Conference Agenda

.

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.