Mars thesis

Universita degli Studi di Parma

DIPARTIMENTO DI SCIENZE DELLA TERRA

Corso di Laurea Specialistica in Conservazione della Natura

Tesi di laurea specialistica

Detailed analysis of Bosphorus Region (35-45S 50-65W, Mars)by geomorphological, spectral and climatic studies

Candidato:Antonio Albano

Relatore:Chiar.ma Prof.ssa Maria Sgavetti

Anno Accademico 2010-2011

I can’t findwhat you are

representing mein the words

of my heroes,of my songs,

of my friends.Maybe becausewhat I’m is only

in my words,in my heroes,in my songs,

in my friends.

Baker et al. (1991), Colaprete & Jakosky (1998), Forget et al. (2006),Simmons et al. (2006), Hauber et al. (2005), Bylander et al. (2005), Head& Marchant (2003), Head et al. (2003), Head III et al. (2004), Head et al.(2005), Head et al. (2006), Kargel et al. (1995), Kress & Head (2008), Laskaret al. (2004), Levy et al. (2007), Levy et al. (2009), Madeleine et al. (2009),Marchant & Head III (2007), McEwen et al. (2007), Mellon et al. (2008),Morgan et al. (2009), Neukum et al. (2004), Pacifici et al. (2009), Sheanet al. (2007), Shean et al. (2005), Soare & Osinski (2009), Mellon et al.(2000), Phillips et al. (2011), Squyres et al. (1992), Ward (1992), Jameset al. (1992), Jakosky & Haberle (1992), Engle (2010), Hayward et al. (2007),Rubincam (1990), Rubincam (1993), Bills (1999), Carr & Head III (2010),Hartmann & Neukum (2001), Head (2006), Bernstein et al. (2005), Quirico& Schmitt (1997), Rossi et al. (2006), Sabins (1987), Christensen et al.(2009), Glotch & Christensen (2005), Hoefen et al. (2003), Christensenet al. (2005), Malin et al. (2007), Christensen et al. (2001), Bandfield (2002),Bandfield (2003), Haberle & Jakosky (1991), Gerlach (2007), Christensenet al. (2000), Hamilton et al. (2001), Glotch et al. (2006), Ruff & Christensen(2002), Kruskal & Wallis (1952)

Contents

I Study of Bosphorus Region 6

1 Introduction 7

2 Geomorphological reconstruction 102.1 Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2 RMCs, LVF and LDA . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2.1 Crater types in LDA/LVF . . . . . . . . . . . . . . . . . 152.2.2 Conformation of the 41◦28’S 62◦26’W crater . . . . . 162.2.3 Stratigraphic relationships LDA/LVF . . . . . . . . . . 19

2.3 Similarities with ADV . . . . . . . . . . . . . . . . . . . . . . . . 24

3 Recognition of cold-desert environment 283.1 Criteria for recognition based on Head et al. (2006) . . . . 283.2 Statistical study applied to Head et al. (2006)’s criteria . . . 34

4 Spectral analysis 384.1 Role of water and CO2 . . . . . . . . . . . . . . . . . . . . . . . 384.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.2.1 Acquisition and improvement of the spectra . . . . . 414.2.2 Comparison of spectra . . . . . . . . . . . . . . . . . . 44

4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5 Framing the Bosporos region within the Mars climate history 515.1 Climate conditions . . . . . . . . . . . . . . . . . . . . . . . . . . 515.2 Predictions of orbital parameters . . . . . . . . . . . . . . . . 515.3 Formation of glaciers . . . . . . . . . . . . . . . . . . . . . . . . 52

5.3.1 Ice near 41◦28’S 62◦26’W the crater . . . . . . . . . . 555.3.2 Typical glacial and periglacial topography . . . . . . 56

6 Summary and conclusions 58

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CONTENTS

II Methods - Instrument Descriptions 59

7 Kruskal–Wallis test 607.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

8 Function used (LabPlot) 628.1 Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

9 Function used (Tutorial JMARS) 649.1 Lat/Lon Grid Layer . . . . . . . . . . . . . . . . . . . . . . . . . 649.2 Map Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669.3 3D Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679.4 TES Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719.5 Viewing TES Data in JMARS . . . . . . . . . . . . . . . . . . . 769.6 THEMIS Stamp Layer . . . . . . . . . . . . . . . . . . . . . . . . 839.7 MOC Stamp Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 919.8 CTX Stamp Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 969.9 HiRISE Stamp Layer . . . . . . . . . . . . . . . . . . . . . . . . 101

10 Data collection instruments 10610.1 Thermal Emission Spectrometer (TES) . . . . . . . . . . . . . 10610.2 Thermal Emission Imaging System (THEMIS) . . . . . . . . 10710.3 Mars Orbiter Laser Altimeter (MOLA) . . . . . . . . . . . . . 10710.4 Mars Orbital Camera (MOC) . . . . . . . . . . . . . . . . . . . 10810.5 Context Camera (CTX) . . . . . . . . . . . . . . . . . . . . . . . 10910.6 High Resolution Imaging Science Experiment (HiRISE) . . 109

11 TES Data Tool 11111.1 Parameters List . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

12 Quality Parameters 12112.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12112.2 Observation Quality . . . . . . . . . . . . . . . . . . . . . . . . . 122

12.2.1 High Gain Antenna Motion . . . . . . . . . . . . . . . . 12212.2.2 Solar Panel Motion . . . . . . . . . . . . . . . . . . . . . 12312.2.3 Algor patch status . . . . . . . . . . . . . . . . . . . . . . 12412.2.4 IMC patch status . . . . . . . . . . . . . . . . . . . . . . 12412.2.5 Momentum Desaturation status . . . . . . . . . . . . . 12512.2.6 Equalization tables status . . . . . . . . . . . . . . . . . 125

12.3 Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12612.3.1 Major Phase Inversions . . . . . . . . . . . . . . . . . . 12612.3.2 Risk of Algor Phase Inversions . . . . . . . . . . . . . 126

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CONTENTS

12.3.3 Calibration Issues . . . . . . . . . . . . . . . . . . . . . . 12712.3.4 Spectrometer Noise . . . . . . . . . . . . . . . . . . . . 12712.3.5 Detector Mask 1 Problem . . . . . . . . . . . . . . . . 12812.3.6 Bolometer Reference Lamp Anomaly . . . . . . . . . 128

12.4 Derived Products Quality . . . . . . . . . . . . . . . . . . . . . 12912.4.1 Thermal Inertia, Spectral and Bolometric . . . . . . 12912.4.2 Pressure-Temperature Profile . . . . . . . . . . . . . . 12912.4.3 Atmospheric Opacity . . . . . . . . . . . . . . . . . . . . 130

12.5 Appendices Quality Parameters . . . . . . . . . . . . . . . . . 13112.5.1 Determining High Gain Antenna and Solar Panel

Motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13112.5.2 Determining Phase Inversions . . . . . . . . . . . . . 13212.5.3 Determining Spectrometer Noise . . . . . . . . . . . . 133

12.6 Detector Mask . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13412.7 Spectra Mask . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

13 Mars Global Surveyor TES (Christensen et al. (2001)) 13713.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13713.2 Spectrometer calibration . . . . . . . . . . . . . . . . . . . . . . 139

13.2.1 Spectrometer algorithm overview . . . . . . . . . . . 13913.2.2 Spectrometer algorithm version . . . . . . . . . . . . 14013.2.3 Precision and accuracy . . . . . . . . . . . . . . . . . . 14413.2.4 Wavenumber sample position and spectral line shape144

13.3 Visible bolometer calibration . . . . . . . . . . . . . . . . . . . 14613.3.1 Visible blometer algorithm overview . . . . . . . . . . 14613.3.2 Visible blometer algorithm version . . . . . . . . . . . 14613.3.3 Precision and accuracy . . . . . . . . . . . . . . . . . . 150

13.4 Thermal blometer calibration . . . . . . . . . . . . . . . . . . . 15213.4.1 Thermal bolometer algorithm overview . . . . . . . 15213.4.2 Thermal bolometer algorithm version . . . . . . . . 153

13.5 Surface temperature determination . . . . . . . . . . . . . . . 15513.6 Thermal inertia determination . . . . . . . . . . . . . . . . . . 15713.7 Atmospheric product determination . . . . . . . . . . . . . . . 158

13.7.1 Atmospheric temperature . . . . . . . . . . . . . . . . . 15813.7.2 Atmospheric optical depth . . . . . . . . . . . . . . . . 16013.7.3 Downwelling flux . . . . . . . . . . . . . . . . . . . . . . 161

13.8 Data quality/anomalies . . . . . . . . . . . . . . . . . . . . . . . 16213.8.1 Spectral ringing . . . . . . . . . . . . . . . . . . . . . . . 16213.8.2 Spectrometer non-zero background calibrated radi-

ance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16313.9 Global mineral distributions on Mars (Bandfield (2002)) . . 163

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CONTENTS

13.9.1 TES instrument and data set overview . . . . . . . . . 16313.9.2 Algorithm description . . . . . . . . . . . . . . . . . . . 16513.9.3 End-member set . . . . . . . . . . . . . . . . . . . . . . . 166

13.10Martian Global Surface mineralogy (Bandfield (2003)) . . . 16913.10.1 Multiple emission angle observations . . . . . . . . . 16913.10.2 Nadir observations . . . . . . . . . . . . . . . . . . . . . 17013.10.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 171

14 TES/THEMIS Glossary 172

Bibliography 177

5

Part I

Study of Bosphorus Region

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Chapter 1

Introduction

Mars looks like a dry dusty world, but in 2002, NASA’s Mars Odysseyspacecraft found the chemical signature of water in huge deposits acrossthe planet. It’s not liquid water on the surface, but huge deposits of iceon Mars.

Astrophysicists had already pointed out the presence of ice on Mars,since it was first detected in the planet’s polar ice caps back in the 1970s.But they didn’t realize that ice was distributed across the entire planet.

Why is the ice distributed throughout Mars? What are the mecha-nisms allowing the formation and preservation of this phase?

We can start from the planet earth. Earth’s obliquity — the angle be-tween its spin and orbital axes, currently 23.4◦ — doesn’t change much,due to the stabilizing influence of the Moon. Mars, in contrast, doesn’thave a large moon, so its obliquity is less constrained, wobbling by up totens of degrees on time scales of 105 – 106 years. The resulting variationin solar heating at the Martian poles has a profound effect on the polaricecaps: substantial amounts of material sublimate and refreeze, creatinga complex structure of overlapping layered deposits. Most of those layersare primarily water ice. But now, radar data from the Mars Reconnais-sance Orbiter reveal at least one large deposit, shown in the Figure 1.1,of solid carbon dioxide.

Because CO2 ice and H2O ice have different indices of refraction, alayer’s composition affects how the radar sees the layers underlying it.If the deposit were vaporized — as it certainly was at some point in thepast, possibly around 600.000 years ago when the obliquity was particu-larly large — it could nearly double the mean atmospheric pressure, from6 mbar to 10.5 mbar. Such an increase would have important implicationsfor the frequency and intensity of dust storms and the stability of liquidwater on the Martian surface (Phillips et al. (2011)).

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Figure 1.1: Reconstruction of the Mars North Polar Ice Cap. Mars hasice caps on both its north and south poles. The ice caps are made ofwater ice and carbon dioxide ice (dry ice). There are two kinds of icecaps on Mars: seasonal ice caps and residual ice caps. Seasonal ice capsaccumulate during the winter season, and evaporate during the summer.The residual caps remain during the entire year. The southern seasonalcap measures about 4.000 km across when it reaches the largest exten-sion during southern winter, and the northern cap measures about 3000km across at its largest, during northern winter. When summer temper-atures rise above 150K (-120 C), the ice sublimes (passes directly fromthe solid state into the gaseous state, bypassing the liquid state) into theatmosphere. Large seasonal changes in the amount of carbon dioxide inthe atmosphere cause large seasonal changes, up to 30% variation, in theatmospheric pressure on Mars (modified from rst.gsfc.nasa.gov/).

According to the authors, extended regions on Mars can experienceCO2 pressures exceeding the triple-point pressures of liquid water, thusallowing liquid water to persist at or near the surface and creating the con-ditions for the permafrost on Mars to be global in extent today. Belowabout a centimeter of depth at the equator and below the upper? Surfaceat higher latitudes the soil temperatures remain below the freezing pointof water all the year round. At higher latitudes, surface soil temperaturesare always cold enough.

It is therefore not surprising that ground ice would have an influ-ence on the geomorphic character of the landscape (Marchant & Head III(2007)). As shown in Figure 1.2, surface glacial activity on Mars is inferredto be restricted to the last 2-2.5 Gy, recorded by glacial and periglacial fea-tures superimposed on older terrains.

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Figure 1.2: Geological activity as a function of time on Mars. The figureshows the relative importance of different processes (impact cratering,volcanism), the time and relative rates of formation of various featuresand units (valley networks, Dorsa Argentea Formation), and types andrates of weathering, as a function of time. The approximate boundaries ofthe major time periods of Mars history are shown (Hartmann & Neukum(2001)), and are compared to similar major time subdivisions in Earthhistory (Head (2006)) (modified from Carr & Head III (2010)).

One of the primary goals of this study is to analyse an old, Noachianregion showing several ground ice features of probable Amazonian ageand examine the environment, the landscape, and the subsurface ice it-self, using a geomorphological reconstruction and the interpretation ofspectral data. To this end a location has been chosen in the BosphorusRegion where ground ice is expected to be abundant a few centimetersbelow the surface.

This region is located in a latitudinal band between 35-45 south and alongitudinal band between 50-65 west.

In this area, different structures with periglacial character can be ob-served, that suggest the presence of ice near the surface (now and in therecent or distant past). The origin of this structures is generally attributedto phenomena like rock-glacier, but the complexity of the erosional formssuggests that the area has been interested by additional types of glacialphenomena. The observed structures are well developed and are associ-ated, in this area, with a field of large dunes.

These preliminary observations suggest an active and complex, geo-morphological and climatic evolution.

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Chapter 2

Geomorphologicalreconstruction

2.1 Study areaThe study area (Figures 2.2 and 2.3) is located inside the Bosphorus

Region in the southern hemisphere of Mars. This area also includes acrater located at 41◦28’S 62◦26’W (Figure 2.4) with a diameter of approxi-mately 50 km and a large debris flow (Figure 2.6) with an area of about24.000 km2, for a total of almost 32.000 km2.

From a topographic point of view, we observe that the crater has adepth of 1.500 m, the base is placed at 500 m above average level, andalso has a central basin of the depth of 350 m (Figures 2.4 and 2.5).

The debris flow (Figure 2.6) can be divided into two streams: the mainflow extends for 250 km with a slight incline of 1.5◦, while the secondaryflow stretches over 70 km.

The freeware JMARS was used for the analysis of the the wholeBosphorus area of Figure 2.2, in order to characterize the diagnostic mor-phologic features, to frame these features within the range of periclacialforma recognized on Mars, to establish stratigraphic relationships (sec-tion 2.2), and to compare the Bosphorus features with terrestrial glacialand periglacial analogues (section 2.3). JMARS (Java Mission-planningand Analysis for Remote Sensing) is a Java-based geospatial informationsystem developed by the Mars Space Flight Facility at Arizona State Uni-versity.

It is currently used for mission planning and scientific data analysisby several NASA missions, including Mars Odyssey, Mars ReconnaissanceOrbiter and the Lunar Reconnaissance Orbiter (Engle (2010)).

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2.1 Study area

Figure 2.1: Maps of Mars’s global topography. The projections are Mer-cator to 70◦ latitude and stereographic at the poles with the south poleat left and north pole at right. This analysis uses an areocentric coordi-nate convention with east longitude positive. Are indicated: (1) Bospho-rus Region (2) Arsia Mons (3) Ascraeus Mons (4) Hecates Tholus (5)Olympus Mons (6) Pavonis Mons (7) Large flow 37◦N (8) Hellas Mons(9) Thaumasia Planum (10) Dichotomy Boundary (modified from photo-journal.jpl.nasa.gov/).

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2.1 Study area

Figure 2.2: Study area inside Bosphorus Region (detail of MOLA shadedrelief NE, modified from Google Earth). Boxes: (a) location of Figures 2.4and 2.5; (b) location of Figure 2.6.

Figure 2.3: 3D of the Study area inside Bosphorus Region (same locationas Figure 2.2, MOLA shaded relief NE, modified from JMARS, MOLA).

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2.1 Study area

Figure 2.4: The 41◦28’S 62◦26’W crater object of this study. The box (a)indicates the location of Figure 2.18 (detail of THEMIS day IR GlobalMosaic, modified from Google Earth). See location in Figure 2.2.

Figure 2.5: 3D of the 41◦28’S 62◦26’W crater (same location as Figure 2.4;(MOLA shaded relief NE, modified from JMARS, MOLA).

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2.2 RMCs, LVF and LDA

Figure 2.6: Morphologic features in Bosphorus Region are indicated:main debris flow in blue, secondary debris flow in green (MOLA shadedrelief NE, modified from Google Earth).

2.2 RMCs, LVF and LDAThe Bosphorus Planum Region is characterized by three types of

structures: Ring-mold craters, lineated valley fill and lobate debris flow(Figures 2.6 and 2.12 to 2.14).

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Ring-mold craters (RMCs) are concentric craters shaped like a trun-cated torus and named for their similarity to the cooking implement (Fig-ure 2.6).

They are abundant in lobate debris aprons (LDA) and lineated val-ley fill (LVF) in the northern mid-latitudes on Mars, and generally absentin the surrounding terrains. LDA and LVF have been interpreted to beoriginated by flows of debris, although this mechanism of flow remainsuncertain (Kress & Head (2008)).

On the basis of their morphologic similarities to laboratory impactcraters formed in ice and the physics of impact cratering into layeredmaterial, the unusual morphology of RMCs was interpreted as the re-sult of impacts onto a relatively pure ice substrate below a thin regolith,with strength-contrast properties. The major factors involved in the de-velopment of the ring-mold shape include spallation, viscous flow andsublimation (Kress & Head (2008)).

2.2.1 Crater types in LDA/LVFBased on the association of features that can be observed superim-

posed on the simple crater bowl shape, RMCs can be assigned to one ofthe following morphological groups (Figure 2.7):

• central pit or bowl;

• central plateau;

• multi-ring;

• central mound.

RMCs form almost 80% of the total crater population on LDA/LVF, andthey are typically larger than bowl-shaped craters. RMC diameters reach≈ 750 m in diameter, with a mean of ≈ 102 m, while the largest bowl-shaped crater diameter is ≈ 356 m and the mean is ≈ 77 m.

According to some authors, the different morphologies of the cratersoccurring in LDA/LVF can be interpreted as terms of a degradational se-quence, considering the bowl-shaped craters "‘fresh"’ and unmodified, andthose with unusual "‘oyster-shell"’ morphologies (like RMCs) "‘degraded"’and progressively highly modified (Kress & Head (2008)).

The crater analyzed in detail in this study can be assigned to the centralpit or bowl group.

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Figure 2.7: Types of RMCs observed in LDA/LVF (modified from Kress& Head (2008)).

2.2.2 Conformation of the 41◦28’S 62◦26’W craterThe impact craters on Earth and on the Moon have a typical central

mound, but on Mars this type of structure can rarely be observed. Thecentral peak of the crater inside the Bosphorus Region has collapsedprobably due to the presence of ice. Why have the craters on icy planets

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this kind of conformation?Two stages characterize the impact process: formation and modifica-

tion.

• The formational stage produces the initial crater morphology, whichis a function of the impact energy of the projectile (depending onmaterial properties, size, velocity, and angle of incidence) and thematerial and structural properties of the target material (composi-tion, density, porosity, layering) (Figure 2.8).

• The primary crater morphology may experience different modifica-tion processes after emplacement. We distinguish short-term mod-ification (slumping or gravitational collapse) from long-term modi-fication (impact, eolian, volcanic, tectonic, or fluvial degradation).Viscous relaxation of craters can bridge short- and long-term modi-fication time scales due to its dependence on crater size, energy cou-pling, geothermal gradient, and material properties of the substrate.Impacts into water ice and ice-silicate mixtures produce distinctlydifferent landforms (Kress & Head (2008)).

Figure 2.8: Depth profiles from impacts at different velocities (modifiedfrom Kress & Head (2008)).

Craters on icy planetary bodies show upwelling of the crater floor andcentral peak, flattening of the crater profile, and viscous domes formingin the central crater pits. Profiles and images of experimental modelsfor viscous relaxation of craters exhibit features similar to the interiormorphologies of some craters on LDA/LVF. On the basis of the scale ofcraters on LDA/LVF (less than 1 km), broad-scale viscous relaxation isunlikely; however, localized viscous flow related to impact heating couldoccur. Recent numerical simulations have shown that a warm core of

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heated ice exists below the impact point, and that ice can form a cen-tral uplifted region (Figure 2.9) that can also migrate outward (Kress &Head (2008)). Impact-induced heating of an ice substrate would dissipateby conduction after the initial impact event, leading to a time-dependentshort-term modification process. In the Bosphorus Region this type ofstructure is not observed, in the 41◦28’S 62◦26’W crater, in which, in con-trast, the collapse of the central mound is clearly developed (Figure 2.10).

According to a debris-covered glacial model for LDA/LVF, a furtheraspect of almost istantaneous crater modification involves the fracturingand ejection of ice and the exposure of fresh fragmented and spalled ice.On the basis of latitudedependent ice stability on Mars, it is clear thatfreshly exposed ice would sublime in a geologically short period of time(Kress & Head (2008)).

Figure 2.9: Moreux Crater, located outside the study area, showing in reda central uplifted region (MOLA shaded relief NE, modified from GoogleEarth).

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Figure 2.10: The 41◦28’S 62◦26’W crater: red circle shows the collapse ofthe central mound (see Figure 2.2 for location, MOLA shaded relief NE,modified from Google Earth).

2.2.3 Stratigraphic relationships LDA/LVFOn the basis of a series of criteria developed for the identification of a

glacial origin for LVF (lineated valley fill) and LDA (lobate debris aprons),we interpret the stratigraphic, topographic, and textural relationships be-tween lineated valley fill and lobate debris apron morphological units asevidence of local and regional glacial overprinting of the landscape duringthe recent Amazonian. We document:

• the stratigraphic relationships between lineated valley fill subunits,including the presence of apparently superposed and small-scalelobate features,

• the regional integration and flow of lineated valley fill material,

• lineated valley fill degradation,

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• the nature and stratigraphic position of lobate debris aprons.

These observations also suggest multiple phases or episodes of midlati-tude valley glacier activity. These observations suggest the possibility ofmidlatitude glacial deposits similar to those recognized over broad por-tions of the Martian dichotomy boundary (Figure 2.11) within the pastseveral hundred million years (Levy et al. (2007)).

Figure 2.11: This image shows an example of the terrain that is presentat the boundary between the southern highlands and the northern low-lands (the famous “dichotomy boundary”). This image is located on thatboundary in Elysium Planitia, not too far north of Gusev Crater. The highground appears to be eroding away, breaking into blocks that almost looklike they are disintegrating (detail of THEMIS PIA04883, modified fromGoogle Earth).

The Bosphorus Region provide evidence for widespread glacial con-ditions during recent Amazonian time.

Questions about LDA/LVF on Bosphorus Region which can be an-swered by morphological and stratigraphical analysis include:

1. the direction (main flow: NW vs SE, secondary flow: NE - SW)and extent (main flow: ≈ 17.000km2, secondary flow: ≈ 7.000km2

(Figures 2.12 to 2.15)), and the continuity of flow between LDA andLVF (the main flow ends into the crater, Figure 2.12).

