In this letter, we discuss the estimation of the location of ionospheric irregularities exploiting the appearance of intensity scintillations in ALOS-2 images, as they are semifocused at different heights. The intensity scintillations (stripes) are not always visible in the single-look complex (SLC) images. However, they start to be visible, as the image is semifocused at different heights with a peak of contrast where electron density irregularities are found (ionospheric height). The slant range between the satellite and the ionospheric plane can be estimated and converted directly into the ionospheric height by autofocusing the stripes. The observations show good agreement with the height of maximum ionization estimated by the International Reference Ionosphere (IRI). Furthermore, we perform an alternative geometric validation based on feature tracking by comparing the shifts between azimuth sublooks. With both methods for the presented dataset, the height of the ionospheric irregularities was estimated to be 330 km.

This letter presents an algorithm for the introduction of ionospheric disturbances into synthetic aperture radar (SAR) simulations in an aperture-dependent manner using subapertures. Its suitability is compared with other methods that follow the beam-center approximation. The method can be generalized to the injection of all kinds of disturbances, and its two main benefits are the accuracy of the squint angle accommodation inside the synthetic aperture and the possibility of neglecting the static ionosphere assumption. For example, realistic ionospheric disturbance maps (phase and intensity scintillation) are introduced into clutter images simulated for the Biomass mission. In this case, with a typical ionospheric irregularity height of 350 km, the limiting azimuth resolution of the irregularities to be injected is around 337 m.

Low-frequency Synthetic Aperture Radar (SAR) images are affected by trans-ionospheric propagation of the radar waves. 2D Total Electron Content maps can be obtained as a product of the ionospheric corrections, allowing for imaging of ionospheric irregularities at very high resolution and broader coverage compared to other sensing technologies (like GNSS, ionosondes, etc). We analyze in this paper the case of the Biomass mission and characterize the errors in the imaging of the ionosphere resulting from the calibration algorithms foreseen for its Ground Processor Prototype: a Faraday rotation-based and an autofocus-approach. The analysis relies on a turbulent power law Rino model for the perturbation of the ionosphere and the spectral behavior of the calibration algorithms. We also discuss the suitability of both approaches for image correction in different scenarios (varying Signal-to-Noise-Ratio and geographic location).

ESA’s BIOMASS is the seventh Earth Explorer mission. It will be devoted to the global monitoring of: 1) above ground forest biomass and biomass change maps with an accuracy90%, a 50-200 m spatial resolution, and 2-12 months temporal resolution; and 3) global forest height maps with an accuracy of 20-30%, 100 m spatial resolution, and 12 months temporal resolution.BIOMASS is based on a P-band SAR (438 MHz center frequency, 6 MHz bandwidth) orbiting in a dawn-dusk, Sun-synchronous orbit at 674 km, that will systematically acquire fully- (quad-) polarized image data in an interferometric mode over all major forested areas on the globe and a tomographic phase (7 images) to retrieve forest vertical structure information. At P-band ionospheric effects are very important and need to be corrected for. This paper describes the current status of the Ionospheric module for the Biomass End-to-end Performance Simulator (BEEPS-IOM).

An intrinsic challenge in the geophysical interpretation of low-frequency synthetic aperture radar (SAR) imagery of semitransparent media, such as ice sheets, is the position ambiguity of the scattering structures within the glacial volume. Commonly tackled by applying interferometric and tomographic techniques, their spaceborne implementation exhibits by orders higher complexity compared to missions relying on single SAR images, making them cost expensive or, in the context of planetary missions, even impossible due to limited navigation capability. Besides, even these sophisticated techniques are commonly biased due to inaccurate permittivity estimates, leading to geometric distortions up to several meters. We present a novel inversion procedure to estimate volume parameters of ice sheets, namely, the depth of the scattering layer within the glacial volume and the dielectric permittivity of the ice, based on single-image single-polarization SAR acquisitions. The information is inherent in the processed SAR data as phase errors on the azimuth signals resulting from uncompensated nonlinear propagation of the radar echoes through ice. We suggest a local map-drift autofocus approach to quantify and spatially resolve the phase errors and an inversion model to relate them to the penetration depth and permittivity. Testing the proposed technique using P-band SAR data acquired using DLR’s airborne sensor F-SAR during the ARCTIC15 campaign in Greenland shows promising results and good agreement with tomographic products of the analyzed test site.

In the above article[1], references [21] and [29] incorrectly provide the publication years. Reference [21] was published in 2020; reference [29] was published in 2021. The references appear in full here: