss soils [1]. Polarimetric analysis of the imaged surface can provide extensive information about the scattering mechanisms present on both the surface and within the subsurface. With the presence and distribution of water-ice a central objective of NASA’s Artemis III mission, Chandrayaan-2’s high resolution SAR datasets can provide an opportunity to enhance our understanding of water-ice deposits at the Lunar South Pole.

A new, novel way of assessing the spatial distribution of water-ice desposits using radar data is by adapting the Water Cloud model (WCM) for use on the Lunar surface [2]. The model, which is traditionally used in Terrestrial applications to separate surface and volume scattering components, is edited and reinterpreted to consider volume under surface and evaluate the expected volumetric scattering signature from subsurface water-ice.

Methodology: We represent the scattering matrix as a Pauli vector and generate the 4x4 Coherency [T] matrix [3] which allows us to visualize the products of the Pauli Decomposition and perform the Claude-Pottier decomposition in Slant Range geometry. Work is currently being completed on geometrically projecting the products for better visualization and analysis. The Pauli vector k allows us to differentiate between surface scattering, dihedral structure scattering, and oriented dihedral or volumetric scattering.

The Claude-Pottier decomposition using the 4x4 Coherency matrix allows us to identify the thermal noise component, which can then be removed from the data [3]. Thermal noise removal significantly improved the polarimetric interpretation, since the SNR of the images was particularly low (corrupting polarimetric information). After noise removal, specific parameters such as H (entropy), which provides information on the disorder of scattering, and α (alpha angle) which indicates if the scattering is dominated by either surface, double-bounce or volumetric interactions, are useful indicators to better understand the properties of the surface and subsurface.

To model the distribution of water-ice, backscatter coefficients are modelled using the WCM with parameters that are expected on the Lunar surface [2]. The model coefficients A-D were optimized through least -square fitting between the observed and modelled version of backscatter, with incidence angle and dielectric constant as inputs to the model. This was then inverted using a generated look-up table (LUT) to estimate the volumetric scattering components across the localized region. Further work is being done on the WCM using FP datasets, and including novel ways to decompose scattering components as described in the POLinSAR abstract submitted by the co-author A. Marino (Testing different methodologies to decompose PolSAR data into a Focus partial target plus a Residual one (1F+R)).

Initial Results: Much of the Lunar surface at the South Pole when viewed with the Pauli decomposition exhibits surface scattering, which is to be expected. Regions around craters, such as the rims and ejecta, present strongly volumetric components, specifically in regions inside the crater walls. This is significant due to the shallow penetration depth of L-band, and the predicted subsurface water ice deposits within the PSRs. Additionally, it can be seen using H and α that these regions are completely depolarising, similar to the areas where volumetric signals are observed. We are currently applying further tests to check if this high entropy targets are indeed volumetric or a mix of close scatterers (increasing entropy, as it is in for Earth artificial targets) or geometry-induced effects in the slant-range plane. If volumetric, this would mean that the subsurface material, be it water-ice deposits or buried boulders, is distributed throughout the walls and not in specific regions.

Acknowledgments: We acknowledge the use of data from the Chandrayaan-II, second lunar mission of the Indian Space Research Organisation (ISRO), archived at the Indian Space Science Data Centre (ISSDC). UK Space Agency for funding this project; grant no. ST/Y005384/1 as part of the UK Government’s Science Bilateral Programme.

References: [1] Bhiravarasu S. S. et al. (2021) Planet. Sci. J., 2, 134. [2] Attema E. P. W. and Ulaby F. T. (1978) Radio Science, 13, 2. [3] Lee J. S. and Pottier E. (2017) CRC press.

Assessing 3D forest structure is essential, as it reflects forest condition and state, offering critical insights into forest development when analysed at relevant spatial resolutions. The scale of observation plays a critical role, since natural, anthropogenic, and climate driven disturbances affect 3D forest structure at varying spatial scales. 3D radar-based methods, such as SAR interferometry, provide a promising approach by exploiting sensitivity to the underlying 3D reflectivity. TanDEM-X with its high spatial resolution allows characterization of structural heterogeneity at fine scales by analysing the centroid of the reflectivity profile. To assess forest structural variation across scales, a multi-resolution wavelet analysis is applied. Spatial heterogeneity is quantified using wavelet-based parameters such as variance and entropy. Results from a temperate forest are analysed and compared with airborne LiDAR-derived structural metrics and forest inventory data. Finally, the effects of the interferometric coherence level and topography on the wavelet results are addressed.

Deforestation is a critical environmental challenge, driving greenhouse gas emissions, biodiversity loss, and disruption of hydrological and energy cycles. Monitoring these processes in Near Real-Time (NRT) is essential, particularly in tropical regions where rapid land-use changes threaten ecosystems and climate regulation. Optical monitoring has traditionally been the main approach, but persistent cloud cover in the tropics often limits its effectiveness. Synthetic Aperture Radar (SAR), unaffected by clouds or illumination, has emerged as a reliable alternative, with missions like Sentinel-1 offering free global acquisitions suitable for operational monitoring. Sentinel-1 provides C-band dual-polarization data (VV and VH), yet most operational systems exploit only one channel or process both independently before merging outputs. This strategy underutilizes the complementarity of the two polarizations and can lead to omission errors, when both channels are required to confirm a change, or commission errors, when a false alarm in one channel is enough to trigger detection. A methodological gap therefore remains in fully exploiting Sentinel-1 polarimetry for early forest loss detection while maintaining operational feasibility.

To address this limitation, the Bayesian polarimetric detector pol-BOCD has been developed to jointly process VV and VH Sentinel-1 time series for NRT deforestation monitoring. The method extends Bayesian Online Changepoint Detection (BOCD) to the polarimetric domain by explicitly modeling the statistical uncorrelation between co- and cross-polarized channels in natural environments. The joint data likelihood is factorized into two univariate distributions, allowing the independent contributions of each channel to be integrated within a single hidden Markov chain, without increasing computational complexity beyond that of the univariate BOCD.

The approach is evaluated in two tropical biomes with heterogeneous land cover and undergoing intense forest conversion: the Cerrado and the Brazilian Amazon. The Cerrado is a biologically rich savanna facing extensive agricultural expansion and exhibiting strong seasonality that complicates SAR-based monitoring. The Amazon rainforest, by contrast, is increasingly affected by small, fragmented clearings that are ecologically disruptive yet difficult to detect. Validation relies on the MapBiomas Alerta reference dataset, covering 8,000 ha in the Cerrado and 13,400 ha in the Amazon in 2020, and including small clearings (0.1–2 ha) as well as larger ones (3 to 50 ha).

Results demonstrate that pol-BOCD improves the detection of small-scale clearings in the Cerrado and large-scale clearings in the Amazon by approximately 10% compared to VH-only BOCD, which in turn outperforms VV-only BOCD (-23% compared to pol-BOCD). In contrast, improvements are marginal for large clearings in the Cerrado and small ones in the Amazon. These outcomes are attributed to the distinct deforestation practices in the two biomes. In the Cerrado, large-scale disturbances—often associated with mechanized soy cultivation—are executed systematically, leaving behind uniformly bare soil. In such conditions, VH polarization is more effective due to the sharp reduction in volume scattering, rendering VV polarization comparatively less informative. In contrast, smallholder agriculture and subsistence farming typically involve artisanal clearing methods, which often leave substantial residual vegetation. Under these less uniform conditions, VV polarization contributes more significantly, complementing VH in detecting subtle structural changes due to its greater sensitivity to ground scattering. In the Amazon, an opposite pattern takes place: large clearings typically involve cutting down the forest, allowing it to dry, and then burning the biomass, as removing extensive vegetation from dense rainforest areas is logistically challenging. A further comparison, shows that pol-BOCD outperforms both the union and intersection of single-polarization results, which suffer from high false alarms and low true detections, respectively. Against operational methods, pol-BOCD surpasses GLAD-L in the Cerrado (+47%) and RADD in the Amazon (+34% for small clearings), benefiting from the higher spatial resolution of the Sentinel-1 input data due to the omission of speckle filtering during pre-processing.

Overall, the findings highlight the potential of joint Sentinel-1 polarimetric processing in pol-BOCD to enhance NRT deforestation detection, particularly in areas where residual vegetation introduces textural complexity in radar signals, underscoring its usefulness for operational monitoring across diverse biomes.

The potential of combining polarimetric interferometric (Pol-InSAR) / tomographic (TomoSAR) SAR measurements at multiple frequencies and lidar measurements towards an improved and / or more complete 3D forest structure characterization is widely recognized. Lower frequencies are more sensitive to larger (i.e. on the order of the wavelength) vegetation elements. At the same time, the reduced attenuation of the canopy increases the “visibility” of lower vegetation layers and of the ground. In contrast, with increasing frequency the sensitivity to smaller vegetation elements increases while the stronger canopy attenuation reduces the “visibility” of the ground. This complementarity of structural information content has recently become especially relevant in a context in which lidar missions like NASA’s GEDI and IceSAT-2, and SAR missions like e.g. ESA P-band BIOMASS, NASA L-band NISAR, ESA L-band ROSE-L, DLR X-band TanDEM-X are already / will be operating at the same or contiguously in time with a number of common forest structure-related objectives, but with different spatial and temporal resolutions and coverages. The development of suitable approaches able to combine multi-frequency Pol-InSAR / TomoSAR and lidar measurements critically depends on the ability to relate the underlying 3D reflectivities to each other. This is in general not established today essentially because there are no electromagnetic models that allow neither to model nor to transfer reflectivity with the required accuracy across a relevant frequency range [1].

In the absence of any model-supported way to transfer reflectivity profiles from one frequency to the other, data-driven approximations remain an appealing (and maybe the only) option. In this context, this work investigates how far polarization coherence tomography (PCT) can support this transfer. To begin with, the investigation is currently focused on the reconstruction of lidar profiles from P-band interferometric measurements (e.g. a BIOMASS-GEDI case) and vice-versa, i.e. on the reconstruction of P-band profiles from lidar ones. PCT formulates the reconstruction of vertical reflectivity profiles as a series expansion in a function basis with coefficients estimated from a limited set of Pol-InSAR measurements [2]. In the presented analysis, function bases are derived from reflectivity profiles available for each data set first [3]. Then, the basis of one data set is used to reconstruct profiles at the other from the available measurements (Pol-InSAR at P-band, full-waveforms for lidar). Finally, the ability to reconstruct specific profile parameters (e.g. the height of and the reflectivity at the canopy local maximum) is evaluated especially as a function of the number of the used basis components. Results obtained by processing real data acquired in the frame of the ESA-supported AfriSAR 2016 campaign over the forest in Mondah in Gabon are shown. During the campaign DLR’s F-SAR and NASA LVIS airborne platforms acquired P-band TomoSAR (multibaseline interferometric) and lidar waveforms, respectively, almost at the same time.

References

[1] M. Pardini, J. Armston, W Qi, S. K. Lee, M. Tello, V. Cazcarra Bes, C. Choi, K. Papathanassiou, R. O. Dubayah, L.E. Fatoyinbo, “Early Lessons on Combining Lidar and Multi-Baseline SAR Measurements for Forest Structure Characterization,” Surveys in Geophysics, vol. 40, pp. 803–837, 2019.