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Figure 2.12: Main morphologic features in Bosphorus Region : main flux(M-LVF) in blue, secondary flux (S-LVF) in green, LDA in brown, crater inred. The boxes indicate the locations of the Figures: (a) 2.13; (b) 2.14;(c) 2.14. (background image: MOLA shaded relief NE, modified fromGoogle Earth).

Figure 2.13: Detail of the Bosphorus Region (37◦30’-38◦18’S 59◦18’-60◦30’W): Channels of flow of detritus in indicated by white arrows (loca-tion in Figure 2.12, background image: MOLA shaded relief NE, modifiedfrom Google Earth).

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2. the sequence of emplacement and/or stratigraphic position of LDA/LVF:Morphological relationships between LVF subtypes suggest a recordof multiple phases and styles of valley glacier activity in the Bospho-rus Region, characterized by sequential reductions in LVF ice vol-ume and flow intensity (Figure 2.16).

Figure 2.16: Interpretation of the relationships between the two fluxesin Bosphorus Region : the main flux (M-LVF in blue) is overlain by thesecondary flux (S-LVF in green). Location in Figure 2.12 (created by LauraDe Matteis and Antonio Albano).

This stratigraphic relationships LDA/LVF may indicate a climatic pro-cess of increasing desiccation with time, leading to increasingly limitedice accumulation and enhanced sublimation of buried ice. Climate con-ditions during more recent history have resulted in sublimation and iceremoval from these glacial terrains, although it is possible that ice re-mains at depth at the present, buried under protective sublimation till.

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2.3 Similarities with ADV

In conclusion, taken together, these lines of evidence suggest a com-plex glacial history for portions of the Bosphorus Region that can havesome similarities with those described along the dichotomy boundary,and a potential link between the stratigraphy of Bosphorus (desiccatedmantle, LVF, LDA) and the global glacial events during the past millionsto hundreds of millions of years.

2.3 Similarities with ADVThe glacial landforms of Mars can be compared with terrestrial land-

forms, in particular those produced by the glacial mechanisms in Artar-tide. The Antarctic Dry Valleys (ADV) (Figure 2.17) are generally classifiedas a hyper-arid, cold-polar desert. The region has long been consideredan important terrestrial analog for Mars because of its generally cold anddry climate and because it contains a suite of landforms at macro-, meso-, and microscales that closely resemble those occurring on the martiansurface. The extreme hyperaridity of both Mars and the ADV has focusedattention on the importance of salts and brines on soil development andphase transitions from liquid water to water ice.

Equilibrium landforms are those features that formed in balance withenvironmental conditions within fixed microclimate zones. Some equi-librium landforms, such as sublimation polygons, indicate the presenceof extensive near-surface ice; identification of similar landforms on Marsmay also provide a basis for detecting the location of shallow ice. Land-forms that today appear in disequilibrium with local microclimate condi-tions in the ADV signify past and/or ongoing shifts in climate zonation;understanding these shifts is supported by documentation of the climaterecord for the ADV (Marchant & Head III (2007)).

24


Figure 2.17: Antarctic Dry Valleys: Location map showing major geo-graphic features. Upper left inset: Black square shows location of DryValleys within Antarctica. Lower right inset: Map showing general rangefor coastal thaw zone (CTZ; blue), inland mixed zone (IMZ; green), andstable upland zone (SUZ; yellow) (modified from Marchant & Head III(2007)).

Detailed analysis of the MOC, THEMIS and MOLA data 41◦28’S 62◦26’Wof the crater studied here suggests that changing environments and lo-cal topographic conditions, such as crater walls and narrow valleys (Fig-ure 2.18), favored accumulation and preservation of snow and ice, andits glacial-like flow down into surrounding areas. In addition, the welldeveloped flow located in the northern portion of the crater rim wouldcontinue to provide a source of debris fed by rockfalls from the valleysides, which would result in much more prominent glacial deposits suchas moraines or till, and eventually a thicker, more continuous debris coverallowing for extended preservation.

25


Figure 2.18: Several debris flows emerges from breached craters, alcoves,tributary valleys, and indentations in valley walls, and merge into a singleflow (location in Figure 2.4, background image: MOLA shaded relief NE,modified from Google Earth).

Similar local ice accumulation zones are typical of debris coveredglacial flow in the already mentioned Antarctic Dry Valleys; these land-forms represent a cold polar desert environment that can be analogousto the environment on Mars. These geomorphologic features are alsotypical of the of regional valley glaciers and plateau icefield landsystemsin Baffin and Ellesmere Islands in the Canadian Arctic. For example,the regional valley glaciers associated with the Ellesmere Island plateauicefield landsystem (Figure 2.19) show very similar relationships amongaccumulation zones, valley fill and down-valley flow, and convergence andice flow deformation (Head et al. (2006)).

Several features in the southern mid-latitudes of Mars have been in-terpreted as being cold-based glaciers similar to what is found in the DryValleys of Antarctica (Head et al. (2006)). Furthermore, detailed analysisof the Bosphorus Region suggest glacial modification comparable to thoseobserved along the dichotomy boundary during the Late Amazonian andformation of integrated valley glacial systems, probably resulting from alarge-scale glaciation over several million km2. This point but this pointwill be discussed in chapter five.

26


Figure 2.19: Eugenie Glacier, Agassiz Ice Cap, Ellesmere Island, Canada,just north of Dobbins Bay (79853VN, 75810VW) showing a portion of themargins of the plateau icefield landsystem. Width of image is ≈ 13 km.Area A shows a series of cirque-like accumulation zones and convergingflow toward the breach in the valley wall. Area B shows constrictionand converging flow of two ice masses and associated deformation. AreaC contains ridges and moraines parallel to the valley and formed byconverging ice masses. Areas D and E show medial moraines that format the convergence of two ice tributaries. Area F shows two small glaciersjoining the large valley glacier and the compression of the ice flow intoa narrow band along, and slightly above, the valley margin. Area G andH show the convergence of ice flow into the southerly trending broadEugenie Glacier (Credit: Advanced Thermal and Reflection Radiometer(ASTER) image obtained 7/31/00, modified from Head et al. (2006)).

27

Chapter 3

Recognition of cold-desertenvironment

A number of features related to and associated with intermontaineglacial systems on Earth have been recognized by Head et al. (2006) asuseful criteria to identify debris covered glacial related terrains in thecold-desert environment of Mars. In this chapter, these criteria have beenapplied to characterize the climate that can have affected the Bosporosarea. First, the features observed in the study area have been comparedwith Head et al. (2006)’s criteria (section 3.1, Figures 3.1 to 3.7) and thenwere further analyzed applying specific statistic tests (Section 3.2).

3.1 Criteria for recognition based on Head et al.(2006)

1. alcoves, theater-shaped indentations in valley and massif walls (localsnow and ice accumulation zones and sources of rock debris cover)(Figure 3.2),

2. parallel arcuate ridges facing outward from these alcoves and ex-tending down slope as lobe-like features (deformed flow ridges ofdebris),

3. shallow depressions between these ridges and the alcove walls (zonesoriginally rich in snow and ice, which subsequently sublimated, leav-ing a depression),

4. progressive tightening and folding of parallel arcuate ridges where

28

3.1 Criteria for recognition based on Head et al. (2006)

abutting adjacent lobes or topographic obstacles (constrained debris-covered glacial flow) (Figure 3.3),

5. progressive opening and broadening of arcuate ridges where thereare no topographic obstacles (unobstructed flow of debris-coveredice),

6. circular to elongate pits in lobes (differential sublimation of surfaceand near-surface ice),

7. larger tributary valleys containing LVF formed from convergence offlow from individual alcoves (merging of individual lobes into LVF)(Figure 3.7),

8. individual LVF tributary valleys converging into larger LVF trunkvalleys (local valley debris-covered glaciers merging into larger in-termontaine glacial systems) (Figure 3.1),

9. sequential deformation of broad lobes into tighter folds, chevronfolds, and finally into lineated valley fill (progressive glacial flowand deformation) (Figure 3.4),

10. complex folds in LVF where tributaries join trunk systems (differ-ential flow velocities causing folding) (Figure 3.5),

11. horseshoe-like flow lineations draped around massifs in valleys andthat open in a downslope direction (differential glacial flow aroundobstacles),

12. broadly undulating along-valley floor topography, including local val-ley floor highs where LVF flow is oriented in different down-valleydirections (local flow divides where flow is directed away from indi-vidual centers of accumulation) (Figure 3.6),

13. integrated LVF flow systems extending for tens to hundreds of kilo-meters (intermontaine glacial systems),

14. rounded valley wall corners where flow converges downstream, andnarrow arete-like plateau remnants between LVF valleys (both inter-preted to be due to valley glacial streamlining) (Head et al. (2006)).

29


Figure 3.1: (A) THEMIS daytime IR image mosaic showing the T-shapedvalley, the lineated valley fill contained within it, and the surroundingplateau. (B) THEMIS mosaic overlain on MOLA topographic map andviewed perspectively from the southwest. Letters show locations of detailsof MOC images of Figures 3.2 to 3.7. (C) THEMIS mosaic with majorflow trends highlighted by arrows. Flow emerges from breached craters,tributary valleys, and indentations in valley walls. (modified from Headet al. (2006)).

30


Figure 3.2: (A and B) Upper accumulation zone: MOC image R0400308showing deformation of tabular blocks at the crater wall marginand downslope streaming and deformation of blocks. Compare tobergschrunds, accumulation zone crevasses, and seracs in the BishopGlacier, Coast Mountains of British Columbia (view is several hundredmeters across) (location in Figure 3.1B, modified from Head et al. (2006)).

Figure 3.3: (C and D) Constriction at the mouth of the breached crater andconvergence of flow lines: MOC image R0301488. Compare to narrow-ing of valley walls and constriction of flow lines (arrow), Yentna Glacier,Alaska Range (view is several kilometers across) (location in Figure 3.1B,modified from Head et al. (2006)).

31


Figure 3.4: (E and F) Convergence and folding: MOC image R0301488showing convergence of flow from two different directions (crater floorand alcove at east end of the T valley); where lineations meet, distinctivefolding is observed, suggesting different velocities for the two ice flows.Looped and folded moraines, Yanert Glacier, Alaska Range, resulting fromglacial surges (view is several kilometers across) (location in Figure 3.1B,modified from Head et al. (2006)).

Figure 3.5: (G and H) Medial and lateral moraines and stranded marginaldeposits: MOC image E1103966 showing linear ridges interpreted to bemedial moraines formed into ridges by preferential sublimation of inter-vening, more ice-rich material. Also observed is a marginal dark terrace(lower left) interpreted to be a marginal deposit stranded along the lowervalley wall by preferential lowering of the glacier interior. Compare tomedial and lateral moraines and terraces (arrow), Speel Glacier, CoastMountains, southeast Alaska (view is several kilometers across) (locationin Figure 3.1C, modified from Head et al. (2006)).

32


Figure 3.6: (I and J) Convergence, folding and shear: MOC imageE0302127 showing very tight folds in the zone where valley ice flowsfrom the upper part of the T are converging and deforming within thenarrower valley. Compare to convergence and deformation of ice in theBering Glacier, south central Alaska (view is 4–5 km across) (location inFigure 3.1B, modified from Head et al. (2006)).

Figure 3.7: (K and L) Lobate lineated valley fill and glacial front: Perspec-tive THEMIS mosaic view of lobate front of the lineated valley fill deposit.Compare to the retreating glacier tongue showing the lineated natureof surface, and lateral spreading of tongues into topographic reentrants,Honeycomb Glacier, North Cascade Range (view is several kilometersacross) (location in Figure 3.1C, modified from Head et al. (2006)).

