As sufficiently recognised in the literature, the quality of SAR measurements may be affected
by the propagation of the radar signals through the ionosphere. The introduced propagation delays and the dispersive nature of the ionosphere may cause strong geolocation errors, defocussing in
range and azimuth in the radar images, as well as the local rotation of the polarisation reference of
fully-polarimetric acquisitions. The impact of the ionosphere is more critical for lower frequencies
and higher bandwidths.
These effects have been assessed for ESA’s Earth Explorer Biomass mission , which will be
the first P-band SAR in space and is expected to be launched in 2023. The baseline approach for
the estimation of ionospheric perturbations in Biomass consists of exploiting the Faraday rotation
estimates provided by the Bickel and Bates algorithm . This approach necessitates very accurate
Faraday rotation estimates (e.g., typically better than one tenth of a degree) if they are to be used
for correcting the phase history of the data and not the depolarisation alone. Such high accuracies
typically require high averaging and low-pass estimates which might be incompatible with strong
As part of the Ground Processor Prototype (GPP) of the mission , we are developing an
autofocus algorithm for the recovery of ionospheric phase signatures which can handle such strong
scintillation cases. To support this development, we have enhanced the Biomass end-to-end performance simulator (BEEPS)  with tailored ionospheric and scene generators. The scene generator
of BEEPS is extended to use real spaceborne SAR reflectivity images (e.g., Sentinel-1) which provide
similar coverage and realistic contrast, essential for the tuning of the autofocus. For the ionospheric
generation, BEEPS is able to create thin-layer realizations including background and turbulent contributions. The incorporation of the background part (based on the NeQuick2 ) in the development
environment is essential for the characterization of integration errors in azimuth. The turbulent part
is based on the well-known Rino’s power law . The superposition of the background and turbulent
components is incorporated in the simulated data as locally-variant phase and delay perturbations,
as well as Faraday rotation.
The classical references on autofocus are typically targeted on the recovery of the contrast of
the image, only minorly worrying about the fidelity of the phase of the images after the correction
. A phase gradient autofocus approach for Biomass was suggested in  to mitigate the effect of
ionospheric irregularities along the synthetic aperture. This approach has the limitation of requiring
the presence of point-like targets within the image, which makes it a difficult choice for operational
environments. We propose in this paper a combined approach based on a map-drift kernel  and
therefore capable of delivering robust phase error estimates over extended areas in the absence of
point-like targets, while at the same time integrating the information of any point-like or coherent
scatterer present in the image  with the purpose of locally improving the estimation accuracy. Due
to the similarity of the phase perturbations for all polarimetric channels, the suggested algorithm
integrates the autofocus estimates of all four polarimetric channels into a single inversion step,
which can be also supported by the residual Faraday rotation estimates as postulated in . An
assessment of the usefulness of estimates of the dispersion in the integration step of the autofocus
will be provided in the final version of the paper. In the paper we will also show how the algorithm
uses the residual errors introduced after each iteration of the algorithm to optimally generate the
ionospheric phase error estimates.
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