[2] S. R. Cloude, "Polarization coherence tomography," Radio Science, vol. 41, no. 04, pp. 1-27, Aug. 2006.

[3] R. Guliaev, M. Pardini, K. Papathanassiou, “Forest 3D Radar Reflectivity Reconstruction at X-Band Using a Lidar Derived Polarimetric Coherence Tomography Basis,” Remote Sensing, vol. 16, 2146, 2024.

The X-band Synthetic Aperture Radar (SAR), with its short wavelength (~3 cm), offers exceptional spatial resolution and sensitivity to surface micro-structures—an attractive asset for precision agriculture. However, its limited penetration into vegetation and soil has historically constrained its utility for deeper biomass or subsurface retrievals.

Previous studies have demonstrated that X-band SAR data are particularly effective in distinguishing between surface roughness and tillage states over bare or sparsely vegetated soils (Baghdadi et al., 2002), owing to their enhanced sensitivity to micro-topographical variations. Nevertheless, several works have emphasized that this sensitivity decreases markedly in the presence of vegetation, as canopy volume scattering masks the soil contribution (Lopez-Sanchez & Ballester-Berman, 2009). More recently, multi-frequency comparative analyses have further confirmed that the X-band signal is predominantly sensitive to surface moisture and roughness, whereas deeper soil layers are better probed by lower-frequency systems (Zhou et al., 2025).

In this study, we explore two complementary aspects of repeat-pass PolInSAR data at X-band for agricultural applications: (1) the potential of repeat-pass interferometric phase coherence to reveal subsurface moisture and drainage features beneath cropped fields; and (2) the value of full polarimetry (orientation sensitivity, depolarisation metrics) to detect sowing ridges, tillage patterns, and crop growth stage via structural anisotropy of the soil–vegetation system.

Firstly, using TerraSAR-X Staring Spotlight acquisitions (single-polarisation, high resolution), we demonstrate that under specific seasonal and hydrological conditions, the interferometric phase (and coherence) signal exhibits sensitivity to subsurface moisture contrasts. In particular, we observe coherent phase-pattern anomalies aligned with known subsurface drainage networks beneath agricultural parcels—a surprising result, given the limited penetration of X-band. We interpret these signals as the result of dielectric contrasts in the shallow subsurface (wet vs dry soil), modulating the scattering phase behavior.

Secondly, employing an airborne, fully-polarimetric X-band sensor (SETHI), we investigate how polarimetric parameters (e.g., orientation angles and depolarization indices) vary with the presence or absence of sowing ridges and as a function of early crop growth stage. Our results show a clear correlation: fields with visible ridging manifest stronger anisotropy in orientation angle domains and lower depolarization, whereas mature crop covers produce more isotropic scattering and higher depolarization. Consequently, we find that the polarimetric signature can serve as a proxy for both soil preparation state (ridges vs flat) and early crop phenology.

Together, these findings illustrate the under‐exploited potential of X-band PolInSAR for precision agriculture: (i) as a sensor of subtle subsurface moisture heterogeneity via coherence/phase analytics, and (ii) as a structural probe of soil and crop architecture via polarimetry.

References

[1] Baghdadi, N., Holah, N., Dubois-Fernandez, P., Prévot, L., Hosford, S., Chanzy, A., ... & Zribi, M. (2004). Analysis of X-Band Polarimetric Sar Data for The Derivation of The Surface Roughness Over Bare Agricultural Fields. A A, 3, 2.

[2] Lopez-Sanchez, J. M., & Ballester-Berman, J. D. (2009). Potentials of polarimetric SAR interferometry for agriculture monitoring. Radio science, 44(02), 1-20.

[3] Zhou, X., Wang, J., Shan, B., He, Y., & Xing, M. (2025). Sensitivity of multi-frequency and multi-polarization SAR to soil moisture at different depths in agricultural regions. Journal of Hydrology, 133513.

This paper introduces Adaptive sum of Kronecker products for QUAntitative- Soil Moisture Anomalies retrieval (AKQUA-SMA), a novel polarimetric framework for SMA estimation. The core innovation lies firstly in the use of Temporal-SKP (T-SKP) [1], which exploits the temporal-polarimetric domain to isolate two scattering components: the moisture-related contribution, namely the Latent contribution, from the other scattering mechanism, namely Ground, i.e., above-ground scattering components. To do so, the SKP solution is chosen through exhaustive search as the one that minimizes the l1-norm of the error between the decomposed coherence values in the Latent structure matrix and the theoretical expected moisture related complex coherences. In particular, the search grid originates from the model proposed in [2], which has been slightly modified to account for a double-scattering mechanism. Then, the Ground component is chosen as the one that minimizes the phase residues, i.e., the most triangular scattering mechanism, and used for phase calibration similarly to what is done for the BIOMASS tomography. Finally, absolute N (zero-mean) SMA values are regressed by LS estimation from the estimated variations in the N(N-1)/2 InSAR pairs.

The AKQUA-SMA algorithm was applied to the Hydrosoil dataset [3], which was collected over a 20m × 58m agricultural field using a C-Band ground-based PolSAR (GB-PolSAR). The campaign aimed to simulate the frequent monitoring capability of the HydroTerra mission [3] for soil moisture and vegetation parameter retrieval. The data comprises two phases: the Barley Crop (March–June 2020), a Dual-Pol dataset (18,055 acquisitions), and the Corn Crop (July–November 2020), a Quad-Pol dataset (12,945 acquisitions), both acquired every 10 minutes. This SAR data is supplemented with essential ancillary information, including probe-based volumetric moisture, plant density, and crop height. Due to the limited field size, the entire area was treated as a single resolution cell. Processing utilized a full overlapping sliding window approach, scanning the dataset in steps of six acquisitions with five temporal samples overlapping between adjacent windows. The first results reveal good estimation accuracy, with a mode RMSE of 0.2% across the entire Corn dataset. Experiments involving the estimation of SMA through exhaustive search using the single polarization channels after phase linking are currently running, in order to assess the benefit of the multi-polarimetric approach w.r.t. the single-polarization one.

References

[1] Tebaldini, Stefano. ”Algebraic synthesis of forest scenarios from multibaseline PolInSAR data.” IEEE Transactions on Geoscience and Remote Sensing 47.12 (2009): 4132-4142.

[2] De Zan, Francesco, et al. ”A SAR interferometric model for soil moisture.” IEEE Trans actions on Geoscience and Remote Sensing 52.1 (2013): 418-425.

[3] Aguasca, Albert, et al. ”Hydrosoil, soil moisture and vegetation parameters retrieval with a C-band GB-SAR: Campaign implementation and first results.” 2021 IEEE Inter national Geoscience and Remote Sensing Symposium IGARSS. IEEE, 2021

Soil moisture is a crucial parameter in hydrology and agronomy applications [1,2] playing an important role in water resource and irrigation management. Synthetic Aperture Radar (SAR) data have been extensively used in several studies to retrieve the soil moisture across various land covers from bare to vegetated soil, including cropland, grassland, and forest [3,4]. Several scattering models have been developed to retrieve soil moisture beneath vegetation [5–7]. Using first–order radiative transfer (RT) backscattering models requires a complete description of vegetation structure. Thus, extensively used semi–physical models such as the Water Cloud Model (WCM) [8], rely on vegetation information (e.g., vegetation water content and biomass) derived from optical vegetation indices including LAI and NDVI. However, such indices provide a partial description of vegetation structure, leading to soil moisture retrieval errors. In this context, Deep Learning (DL) techniques can be synergically used with physics–based models to optimize their parameters and compensate the lack of vegetation structure description.

In this study, we apply the Physics-informed Residual Network (ResNet) model RTNet, proposed and validated using airborne data in our previous work [9], and specifically designed for accurate and physically meaningful soil moisture retrieval. Particularly, we extend the use of RTNet to spaceborne data from ALOS–2 acquisitions at HV polarization, together with multi-source remote sensing (RS) data, e.g., vegetation water content (VWC), soil texture, and weather information. While the HV polarization is used as input to the AI model, the HH polarization is employed, as described later, for the model optimization task.

The RTNet is designed to simultaneously perform two optimization tasks. The first one aims to estimate the parameters of a first–order RT model, namely the four scattering contributions (i.e., surface, double, volume, and triple scattering) and the attenuation term. The second task focuses on soil moisture retrieval starting from the optimized scattering components, inspired by the purely physical modeling approach proposed in [10]. Thus, two loss functions are defined: the first minimizes the difference between the measured total backscatter at HH polarization and the estimated one, computed as the sum of the optimized attenuated scattering components, while the second minimizes the difference between the retrieved soil moisture and in–situ measurements. These two losses are properly combined to guide the RTNet training, ensuring an accurate and consistent soil moisture retrieval by coupling it with the optimization of the scattering mechanisms based on physical modeling. To guarantee the physical validity of the estimation and to avoid ill–posed solutions, additional physical constraints are imposed within the loss function.

The methodology is validated using soil moisture measurements from the SCAN network dataset [11] and ALOS–2 L–band acquisitions aggregated to a 100 meter resolution over six test sites characterized by different land cover types. On the other hand, the optimized RT parameters, i.e., the scattering mechanisms, are evaluated through a comparison with well–established polarimetric decompositions, e.g., the Freeman–Durden three–component decomposition (FD3) [12], the Yamaguchi four–component decomposition with rotation of the coherency matrix (Y4R) [13], to assess the strengths and limitations of the proposed approach.

As a future development, the framework will be applied to L–band NISAR data, offering the opportunity to evaluate the robustness of the proposed framework against global soil-vegetation condition variabilities.

To conclude, this methodology goes beyond retrieving soil moisture. Instead, through the ingestion of data from heterogeneous sensors and the decomposition of the radar signal into its scattering components, it provides a comprehensive tool useful for investigating soil–vegetation interactions, seasonal changes, and other applications such as land cover characterization and crop type identification, opening new possibilities for development, training, and validation of explainable physics–informed AI models.

References

[1] Schaufler, G., Kitzler, B., Schindlbacher, A., Skiba, U., Sutton, M.A., & Zechmeister-Boltenstern, S. (2010). Greenhouse gas emissions from European soils under different land use: effects of soil moisture and temperature. European Journal of Soil Science, 61.

[2] Sheffield, J., Goteti, G., Wen, F., & Wood, E.F. (2004). A simulated soil moisture based drought analysis for the United States. Journal of Geophysical Research, 109.

[3] Hosseini, M., & Mcnairn, H. (2017). Using multi-polarization C- and L-band synthetic aperture radar to estimate biomass and soil moisture of wheat fields. Int. J. Appl. Earth Obs. Geoinformation, 58, 50-64.

[4] Baghdadi, N.N., El-Hajj, M., & Zribi, M. (2016). Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 1229-1243.

[5] Baghdadi, N.N., & Zribi, M. (2006). Evaluation of radar backscatter models IEM, OH and Dubois using experimental observations. International Journal of Remote Sensing, 27, 3831 - 3852.

[6] Fung, A.K., & Chen, K. (2004). An update on the IEM surface backscattering model. IEEE Geoscience and Remote Sensing Letters, 1, 75-77.

[7] Bracaglia, M., Ferrazzoli, P., & Guerriero, L. (1995). A fully polarimetric multiple scattering model for crops. Remote Sensing of Environment, 54, 170-179.