33

3.2 Statistical study applied to Head et al. (2006)’s criteria

3.2 Statistical study applied to Head et al. (2006)’scriteria

To give a more analytical characterization of the study area, geomor-phological observations were input in statistically based studies specificallyfocused on the Bosphorus Region.

The Kruskal-Wallis test is a nonparametric test that has the same pur-pose of ANOVA (Analysis Of Variance) that verify the homogeneity of thegroups considered; the method requires a transformation in ranks of thescores, then the ranks are added and was calculated a parameter K whichis then compared with the value of the printout to verify the significanceof the test (see Chapter 7 for a complete description of this method). Inthis case, were also computed the confidence limits of group means withthe intent to understand if the score assigned to the Bosphorus Regionmay fall into the category of areas defined glacial as literature.

Comparisons were made between 3 types of groups:

Group I : glacial areas as defined in the literature, belonging to the Thar-sis volcanic province Arsia Mons, Ascraeus Mons, Hecates Tholus,Olympus Mons, Pavonis Mons Large (Figure 3.8) and Large flowlocated 37◦N 25◦E.

Figure 3.8: Tharsis Volcanic Province: Olympus Mons, AscraeusMons, Pavonis Mons, Arsia Mons (MOLA Colorized Terrain, Credit:MSSS/NASA/JPL/DLR (RPIF)).

34


Group II : Bosphorus Region and 12 craters showing similar geomor-phological features, characteristic of periglacial areas (Figure 3.9).In the Figures 3.11 and 3.12 are indicated the 11 features (2-3-4-5-7-8-9-10-11-12-13) that are described in the Head et al. (2006)’s criteria.

Figure 3.9: Periglacial areas object of study (Viking Color Imagery, mod-ified from Google Earth).

Group III : areas devoid of signs of ice (Figure 3.10).

Figure 3.10: Absence of areas useful for comparison (Viking Color Im-agery, modified from Google Earth).

35


Figure 3.11: Features 4-5-7-8-9-10 by criteria of Head et al. (2006) insideBosphorus Region (location in Figure 2.4, MOLA shaded relief NE, mod-ified from Google Earth).

Figure 3.12: Features 2-3-11-12-13 by criteria of Head et al. (2006) in-side Bosphorus Region (location in Figure 2.2, MOLA shaded relief NE,modified from Google Earth).

36


As Table 3.1 shows, the geomorphological observations reflect the sta-tistical results, indicating both periglacial and glacial characteristics of theBosphorus Region; only spectroscopy studies may clarify this point.

Group I Group II Bosphorus Region Group IIIglacial periglacial no glacial

[10 - 12 - 14] [5 - 8 - 11] 11 [0 - 2 - 5]

Table 3.1: Head et al. (2006)’s criteria calculated for 3 types of grouping.For each group is reported the median as central datum and the respectiveconfidence intervals. In red is indicated the Bosphorus Region that is theonly area periglacial with a critical value that is comparable to the valuesof the Head et al. (2006)’s criteria for the glacial areas.

37

Chapter 4

Spectral analysis

4.1 Role of water and CO2

The stability of equatorial ground ice is governed by the rate at whichH2O molecules can diffuse through the regolith and into the atmosphere.This process is complicated by the fact that the mean free path of anH2O molecule in the Martian atmosphere is ≈ 10µm. When the ratio ofthe pore radius r to the free path λ of the diffusing molecules is large(r/λ > 10), bulk molecular diffusion is the dominant mode of transport;the movement of molecules through the pore system occurs in responseto repeated collisions with other molecules present in the pores. Onthe other hand, for very small pores (r/λ < 0.1) H2O diffusion greatlyoutnumber those that occur with other moleculs, leading to the processknown as Knudsen diffusion. Because the frequency of pore wall colli-sions increases with decreasing pore size, small pores can substantiallyreduce the effciency of the transport process. For pores of intermediatesize (0.1 < r/λ < 10), the contributions of both processes must be takeninto account (Squyres et al. (1992)).

The most likely volatile to have formed the glaciers is water-ice, as theonly alternative, CO2, is particularly unstable at low latitudes under anyconceivable atmospheric conditions. The water source could have beenprecipitation or groundwater that freezes when coming into contact withice (Hauber et al. (2005)).

The impact of CO2 and water caps has been respectively studied byRubincam (1990), Rubincam (1993) and Bills (1999) using linear approx-imations for the obliquity dynamics and global mass redistribution. Be-cause both the volatile response to obliquity forcing and martian inter-nal parameters (density, elasticity, rigidity, viscosity) are still poorly con-

38

4.2 Analysis

strained, long-term estimation of the climate impact on ice mass friction isvery uncertain. Similar analyses relative to Earth suggest that obliquity-oblateness feedback has probably not changed the Earth’s obliquity bymore than 0.01◦ per Myr during the severe recent Pliocene-Pleistoceneglaciations (≈ 0.3 Ma) (Levrard and Laskar, 2003).

Since the martian caps are about one order of magnitude less massivethan the water/ice mass involved in typical terrestrial ice age, its impact isexpected to be negligible over the last 20 Myr, compared to other sourcesof uncertainty, and it was not taken into account in the long-term obliquitysolutions (Laskar et al. (2004)).

4.2 AnalysisDespite the objectivity effort in qualitative descriptions, the problem

of geomorphological observations is that the geomorphic convergence offeatures originated by even very different processes. As a consequence,different authors may arrive at different conclusions. So, the conclusionsreached in the previous chapter, are subject to limits of the method. Thespectral analysis is certainly one of the most effective way to reach notquestionable, or at least reproducible, results to define the exact nature ofthe geomorphological units recognized in the Bosphorus Region.

The spectroscopic characteristics of pure water and CO2/H2O ice mix-tures can be observed in the laboratory spectra reported in the Figures 4.1,4.2, 4.3 showing the positions of the absorption band maxima (Figure 4.1),transmission minima (Figure 4.3) and the vibrational modes responsiblefor the spectral features (Figure 4.2).

Figure 4.1: Absorbance spectrum of water (modified from Squyres et al.(1992)).

39

4.2 Analysis

Figure 4.2: Peak areas for solid CO2 in H2O; a: Mode assignments fromQuirico & Schmitt (1997), b: Peak areas are normalized to the CO2 peaknear 2340 cm−1 (4.274 µm). The relative numbers can be converted toabsolute values by multiplying by ≈ 2x10−16 cm/molec, c: These are notCO2 peaks, they are probably caused by H2O, d: This area probably rep-resents a lower limit, this peak is difficult to integrate accurately becauseof its proximity to the 3 µm OH band of H2O (modified from Bernsteinet al. (2005)).

Figure 4.3: The 1.75–22 µm (5700–450 cm−1) IR spectrum of an H2O/CO2= 5 ice mixture at 15 K. In addition to the broad absorptions of amorphoussolid H2O and the sharper CO2 fundamentals one sees the ‘forbidden’ 2ν3overtone of CO2 at 2.135 µ m (4684 cm−1) (modified from Bernstein et al.(2005)). For this analysis, mid-infrared spectra acquired by the ThermalEmission Spectrometer (TES) on Mars Global Surveyor mission to Marshave been used (see Chapter 11, TES data tool).

40

4.2 Analysis

The spectral analysis was divided into 3 phases:

1. acquisition of the spectra,

2. improvement of the spectra,

3. comparison of the spectra.

4.2.1 Acquisition and improvement of the spectraThe TES Layer (see Chapter 9 Section 4) allows users to display spec-

tral data collected by ASU’s Thermal Emission Spectrometer on MarsGlobal Surveyor and is mostly used for reconnaissance purposes, allow-ing users to identify spectra of interest which can then be downloadeddirectly from the database and analyzed using specific tools. The TESLayer is currently only available in the THEMIS Team Release of JMARS.

The procedure involves:

• open the TES Layer and then click on the TES Tab to access thefocus panel. The focus panel will appear blank since there is not anopen “context”. A context is the set of database search parametersand display options which tells the TES Layer what data must beretrieved from the TES database (due to the large amount of data inthe TES database, the TES Layer can only display a small fractionof it) (Figure 4.4),

• add a New Context. The focus panel will then fill with the variousfields needed for creating a context (Figure 4.5),

• specify the Context Parameters: enter values in the various fieldsand drop-down menus to create a context (Figure 4.6).

See Chapter 9 for a complete description of the procedures JMARS.The amount of TES data downloaded by a context can be extremely

large, which can make the TES Layer very slow (Engle (2010)).Finally, the spectra have been enhanced with software freeware Lab-

Plot (Figure 4.7) through fitting and smoothing methods, see Chapter 8for a complete description of this function.

41

4.2 Analysis

Figure 4.4: Example TES Layer inside Bosphorus Region (MOLA shadedrelief NE, modified from JMARS, TES).

Figure 4.5: Context Parameters (MOLA shaded relief NE, modified fromJMARS, TES).

42

4.2 Analysis

Figure 4.6: Final spectrum (MOLA shaded relief NE, modified fromJMARS, TES).

Figure 4.7: Interface software freeware LabPlot (modified from LabPlot).

43

4.2 Analysis

4.2.2 Comparison of spectraWith the advent of the Mars Global Surveyor Thermal Emission Spec-

trometer (TES), it is now possible to examine the thermalinfrared (TIR)spectral character (≈ 1670˘200cm−1) of Mars at a combined spatial andspectral sampling (3 x 6 km and 10 cm−1, respectively) previously notavailable. To date, surface studies using TES spectra have focused ondark regions, where basalt, basaltic andesite, and hematiterich materialshave been identified.

Thermal-IR spectra of Mars are dominated by atmospheric absorp-tions. In order to distinguish the spectral features of the surface fromthose of the atmosphere, it is necessary first to recognize the multitudeof atmospheric features.

The TES spectra are plotted as apparent emissivity versus wavenum-ber. The measured spectrum has been calibrated to radiance and thendivided by a Planck curve at the maximum brightness temperature cal-culated from the radiance spectrum (Ruff & Christensen (2002)).

Therefore, the comparison of the spectra has been done on series ofthe parameters:

Carbon Dioxide Gas: .For the CO2 spectrum, the following conditions, were applied: the at-mospheric temperature profile was 260 K at surface level (6.1 mbarsurface pressure), decreasing 3 K km−1 with altitude to 300 km,where it becomes isothermal; the surface temperature was 300 K.Several CO2 absorptions are evident throughout the TES spectralrange. Most dominant is the one centered at 667 cm−1 (15 µm) aris-ing from the fundamental bending mode of gaseous CO2. Althoughit appears as a single feature in a TES spectrum, additional CO2bands related to rotation-vibration coupled modes, CO2 isotopes,and Fermi resonances produce the observed shape. The opacityof this feature at its center is so high that surface radiance is com-pletely absorbed and thus does not reach an orbiting instrument.For a typical daytime TES spectrum, the surface radiance compo-nent only becomes significant at >780 and <560 cm−1.Outside of the 667 cm−1 CO2 feature is a set of “hot bands”, whichare absorptions that arise from vibrational transitions originatingfrom above the ground state. Two notable hot bands are readilyapparent on either side of the 667 cm−1 feature, one at ≈ 544 cm−1

and the other at ≈ 790 cm−1 (Figure 4.8). A pair of weak doublets,

44

4.2 Analysis

one centered at ≈ 960 cm−1 and the other at ≈ 1060 cm−1 also arehot bands. These are less discernable in TES spectra because theyoverlap with the prominent aerosol dust and water-ice absorptions.Hot band absorptions are very sensitive to temperature, increasingin intensity with increasing atmospheric temperature.A final set of CO2 features arises from the various isotopes of oxy-gen and carbon that can be found in the molecule. Most significantfor TES spectra are the weak doublets centered at 1260 and 1365cm−1 that are the result of a Fermi resonance between fundamentalmodes of the 18O12C16O isotope. Compared with hot bands, theseabsorptions are much less sensitive to the temperature of the atmo-sphere (Ruff & Christensen (2002)) (Figure 4.12).

