Conference Agenda

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.