[8] Attema, E., & Ulaby, F.T. (1978). Vegetation modeled as a water cloud. Radio Science, 13, 357-364.

[9] Huang, X., Papale, L.G., Lavalle, M., Del Frate, F., Fattahi, H., Chan, S.K., Lohman, R.B., Xu, X., & Kim, Y. (2025). Optimizing the Radiative Transfer Model Using Deep Neural Networks for NISAR Soil Moisture Retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 12697–12712.

[10] Hajnsek, I., Jagdhuber, T., Schon, H., & Papathanassiou, K. P. (2009). Potential of estimating soil moisture under vegetation cover by means of PolSAR.IEEE Transactions on Geoscience and Remote Sensing, 47(2), 442-454

[11] Schaefer, G.L., Cosh, M.H., & Jackson, T.J. (2007). The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN). Journal of Atmospheric and Oceanic Technology, 24, 2073-2077.

[12] Freeman, A., & Durden, S.L. (1998). A three-component scattering model for polarimetric SAR data. IEEE Trans. Geosci. Remote. Sens., 36, 963-973.

[13] Yamaguchi, Y., Sato, A., Sato, R., Yamada, H., & Boerner, W. (2010). Four-component scattering power decomposition with rotation of coherency matrix. 2010 IEEE International Geoscience and Remote Sensing Symposium, 1327-1330.

Observing changes in soil moisture with differential Synthetic Aperture Radar (SAR) interferometry (D-InSAR) has gained relevance due to shorter satellite revisit times, bigger swath widths, higher spatial resolution of SAR systems, and the introduction of L-band satellites. The geometric configuration of D-InSAR mitigates topographic effects, baseline-induced distortions, and geometric decorrelation.

In this context, the separation of volume, surface and dihedral scattering in the SAR signal is essential for limiting uncertainty in the accurate estimation of soil moisture. Using D-InSAR recent soil moisture forward models are able to account for varying soil states and surface and subsurface volume scattering combinations, although they assume the absence of topographical variations and vegetation between acquisitions [1], [2]. Whereas former can be approximately removed by using phase triplets, separating above ground volume from surface scattering remains a challenge. To address this, polarimetric information has been shown to be sensitive to vegetation [3].

To enhance soil moisture estimation we combine existing D-InSAR electromagnetic forward models with polarimetric information. Here, the copolar phase differences (CPD) [4] can been used to counteract the need of auxiliary geometries and fully-polarimetric systems. CPD has been shown to be highly sensitive to volume scattering due to the structural anisotropy of vegetation.

This analysis will focus on quantifying the performance of Zwieback et al. [2] at L-Band and HH/VV polarizations by deploying it on the recent AgriROSE-L dataset [5]. The limits of the model will be explored in respect to its parameterization, the degree of vegetation and numerous soil moisture levels. First, we test the model performance over bare ground. Secondly, we analyze the value of CPD for identifying vegetated and non-vegetated pixels. Third, we provide a first approach of integrating a CPD indicator into current soil moisture forward models.

References

[1] F. De Zan, A. Parizzi, P. Prats-Iraola, and P. López-Dekker, “A SAR interferometric model for soil moisture,” IEEE Transactions on Geoscience and Remote Sensing, 2013.

[2] S. Zwieback, S. Hensley, and I. Hajnsek, “A polarimetric first-order model of soil moisture effects on the DInSAR coherence,” Remote Sensing, vol. 7, no. 6, pp. 7571–7596, 2015.

[3] G. Anconitano, M. Lavalle, M. A. Acuña, and N. Pierdicca, “Sensitivity of polarimetric SAR decompositions to soil moisture and vegetation over three agricultural sites across a latitudinal gradient,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 3615–3634, 2023.

[4] V. Brancato and I. Hajnsek, “Analyzing the influence of wet biomass changes in polarimetric differential SAR interferometry at L-band,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 5, pp. 1494–1508, 2018.

[5] H. I. Schauer, N. Basargin, and I. Hajnsek, “Airborne L-Band soil moisture retrieval over agricultural areas in preparation for ESA ROSE-L mission,” 2025.

Recently, the German Aerospace Center (DLR) conducted the AgriROSE-L 2025 (CROPEX 2025) airborne F-SAR campaign in preparation for the upcoming ESA ROSE-L mission. Multiple flights acquired a long and dense (6-day revisit) time series of fully polarimetric SAR acquisitions over agricultural areas between April and July 2025. Ground teams accompanied each flight to collect in situ measurements and record the soil and vegetation conditions. Agricultural areas are subject to rapid changes caused by crop growth, transitions of phenological stages, and alterations in soil conditions, including changes in soil moisture and roughness. The campaign provides a valuable dataset for analyzing the dynamics of the polarimetric signal over time and developing new methods for geophysical parameter retrieval.

In this study, we provide an early evaluation of the new dataset, focusing on soil moisture estimation, an essential climate variable (ECV) important for hydrology and agriculture. A significant challenge for SAR-based high-resolution soil moisture estimation is the presence of vegetation, which interferes with the ground signal. Here, the use of longer wavelengths (L-band) with deeper penetration is beneficial in minimizing the influence of vegetation. Additionally, using fully polarimetric data enables a certain degree of separation between the ground and vegetation contributions.

We apply a model-based tensor decomposition [1] to separate the signal into surface, dihedral, and volume contributions. The method jointly exploits polarimetric and temporal information, analyzes a time series of acquisitions, and provides estimates for different geophysical parameters, including soil moisture. We validate the accuracy across different crop types and growth stages using the in situ measurements acquired during the campaign.

References

[1] N. Basargin, A. Alonso-González, and I. Hajnsek, "Model-based tensor decompositions for geophysical parameter retrieval from multidimensional SAR data," Submitted to IEEE Transactions on Geoscience and Remote Sensing, under review.

The AGRIROSE-L campaign coordinated and conducted by the German Aerospace Center (DLR) was conducted in cooperation with the LMU, GFZ and CzechGlobe over an agricultural area in southern Germany called Puch. The campaign's primary goal is to provide calibration and validation data to support future Earth observation missions, specifically ROSE-L and CHIME, with a focus on improving the monitoring of soil moisture and health, crop growth, and other agricultural parameters from space. The data collected is crucial for developing and testing algorithms for sustainable agriculture.

For this the DLR’s F-SAR system recorded a globally unique dataset across four different frequency ranges (the X, C, S and L bands). In total, the radar team carried out 23 measurement flights between April and July covering the whole agricultural vegetation season. On selected days, the flights took place in the morning, at midday and in the evening to record any daily changes in the soil and vegetation.

The data was collected using innovative imaging techniques such as polarimetry, interferometry and tomography. Experienced DLR test pilots flew specified paths with metre-level precision, supported by the satellite-based navigation system integrated into the F-SAR. Parallel to each flight, a team from LMU collected ground measurements of soil and vegetation parameters, such as soil moisture, surface roughness, plant water content and plant biomass. In addition, four times also the DLR’s hyperspectral sensor HySpecs was flown over the same area and one-time CzechGlobes hyperspectral sensor acquired data.

In this research work the campaign, its collected data and the first performance analysis are presented.

Provided that there is enough penetration in the medium, multibaseline interferometric acquisitions can be used to reconstruct the vertical profile of the scene via its power spectral density [1]. This technique, known as radar tomographic imaging, is particularly advantageous because it does not alter the volume of interest and provides information from the whole scene, contrasting with other methods that only retrieve a profile for a single datapoint. [2,3]

We have conducted our tomographic experiment with KAPRI, a Ku-band ground-based interferometer with full-polarimetric capabilities [4]. In order to create a tomographic array, it was necessary to use two KAPRI devices (G21 and G22) operating in bistatic mode. The devices were placed in different locations, resulting in a separation of a few-meters in the horizontal and vertical directions that together with the local geometry determined the effective baseline. The KAPRI units were used sequentially. First, the G21 acted as both transmitter and receiver (master) while G22 was used as passive receiver only (slave). In the next step, the devices exchanged roles. The temporal baseline between transmissions was kept below 3 minutes, which allowed to consider consecutive acquisitions as simultaneous, hence, increasing the density of the tomographic array.

The tomographic imaging was performed in two locations. The first campaign took place in mid-February, a five-hour long time series were retrieved from the Jungfraufirn region (Aletschglacier, Switzerland), in a flat and homogeneous snow-covered region of the glacier. Additionally to the radar measurements, in-situ glacier investigations were done as a support for later data processing and interpretation. The complementary fieldwork consisted of installing two corner reflectors on the glacier surface for radar measurements, snow-depth investigations, recording the temperature and density of snow, taking images of snow grains and using a metallic scatterer inside the snowpack for depth penetration estimation. A second campaign was done in ETH Hönggerberg on a meadow area with the purpose of helping with the processing of the previous dataset and investigating decorrelation phenomena of unclear source on the Jungfraujoch campaign.

The data pre-processing is proving to be particularly challenging given the fact that Ku-Band has such a small wavelength (1.74cm) and makes the system very sensitive to small inaccuracies of the horizontal baseline. Thus, making the coregistration step very time consuming. Furthermore, the temporal baseline, despite being so small, is enough to cause decorrelation at this wavelength, in turn, resulting into noisy interferograms. Such problem had to eventually be solved by using very strong adaptive filtering (Goldstein filter).

Even though preprocessing is still on-going, there are results that are valuable due to the lack of investigations of different media using Ku-Band. Our specific radar configuration leads to a narrow “usable” region of the scene due to horizontal decorrelation. However, this limitation allows us to observe that the snow and vegetation datasets exhibit different decorrelation behaviors. We interpret this as the meadows behaving closer to a surface scatterer while the snowpack to a volume scatterer. Further analysis of the dataset will determine if the volume information contained in the radar signal can be exploited to reconstruct the vertical profile.

References

[1] Stoica, P., & Moses, R. L. (2005). Spectral Analysis of Signals. Prentice Hall.

[2] Tebaldini, S., et al. (2013). High-resolution 3D imaging of a snowpack from ground-based SAR at X and Ku band. IGARSS.

[3] Frey, O., et al. (2023). Time-series analysis of snow vertical profiles by SAR tomography at L/S/C, Ku, and Ka bands vs. snow characterization. IGARSS, 754–757.

[4] Werner, C., et al. (2012). The GPRI multi-mode differential interferometric radar for ground-based observations. EUSAR, 304–307.

NASA/JPL’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) has been deployed to collect multi-baseline interferometric SAR observations across a diverse range of forest biomes, including tropical forests in Africa and Central America, temperate forests in California and Maine, and boreal forests in Alaska and Saskatchewan, Canada. For each site, the multi-baseline data acquisition was designed and optimized based on expected canopy height, radar frequency (L- or P-band), and the number of interferometric tracks achievable within a single flight mission. The acquired multi-baseline data are processed, polarimetrically calibrated, and co-registered into stacks of single-look complex (SLC) images. To remove residual phase screens among SLCs, we apply the phase center double localization (PCDL) method for inter-track phase calibration. The resulting calibrated SLC stacks serve as input for subsequent polarimetric interferometric SAR (PolInSAR) and tomographic SAR (TomoSAR) processing. PolInSAR processing is performed using Kapok, an open-source software developed at JPL, to compute the full PolInSAR covariance matrix from the calibrated SLC stack. For TomoSAR processing, we use the Capon beamforming algorithm to reconstruct 3-D radar backscatter voxels (tomographic cubes). To retrieve canopy height and ground elevation beneath vegetation, we developed a SAR-Lidar data fusion workflow capable of ingesting a variety of PolInSAR and/or TomoSAR input, or a combination of both. In this presentation, we summarize the data processing and machine learning framework used to generate PolInSAR/TomoSAR-based canopy height and bare surface topography retrievals, and evaluate the performance and generalizability of data fusion models trained with different number of baselines, radar frequencies, and forest types.