• CO2-CONT-TEMP: Mean of brightness temperature just out-side the wings of the 667 cm−1 CO2 absorption band. For 10cm−1 and 5 cm−1 data, all wavenumbers between 508 to 531cm−1 and 805 to 827 cm−1 are used with a total of 6 and 10wavenumbers used in the calculation, respectively.• CO2-DW-FLUX: Down-welling flux from 667 cm−1 CO2 band

(Figure 4.8).• TOTAL-DW-FLUX: Down-welling flux from CO2 and atmospheric

aerosols.

Aerosol Dust: .The atmosphere of Mars is never free from dust although seasonalvariability is well documented. Because the dust is such a strongabsorber, it results in the next most prominent feature in TES spec-tra relative to CO2. Centered at 1075 cm−1 (9 µm), aerosol dustproduces a prominent V-shaped absorption that is accentuated bythe presence of a CO2 hot band roughly at the center of the trough.The V-shape is relatively symmetric but becomes increasingly asym-metric when combined with a water-ice absorption. On the lowwavenumber side of the 667 cm−1 CO2 feature, atmospheric dusthas another absorption centered at 470 cm−1 (Ruff & Christensen(2002)) (Figure 4.12).

• TOT-DUST: Dust extinction at ≈ 9µm.• PERC-DUST1: Concentration of dust1 endmember; Dust LowCO2.

45

4.2 Analysis

• PERC-DUST2: Concentration of dust2 endmember; Water HighIce CO2.

Water-Ice Clouds and Water Vapor .The occurrence of water-ice clouds and hazes is highly variable inspace and time. Water ice has a deep and relatively broad absorp-tion centered at 825 cm−1 and a narrower absorption at 230 cm−1

(Ruff & Christensen (2002)).Water-vapor concentration in the Martian atmosphere is variable inspace and time but is in the range of a few to several tens of precip-itable microns. The concentration of Martian water vapor is neversufficient to produce deep absorption features but numerous shallow,narrow features exist in the spectral regions >1400 and <500 cm−1.At wavenumbers >1400 the absorptions are due to the fundamen-tal bending mode vibration and coupled rotation-vibration modes ofthe H2O molecule while those at low wavenumbers (<500) are dueto molecular rotations. In both cases, many individual absorptionsare narrower than the 10 cm−1 (or even 5 cm−1) spectral samplingof the TES instrument. The end result is a sawtooth appearance inthe water-vapor regions of TES spectra (Ruff & Christensen (2002))(Figure 4.12).

• TOT-ICE: Ice extinction at ≈ 11µm.• PERC-ICE1: Concentration of ice1 endmember; Water Ice Cloud

(High Latitude).• PERC-ICE2: Concentration of ice2 endmember; Water Ice Cloud

(Low Latitude).

PERC-BAS1: Concentration of andesite endmember; comparision withspectre of Acidalia Type Surface (Figure 4.9).

PERC-BAS2: Concentration of basaltic endmember; comparision withspectre of Syrtis Type Surface (Figure 4.9).

PERC-HEM: Concentration of hematite endmember; comparision withspectre of Hematite (TT derived) (Figure 4.10 and 4.11).

See Chapter 11 for a complete description of parameters TES andChapter 12, 13 for description of the algorithms.

46

4.2 Analysis

Figure 4.8: Typical TES spectra of CO2, atmospheric dust, water-iceclouds, dust and cloud mixtures, and mixtures of atmospheric and sur-face components. The “cloud” spectrum was acquired on orbit P48 (LS2238), ICK 1118, Detector 1, and is scaled by 50%; the “dust” spectrum isthe average of all detectors for ICKs 800–950 on orbit P61 (LS 2348); the“dust and cloud” spectrum was acquired on orbit P35 (LS 2128), ICK 1157,Detector 1; the “dust and surface” spectrum is the average of all detectorsfor ICKs 1663–1675 on orbit P219 (LS 3058) (modified from Christensenet al. (2000)).

Figure 4.9: Martian surface spectra acquired by TES overlaid on the spec-tral field classification diagram. Fields are defined by spectra of terres-trial rocks at 10 cm−1 sampling. Basaltic rocks (Syrtis Type Surface) fallwithin the gray shaded zone, and andesitic rocks (Acidalia Type Surface)lie between the black lines (modified from Hamilton et al. (2001)).

47

4.2 Analysis

Figure 4.10: The effect of packing on spectral shape. Scaled spectral fea-ture shapes for a suite of hematite grain sizes and packing are comparedto the measured TES spectrum. The spectra of submicron grains do notchange shape with packing, are unlike those of larger particle sizes, anddo not provide a good match to the TES data. (modified from Christensenet al. (2000)).

Figure 4.11: Mini-TES observation of Tamanend Park compared to thetarget transformation (TT) derived outcrop rock spectral shape scaled forcontrast. The spectral emissivity measured by Mini-TES is a combinationof outcrop rock, basaltic sand, surface dust, and atmosphere. (modifiedfrom Glotch et al. (2006)).

48

4.2 Analysis

Figure 4.12: TES spectra that display the combined features of atmo-spheric CO2 gas, dust, water vapor, and water ice which are the medianof 25 individual spectra from a common location in Bosphorus Region.This spectrum is displayed with 10 cm−1 sampling.

To prove the periglacial nature of the Bosphorus Region two groupsof spectroscopically charactherized areas based on Head et al. (2006)’scriteria (See Chapter 3 Section 1) were compared:

Group I : glacial areas defined as the bibliography (Arsia Mons, AscraeusMons, Hecates Tholus, Olympus Mons, Pavonis Mons, Large flow37◦N 25◦E),

Group II : Bosphorus Region and 12 craters that have the same geomor-phological features (periglacial areas),

49

4.3 Discussion

The Kruskal-Wallis test (See Chapter 3 Section 2) was performed tocheck homogeneity within the groups and to evaluate significant differ-ence between them.

The results of the tests are reported in Table 4.1:

Parameters Group I Group II Group I vs Group IIglacial periglacial comparison

CO2-CONT-TEMP homog. NOT HOMOG. I + Bosphorus Region > IICO2-DW-FLUX homog. NOT HOMOG. I + Bosphorus Region > II

TOTAL-DW-FLUX homog. NOT HOMOG. I + Bosphorus Region > IITOT-DUST homog. homog. I > II

PERC-DUST1 homog. homog. I > IIPERC-DUST2 homog. homog. I > II

TOT-ICE homog. homog. I > IIPERC-ICE1 homog. homog. I > IIPERC-ICE2 homog. homog. I > IIPERC-BAS1 homog. homog. I = IIPERC-BAS2 homog. homog. I = IIPERC-HEM homog. homog. I < II

Table 4.1: Test results for comparison of spectroscopy data

4.3 DiscussionThe parameters related to ice-water reported in the Table 4.1, how

many the glacial regions on Mars have a statistically higher concentra-tion with respect to the Bosphorus Region and its parent group.

However, the most interesting thing is about the CO2-flow (relatedto both the nature geomorphological and climatic history), because thegroup presents at not onogeneità statistical to do with the periglacial re-gions of study, the region has a concentration of CO2-flux comparablewith glacial regions but statistically higher than the periglacial regions.

In addition, the group II shows a concentration of iron and sulfides(but only in the dunes in craters) statistically higher than those of theglacial regions. This result may be due to the fact that in the periglacialregions there is a greater presence of material reshuffled by various ge-omorphological events.

50

Chapter 5

Framing the Bosporos regionwithin the Mars climate history

5.1 Climate conditionsMars is at present in an "‘interglacial"’ period, and the ice-rich deposits

are undergoing reworking, degradation and retreat in response to thecurrent instability of near-surface ice (Head et al. (2003)).

Present climate conditions on Mars are notably different from thoseon Earth. In particular the annual mean temperatures are significantlylower on Mars, with a global annual mean of 204 K and a range from 234K at the equator to 159 K at the poles. Such low mean temperatures makesthe potential for freeze-thaw processes in the current climate extremelyunlikely. In addition, the rheological response of the permafrost to in-duced stresses, such as thermal contraction, differs from that on Earth inthat at these low temperatures the ice-rich material is more resistant toviscous relaxation, allowing high tensile stresses to develop.

Under such conditions thermal contraction cracks easily form in cohe-sive ice-rich permafrost and thus polygonal-patterned ground is expectedto be widespread (McEwen et al. (2007)).

5.2 Predictions of orbital parametersObliquity, eccentricity and argument of perihelion have a significant

impact on the stability of the glaciers, and ongoing sensitivity studies willprovide major insight into the understanding of the observed glacial cy-cles (Madeleine et al. (2009)).

The parameters of Mars orbit and spin axis orientation control the

51

5.3 Formation of glaciers

global distribution and seasonal intensity of the solar insolation, and it iswidely accepted that astronomical variations could have had a profoundinfluence on the planet’s climatic history. These changes are probablycharacterized by a redistribution of the major martian volatiles (CO2, dustand water) and variations in their partition between atmospheric, surface,and subsurface reservoirs.

New solutions and predictions for historical variations in the spin or-bital parameters of Mars show that the obliquity of Mars varied signifi-cantly from its present unusually low value (25◦19’). These new solutionspermit robust predictions of parameter variations over the last ≈ 20 Ma,but prior to that time the solutions are chaotic and non-unique. The so-lutions predict that the maximum obliquity over the history of Mars mayhave reached 82◦; the standard model of insolation parameters over 4Gyr predicts a most probable obliquity value of ≈ 46◦ (Head et al. (2006)).

However, as the obliquity of Mars is strongly chaotic, it is not possibleto give a solution for its evolution over more than a few million yearsand due to the nonuniqueness of solutions for orbital parameter varia-tion during this time (late Amazzonian), one must rely on the geologicalrecord to provide evidence for the nature of climate change.

5.3 Formation of glaciersThe formation of glaciers on Mars appears to be the product of the

same martian climate system as that of today, except that high obliquityincreased the atmospheric water content and amplified the circulation.Indeed, tropical mountain glacier deposits have been documented in theequatorial regions of Mars and glacial flow simulations suggest significantatmospheric precipitation and accumulation in these areas (Forget et al.(2006)).

In reality, the complex variations of orbital parameters probably led toseveral different types of regimes in the past, with water ice alternativelymobilized from the poles to tropical and midlatitude glaciers, also drivenby seasonal effetcs (Forget et al. (2006)).

In particular, the simulations show that, during the southern summerthe southern ice cap is able to sublime and release large amounts of watervapor to the polar atmosphere. This water vapor cannot be easily trans-ported toward the equator because the south polar region is isolated bya midlatitude westward summer vortex, except where local topographicconfigurations can create favourable conditions for air mass flows.

52


In fact, glacial/periglacial features have been observed in Thauma-sia highlands (Rossi et al. (2006)) and in a few other sites at mid andlow southern latitudes (Forget et al. (2006)), thus suggesting the possibleoccurrence of thermal gradients and contrasts resulting in strong con-densation and precipitation. Figure 5.1 shows the seasonal curves of iceaccumulation for different areas in both the northern and southern hemi-spheres (Forget et al. (2006)).

Some of these topographic configurations can be represented by promi-nent topographic highs. The inferred mechanism suggested by climatemodels is that upwelling of water-rich air on these highs causes adiabaticcooling, snow precipitation and rapid accumulation sufficient to producesustained snow and ice cover and glacial flow (Forget et al. (2006)).

Figure 5.1: Seasonal evolution of the ice accumulation over one mar-tian year at several locations for a 45◦ obliquity simulations with a waterice northern polar cap (solid lines) and one simulation with a water icesouthern polar cap (dashed line) near Pavonis Mons (117◦W 0◦N), nearArsia Mons (122◦W 6◦S), near Ascraeus Mons (107◦W 12◦N), near Olym-pus Mons (137◦W, 16◦N), in Eastern Hellas (110◦E, 40◦S), near Thaumasia(colorized blue), in Bosphorus Region (colorized red). Solar longitude (Ls)is the areocentric longitude of the sun, with Ls equal to 0◦ at northernspring equinox, 90◦ at summer solstice, 180◦ at autumn equinox, and 270◦at winter solstice (modified from Forget et al. (2006)); the curves for Thau-masia and Bosphorus are calculated by method described by Forget et al.(2006) with the software LabPlot.

