Conference Agenda

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.