Tropical forests are particularly important. Although they only cover about 6% of Earth’s surface, they are home to approx. 50% of the world’s animal and plant species. Their trees store 50% more carbon than trees outside the tropics. At the same time, they are one of the most endangered ecosystems on Earth: about 6 million of hectares per year are felled for timber or cleared for farming. Compared to the other components of the carbon cycle (i.e. the ocean as a sink and the burning of fossil fuels as a source), the uncertainty in the land local carbon stocks and the carbon fluxes are particularly large. This is especially true for tropical forests, which remain poorly characterized compared to other ecosystems on the planet. More than 98% of the land use change flux should be due to tropical deforestation, which converts carbon stored as woody biomass (of which around 50% is biomass) into emissions.

In the frame of the ESAs BIOMASS mission, selected in May 2013 as the 7th Earth Explorer mission to meet the pressing need for information on tropical carbon sinks and sources through estimates of forest height and biomass a first airborne campaign over tropical forest in Gabon was conducted. The campaign, called AFRISAR took place over four forest sites in Gabon where two acquisitions at different season where made, the first one was conducted by ONERA (SETHI system, July 2015) and the second one by DLR (F-SAR system, February 2016).

After 7 years a second campaign, called AFRISAR-2/GABONX, was conducted in 2023 exactly over the same test site and three further one selected by the Gabonese partners (the space agency AGEOS, CENAREST and the Ministry of Forest) with the same configuration. This time the campaign served also the mission objectives of ROSE-L, with its six days repeat-pass cycles acquiring fully polarimetric and multi-baseline data sets in L- and P-band. The main objective of this campaign was to observe short term changes in terms of decorrelation on the interferometric coherence and long term changes over 7 years.

A third campaign was conducted in late 2025 underflying the BIOMASS satellite in P-band for signal calibration and validation. First results of the campaign will be presented and a change analysis will be provided for the 7 and 9 years difference.

Terrestrial laser scanning (TLS) is being recognized as a key technology in forest monitoring by providing highly detailed 3D point clouds of the ecosystem. Recent algorithmic and computational advances now allow for the near-automated processing of the raw point clouds into 3D reconstructions of real forests. Here, we show how these 3D ‘virtual forest’ replicas, combined with the parameterization of its components (e.g. leaves, stems, soil), can serve as input for microwave interaction models (MIM) to study the interaction of electromagnetic waves with forests scenes in a realistic simulation environment.

First, we present ongoing work on in-vivo stem dielectric permittivity estimation with wood penetrating radar (WPR). Novel experimental WPR sensors are currently being tested, which non-destructively measure the forward and back scatter of multi-frequency microwaves emitted through the tree trunk by placing two antennas on opposite sides of the stem. Two such sensors have been installed on a sycamore tree in the Ghent University forest experimental site (Belgium) and have been measuring at a 20-minute time interval since February 2024. Concurrently, weather and microclimate variables are recorded and monthly TLS scans of the tree are made to capture the 3D dynamics (e.g. seasonality, growth, branch loss) of the tree. From the WPR measurements, the (dynamic) dielectric permittivity can be estimated, which holds potential to parametrize the woody components of the virtual forest for microwave MIM. We show preliminary results of how the dielectric permittivity relates to environmental and phenological variability.

Secondly, we demonstrate the use of microwave MIM using data from the Caxiuanã research site in the eastern Amazonia (Para, Brazil), the longest running drought experiment in the tropics. Both the 1-ha control plot and 1-ha rain throughfall exclusion (TFE) plot have been reconstructed into a 3D virtual forest from TLS acquired in November 2024. Additionally, for both plots, a tower radar system is installed centrally in the plot and 21 trees in the field of view of the radar are equipped with FDR sensors to estimate the stem water content. By combining these data sources, we aim to parameterize the MIPERS-4D microwave MIM and will show preliminary results of how simulations compare to actual measurements.

With these two use cases, we aim to demonstrate that the combination of structurally accurate 3D virtual forests with a parameterized microwave RTM would allow for a powerful instrument to facilitate the calibration and validation of remote sensing signals and derived biophysical products such as forest water status or biomass.

ESA’s BIOMASS launched in April 2025 is the first spaceborne P-band Synthetic Aperture Radar (SAR) ever, fully polarimetric (PolSAR), with primary objective of globally estimating forest properties and secondary goals among which the estimation of Digital Terrain Model (DTM) under vegetation. This can be done through multi-baseline InSAR processing of 3/7 acquisitions, depending on mission phase, separated by a 3-days lag [1].

Preliminary to forest products or DTM estimation, phase calibration of the multi-baseline interferometric (InSAR) stack is mandatory. The three main phase disturbances to be compensated are baseline errors, ionosphere and troposphere. BIOMASS InSAR calibration addresses the first two with a dedicated processing [2] and subsequently produces additional ground phases with the two-fold purpose of residual phase calibration and ground steering, i.e., setting height reference to terrain topography. This allows generating data stacks ready for tomographic (TomoSAR) processing and estimation of forest products.

We discuss in this presentation the strategies devised for BIOMASS DTM retrieval, starting from ground phases. First, we review different approaches to retrieve ground phases (i.e., purely InSAR versus PolInSAR) and address precise topography locking with high-resolution spectral estimation methods [3].

To finally estimate DTM, topography must be separated from residual low-pass disturbances, corresponding mainly to troposphere (APS, i.e., Atmospheric Phase Screen in InSAR literature). Effective APS compensation is challenging in difficult environments such as dense forests, where volume scattering, water vapor variability and a limited number of acquisitions make difficult to resort to traditional InSAR approaches [4]. We discuss a data-driven InSAR APS correction strategy designed for BIOMASS, first removing stratified troposphere, then reconstructing full turbulent phase from open areas. We also assess the superior performance and independence of this approach with respect to external correction services [5], which is desirable for an operational BIOMASS algorithm.

References

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

[2] S. Tebaldini, F. Salvaterra, F. Banda, and M. Pinheiro, “Multi-layer ionosphere correction in BIOMASS interferometry,” Submitted to POLINSAR 2026

[3] Salvaterra, Francesco; Ferro-Famil, Laurent; Banda, Francesco; Tebaldini, Stefano, “High-Resolution Techniques for Topography Estimation and Terrain Ground Steering within the ESA BIOMASS Processor”, submitted to POLINSAR 2026

[4] A. Ferretti, C. Prati, and F. Rocca, “Permanent scatterers in SAR interferometry,” IEEE Transactions on geoscience and remote sensing, 2002

[5] C. Yu, Z. Li, N. T. Penna, and P. Crippa, “Generic atmospheric correction model for interferometric synthetic aperture radar observations,” Journal of Geophysical Research: Solid Earth, 2018

The ESA BIOMASS mission is a single platform, fully polarimetric P-band SAR launched in April 2025 with the task of monitoring world forest and Above Ground Biomass distribution.

Besides supporting the generation of primary mission products, i.e. above ground biomass, forest height, the interferometric acquisitions can also be used to estimate the ground topography.

The estimation of a DTM over forested areas requires to separate the response of the ground from the one of the overlying volume, and to accurately estimate the elevation from which it originates. TomoSAR is a natural solution to this problem as it allows spatial discrimination in the vertical direction to identify the different scattering sources [1-3].

The small bandwidth of the signals measured by the BIOMASS sensor limits the vertical resolution to coarse values in the tomographic mode and to extremely coarse ones in the dual-baseline case. The resulting lack of accuracy and a limited contrast between the responses of the ground and of the canopy may seriously affect the performance of DTM retrieval using classical tomographic imaging techniques or phase center estimation [5].

High-Resolution (HR) spectral analysis techniques represent an alternative to Fourier-based approaches, characterized by significantly improved resolution values. Whereas Fourier-based techniques discriminate sources from reconstructed intensity profiles, HR techniques separate sources by associating the response to low-rank models whose simplicity guarantees both identifiability and robustness [4,5].

In the BIOMASS ground phase estimation processor, the HR spectral analysis is applied after compensating the image stack for low-pass phase screens, mainly caused by tropospheric disturbances. This compensation can be performed using a Phase linking approach (PL) or SKP decomposition.

In BIOMASS processor, the outputs of the HR spectral analysis and the low-pass phase screen calibration are combined to estimate the ground phase, defined as the interferometric phase corresponding to the ground scattering, necessary for ground steering.

This contribution evaluates the performance of HR methods for the estimation of ground phases in the context of the BIOMASS mission, and addresses the following points:

1) association with the SKP and PL methods.

2) assessment of the robustness of the proposed method with respect to residual phase screens.

Performance evaluation is carried out using data from ESA’s TropiSAR, AfriSAR, and TomoSense campaigns, analyzing the influence of polarization choice and calibration methods. Results derived from the BIOMASS ground processor will also be presented.

[1] Soja, M.J., Quegan, S., d’Alessandro, M.M. et al. (2021) Mapping above-ground biomass in tropical forests with ground-cancelled P-band SAR and limited reference data. Remote Sensing of Environment, 253. 112153. ISSN 0034-4257

[2] Y. Huang, L. Ferro-Famil and A. Reigber, "Under-Foliage Object Imaging Using SAR Tomography and Polarimetric Spectral Estimators," in IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 6, pp. 2213-2225, June 2012.

[3] Y. Huang and L. Ferro-Famil, "3-D Characterization of Urban Areas Using High-Resolution Polarimetric SAR Tomographic Techniques and a Minimal Number of Acquisitions," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 11, pp. 9086-9103, Nov. 2021.

[4] Y. Huang, Q. Zhang, and L. Ferro-Famil, “Forest Height Estimation Using a Single-Pass Airborne L-Band Polarimetric and Interferometric SAR System and Tomographic Techniques,” Remote Sensing, vol. 13, no. 3, p. 487, Jan. 2021

[5] P. -A. Bou, L. Ferro-Famil, F. Brigui and Y. Huang, "Tropical forest characterisation using parametric SAR tomography at P band and low-dimensional models," in IEEE Geoscience and Remote Sensing Letters

Digital elevation modeling in desert environments presents unique challenges for radar-based remote sensing. The varying penetration capabilities of radar bands result in terrain representations that differ significantly in depth and detail. X-band signals penetrate only a few centimeters, C-band up to approximately 50 cm, and L-band between 2–3 meters. In contrast, the P-band, used by the Biomass satellite currently in orbit, can penetrate more than 5 meters—potentially reaching bedrock in areas with shallow sand cover. In areas where radar penetration is deep, advanced image processing techniques become crucial for distinguishing between the multiple subsurface layers contributing to the signal.

