<p><strong>Introduction</strong></p> <p>Bright basal reflections detected by MARSIS at Ultimi Scopuli (Orosei et al., 2018; Lauro et al., 2021) started a wide scientific debate on the possible materials capable to generate such strong echoes from the base of the South Polar Layers Deposits (SPLD). Different interpretations were suggested: some involving the presence of briny water at the base of the SPLD (Orosei et al., 2018; Lauro et al., 2021; Mattei et al., 2022; Stillman et al., 2022) and others the existence of conductive materials, like saline ice and hydrated clays (Bierson et al., 2021; Smith et al., 2021) or ilmenite-rich basaltic rocks (Grima et al., 2022).</p> <p>The original study (Orosei et al., 2018) was based on an inversion approach of MARSIS data (Lauro et al., 2019) from which the basal permittivities were retrieved. Such permittivity values are estimated from the amplitude of the reflected signal (Orosei et al., 2018), which does not allow to separately compute real and imaginary parts of the complex permittivity but only the apparent permittivity (e<sub>a</sub>) (Mattei et al., 2022). This is a real single quantity (to not be confused with the real part of permittivity, e&#8217;) that accounts for both polarization and conductive processes and fully describes the dielectric property of a material. In other words, the apparent permittivity is the physical quantity associated to a material lying below the SPLD that MARSIS measure.</p> <p>The analysis of MARSIS data at Ultimi Scopuli defined the presence of two distinct distributions of apparent permittivity values.&#160; A distribution with high values, inside the so-called bright area, which were interpreted as evidence of basal salty liquid water and a distribution with low values typical of dry rocks/soil, outside the bright area (Orosei et al., 2018). The presence of other wet areas was subsequently confirmed applying a different analysis based on a signal processing approach commonly used in terrestrial Radar Echo Sounding (RES) studies to discriminate between wet and dry subglacial basal conditions (Lauro et al., 2021). Moreover, other indirect evidence supports the existence of liquid water below the ice at Ultimi Scopuli (Carrer and Bruzzone, 2021).</p> <p>&#160;</p> <p><strong>Results and discussions</strong></p> <p>The main argument against the possible presence of basal briny water is the very low temperature inferred from thermal models at the base of the SPLD (~180K), which was believed to require a large amount of salt to maintain the water in a liquid state (e.g., Sori and Bramson, 2019). Based on laboratory measurements, however, recent papers have discarded such requirement showing that few hundreds of mM of perchlorate salts are capable to maintain the water liquid at temperature lower than 200K (Mattei et al., 2022; Stillman et al., 2021; Stillman et al., 2022). Moreover, neither dielectric theory nor extensive experimental data support the hypothesis that saline ices or hydrated salts and clays can produce the bright basal reflections detected by MARSIS at the base of the SPLD (Mattei et al., 2022; Stillman et al., 2022). On the other hand, the largest amount of ilmenite content detected so far on Mars is &#163;5% (e.g., Morris et al., 2006) which is largely insufficient to create strong radar basal reflections (Hansen et al., 1973). Another puzzling aspect in this controversy, is the presence of other bright areas detected by MARSIS below the South polar cap, sometime where the ice is thinner than 1.5 km (Khuller and Plaut, 2021). It should be notice, however, that the data analyzed by Khuller and Plaut (2021) are not the same (on-board standard mode) as those used in Orosei et al. (2018) and Lauro et al. (2021) (super frame and flash memory mode).</p> <p>We present here the results of a large literature review on the dielectric properties of the materials suggested to be present at the base of the SPLD, as a function of temperature and composition. For these materials we computed the apparent permittivity which we compared to the apparent permittivity values retrieved by MARSIS (Fig.1). The results are discussed in the framework of the thermal state at the base of the SPLD and show that only perchlorates solutions can generate the basal bright reflections detected by MARSIS at Ultimi Scopuli.</p> <p><img src="" alt="" width="844" height="520" /></p> <p>Fig.1 Box plot of the apparent permittivity. The plot indicates the basal apparent permittivity retrieved inside the main bright area (blue) and outside the bright areas (red). Color bars indicate a range of apparent permittivity values for several lithologies potentially present at the base of the SPLD, measured mostly at MARSIS frequencies and 200 K.</p> <p><strong>References</strong></p> <p>Bierson, C. et al.&#160; Geophysical Research Letters, 48(13), doi.org/10.1029/2021GL093880, (2021).&#160;</p> <p>Carrer L. and L. Bruzzone,&#160; IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 4600915, doi: 10.1109/TGRS.2021.3111814.</p> <p>Grima, C.,&#160; et al. (2022). Geophysical Research Letters, 49(2), e2021GL096518, doi.org/10.1029/2021GL096518. &#160;</p> <p>Khuller, A. R., & Plaut, J. J. (2021). <em>Geophysical Research Letters</em>, <em>48</em>(13), e2021GL093631, doi.org/10.1029/2021GL093631&#160;</p> <p>Lauro, S. E., et al. (2019). <em>Remote Sensing</em>, <em>11</em>(20), 2445.<em> Remote Sens.</em> <strong>2019</strong>, <em>11</em>(20), 2445, doi.org/10.3390/rs11202445.</p> <p>Lauro, S.E., et al.</p> <p>Mattei, E., et al. (2022). Earth and Planetary Science Letters, 579, 117370, doi.org/10.1016/j.epsl.2022.117370.</p> <p>Morris, R. V., et al. (2006). Journal of Geophysical Research: Planets, 111(E2).</p> <p>Orosei, R., et al. (2018). Science, 361(6401), 490-493, doi: 10.1126/science.aar7268.</p> <p>Smith, I. B., et al. (2021).</p> <p>Sori, M. M., & Bramson, A. M. (2019). Geophysical Research Letters, 46(3), 1222-1231, doi.org/10.1029/2018GL080985.</p> <p>Stillman, D. E., et al. (2022). LPI Contributions, 2678, 2133.</p> <p>Stillman, D. E., et al.(2021).LPI Contributions, 2614, 6028.</p>
Sea ice monitoring is important for both climate change studies and potential trans-Arctic shipping. Ground Penetrating Radar (GPR) has been demonstrated to be a powerful method to retrieve sea ice thickness and gain information about its internal structure. Nevertheless, its applicability can be strongly limited in the case of very low ice thickness and high salinity content. This paper presents results from a field experiment performed under such conditions which integrated GPR data and s-parameters measurements with Vector Network Analyzer (VNA) on artificial sea ice grown at the SERF research site in Winnipeg, Canada. The observed dielectric behavior has been used to monitor sea ice growth, relating the electrical conductivity to temperature evolution and brine content. Results demonstrate the capability of both GPR and VNA techniques in the investigation of sea ice properties under non-ideal conditions.
