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Phase unwrapping, also known as ambiguity resolution, is an underdetermined problem in which assumptions must be made to obtain a result in SAR interferometry (InSAR) time series analysis. This problem is particularly acute for distributed scatterer InSAR, in which noise levels can be so large that they are comparable in magnitude to the signal of investigation. In addition, deformation rates can be highly nonlinear and orders of magnitude larger than neighboring point scatterers, which may be part of a more stable object. The combination of these factors has often proven too challenging for the conventional InSAR processing methods to successfully monitor these regions. We present a methodology which allows for additional environmental information to be integrated into the phase unwrapping procedure, thereby alleviating the problems described above. We show how problematic epochs that cause errors in the temporal phase unwrapping process can be anticipated by the machine learning algorithms which can create categorical predictions about the relative ambiguity level based on the readily available meteorological data. These predictions significantly assist in the interpretation of large changes in the wrapped interferometric phase and enable the monitoring of environments not previously possible using standard minimum gradient phase unwrapping techniques.

The Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) onboard Mars Express, which operates between 0.1 and 5.5 MHz, suffered from a complete blackout for 10 days in September 2017 when observing on the nightside (a rare occurrence). Moreover, the Shallow Radar (SHARAD) onboard the Mars Reconnaissance Orbiter, which operates at 20 MHz, also suffered a blackout for three days when operating on both dayside and nightside. We propose that these blackouts are caused by solar energetic particles of few tens of keV and above associated with an extreme space weather event between 10 and 22 September 2017, as recorded by the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission. Numerical simulations of energetic electron precipitation predict that a lower O-2(+) nighttime ionospheric layer of magnitude similar to 10(10) m(-3) peaking at similar to 90-km altitude is produced. Consequently, such a layer would absorb radar signals at high frequencies and explain the blackouts. The peak absorption level is found to be at 70-km altitude. Plain Language Summary Several instrument operations, as well as communication systems with rovers at the surface, depend on radio signals that propagate throughout the atmosphere of Mars. This is the case also for two radars that are currently working in Mars' orbit, sounding the ionosphere, surface, and subsurface of the planet. In mid-September 2017, a powerful solar storm hit Mars, producing a large amount of energetic particle precipitation over a 10-day period. We have found that high-energy electrons ionized the atmosphere of Mars, creating a dense layer of ions and electrons at similar to 90 km on the Martian nightside. This layer attenuated radar signals continuously for 10 days, stopping the radars to receive any signal from the planetary surface. In this work, we assess the properties of this layer in order to understand the implications of this kind of phenomenon for radar performance and communications.

We present a new method for determining the total electron content (TEC) in the Martian ionosphere based on the time delay of received radar pulses of the Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS) on board the Mars Express spacecraft. Previous studies of the same dataset have produced differing results for the day-side ionosphere, so it is useful to have an alternative way to compute the TEC in this region. This method iterates a model ionosphere in order to simultaneously match the ionospheric delays of the signals received by the radar’s two channels by finding the model which minimizes the root mean square error (RMSE) between the measured and simulated delays. Topographical information is obtained from data from the Mars Orbiter Laser Altimeter (MOLA) instrument. The model parameters are held constant for a given orbit, and a very good agreement between the simulated and measured delays is obtained. The TEC can then be inverted from the ionospheric model. Matching the delays of both channels simultaneously applies an additional constraint to the model which has not been made in previous studies. The model is additionally validated by matching the simulated pulses with the raw range-compressed measurements for one orbit. Finally, typical model parameters are compared to those obtained by previous studies, which are also simulated. The method is applied to orbits during moderate solar activity, and results show very good agreement with previous studies.