This study investigates the potential of P-band imagery to enhance terrain modeling in desert regions by comparing it with DEMs derived from X, C, and L-band data. The objective is to provide a preliminary assessment of P-band’s ability to represent subsurface morphology and to anticipate the performance of advanced processing techniques—such as tomography and Polarimetric Interferometric SAR (PolInSAR)—that will be applied to Biomass data for topographic extraction beneath desert surfaces.

The analysis utilizes existing data such as Copernicus (X-band) and SRTM (C-band) DEMs, alongside an InSAR-derived DEM based on ALOS PALSAR imagery over an area in Egypt. Validation is conducted using two complementary approaches:

• External validation compares DEMs against ground truth data, primarily Ground Penetrating Radar (GPR) measurements, to evaluate elevation and slope accuracy compared with bedrock elevation reference.

• Internal validation assesses geomorphological consistency based on physical and statistical principles.

This experiment will be completed by 2D analysis of Biomass polarimetric features to assess to sensitivity of P-band to deeper targets.

The outcomes of this study contribute to the development of a validation framework for future bedrock DEMs generated from Biomass P-band data. This framework aims to clarify the capabilities and limitations of P-band radar for subsurface terrain modeling in arid regions, with implications for geoscientific research and environmental monitoring.

The European Space Agency’s (ESA) BIOMASS mission represents the first spaceborne Synthetic Aperture Radar (SAR) operating at P-band, providing an unprecedented perspective for Earth observation. It is also the first mission to systematically utilize SAR tomography for three-dimensional mapping of terrestrial structures. While its primary objective is to investigate the global biosphere, the mission also offers significant potential for imaging subsurface geological structures in arid regions.

This paper presents a preliminary interferometric analysis of ESA BIOMASS data over desert regions. After its launch, the satellite entered a six-month commissioning phase, during which it collected repeat-pass interferometric pairs with a three-day revisit cycle and a range of spatial baselines, from near-zero to variable separations. Using a straightforward regional InSAR processing approach, the resulting interferograms yields the following initial findings: (i) time-series BIOMASS interferometric data with nearly zero spatial baselines and three-day temporal separation enable the detection of spatiotemporal sand dune movements through interferometric phase measurements; (ii) BIOMASS interferometric data with appropriate spatial baselines facilitate elevation estimation of potential subsurface structures at a study site in the eastern Sahara. These preliminary results indicate that the interferometric capabilities of BIOMASS mission may unlock new opportunities for desert environment research. A thorough analysis will follow upon completion of precise calibration and rigorous validation.

This study presents initial findings from the analysis of Biomass P-band polarimetric synthetic aperture radar (PolSAR) data acquired over the Tibesti Mountains, an arid region characterized by predominantly sedimentary and volcanic rocks, as well as ancient calderas. Only one P-band scene was available for this area, but it provided a valuable opportunity to assess the potential of P-band data for geological applications. We compiled comprehensive geological information, including the integration of hyperspectral datasets from Prisma satellite, and performed classification using various methods. Multiple polarimetric decompositions were evaluated for their suitability in geological mapping and compared with results from historical L-band PolSAR data (ALOS, SAOCOM).

The analysis began with the L1a SCS product, which was ingested using a Jupyter notebook reader. The data were subsequently converted to the DIM format and processed within the Python environment using SNAPISTA and SNAP software. Terrain correction was applied to ensure compatibility with GIS layers and hyperspectral data. Preliminary results allow to analyse overall data quality and reveal intriguing geological features that have not been previously documented, highlighting the unique value of P-band SAR in resolving surface roughness and subsurface structures due to its enhanced penetration capabilities. While the geology of the examined area is relatively straightforward, this work demonstrates the added perspective that P-band data can offer. Future work will focus on analyzing data from the northern slopes of the Tibesti Mountains, where the geology is more complex and includes significant metamorphic rock formations.

BIOMASS ice flow mapping

Antarctic ice flow mapping from satellite SAR is a well-established technique, with several products available for users [1][2]. These products rely heavily on Sentinel-1 data, but the C-band wavelength means that phase-based InSAR techniques fail on fast moving glaciers due to loss of coherence from excessive fringe rates and high sensitivity to surface conditions. The fallback technique, amplitude-based offset-tracking, results in noisier velocity maps. For BIOMASS, only InSAR methods are expected to work, due to the coarse range resolution resulting from the 6MHz bandwidth.

The acquisition scenario and radar parameters of BIOMASS represent opportunities but also potential challenges when using BIOMASS for ice flow mapping. During the tomographic and InSAR phases, a given ground track is acquired in sets of images with 3-day temporal separation (7 images in each set in the tomographic phase, 3 in the InSAR phase) and a spatial separation of 15% of the critical baseline[3] in the tomographic phase and even larger baselines in the InSAR phase, so unlike Sentinel-1, a consistent dense temporal sampling cannot be achieved.

Compared to existing sensors, the increased penetration of the 70 cm wavelength reduces the adverse impact of changes in surface conditions, and in combination with the short temporal baselines, this is expected to result in reduced temporal decorrelation. Also, the long wavelength reduces fringe rates and simplifies phase unwrapping, although the low range resolution to some extent counteracts this benefit.

On the other hand, the increased penetration can result in increased volume decorrelation for baselines much smaller than the critical baseline, and this might well be an issue, considering the relatively large spatial baselines mentioned above. The long wavelength also means a significant sensitivity to ionospheric scintillations.

In this contribution, we present InSAR ice velocity maps generated from BIOMASS data acquired over Antarctica during the commissioning phase and investigate the impact of spatial baseline drift by comparing velocity maps generated from 0-baseline data with velocity maps generated from larger baseline data. Also, the impact of residual ionospheric signal on ice flow mapping is investigated.

References

[1] Rignot, E., J. Mouginot, and B. Scheuchl. 2017. MEaSUREs InSAR-Based Antarctica Ice Velocity Map, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/D7GK8F5J8M8R.

[2] J. Wuite, M. Hetzenecker, T. Nagler and S. Scheiblauer, ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly velocity from 2017 to 2020, derived from Sentinel-1, v1, NERC EDS Centre for Environmental Data Analysis, 2021.

[3] Shaun Quegan, 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.

Microwave remote sensing has become an indispensable tool for monitoring polar regions, as its ability to penetrate dry snow and ice makes Synthetic Aperture Radar (SAR) particularly sensitive to subsurface structures. This sensitivity increases with decreasing frequency, enabling the detection of internal layers and features that remain invisible at higher frequencies.

ESA’s BIOMASS mission marks a significant milestone as the first spaceborne SAR system operating at P-band (435 MHz), providing unprecedented penetration depths and the potential to observe structural information from tens or even hundreds of meters below the surface [1]. Beyond its primary objective of global forest observation, BIOMASS offers new opportunities for cryospheric research through its fully polarimetric mode, which enables detailed characterization of scattering mechanisms within the Antarctic ice sheet.

This study focuses on an in-depth polarimetric analysis of BIOMASS data over Antarctica [2]. A dedicated test site will be selected based on the availability of ground reference measurements, such as sounder data, ground-penetrating radar (GPR) profiles, and ice core observations, which will be essential for validation and physical interpretation of the satellite data.

The analysis builds on previous experience with P-band SAR data in polar environments, gained during an airborne campaign with DLR’s F-SAR sensor in Greenland. There, multiple test sites across distinct glacial zones revealed key insights into polarimetric signatures. In the percolation zone, the anisotropic microstructure of the firn was shown to induce significant co-polar phase differences (CPD), and a corresponding CPD-based model established a quantitative relationship between polarimetric SAR measurements and firn thickness [3]. In the ablation zone, a Pauli decomposition in combination with entropy and the mean alpha angle distinguished areas with potential subtle differences in water content and density [4]. Furthermore, crevasses were effectively detected through characteristic combinations of volume and dihedral scattering, with polarization-dependent contrasts aiding their detection and characterization.

Preliminary investigations of BIOMASS P-band data indicate similar scattering mechanisms to those observed in the airborne campaign. However, several signatures identified in the BIOMASS polarimetry cannot yet be explained. To address this, the planned analysis will incorporate complementary decomposition techniques (e.g., eigenvalue-based and model-based approaches) to better characterize the dominant scattering mechanisms and retrieve their glaciological origin. A particular focus will be on investigating subsurface structures and exploring unusual scattering behaviors that may reveal previously unknown processes within the ice.

By integrating polarimetric analysis with reference data and advanced decomposition methods, this work aims to improve the physical understanding of P-band interactions with Antarctic ice. The resulting framework will provide the basis for fully exploiting BIOMASS P-band observations in cryospheric remote sensing and advancing the study of subsurface structures and ice-sheet dynamics.

[1] Rignot, E., et al. (2001). Penetration depth of interferometric synthetic-aperture radar signals in snow and ice. Geophysical Research Letters, 28(18), Art. no. 18.

[2] Cloude, S. (2010). Polarisation: applications in remote sensing (1st ed.). Oxford: Oxford University Press.

[3] Fischer, G. et al. (2019). Modeling Multifrequency Pol-InSAR Data from the Percolation Zone of the Greenland Ice Sheet. IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 4

[4] Schlenk, S. et al. (2025). Characterization of Ice Features in the Southwest Greenland Ablation Zone Using Multi-Modal SAR Data. The Cyrosphere

Ice mapping is one of the secondary objectives of ESA's fully polarimetric P-band SAR mission, BIOMASS, recently launched on 29 April 2025 [1]. The use of P-band allows for deeper penetration into ice sheets and glaciers than what has been possible with the higher frequency spaceborne systems, used until now. The BIOMASS mission potentially allows for the mapping of subsurface features such as ice inclusions in the firn-pack, aquifers, and firn depths though the employment of advanced SAR techniques, namely Polarimetric SAR Interferometry (PolInSAR) and SAR Tomography (TomoSAR). Over the course of the BIOMASS mission, data will be acquired in two acquisition phases with orbits designed specifically for each of the two techniques. Furthermore, during the BIOMASS commissioning (COM) phase, data will be gathered with large spatial baselines over the Antarctic continent, potentially allowing for TomoSAR mapping of ice sheets with high vertical resolution.

From 11 December 2023 to 14 February, the Technical University of Denmark completed an airborne radar campaign in Antarctica. The primary objective was to gather airborne P-band data from the Antarctic continent in support of BIOMASS. Data was acquired with the POLARIS instrument, which was developed by the university, and commissioned by ESA [2]. The POLARIS instrument is a fully polarimetric P-band radar capable of operating both as an ice sounder and in a SAR configuration. During the campaign, PolInSAR data was acquired around the Dome C region in the dry snow zone, where no summer melt occurs. Also, both PolInSAR, TomoSAR, and ice sounder data was acquired at the Shackleton ice shelf in the percolation zone, where summer melt percolates down through the firn-pack, thus forming ice inclusions.

Previously, PolInSAR and TomoSAR analyses of ice sheets have been carried out in Greenland. However, this was in the ablation and percolation zone [3][4][5]. However, 90% of the Antarctic ice sheet is in the dry snow zone. Furthermore, the Antarctic continent is subject to specific meteorological conditions, which are not present in other snow-covered regions. Most notably, the surface at the Dome C region is dominated by longitudinal snow dunes [6]. These surface features lead to highly anisotropic backscatter while also potentially impacting the polarimetric signature of SAR images [7].