53


Other models also show that, assuming a relatively dusty atmosphere,the resulting climate favors the formation of a thick cloud belt in thesouthern mid-latitudes of Mars, with precipitation and accumulation ofice in regions in good agreement with geological observations (Madeleineet al. (2009)).

Recent advances in global climate modeling have permitted the ex-amination of the fate of volatiles such as H2O in response to changes inglobal patterns of orbital parameter-driven insolation. These studies showthat at obliquities of 45◦ or more, water vapor mobilized from the polarregions is redistributed at mid-latitudes, is stable there as ice, and willaccumulate if obliquity remains at these values. Although robust predic-tion of obliquity beyond ≈ 20 Ma is not possible, statistical studies of thepossible behavior of obliquity over the last 250 Ma have been performedand the range of solutions include scenarios from about 50 to 250 Mawith obliquity values from ≈ 5◦ to ≈ 65◦ (Head et al. (2006)).

The geological evidence reported in this study relative to BosphorusRegion supports orbital parameter scenarios where mean obliquity ex-ceeds 45◦ during the Late Amazonian for a sufficiently long period tocause the observed ice accumulation and glacial flow.

In addition, the Bosporos region is bounded to the west by the highNE-SW trending relief of the Thaumasia highlands. Therefore, it is pos-sible to argue that this physiographic element has affected the circulationof the polar air masses. This, combined with the morphological and statis-tical observations reported in chapter 2 for Bosphorus Region encouragethe hypothesis that conditions similar to those simulated by the modelscould have occurred also in this area, during periods of higher obliquity.

According to this hypothesis, the water-rich polar air coming from thewestern flank of Thaumasia ascended and underwent adiabatic cooling,causing snow precipitation, regional ice accumulation, and valley glacialsystems in this mid latitude southern area. This hypothesis parallels theDeuteronilus-Protonilus Mensae example as well as other examples de-scribed for different areas along the several kilometer-high dichotomyscarp siscussed discussed by several authors (Head et al. (2006), Levyet al. (2007)) and the implication the authors suggest for the late Amazio-nial period.

54


5.3.1 Ice near 41◦28’S 62◦26’W the craterIn agreement with the interpretations relative to other glacial and

periglacial regions in tropical to mid-latitude areas (Head et al. (2005)), thepresence of the linear depression in the crater analyzed in the Bospho-rus Region gives further support to the fact that this may have been theregion of snow and ice accumulation (Figure 5.2); the present depressedtopography may represent typical proximal high concentrations of icewhere more complete sublimation would take place.

Figure 5.2: Glacial related morphologic features in the crater insideBosphorus Region: depression linear zone in blue, eroded sides of thecrater in green and accumulation zone in red (symbols overlain on aTHEMIS Day IR image, modified from Google Earth).

55


5.3.2 Typical glacial and periglacial topographyActive glaciers form characteristic topography, flow fronts, and stream-

lines as evidence of movement and direction under the influence of grav-ity. Rock glaciers and debris-covered glaciers form many diagnostic fea-tures without any expression of exposed surface ice. Past glacial flowcan leave characteristic landforms such as moraines, eskers, and kettlesholes after the ice has been lost by melting or sublimation. The occur-rence of such features and their morphological details (including boulderdistributions) not only provide evidence for the existence of past ice de-posits, but also the climate conditions under which the ice occurred andwas eventually lost. Periglacial landforms are perhaps the most commonperiglacial features in Earth’s cold regions. Solifluction lobes and stonecircles can be diagnostic of freeze thaw cycles. Large expanses of dunescan indicate permanently frozen, ice-rich soil deposits (Figure 5.3).

Figure 5.3: Dune of the Bosphorus Region is classified as: Barchanoid(row of connected crescents in paln view, 1 splip face), Star (central peakwith 3 or more arms, 3 or more slip faces) and Sand Sheet (sheetlike withbroad, flat surface, no splipfaces) (modified from Hayward et al. (2007))(location in Figure 2.4, THEMIS daytime IR image mosaic, image Credit:NASA/JPL/ASU).

56


The field of dunes has been linked with 11 other surrounding areas,that the ASU library has defined the same (Barchanoid: row of connectedcrescents in paln view, 1 splip face, Star: central peak with 3 or more arms,3 or more slip faces and Sand Sheet: sheetlike with broad, flat surface,no splipfaces). The area studied has different characteristics from thoseof other craters from both geomorphological point of view (the dune isin contact with LDA and with all the material mixing) and spectral pointof view (it has a statistical inhomogeneity on flow of ice from CO2). So,probably the area of dunes is of glacial origin, to follow over the years,has definitely endured remodeling by wind power, also facilitated by theparticular shape of the crater in the north-west (the lines are not definedbut highly eroded).

Characteristics such as the spatial distribution of rocks, plan view pat-terns, and micro-topography can help distinguish processes and history(McEwen et al. (2007)).

However, the probable preservation of ice beneath a substantial por-tion of this deposit located at mid-latitude in the southern hemispheresupports the probability that many of the other debris aprons in this areaof Mars also represent very ice-rich debris-covered glaciers, by analogyto what has been already suggested for mid-latitude areas in the northernhemisphere (Head et al. (2005)).

57

Chapter 6

Summary and conclusions

The study of the Bosphorus Region shows that the morpho-stratigraphicrecord preserved the most sensitive abiotic indicators of climate change,stability and flow of snow and ice. A possible analogy is the Little IceAge on Earth (late sixteenth to early twentieth centuries), glaciers at highlatitude and altitude advanced several kilometres on average and manyare receding today in concert with warming trends. On Mars, shallowsubsurface water-ice stability in the current climate conditions should belimited to latitudes higher than about 60◦, a theoretical prediction borneout by spacecraft observation. At present, the spin-axis obliquity of Mars,thought to be among the major factors in climate change, is about 25◦, butcalculations show that there were several periods of increasingly higherobliquity in the last several millions of years of the history of Mars. Gen-eral circulation models show that increased obliquity warms ice-rich po-lar regions and redistributes water-ice and CO2-ice deposits equatorward,and this variation in soil composition can also be demonstrated by spec-troscopic data. Indeed, geological observations show evidence:

1. for a recent ice age in the last several million years in the form ofa latitude-dependent dust–ice mantle extending from high latitudesdown to about 30◦ latitude in both hemispheres,

2. for localized tropical mountain glacier deposits that formed duringthe early Late Amazonian period on Mars tens to hundreds of mil-lions of years ago.

Furthermore, there are numerous morphologic features that might in-volve ice-rich material at low to mid-latitudes throughout the history ofMars (such as large expanses of dunes, debris aprons and rock glaciers).

The origins, sources, amounts and state of water in these materialshas been controversial (Head et al. (2005)).

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Part II

Methods - InstrumentDescriptions

59

Chapter 7

Kruskal–Wallis test

In statistics, the Kruskal–Wallis one-way analysis of variance by ranks(named after William Kruskal and W. Allen Wallis) is a non-parametricmethod for testing whether samples originate from the same distribution.The factual null hypothesis is that the populations from which the samplesoriginate, have the same median. It is identical to a one-way analysis ofvariance with the data replaced by their ranks. It is an extension of theMann–Whitney U test to 3 or more groups.

Since it is a non-parametric method, the Kruskal–Wallis test does notassume a normal population, unlike the analogous one-way analysis ofvariance. However, the test does assume an identically-shaped and scaleddistribution for each group, except for any difference in medians.

7.1 Method1. Rank all data from all groups together; rank the data from 1 to N

ignoring group membership. Assign any tied values the average ofthe ranks they would have received had they not been tied.

2. The test statistic is given by:

K = (N − 1)∑g

i=1 ni(ri· − r)2∑gi=1∑ni

j=1(rij − r)2

where:

• ni is the number of observations in group i,• rij is the rank (among all observations) of observation j from

group i,

60

7.1 Method

• N is the total number of observations across all groups,

• ri· =∑ni

j=1 rijni ,

• r = 12 (N + 1) is the average of all the rij .

3. Notice that the denominator of the expression for K is exactly (N −1)N(N + 1)/12 and r = N+1

2 . Thus:

K = 12N(N + 1)

g∑

i=1

ni(ri −

N + 12

)2

= 12N(N + 1)

g∑

i=1

nir2i − 3(N + 1)

4. Notice that the last formula only contains the squares of the averageranks. A correction for ties can be made by dividing K by 1 −∑g

i=1(t3i −ti)N3−N , where G is the number of groupings of different tied ranks,

and ti is the number of tied values within group i that are tied at aparticular value. This correction usually makes little difference inthe value of K unless there are a large number of ties.

5. Finally, the p-value is approximated by Pr(χ2g−1 ≥ K). If some ni

values are small the probability distribution of K can be quite dif-ferent from this chi-square distribution. If a table of the chi-squareprobability distribution is available, the critical value of chi-square,χ2α:g−1, can be found by entering the table at g - 1 degrees of freedom

and looking under the desired significance or alpha level. The nullhypothesis of equal population medians would then be rejected ifK ≥ χ2

α:g−1. Appropriate multiple comparisons would then be per-formed on the group medians (Kruskal & Wallis (1952)).

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Chapter 8

Function used (LabPlot)

LabPlot is a platform-independent open-source cross-platform com-puter program for interactive scientific graphing and data analysis, writ-ten for the KDE desktop. It is similar to Origin and is able to importOrigin’s data files.

It uses the Qt widget set for its graphical interface. It is integrated withthe KDE desktop and has drag and drop support with KDE’s applications.The handbook is written in KDE and conforms to the Khelpcenter stan-dards. It is scriptable using Qt Script for Applications (QSA). 2D and 3Dplots of data can be rendered in a “worksheet”, either by directly readingdatafiles or from a spreadsheet, which LabPlot supports. It has interfacesto several libraries, including GSL for data analysis, the Qwt3d librariesfor 3D plotting using OpenGL, FFTW for fast Fourier transforms andsupports exporting to 80 image formats and raw PostScript. Other keyfeatures include support for LaTeX and Rich Text labels, data masking,multiple plots in the same worksheet, pie charts, bar charts/histograms,interpolation, data smoothing, peak fitting, nonlinear curve fitting, regres-sion, deconvolution, integral transforms, and others1.

8.1 SmoothingIn my study I used the smoothing function. In statistics and image

processing, to smooth a data set is to create an approximating functionthat attempts to capture important patterns in the data, while leaving outnoise or other fine-scale structures/rapid phenomena. Many different al-gorithms are used in smoothing. One of the most common algorithms

1labplot.wiki.sourceforge.net/Translations

62

8.1 Smoothing

is the “moving average”, often used to try to capture important trends inrepeated statistical surveys. In image processing and computer vision,smoothing ideas are used in scale-space representations.

Smoothing may be distinguished from the related and partially over-lapping concept of curve fitting in the following ways:

• curve fitting often involves the use of an explicit function form forthe result, whereas the immediate results from smoothing are the“smoothed” values with no later use made of a functional form ifthere is one;

• the aim of smoothing is to give a general idea of relatively slowchanges of value with little attention paid to the close matching ofdata values, while curve fitting concentrates on achieving as close amatch as possible.

• smoothing methods often have an associated tuning parameter whichis used to control the extent of smoothing2.

Smooth: This function does the same as average but for every data point.So you will get a smoothed graph with the same number of datapoints. (Analysis->Filter->Smooth (Alt-s), Opens the Smooth Dialogand here you can create a new graph from the smoothed data ofany other graph).

