Syrtis Major is a near equatorial dark region of Mars and its composition is known to be basaltic. It is also known that it is a region undergoing high albedo variations. In this study we analyzed MGS TES ancillary data (i.e. surface... more
Syrtis Major is a near equatorial dark region of Mars and its composition is known to be basaltic. It is also known that it is a region undergoing high albedo variations. In this study we analyzed MGS TES ancillary data (i.e. surface albedo, thermal inertia and atmospheric dust opacity) in order to relate these variations to different surface conditions. Looking at these data (spanning two separated biennia - i.e. 1999-2000 and 2003-2004) we noticed that dust opacity raises with albedo, but thermal inertia does not decrease with albedo, as expected if albedo variations was due to dust redeposition. So we found out that TES albedo is not the pure surface albedo, but it is linearly related with dust opacity.
Several works on TIR data from the Thermal Emission Spectrometer, onboard the NASA Mars Global Surveyor mission, show that statistical analysis could give a complete description of atmospheric non-gaseous components (mineral dust and... more
Several works on TIR data from the Thermal Emission Spectrometer, onboard the NASA Mars Global Surveyor mission, show that statistical analysis could give a complete description of atmospheric non-gaseous components (mineral dust and water ice cloud). It is possible to describe spectral observation as a linear combination of several spectral shapes extracted from the data by statistical techniques and therefore extract also surfaces spectra for direct analysis. In this work we use a similar approach, applying the Factor Analysis and the Linear Deconvolution Algorithm on the Planetary Fourier Spectrometer, onboard the Mars Express ESA mission (PFS-MEX). These techniques have been shown very reliable and fast in order to analyze a huge set of data. The large temporal separation between the two experiments and the higher spectral resolution of PFS can give new insights in composition, distribution and evolution of aerosol components. The analyzed subset spans from Ls 320-360 (first mission year) to Ls 0-210 (second mission year) covering nearly a Martian year. Thanks to this large seasonal coverage (i.e. thanks to the great variability of the atmospheric components as suspended mineral dust and water ice clouds) we were able to successfully separate the spectral signatures of the ground from the atmosphere. Although the PFS data have been obtained several years after the TES ones, our results show that the derived atmospheric components are in good agreement as well. An independent analysis of PFS-MEX data, based on retrieval algorithm BDM (Grassi,D.), show a similar latitudinal and seasonal behaviour of the atmospheric components.