In this contribution, we present polarimetric analysis and PolInSAR results for both sites based the Uniform Volume under Surface (UVuS) model [8] (Dome C) and a more complex coherence model, accounting for both a surface and a subsurface scattering layer [3] (Shackleton). Furthermore, the presence of both PolInSAR, TomoSAR, and ice sounder data at the Shackleton site allows for a very thorough assessment of the feasibility of subsurface mapping of ice sheets through PolInSAR and TomoSAR techniques. At this site, subsurface structures observable in TomoSAR and ice sounder profile was predicted by PolInSAR model inversion, signifying an excellent level of cohesion between techniques. Finally, degradation of airborne P-band data allows for the direct assessment of BIOMASS feasibility regarding the subsurface mapping of ice sheets through the employment of TomoSAR and PolInSAR techniques.

References

[1] Shaun Quegan et al. “The European Space Agency BIOMASS mission: Measuring forest above-

ground biomass from space”. eng. In: Remote Sensing of Environment 227 (2019), pp. 44–60. ISSN:

18790704, 00344257. DOI: 10.1016/j.rse.2019.03.032.

[2] Jørgen Dall et al. “ESA’S POLarimetric Airborne Radar Ice Sounder (POLARIS): design and first

results”. eng. In: I E T Radar, Sonar and Navigation 4.3 (2010), pp. 488–496. ISSN: 17518784,

17518792. DOI: 10.1049/iet-rsn.2009.0035.

[3] Georg Fischer, Konstantinos P Papathanassiou, and Irena Hajnsek. “Modeling multifrequency pol-

InSAR data from the percolation zone of the Greenland ice sheet”. In: IEEE Trans. Geosci. Remote

Sens. 57.4 (Apr. 2019), pp. 1963–1976.

[4] Georg Fischer et al. “Modeling the Vertical Backscattering Distribution in the Percolation Zone of

the Greenland Ice Sheet With SAR Tomography”. eng. In: Ieee Journal of Selected Topics in Applied

Earth Observations and Remote Sensing 12.11 (2019), pp. 4389–4405. ISSN: 19391404, 21511535.

DOI: 10.1109/JSTARS.2019.2951026.

[5] Francesco Banda, Jørgen Dall, and Stefano Tebaldini. “Single and multipolarimetric P-band SAR

tomography of subsurface ice structure”. In: IEEE Trans. Geosci. Remote Sens. 54.5 (May 2016),

pp. 2832–2845.

[6] Marine Poizat et al. “Widespread longitudinal snow dunes in Antarctica shaped by sintering”. en. In:

Nat. Geosci. 17.9 (Sept. 2024), pp. 889–895.

[7] Jayanti J Sharma et al. “Polarimetric decomposition over glacier ice using long-wavelength airborne

PolSAR”. In: IEEE Trans. Geosci. Remote Sens. 49.1 (Jan. 2011), pp. 519–535.

[8] Jørgen Dall, Konstantinos Papathanassiou, and Henning Skriver. “Polarimetric SAR interferometry

applied to land ice: modeling”. eng. In: Proceedings of the Eusar 2004 Conference (2004), pp. 247–

250.

Understanding structure and dynamics of ice sheets and glaciers is of crucial importance [1], as ice masses represent a major storage of freshwater and influence global water circulation. Loss of ice masses is exacerbated by warming climate, with a negative impact on sea level rise. There is thus an urgent need for globally improving knowledge about ice sheets and glaciers, which is difficult to achieve only with in situ studies, due to limited coverage and often access difficulties. Satellite remote sensing can provide a bridge, with Synthetic Aperture Radar (SAR) able to acquire information in all-weather, day and night conditions. SAR interferometry (InSAR) and tomography (TomoSAR) at long wavelengths (P and L bands) give access to the internal structure of ice by combining multiple SAR surveys from slightly different viewpoints [2].

ESA’s BIOMASS [3], successfully launched April 29, 2025, features the first spaceborne P-band SAR with fully polarimetric capabilities and orbits enabling InSAR and TomoSAR. BIOMASS primary target are world’s forests, though the long wavelength of about 70 cm is an unprecedented opportunity for more and equally important applications, among which the investigation of subsurface structures, including icy regions. In particular, orbits deployed for antenna pattern characterization over BIOMASS transponder during In-Orbit Commissioning (IOC) phase are suitable for TomoSAR imaging over Antarctica.

In this contribution we present preliminary BIOMASS TomoSAR results over Antarctica. We processed a dataset acquired in the July/August 2025 IOC phase over the Shackleton Ice Shelf System [4], an increasingly studied site gaining attention due to its vulnerability and important role played in stabilizing part of the East Antarctic ice sheet. Contextually, we extended Multi-Squint InSAR (MS-InSAR) presented in a companion contribution [6] to multi-baseline, to phase calibrate the data stack and counteract coherence losses due to severe ionosphere at high latitudes.

References

[1] P. L. Whitehouse, N. Gomez, M. A. King, and D. A. Wiens, “Solid Earth change and the evolution of the Antarctic Ice Sheet,” Nature communications, 2019

[2] F. Banda, J. Dall, and S. Tebaldini, “Single and multipolarimetric P-band SAR tomography of subsurface ice structure,” IEEE Transactions on Geoscience and Remote Sensing, 2015

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

[4] Thompson, Sarah S et al., “Glaciological history and structural evolution of the shackleton ice shelf system, east antarctica, over the past 60 years,” The Cryosphere, 2023

[6] S. Tebaldini, F. Salvaterra, F. Banda, and M. Pinheiro, “Multi-layer ionosphere correction in BIOMASS interferometry,” Submitted to POLINSAR 2026

Lakes are common features of arctic lowland permafrost regions. Increasing temperatures and changing precipitation regimes at higher latitudes affect the ice forming seasonally at their surface. In particular, the thinning of this ice layer can lead to a shift from bedfast-ice regime, where the ice reaches the bottom of the lake, to a floating-ice regime, where there still remains liquid water below the ice layer. This in turn can lead to potential greenhouse gas release from the newly unfrozen ground at the lake bottom [1]. Monitoring the ice thickness and the ice regime is therefore crucial. To this end, Synthetic Aperture Radar (SAR) is well adapted because the radar waves penetrate into the ice volume at all bands. Though the polarimetric signature of these lakes has been well studied [2], no study yet has investigated the vertical reflectivity profile from the ice layer which can be retrieved by combining SAR acquisitions coherently, in a SAR interferometry (InSAR) or SAR tomography (TomoSAR) framework. This contribution aims at reconstructing this profile in a number of lakes by exploiting the sensitivity in the height direction of multi-baseline SAR data and at improving the understanding of scattering in the lake ice volume and at its interfaces: air/ice and ice/water interfaces in the floating-ice case; air/ice and ice/frozen-ground interfaces in the bedfast-ice case.

For this analysis, we propose to use the PermASAR19 airborne TomoSAR dataset, which was collected by the German Aerospace Center (DLR) in the Canadian low Arctic in the late winter season of 2019. The acquisitions are fully polarimetric, and were performed at several bands (X-, C- and L-band) with submeter spatial resolution in both range and azimuth directions, within a two-hour time window. The SAR footprint covers several lakes which are known to be shallow (only a few meters depth), and which ice thickness is expected to reach approximately 1 meter [3] [4]. Challenges arise from the thinness of the ice layer with respect to achievable resolution in height from usual beamforming methods, suggesting that high-resolution tomographic techniques like Capon beamforming are required.

First analyses with separated polarizations show that scattering occurring within the ice volume and at interfaces can be observed in the reconstructed profiles, at X-band and C-band over several lakes. Combining polarimetric channels coherently in a polarimetric tomographic SAR (Pol-TomoSAR) framework will improve the physical understanding of the retrieved profiles [5]. A Pol-TomoSAR analysis over several lakes of the testsite will be presented. The results of several bands, in particular X-band and C-band, will be compared. The obtained estimated structure information will be assessed with regards to ground measurements of lake bathymetry and ice thickness [3] [4].

[1] C. D. Arp, B. M. Jones, Z. Lu, and M. S. Whitman (2012), “Shifting balance of thermokarst lake ice regimes across the Arctic Coastal Plain of northern Alaska”, Geophysical Research Letters, vol. 39, L16503, doi: 10.1029/2012GL052518

[2] D. K. Atwood, G. E. Gunn, C. Roussi, J. Wu, C. Duguay, and K. Sarabandi (2015), “Microwave Backscatter From Arctic Lake Ice and Polarimetric Implications”, IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 11, p. 5972-5982

[3] E. J. Wilcox (2025), "Ice thickness, snow depth and lake properties for sampled lakes in and around the Trail Valley Creek watershed, NT", https://doi.org/10.5683/SP3/VP1UMC, Borealis

[4] E. J. Wilcox (2025), "Lake bathymetry measurements for sampled lakes in and around the Trail Valley Creek watershed, NT", https://doi.org/10.5683/SP3/7XADY4, Borealis

[5] L. Ferro-Famil, Y. Huang, and A. Reigber (2012), “High-resolution SAR tomography using full rank polarimetric spectral estimators”, 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, pp. 5194-5197

Shipping in the Arctic is a huge commercial operation. The presence of icebergs therefore poses a hazard to such operations. Of particular interest are icebergs within sea ice, and a need for automated detection methods. In this work, we utilise a convolutional neural network (CNN) for iceberg detection in fast ice environments. Fast ice is a type of sea ice that forms off of coastlines and remains attached to surrounding land or sea floor. This means that fast ice generally remains in place and is not affected by currents or wind. In Arctic seas, fast ice can extend down to 20 m and has a varied topology depending on environment. Fast ice can be distinguished from drift ice since it does not contain large cracks and fractures. We utilise Sentinel-1 SAR data acquired over Franz-Josef region for this work. Although icebergs show up clearly in optical data, the dependence on conditions such as cloud cover means training with optical data would lead to a less robust program. As such, SAR data is used alongside a Sentinel-2 optical dataset. This SAR data is split by horizontal (HH) and horizontal-vertical (HV) polarisation, with icebergs being clearer in HV polarisation. We also make use of a land mask from the Polar Geospatial Center which helped to aid the training process.

A detection filter used to identify icebergs was proposed by Marino et al (2016). This filter is known as the dual intensity polarisation ratio anomaly detector (iDPolRAD) and has been successfully used in a previous study by Soldal et al (2019) to separate, identify and detect icebergs in sea ice environments. For this work, an iDPolRAD filter is applied to the SAR data to produce training images for a YOLO v8 detection model.

We perform training for 50 epochs with a batch size of 16 and a learning rate of 0.001. For YOLO v8, precision, recall, F1 score and mean average precision (mAP) are used for evaluating the detection performance. Precision measures the ratio between true positives and any new detections (false positives), while recall measures the ratio between true positive and objects the model failed to detect (false negatives). The F1 score acts as a ratio between precision and recall and can be used to determine the optimum confidence score for the algorithm. The mAP score is defined as the total accuracy of the model and is found by taking the area under the Precision-Recall (PR) curve. For model evaluation, we obtained a precision of 0.759, recall of 0.706, F1 score of 0.732 and a mAP of 0.789, giving the model an accuracy of 79%. These results are acceptable for feasible operational use.

