Mawrth Vallis contains one of the largest exposures of phyllosilicates on Mars. Nontronite, montmorillonite, kaolinite, and hydrated silica have been identified throughout the region using data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM). In addition, saponite has been identified in one observation within a crater. These individual minerals are identified and distinguished by features at 1.38–1.42, ∼1.91, and 2.17–2.41 μ m. There are two main phyllosilicate units in the Mawrth Vallis region. The lowermost unit is nontronite bearing, unconformably overlain by an Al‐phyllosilicate unit containing montmorillonite plus hydrated silica, with a thin layer of kaolinite plus hydrated silica at the top of the unit. These two units are draped by a spectrally unremarkable capping unit. Smectites generally form in neutral to alkaline environments, while kaolinite and hydrated silica typically form in slightly acidic conditions; thus, the observed phyllosilicates may reflect a change in aqueous chemistry. Spectra retrieved near the boundary between the nontronite and Al‐phyllosilicate units exhibit a strong positive slope from 1 to 2 μ m, likely from a ferrous component within the rock. This ferrous component indicates either rapid deposition in an oxidizing environment or reducing conditions. Formation of each of the phyllosilicate minerals identified requires liquid water, thus indicating a regional wet period in the Noachian when these units formed. The two main phyllosilicate units may be extensive layers of altered volcanic ash. Other potential formational processes include sediment deposition into a marine or lacustrine basin or pedogenesis.
Hyperspectral near-infrared scenes have been simulated to analyze the contributions of surface minerals, atmosphere and sensor noise on images of Mars. Modeling the remote sensing process creates a means for independent analysis of the influence of the environment and instruments on detection accuracy of the surface composition. The system models surface reflectance based on laboratory sample spectra, creates atmospheric effects using DISORT, simulates the instrument response function using CRISM data files and adds instrument noise from thermal and other sources. The purpose of this work is understanding the hyperspectral remote sensing process to eventually enable elevated detection accuracy of minerals on the surface of Mars.
Introduction: The Mars Exploration Rover (MER) in Gusev Crater has exposed in its tracks an unusual occurrence of a soil high in sulfur and high in phosphorus [1-3] at a site called Paso Robles. Mossbauer (MB) measurements also suggested the presence of ferric sulfate minerals [4]. Ferric sulfates account for about 25–29% of the Paso Robles composition [1]. The sulfate-rich soil is concentrated in the parts exposed by the MER tracks and is typically bright. This bright salty material has been found in other locations such as Arad and Tyrone. The focus of this work is the interpretation of Panoramic Camera (Pancam) [5] images in the attempt to identify both the dominant and the low abundance phases through their spectral signatures in Paso Robles, Arad and Tyrone soils using automated statistical algorithms as an alternative and/or an aid to expert assessment. Discussion: Our analysis of Pancam data showed that most scatterplots of the data cloud tend to be tearshaped or deltoid, radiating away from the so called dark point (greenish area in the right – bottom part of figure 1), the scanner response to a target of zero reflectance in all bands [6] which is close in concept to a virtual endmember as described by [7].
We consider the problem of factorizing a hyperspectral image into the product of two nonnegative matrices, which represent nonnegative bases for image spectra and mixing coefficients, respectively. This spectral unmixing problem is a nonconvex optimization problem, which is very difficult to solve exactly. We present a simple heuristic for approximately solving this problem based on the idea of alternating projected subgradient descent. Finally, we present the results of applying this method on the 1990 AVIRIS image of Cuprite, Nevada and show that our results are in agreement with similar studies on the same data.