Liquid water was present on the surface of Mars in the distant past; part of that water is now in the ground in the form of permafrost and heat from the molten interior of the planet could cause it to melt at depth. MARSIS (Mars Advanced Radar for Subsurface and Ionosphere Sounding) has surveyed the Martian subsurface for more than fifteen years in search for evidence of such water buried at depth. Radar detection of liquid water can be stated as an inverse electromagnetic scattering problem, starting from the echo intensity collected by the antenna. In principle, the electromagnetic problem can be modelled as a normal plane wave that propagates through a three-layered medium made of air, ice and basal material, with the final goal of determining the dielectric permittivity of the basal material. In practice, however, two fundamental aspects make the inversion procedure of this apparent simple model rather challenging: (i) the impossibility to use the absolute value of the echo intensity in the inversion procedure; (ii) the impossibility to use a deterministic approach to retrieve the basal permittivity. In this paper, these issues are faced by assuming a priori information on the ice electromagnetic properties and adopting an inversion probabilistic approach. All the aspects that can affect the estimation of the basal permittivity below the Martian South polar cap are discussed and how detection of the presence of basal liquid water was done is described.
<p>Snow-mantle extent (or area), its local thickness (or height) and mass (often expressed by the snow water equivalent, SWE) are the main parameters characterizing snow deposits. Such parameters result of particular importance in meteorology, hydrology, and climate monitoring applications. The considerable geographical extension of snow layers and their typical spatial heterogeneity makes it impractical to monitor snow by means of direct or indirect in situ measurements, suggesting the exploitation of satellite technologies. Space-borne C-band synthetic aperture radar (SAR) sensors (such as those operating in Sentinel-1 A and B missions) are particularly suitable for the analysis of snow deposits, providing data with resolutions up to some meters with global coverage and 6-day revisit time. Most of the satellite remote sensing applications have been focused on major mountain systems, such as the Andes, the Alps, or the Himalayan region. Other important mountain systems, like the Italian Apennines, have not been extensively considered, probably due to their complex orography and the high variability of their snow cover. Nevertheless, the central Apennine has a central role for the meteorological dynamics in the Mediterranean area, and it hosts the southernmost European glacier &#8211; namely, the Calderone glacier whose evolution represents a relevant indicator, at least for the medium latitudes, of climatic changes.</p><p>The implementation of the objectives of the SMIVIA (Snow-mantle Modeling, Inversion and Validation using multi-frequency multi-mission InSAR in central Apennines) project is based on the development of innovative simulation techniques and snow parameter estimators from SAR and differential interferometric SAR (DInSAR) measurements, based on the synergy with spatial measurements from optical remote sensing sensors, data from ground weather radar and simulations from dynamic snow cover models and on an inverse problem approach with a robust physical-statistical rationale. Furthermore, the scientific validity of the achievable results is supported by an enormous systematic validation effort in the Apennine area with in-situ measurements, identifying 3 pilot sites manned with meteorological and snow measurements, dielectric and georadar measurements, trenches and micro-macrophysical sampling, 6 sites of semi-automatic verification, 31 remote auxiliary sites and 1 site of glaciological interest (Calderone) with ad hoc campaigns. SAR data processing can be performed in different ways to retrieve snow parameters.</p><p>In this work we exploit SAR backscattering coefficient to study the effects of backscattering at the air-snow interface, at the snow-ground interface, together with the volumetric effects of the snow layer. The distinction between wet and dry snow is obtained exploiting the copolar and cross-polar SAR returns. DInSAR is exploited to analyze the effects of air-snow refraction and the snow-ground reflection, together with the coherence and phase-shifts between two sequential images. In this work we will present the Sentinel-1 DInSAR processing chain to estimate snowpack height (SPH) combined with SAR-backscattered data for wet snow discrimination. The potential of using physically based analytical and statistical inversion algorithms, trained by forward electromagnetic and snowpack models, is introduced, and discussed. The processing chain is tested in central Apennines, using validation sites with snow-pit in-situ measurements, discussing potential developments and critical issues.&#160;</p>
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)