Nonlinear Fit: With this function you can fit a graph in a nonlinear fash-ion. You can select one of 12 different models or any user definedfunction with up to 9 parameters. Please note that fitting especiallyexponential models is very sensitive to the initial values. The result-ing fit parameter are shown in the bottom field and automaticallyreplaced as initial values for further fitting. The results are addedto the plot as label (Gerlach (2007)).

2http://en.wikipedia.org/wiki/Smoothing

63

Chapter 9

Function used (Tutorial JMARS)

9.1 Lat/Lon Grid LayerThe Latitude/Longitude Grid Layer, which is available in all releases

of JMARS, draws latitude and longitude lines in the Viewing Window andallows users to measure distances.

Figure 9.1: Frame open JMARS (modified from JMARS, Lat/Lon GridLayer).

64

9.1 Lat/Lon Grid Layer

Display a Latitude/Longitude Grid1. Open the Lat/Lon Grid Layer: The Lat/Lon Grid is one of two default

layers in JMARS that will open automatically whenever JMARS isstarted (if you need to re-open the Lat/Lon Grid Layer after closingit, go to the Layer Manager, choose “Add New Layer” -> “Lat/LonGrid”).

2. Edit the Default Grid Settings: To change any of the layer’s defaultsettings (ie: line spacing, line color, etc), click on the “Lat/Lon Grid”tab in the Layer Manager to access the focus panel.

3. Select Major Line Frequency: JMARS defaults to placing major lat-itude and longitude lines at 10 degree intervals, but this can bechanged by editing the “Major Lines” box. Users can also chosewhether or not the major lines are displayed in the Main and Pan-ning Views by clicking the appropriate checkboxes.

Figure 9.2: Frame Layer Manager (modified from JMARS, Layer Man-ager).

4. Select Major Line Color: The default color for the major lines isblack, but it can be changed by clicking on the black color box andchoosing a new color.

5. Select Minor Line Frequency: JMARS defaults to placing minor lat-itude and longitude lines every 2 degrees, although it does not auto-matically display them in either the Main or Planning Views. Users

65

9.2 Map Layer

can add the minor lines to either view by clicking on the appropriatedisplay checkbox.

6. Select Minor Line Color: The default color for the minor lines isgray, but it can be changed by clicking on the gray color box andchoosing a new color.

Measure DistancesThe Latitude/Longitude Grid Layer also allows users to measure dis-

tances on the maps in the Main View. To use this feature, left click on apoint and drag the mouse to another point. At the bottom left of the theViewing Window the measured distance is given in units of degrees andkilometers.

9.2 Map LayerThe Map Layer allows users to load and display global maps of Mars

and other planetary bodies. All versions of JMARS include the MapLayer, which offers users two options: Graphic/Numeric Maps and Ad-vanced/Custom Maps.

Figure 9.3: Frame Maps Layer Manager (modified from JMARS, MapLayer).

66

9.3 3D Layer

Graphic/Numeric MapsGraphic/Numeric Maps usually serve as the base layer of the Viewing

Window with other layers (such as the ROI Layer, Groundtrack Layer,etc) stacked on top of them.

Advanced/Custom MapsAdvanced/Custom Maps are developed by users to display datasets not

included in the standard version of JMARS. These maps can contain bothgraphic and numeric components and be either regional or global in size.The data used to build these maps are contained in files (usually eitherISIS cubes or standard image files), which must be loaded into JMARS bythe user.

9.3 3D LayerThe 3D Layer is unlike many of the JMARS layers because it does

not load any visual data into the Viewing Window. Instead, it opens anew window and displays a three-dimensional version of the scene in theMain View. This allows the user to observe the altitude of the terrain di-rectly, instead of relying on the shadowing of the 2D maps to determinethe height or depth of certain features. The 3D Layer is available in theTHEMIS and Public releases of JMARS.

Navigating in the 3D Layer

1. Display a Map in the Viewing Window: Before loading the 3D Layer,the user must display a graphical map in the Viewing Window. Userscan also display the Lat/Lon Grid Layer, the Groundtrack Layer, theTHEMIS Planning Layer or any other layer which draws visible datain the Viewing Window.

2. Open the 3D Layer: Chose “Add New Layer” -> “3D Layer”.

3. Navigate in the View: Using the following mouse and keyboard con-trols, the user is able to navigate in the 3D Layer’s window:

4. Reset the 3D View: If the area displayed in the Main View is changed,the area in the 3D View will not automatically change. Clicking the“Reset Camera” button in the 3D Layer focus panel will refresh the3D View to match the Main View.

67

9.3 3D Layer

Figure 9.4: Frame 3D Layer Manager (modified from JMARS, 3D Layer).

Figure 9.5: Frame 3D view (modified from JMARS, 3D Layer).

68

9.3 3D Layer

Figure 9.6: Frame 3D viewer (modified from JMARS, 3D Layer).

Changing the 3D Layer Settings

1. Display the Focus Panel: At the top of the Layer Manager, click onthe "3D Viewer" tab.

2. Scene Properties: The scene properties control the appearance ofthe surface displayed in the 3D Window.

• Bottom: Selecting this option will apply a color to the bottomof the 3D surface in the 3D window. This makes it easier tomake sure the user is viewing the top of the surface instead ofthe bottom.• Update Scene: If the position of the Main View has changed,

click this button to refresh the view displayed in the 3D window.• Z Scale: This option allows the user to exaggerate the altitudes

displayed in the 3D window, which is particularly useful whendisplaying relatively flat terrain. By default, the elevation (z-axis) has a scaling multiplier of 1.0. To change this value simplychange the multiplier and click “Update Scene”.

69

9.3 3D Layer


• Altitude: This option allows users to display altitude data fromdifferent sources. The default source is the MOLA altitude data.

3. Orientation: This section of the focus panel allows the user to con-trol and modify the view in the “3D View” Window.

4. Directional Light: This section of the focus panel gives users theability to add an artificial light source to the 3D window in order thechange the appearance of shadows on the 3D terrain.

• Light On: This check-box will turn on the artificial light source.The default setting is off.• Light Color: This button will give users a color palette from

which they can choose the color of the light source.• Light Source Position: The lighted circle represents the posi-

tion of the artificial light source. By default, the light source ispositioned directly above the 3D terrain. To change this posi-tion, click on the center of the lighted circle and drag it aroundthe box. Release the click to see what the terrain looks likewith the light source in the new position.

70

9.4 TES Layer


9.4 TES LayerThe TES Layer allows users to display spectral data collected by ASU’s

Thermal Emission Spectrometer on Mars Global Surveyor. It is mostlyused for reconnaissance purposes, allowing users to identify spectra ofinterest which can then be pulled directly from the database and ana-lyzed using other tools. The TES Layer is currently only available in theTHEMIS Team Release of JMARS.

Figure 9.9: Frame TES Layer (modified from JMARS, TES Layer).

71

9.4 TES Layer

Open the TES Layer

1. Open the TES Layer: Chose “Add New Layer” -> “TES” and thenclick on the “TES” Tab to access the focus panel.

• The focus panel will appear blank since there is not an open“context”.• A context is the set of database search parameters and display

options which tells the TES Layer what data must be retrievedfrom the TES database. (Due to the large amount of data in theTES database, the TES Layer can only display a small fractionof it.)

2. Add a New Context: Click on the “Add” button at the bottom of thefocus panel. The focus panel will then fill with the various fieldsneeded for creating a context.

Specify the Context Parameters: Enter values in the various fieldsand drop-down menus to create a context:• The amount of TES data pulled by a context can be ex-

tremely large, which can make the TES Layer very slow.Users should start by over-constraining their search forTES data and then gradually relax those constraints asneeded.• To help the TES Layer refresh faster, reduce the size of

the Viewing Window as much as you can. Next, increasethe zoom of the Main View as much as you can. If youwould like the TES Layer to load even faster, un-check the“P” next to the TES Layer in the Main Tab of the LayerManager. This will prevent TES data from being displayedin the Panner window.

Title: Users can specify the name of the current context by chang-ing the name in this field. The focus panel will allow the userto open multiple contexts, which are listed at the bottom of thefocus panel as tabs with the specified titles.

Real Data: If users view TES data at 128 ppd or lower resolutions,by default JMARS will not display the data at it’s full resolu-tion in order to reduce the time needed to load all the data.However, if you want to view the full resolution data at low

72

9.4 TES Layer

Figure 9.10: Frame TES Layer Mananger (modified from JMARS, TESLayer).

zoom levels, check this box. At zoom levels of 256 or higherthis checkbox has no effect since the real data is already beingdisplayed.

Fields: Since the TES database contains 153 fields, which all havedata for every TES observation, JMARS does not load in anydata by default. Instead, it allows users to specify which databasefields they want to load into JMARS. There are two ways to adda field: 1) type the name of the field in the input box and click“Add” or 2) click on the “?” button to the left of the input box toget a list of all available fields, highlight the field you want toadd, click the “To Fields” button on the right side of the pop-upscreen and then click “Add” in the “Fields” section of the focuspanel.

Selects: This section allows users to specify which data from theselected fields to load into JMARS. In the input box in the “Se-lects” section, users must enter a field name (which also has tobe listed in the “Fields” section above), a minimum value anda maximum value. Users must then click “Add” to incorporatethe information into the context.

Order: The “Order” section allows users to specify the order inwhich JMARS draws the selected observations footprints (thefootprints can be drawn by orbit number in ascending order,surface temperature in descending order, etc).

Color: The “Color” section allows users to colorize the TES data

73

9.4 TES Layer

displayed in the Viewing Window based on the value of thedata. First, select a color scheme by right-clicking on one ofthe sliders on the color bar and then either “Set Tab Color” (toselect simple colors) or “Built-In Colors” (to select more com-plex color spectra). Second, users must select which databasefield will be used to colorize the data from the available fieldsin the drop-down menu in the “Color” section. Third, usersmust enter a minimum and maximum values in the boxes tothe right of the pull-down menu. If the field you wish to col-orize is loaded into the “Fields” section but is not visible in the“Color” section’s drop-down box, this means that the data in thatfield is not compatible with the colorization tool. (ie: it does notcontain single-value data)

Context Control Buttons: .

• Add: Creates a new TES context. Multiple contexts can beopen simultaneously, but only one can be displayed in theViewing Window at a time.• Delete: Deletes the selected context.• Duplicate: Creates a copy of the selected context.• Revert: Returns the selected context the its state when the

last “Submit” was made.• Submit: Submits a request for data to the TES database

based on the specifications in the selected context.Submit Data Request: After parameters have been entered for all

of the necessary fields, clicking the “Submit” button at the bot-tom of the focus panel will request the specified data from theTES database. Depending on the amount of data requested, itmay take a while for the TES Layer to draw the data footprintsin the Viewing Window.

Viewing TES Data

Selecting a Footprint: Once the TES footprints have been displayed inthe Main View, clicking on a footprint will open a data window withthe data associated with the selected footprint.

• The data window will only display data for the fields listed inthe “Fields” section of the focus panel.

74

9.4 TES Layer

• Since many TES observations can overlap each other, the datawindow will display information for all of the observations thatcover the selected point.

Viewing Single-Value Data: Fields that contain single-value data (ie: tem-perature, mirror pointing angle, etc) will be shown in the top halfof the data window as a table of values. This table can be sorted byany of the fields by clicking on the title of any column.

Graphing Array Data: Fields that contain multiple values (ie: raw radi-ance, etc) can not be easily shown in the data table, but they can begraphed by selecting the field name from the pull-down menu inthe middle of the data window. Then select one of the observationrows from the data table at the top of the window and the specifiedfield for that observation will be graphed.

• Users can graph data from multiple observations by selectingmultiple observations (Ctrl+left-click).

Figure 9.11: Frame Selection Context-1 (modified from JMARS, TESLayer).

Saving the TES Data: To save the TES data displayed in the data window,click the “Save” button at the top of the window and specify a filename and path.