The main limitations of this work amount to a lack of an available automated iceberg training dataset, which was addressed by creation of a manual dataset and the continued lack of coverage which can be addressed by future SAR missions (ROSE-L and NISAR). It is hoped that our detection system can be further improved in the future for potential commercialisation.

Height information of semi-transparent media is usually derived with interferometric or tomographic SAR techniques, such as forest height or the penetration depth into glaciers. In contrast, BIOMASS data present the opportunity to derive the thickness of icebergs solely from SAR polarimetry. The large penetration of the long-wavelength signals into ice and the quad-pol polarimetry allow to observe signals originating from the bottom of an iceberg, the ice-water interface. This bottom signal appears like a range-delayed replica of the direct signal from the top of the iceberg with distinct polarimetric characteristics.

The targets of interest are tabular icebergs with sizes of often several kilometers. They are calving from ice shelves, as opposed to their non-tabular, irregularly shaped, smaller siblings calving from marine terminating glaciers. Further, tabular icebergs have a more or less rectangular profile, with a flat top, steep sides, and a relatively flat bottom. Even though this rectangular profile degrades over time, it allows to formulate a first-order model of the range delay between the backscatter from the top and the bottom of the iceberg. More specifically, the range distance between the top and bottom signals, as well as the length of the delayed bottom signal, depends on the thickness and the ground-range length of the iceberg. The permittivity of ice is considered for calculating refraction angles and range delays. Using Archimedes principle, the thickness can be separated into freeboard and submerged draft in order to consider the signals penetrating not only through the top of the iceberg, but also through the frontal freeboard wall.

In single-pol backscatter images, it can be difficult to discriminate the iceberg bottom signal from a top signal. Further, the bottom signal can be also hidden in the surrounding sea ice backscatter. The quad-pol data of BIOMASS allows a clear distinction, in most cases, between the top and bottom signals of an iceberg. An interesting polarimetric pattern can be observed: Icebergs that show a surface-scattering mechanism in the top backscatter, with low polarimetric entropy and alpha parameters, have a strong dihedral contribution in the range-delayed bottom signal, with high alpha values and large phase differences between HH and VV. In contrast, icebergs that show a medium entropy and medium alpha scattering mechanism in the top backscatter, have similar scattering properties also in their bottom signal. A first theory is a stronger volume scattering contribution from inside the iceberg causing both the top and the bottom signal to appear volume-like.

So far, the investigation concentrated on one BIOMASS acquisition from the commissioning phase, where channel imbalance phase and Faraday rotation were corrected, so that the phase differences between different channels have been widely calibrated, making the data ready for polarimetric analysis.

A first estimation, with the first-order geometry model and by discriminating top and bottom signals according to their polarimetric characteristics, resulted in a thickness of 130 m. This iceberg is located in front of the Jelbart ice shelf, which has reported ice thicknesses of about 200 m at the calving front. Further investigations will refine the model formulation and the understanding of the polarimetric characteristics, as well as increase the number of estimated icebergs and include validation.

Snow water equivalent (SWE) is a critical parameter of seasonal snow cover for meteorology and hydrology in northern China and other high-latitude or high-altitude regions with abundant snow resources. However, our ability to accurately measure and monitor SWE change from satellite remote sensing remains a challenge. Traditional passive microwave remote sensing provides daily and large-scale SWE observations, but is limited by its coarse spatial resolution, which is typically tens of kilometers in scale. Repeat-pass Interferometric Synthetic Aperture Radar (InSAR) offers a promising approach to obtaining SWE change at high spatial resolution and accuracy. For this technique, low-frequency (e.g., L-band) radar signals and shorter revisit times are essential for minimizing temporal decorrelation in frequent snowfall regions. This technique has been available until recently due to its limited observations with the optimal radar frequencies and temporal repeat intervals.

This study presents the first demonstration of spaceborne repeat-pass L-band InSAR observations from the Chinese Lutan-1 mission for retrieving SWE changes at Altay, Xinjiang Province, during the winter of 2023–2024. Consecutive 4-day and 8-day repeat-pass interferometric pairs were processed to phase changes, and then related to SWE variations. An InSAR processing chain was developed, including atmospheric phase delay correction (both ionospheric and tropospheric effects), orbital error removal, filtering parameter optimization, and phase calibration. These procedures establish a comprehensive workflow for time-series InSAR SWE retrieval using L-band Lutan-1 data.

The retrieved SWE change shows a good agreement with in-situ SWE observations during the dry snow period (January 12 to February 9, 2024), yielding a root mean square error (RMSE) of 9 mm and a correlation coefficient (R) of 0.48 for the 4-day temporal baselines (p-value << 0.05). However, the accuracy decreases significantly for the 8-day baselines (February 17 to March 28, 2024), mainly due to temporal decorrelation associated with snowfall and snowmelt events. A heavy snowfall observed from February 9 to 17, 2024, induced severe decorrelation, leading to phase unwrapping errors and preventing the retrieval of SWE. This finding emphasizes the necessity of using shorter temporal baselines, such as 4 days, in regions characterized by rapid snow accumulation and ablation processes.

Overall, this study demonstrates the capability of spaceborne repeat-pass L-band InSAR with short revisit intervals to effectively retrieve SWE change under appropriate snow cover conditions. The results also highlight the potential and challenges of operational SWE monitoring from existing and upcoming L-band SAR missions, such as JAXA’s ALOS-4, NASA’s NISAR, and ESA’s ROSE-L, which feature short repeat cycles, wide swath coverage, and high spatial resolution. Future work will focus on improving SWE retrieval accuracy by investigating the impacts of meteorological and environmental factors on InSAR phase.

Plastic pollution represents a major threat to marine ecosystems, leading to biodiversity loss and posing risks to human health and safety. The detection of floating plastic objects through in-situ surveys and direct observations remains a challenging task, as these materials are in constant motion across vast and often inaccessible marine regions. In this context, satellite data can play an important role for monitoring and detecting plastic accumulations thanks to their frequent temporal sampling and broad spatial coverage. While most existing methods for detecting floating plastic islands rely on satellite optical data, the use of Synthetic Aperture Radar (SAR) images for plastic marine litter monitoring has so far been limited, although they offer the advantage of being acquired regardless of sunlight or weather conditions. This study aims at investigating the capability of multi-frequency and dual-polarization SAR data to identify a small floating plastic island, approximately 30 m x 3 m in size, deployed in a controlled marine environment in the Gulf of La Spezia (Liguria, Italy) in April 2025. During the experimental campaign, X-band dual-polarization COSMO-SkyMed Second Generation (CSG) and C-band dual-polarization Sentinel-1 (S1) images were acquired with different spatial resolutions and imaging geometries, providing the opportunity to assess the detectability of plastic marine litter using different SAR configurations. The dual-polarization covariance matrix was extracted from both datasets, and different polarimetric techniques were applied, including the H-alpha decomposition and the m-chi decomposition. The objective of the study is to identify an optimal polarimetric parameter capable of detecting the floating plastic island by distinguishing it from the surrounding water. In fact, while calm water surfaces are expected to reflect most of the radar signal away from the sensor, the presence of plastic objects increases the surface roughness, resulting in the signal being scattered in multiple directions. The results of the analysis, which is still ongoing, will be presented during the workshop. However, the preliminary findings already provide promising indications regarding the potential to extract different parameters suitable for this challenging task, also considering the very limited number of literature studies that have explored the use of SAR data for similar applications.

The primary source of ocean surface signatures in SAR images is the surface wave field generated by local wind stress. The normalized radar cross section (NRCS) wave spectrum offers a statistical representation of the surface roughness induced by these wind waves and is closely linked to wind speed, wind direction, and overall sea state. Wind-generated waves affect the backscattered radar signal through three main mechanisms: specular reflection, Bragg scattering, and wave breaking. The relative contribution of each mechanism to the received signal depends on the radar observing frequency. For instance, at P-band wavelengths, Bragg scattering is expected to be the dominant mechanism, primarily interacting with wave features on the order of one meter. Comparing wave spectra retrieved across different frequencies enhance understanding of upper ocean dynamics under varying sea state conditions. Since different radar wavelengths are sensitive to different ocean wave scales, multi-frequency analyses also reveal how wave energy is distributed across scales, improving interpretation of sea surface processes.

In this project, we will analyze the NRCS wave spectrum to investigate ocean surface roughness and the structure of the wave field. The Biomass mission offers a unique opportunity to investigate radar observables at longer wavelengths, enabling assessment of NRCS signatures in P-band SAR observations over the ocean. This analysis allows testing the assumption that Bragg scattering dominates the received signal at P-band. In addition to Biomass, we perform multi-frequency comparisons of wave spectra retrieved from Sentinel-1 (C-band) and SAOCOM-1 (L-band). This analysis will further improve understanding of frequency-dependent scattering behavior over the ocean.

Polarimetric SAR Interferometry (PolInSAR) merges polarimetric SAR, offering the ability to distinguish between different scattering types within individual resolution cells, and interferometric SAR, which provides sensitivity to the vertical stratification of scatterers.

The PolInSAR two layer model represents the signal as a combination of the ground and the volume contributions. It has been especially useful to model the PolInSAR signal over vegetation. Taking advantage of the fact that these two components have different polarimetric and interferometric characteristics, it is possible to extract physical parameters such as the ground phase or the height of the forest [1]. Recently, this model has also been used to separate and extract fully polarimetric signatures from the ground and volume, as a polarimetric decomposition [2].

When several baselines are available, the vertical reflectivity profile of the vegetation can be estimated. This is equivalent to a synthetic aperture in the across-track dimension, as done in SAR Tomography. In this sense, the vertical reflectivity profile estimation is converted into a spectral estimation. Assuming the PolInSAR two layer model, the vertical reflectivity of the ground and volume components define their interferometric coherences, linking polarimetric and interferometric dimensions of the data [3]. In this work, we propose exploiting the spatial variability of the volume layer height across the image, instead of requiring additional baselines, to estimate its profile. The additional assumption then is that the volume profile of spatially close stands may be seen as the same normalized profile scaled in height.

The proposed technique has been evaluated with airborne data from the DLR FSAR sensor over the AfriSAR campaign at P-band, providing a reasonable volume profile over tropical forests when using a single baseline. This technique could be very relevant for the BIOMASS mission, where only a limited number of baselines will be available during the interferometric phase, allowing the analysis of the forest vertical profiles and its changes over time.

References

[1] S. Cloude and K. Papathanassiou, “Three-stage inversion process for polarimetric SAR interferometry,” IEE Proceedings-Radar, Sonar and Navigation, vol. 150, no. 3, pp. 125–134, 2003.

[2] A. Alonso-Gonzalez and K. P. Papathanassiou, “Multibaseline two layer model polinsar ground and volume separation,” in EUSAR 2018; 12th European Conference on Synthetic Aperture Radar, 2018, pp. 1–5.

[3] A. Alonso-González, N. Romero-Puig, C. López-Martínez, and K. P. Papathanassiou, “Polarimetric Ground and Volume Decomposition by means of PolInSAR two layer model,” Submitted to IEEE Transactions on Geoscience and Remote Sensing, under review.

