Conference Agenda

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

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

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

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

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

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

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

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

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

coherence, facilitating the implementation of interferometric techniques such as

SAR tomography.

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

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

Faraday rotation affecting the polarimetry, and phase errors that introduce

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

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

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

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

However, the Faraday rotation sensitivity to small irregularities decreases

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

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

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

differential ionospheric phase screen can be resolved by exploiting the induced

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

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

scenes highly compromise the performance.

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

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

to reduce the uncertainty in retrieving the ionospheric distortion map when

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

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

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

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

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

resolution ionospheric imaging.

References

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

Correcting distortion of polarimetric SAR data induced by ionospheric scin-

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

6319-6335.

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

Towards an Interferometric Autofocus for the Estimation of Ionospheric Sig-

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

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

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

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

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

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

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