Advanced spaceborne 3D Tomographic SAR (Tomo-SAR) [1-5] missions based on companion/tandem (i.e. formation-flying) configurations are emerging for remote sensing of forests [2-4,6], key in the CO2 cycle matters. In fact, this implementation of Tomo-SAR, obtaining the height profile from a sequence of track-pair only coherence data, bypasses typical long-term decorrelation issues of repeat pass monostatic SAR configurations [2,3]. However, because of safety/orbit control issues, the formation satellites do not pass exactly simultaneously [6,7]. Therefore, wind-induced short-term (fractions of second scale) decorrelation phenomena can still affect tandem Tomo-SAR processing [7] in forest scenarios. Also, it is noted that the Tomographic blurring sources are height-localized [2,3].

In our work, a methodology is thus reported for new analyses of 4D (3D + Time [5]) Differential Tomography type of short-term forest temporal decorrelation phenomena, exploiting a ground-based X-band miniradar array (ack. IDS Italy), placed vertically, with very quick acquisition capabilities (up to 1000 array firings per second) [7]. In particular, multiple vertical beamformings are performed, one for each array snapshot, followed by height-by-height statistical analyses along the quickly sampled temporal domain.

Moreover, corresponding real data results have been obtained, developing characterizations of both height- and time-varying behaviours of short-term decorrelation in a representative experiment for a stand of elms and poplars trees in Italy (UniPi PisaScat experiment [7]). The data were acquired in two different wind conditions and seasons.

For example, advanced height-varying Doppler spectra estimates have been obtained; increasing Doppler bandwidths with height and for stronger wind velocity resulted, indicating corresponding increasing velocity of the random motions of windblown branches and leaves.

Through the well-known signal bandwidth-coherence time relation, short-term coherence times (for coherence to decay to a steady-state) have been also measured; these can be useful to state if the short-term decorrelation issue influences a Tomo-SAR tandem satellite system, according to its formation-flying time-lag [6], or if this issue can be neglected. Noteworthy, a variability of coherence time along the trees layers (decreasing with higher heights), and a decorrelation timescale of small fractions of second, have resulted to be apparent. As a comparison, ensemble (height-integrated) Doppler measures have also been carried out, resulting mostly sensitive just to the lower (less critical) heights.

Other obtained findings include height-varying short-term steady-state coherence levels (which can tell about influence degree for tandem satellite Tomo-SAR); time-varying height scattering profiles and (sliding window-based) mean Doppler shifts under wind gusts, for the different heights; and direct autocorrelation function measures of the scattering at very short sampling scale (representing a physical view of the height-varying short term decorrelation process and a confirmation of the Doppler-based coherence time results). These will be presented at the conference, together with physical comments, and other acquisition system, scenario and methodology details.

Such investigations of height-varying characteristics of short-term decorrelation processes complement ESA radar tower [3] measures, which are instead at some seconds scale and basically of ensemble kind.

Our advanced characterization methodology and the obtained findings (possibly carrier frequency-scaled) may be useful for development, or definition refinement of the operative envelope, of the emerging tandem satellite programs and missions; examples are LuTan-1, already operative (as TanDEM-X), the NASA DART, and SAOCOM-CS-like system [6] concepts, currently being developed.

Ack.: This work has been partially supported by the Italian Ministry of Education and Research (MUR) in the framework of the FoReLab project (Departments of Excellence).

[1] T.G. Yitayew, L. Ferro-Famil, T. Eltoft, “High Resolution Three dimensional Imaging of Sea Ice using Ground-based Tomographic SAR Data,” Proc. 10th EUSAR, Berlin, Germany, 2014, pp.1325-1328.

[2] M. Lavalle, M. Simard, S. Hensley, “A Temporal Decorrelation Model for Polarimetric Radar Interferometers,” IEEE TGRS, 50(7), pp.2880-2888, 2012.

[3] T. Dinh Ho Minh, et al., “Vertical Structure of P-Band Temporal Decorrelation at the Paracou Forest: Results from TropiScat,” IEEE GRSL, 11(8), pp.1438-1442, 2014.

[4] A. Reigber, A. Moreira, “First Demonstration of Airborne SAR Tomography using Multibaseline L-band Data,” IEEE TGRS, 38(5), pp.2142-2152, 2000.

[5] D. Reale, G. Fornaro, A. Pauciullo, X. Zhu, R. Bamler, “Tomographic Imaging and Monitoring of Buildings with Very High Resolution SAR Data,” IEEE GRSL, 8(4), pp.661-665, 2011.

[6] P. López-Dekker, H. Rott, P. Prats-Iraola, B. Chapron, K. Scipal and E. D. Witte, “Harmony: an Earth Explorer 10 Mission Candidate to Observe Land, Ice, and Ocean Surface Dynamics,” Proc. 2019 IEEE IGARSS, Yokohama, Japan, 2019, pp.8381-8384.

[7] F. Lombardini, G. Buttitta, “On the Issue of Short-term Decorrelation in Forest Tomography with Companion SARs,” Proc. 2024 IEEE IGARSS, Athens, Greece, 2024, pp.3104-3107.

Forest SAR tomography is a key tool for monitoring biomass. It consists of processing a stack of coherent SAR images to retrieve several observables related to the biomass. One of the challenges of forest SAR tomography is that the shape of the forest’s reflectivity profile is unknown. Usually, some prior is introduced, e.g., the reflectivity profile is assumed to have a Gaussian or an exponential shape. However, the choice of this prior impacts the estimation performance, as an incorrect model induces estimation biases.

This communication focuses on estimating the mean height, the spread, and the total backscattered power of the forest reflectivity profile. It presents a new estimation method that does not require, nor assume, any shape for the reflectivity profile of the forest. Instead, the unknown reflectivity distribution is characterized by its central moments.

First, a discussion on the observability of the forest SAR tomography problem justifies the introduction of the moments to characterize the distribution of scatterers. The key idea is that the spread of the scatterers is small with respect to the system ambiguity, typically 5 meters vs. 90 meters. Therefore, the spectrum of the reflectivity density is smooth and can be approximated by the first terms of its Taylor expansion, which involves the moments of the distribution.

Based on this observation, a new model for the covariance matrix is obtained by cutting the Taylor expansion at some order D, which is a hyperparameter to set. In this model, the first D moments are (new) parameters to estimate, along with the usual parameters. Then, a new estimation algorithm is proposed by applying Covariance Matching Estimation Techniques (COMET) to fit the empirical covariance of the measurements with the proposed model using a least-squares minimization. The main advantage of this model is its computational complexity. All the parameters but one, the mean height of the forest, enter linearly in the model of the covariance matrix. Consequently, the estimation algorithm requires only the optimization of a single parameter. Moreover, as the model relies on the moments and does not assume any prior on the shape of the reflectivity profile, it is robust to incorrect modeling. However, the cut-off induces a misspecification and potentially estimation biases.

The performance of the algorithm is assessed in realistic SAR tomography simulations. Scenarios are proposed to estimate the forest profile with and without ground cancellation. The estimation algorithm is compared with the maximal-likelihood estimator (MLE), which assumes a shaping distribution. As expected, when well-specified, the MLE obtained the best performance, but it is outperformed by the proposed algorithm when the assumed shape is incorrect. Finally, the choice of the hyperparameter is discussed and a tradeoff is highlighted. Large cutting orders allow better reconstructions with smaller biases but are slower to converge, while small cutting orders are more robust but present larger biases.

Due to its robustness and its low complexity, the proposed algorithm is particularly suited for forest SAR tomography, where it can efficiently process wide areas. The next step will be to apply it to Biomass data.

This study aims to characterize forest structure using multi-baseline Synthetic Aperture Radar (SAR) tomography, leveraging ESA’s Biomass Tomography Mission P-band SAR products for calibration and validation (Cal/Val). The study focuses on retrieving 3D forest structure, estimating forest height, and analyzing biomass distribution using advanced SAR tomographic techniques. The Biomass Tomography Mission provides P-band SAR data specifically designed for forest parameter estimation, offering deep penetration capabilities to extract vertical structure information.

Materials:

For this study, we have chosen two forest sites over Gabon which is taken in descending pass and over Amazon forest which is taken in both ascending and descending passes from the 32 datasets that are provided. We will use Level 1A - SCS products for this study. We intended to use only June GEDI sample points, but currently, it is not published for June 2025. Hence, GEDI LiDAR data from Jan 01 2025 till 02 May 2025 over the two sites is considered.

Methodology:

We will employ multi-baseline SAR tomography techniques, including spectral estimation methods such as Beamformer, Capon, MUSIC, and Compressive Sensing. These techniques enable the separation of different scattering contributions within forested areas, leading to a more accurate representation of vertical forest structure. The processing will include interferometric phase calibration, coherent combination of SAR acquisitions, and tomographic inversion for reconstructing vertical reflectivity profiles.

The Biomass Tomography Mission's P-band SAR products, particularly tomographic forest height and biomass estimates, will be used as reference datasets to validate our reconstructions. The calibration and validation process will involve comparison with ground truth data, including GEDI LiDAR-derived forest height measurements, in-situ biomass inventories, and other independent datasets. Additionally, error estimation and uncertainty quantification will be performed to assess the robustness of the tomographic inversion results.

Expected Outcomes:

High-resolution 3D forest structure maps: Using multi-baseline SAR tomography, we will generate spatially detailed representations of forest height and biomass distribution. Improved biomass estimation models: By incorporating validated tomographic inversion results, we aim to refine biomass retrieval algorithms and enhance their accuracy.

Comparative assessment with Biomass Tomography Mission products: A detailed evaluation of our tomographic retrievals against ESA’s Biomass Mission P-band SAR products and also GEDI LiDAR retrievals will be conducted to assess the consistency and accuracy.

Uncertainty analysis: A comprehensive error assessment will be carried out to quantify uncertainties in tomographic height and biomass estimates.

Datasets and reports: The results of this study, including tomographic forest structure datasets, will be available for further research and applications in global biomass mapping.

Synthetic Aperture Radar (SAR) tomography (TomoSAR) exploits multi-baseline acquisitions coherently combined to estimate a three-dimensional reflectivity distribution of the scene. Classical reconstruction methods, however, strongly depend on missions with a large number of tracks to achieve high-quality tomograms in terms of resolution and effective ambiguity suppression. This challenge is particularly pronounced in forested regions, where scattering contributions from vegetation volume and ground surfaces overlap along the reconstruction direction, increasing the dependence of the tomographic result on the stack size.

We propose using a U-Net, a deep learning architecture designed for dense prediction tasks, to directly map reduced TomoSAR stacks into high-quality vertical profiles, comparable to those obtained with significantly larger baseline stacks using classical methods.

The reference data is obtained from Capon vertical profiles computed using a highly dense stack. We further leverage a model of the expected vertical backscattering reflectivity distribution (defined by a set of physical parameters) to incorporate physical prior knowledge into the reference generation, also helping the proposed network to fit the data.

The proposed method is evaluated using L-band tomographic data acquired by DLR’s F-SAR system over the Traunstein temperate forest site in southern Germany in 2017, demonstrating its potential to reduce acquisition requirements to one-fifth of the original amount while maintaining reconstruction quality. The reconstruction quality is evaluated against high-quality profiles using quantitative similarity measures applied both to the model parameters and to the reconstructed tomographic profiles.