Clearing the Selected TES Data: Clicking on the “clear” button at thetop of the data window will remove all of the displayed data.

75

9.5 Viewing TES Data in JMARS

9.5 Viewing TES Data in JMARSThe purpose of this tutorial is to walk users through the process of

opening JMARS, navigating to the MER-B (Opportunity) landing site atMeridiani Planum, opening the TES Layer and using the TES Layer toview spectral data in JMARS. The TES Layer is currently only available inthe THEMIS Team Release of JMARS, which will be used for this tutorial.

Step 1: Opening JMARS and Navigating to Meridiani Planum

• Open a terminal window and type: “jmars clean 0 0”

• Enter your JMARS user name and password. If you do not have auser name and password, follow the instruction under the appropri-ate”Getting Started” link on the Main Page.

• At this point, the Layer Manager should only have the MOLA ShadedRelief Layer and the Lat/Lon Layer loaded.

• In the Lon/Lat box in the upper-left corner of the Viewing Window,enter the coordinates “-5.94E, -1.98”

• The Main View should now be centered over Meridiani Planum.

Figure 9.12: Frame Viewing TES Data (modified from JMARS, TES Layer).

76


Step 2: Opening the TES Layer

• Using the “Zoom” menu in the top-right corner of the Viewing Win-dow, zoom the Main View to 256 ppd.

• Left-click in the Panner View, select “Zoom” and select “128 Pix/Deg”.

• The TES Layer requires a large amount of data to be loaded fromthe TES Database. The less area displayed in the Main and PannerViews the faster the layer will refresh when changes are made.Depending on your computer and internet connection, this refreshtime may still be large.

• In the Layer Manager, click “Add New Layer” -> “Spectra” -> “TES”,then click on the “TES” tab to access the focus panel.

• The focus panel should be blank except for the five buttons at thebottom.

Figure 9.13: Frame Layer Manager Viewing TES Data (modified fromJMARS, TES Layer).

77


Step 3: Viewing TES Hematite Concentration Data

• Click “Add” at the bottom of the TES focus panel to create a newTES context.

• The focus panel will fill with a number of fields for loading, sortingand colorizing TES data.

• Click on the “?” button to the left of the first input line to view a listof all available TES database fields.

Figure 9.14: Frame Field Prompt (modified from JMARS, TES Layer).

• Load Database Fields into the TES Layer:

• In the list of database fields, highlight “orbit” and click the “To Fields”button.

• In the “Fields” section of the focus panel, “orbit” will appear in theinput box. Click “Add” to load it into the TES Layer. (The “orbit” willmove from the input box into the list of loaded fields.)

• Repeat this process for the “pnt-angle” (TES pointing mirror angle),“perc-hem” (Hematite concentration) and “emissivity” fields.

• Select Filtering Values for the Loaded Database Fields

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• In the list of database fields, highlight “orbit” and click the “To Se-lects” button.

• In the “Selects” section of the focus panel, “orbit” will appear in thefirst input box. The second and third input boxes are the mini-mum and maximum allowable values, respectively. Enter a mini-mum value of “0” and a maximum value of “10000”.

• Repeat this process for “pnt-angle” with a minimum of “0” and a max-imum of “5”. This will ensure that only nadir-pointing observationsare displayed.

• Specify Ordering Parameters

• In the list of database fields, highlight “orbit” and click the “To OrderBys” button.

• In the “Order” section of the focus panel, “orbit” will appear in theinput box. Select “Ascending” from the drop-down menu and thenclick “Add”.

• This will draw the TES observation footprints in the Viewing Windowstarting with the earliest observation.

• Specify Coloring Parameters

• In the “Color” section of the focus panel, select “perc-hem” from thedrop-down menu.

• Right-click on the right-hand slider on the color bar, then select“Built-in Color” -> “Inverted Spectrum”

• The two input boxes represent the minimum and maximum valuesof the given color spectrum. Enter “-0.5” for the minimum and “0.5”for the maximum.

• Values higher than the maximum value will be colorized as if theywere the maximum value. Values lower than the minimum valueswill be colorized as if they were the minimum value.

• Although a negative percent composition is not possible, the “-0.5”value is used to make the coloring more intuitive in this case. (Thelow concentrations will be displayed as cooler hues and the higherconcentrations will be displayed as warmer hues.)

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• Un-check the “Draw Null” checkbox at the bottom-right corner ofthe focus panel.

• This will prevent TES footprints without “perc-hem” data from beingdrawn in the Viewing Window.

• Click on the “Submit” button at the bottom of the focus panel torequest the specified information from the TES Database.

• Before submitting the request, the focus panel should look like thefirst example below.

• Depending on your computer and internet connection, it may takethe TES Layer a few minutes to refresh.

• After the request data has been loaded, the Viewing Window shouldlook like the second example below.

Figure 9.15: Frame Layer Manager Viewing TES Data (modified fromJMARS, TES Layer).

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Step 4: Viewing Actual TES Data Values

• To see the TES detector footprints more clearly, zoom the MainView to 512 ppd.

• In the “Selects” section of the TES Layer focus panel, highlight the“orbit” row and click the “Delete” button to the right.

• Add a new search constraint using the three input boxes at thebottom of the “Selects” section. In the first box, type “orbit”, thesecond type “5500” and in the third type “6000”.

• Click “Submit” at the bottom of the focus panel to submit the newdata request.

• After the TES Layer has refreshed, it is much easier to see theindividual TES detector footprints.

• To view the data associated with one of the footprints, left-click onit. This will bring up a data display window.

• If multiple footprints overlap the point you clicked on, multiple rowswill appear in the data display window.

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• The data table displays values for all of the loaded fields (listed inthe Fields section of the focus panel) which have single-value data.(ie: mirror pointing angle, surface temperature, etc)

• Note that emissivity is not included in the table because it is not asingle-value data field.

• To graph the emissivity in the plotting field at the bottom of thewindow, select “emissivity” from the drop-down menu. Then clickon one of the rows in the data table. The emissivity graph for thatparticular footprint will then appear in the plotting field.

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9.6 THEMIS Stamp Layer

Figure 9.18: Frame Selection Context-1 (modified from JMARS, TESLayer).

9.6 THEMIS Stamp LayerThe THEMIS Stamp Layer will display outlines (or “stamps”) for all VIS

and IR observations acquired by the Thermal Emission Imaging System(THEMIS) camera onboard Mars Odyssey. Since the THEMIS dataset isso large, there are numerous search parameters that allow users to re-trieve stamps for only the observations they are interested in viewing.

Open the THEMIS Stamp Layer

1. Open the Stamp Layer: In the Layer Manager chose “Add NewLayer” -> “Stamps” -> “THEMIS Stamps”.

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Figure 9.19: Frame THEMIS Layer (modified from JMARS, THEMISLayer).

2. Search Parameter Categories: The search parameters are dividedinto categories since there are so many of them. Clicking on thedown arrow on the right side of the category name will reveal allthe search parameters in that category. The categories are:

• Image Location Parameters• Viewing Conditions Parameters• Observation Parameters• IR Derived Science Parameters• Quality Parameters• Publication Parameters

3. Enter Search Parameters: It is not necessary to enter values for allparameters, but the more specific your search the faster it will be.The allowable values for each field are given in the quick referencetable below.

4. Perform Image Search: Clicking OK will make the Stamp Layerperform the search and display the results. Depending on howspecific the search parameters are, it may take the Stamp Layer afew minutes to find and create stamps for all of the images. Oncethe stamps are displayed in the Viewing Window, users can right-click on an outline to either render the image (display the imagedata in JMARS) or view the image in a web browser.

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Figure 9.20: Frame THEMIS Parameter (modified from JMARS, THEMISLayer).

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86



87



88



89



90

9.7 MOC Stamp Layer

9.7 MOC Stamp LayerThe MOC Stamp Layer will display stamps for all images, both narrow-

angle and wide-angle, acquired by the Mars Orbital Camera (MOC) on-board Mars Global Surveyor. While the search interface is similar tothe THEMIS Stamp Layer, there are some differences that allow users tosearch for images based on the unique parameters associated with MOCimages.

Figure 9.26: Frame MOC Layer (modified from JMARS, MOC Layer).

Open the MOC Stamp Layer

1. Open the Stamp Layer: Chose “Add New Layer” -> “Stamps” ->“MOC Stamps”.


• Basic Parameters• Advanced Parameters

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9.7 MOC Stamp Layer

3. Enter Search Parameters: It is not necessary to enter values for eachparameter, but the more specific your search the faster it will be.The allowable values for each field are given in the quick referencetable below.


Figure 9.27: Frame MOC Layer (modified from JMARS, MOC Layer).

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9.7 MOC Stamp Layer

Figure 9.28: Frame MOC Parameter (modified from JMARS, MOCLayer).

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9.7 MOC Stamp Layer


94

9.7 MOC Stamp Layer


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9.8 CTX Stamp Layer

9.8 CTX Stamp LayerThe CTX Stamp Layer will display stamps for all images acquired

by the Context Camera (CTX) aboard the Mars Reconnaissance Orbiter.While the search interface is similar to the THEMIS Stamp Layer, thereare some differences that allow users to search for images based on theunique parameters associated with CTX images.

Figure 9.31: Frame CTX Layer (modified from JMARS, CTX Layer).

Open the CTX Stamp Layer

1. Open the Stamp Layer: Chose “Add New Layer” -> “Stamps” -> “CTXStamps”.



3. Enter Search Parameters: It is not necessary to enter values for eachparameter, but the more specific your search the faster it will be.

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9.8 CTX Stamp Layer

The allowable values for each field are given in the quick referencetable below.


Figure 9.32: Frame CTX Layer (modified from JMARS, CTX Layer).

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9.8 CTX Stamp Layer

Figure 9.33: Frame CTX Parameter (modified from JMARS, CTX Layer).

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9.8 CTX Stamp Layer


99

9.8 CTX Stamp Layer


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9.9 HiRISE Stamp Layer

9.9 HiRISE Stamp LayerThe HiRISE Stamp Layer will display stamps for all images acquired

by the High Resolution Imaging Science Experiment (HiRISE) onboardthe Mars Reconnaissance Orbiter. While the search interface is similarto the THEMIS Stamp Layer, there are some differences that allow usersto search for images based on the unique parameters associated withHiRISE images.

Figure 9.36: Frame HiRISE Layer (modified from JMARS, HiRISE Layer).

Open the HiRISE Stamp Layer

1. Open the Stamp Layer: Chose “Add New Layer” -> “Stamps” ->“HiRISE Stamps”.



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3. Enter Search Parameters: It is not necessary to enter values for eachparameter, but the more specific your search the faster it will be.The allowable values for each field are given in the quick referencetable below followed by more detailed descriptions of each searchparameter.


Figure 9.37: Frame HiRISE Layer (modified from JMARS, HiRISE Layer).

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Figure 9.38: Frame HiRISE Parameter (modified from JMARS, HiRISELayer).

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Chapter 10

Data collection instruments

10.1 Thermal Emission Spectrometer (TES)The Thermal Emission Spectrometer (TES) is an instrument on board

Mars Global Surveyor. TES collects two types of data, hyperspectral ther-mal infrared data from 6 to 50 mu and bolometric visible-NIR (0.3 to 2.9µ) measurements. TES has six detectors arranged in a 2x3 array, andeach detector has a field of view of approximately 3×6km on the surfaceof Mars. The TES instrument uses the natural harmonic vibrations ofthe chemical bonds in materials to determine the composition of gases,liquids, and solids (Hoefen et al. (2003)).

Particulary, the MGS-TES instrument is a Fourier-Transform Michel-son Interferometer (FTIR) that covers the wave number range from ≈1700 to 200 cm−1 (≈ 6 to 50 µm) at 5 or 10 cm−1 spectral sampling. Eachdetector has an instantaneous field of view of ≈ 8.5 mrad, providing a spa-tial resolution of ≈ 3 by 3 km. From the final TES mapping orbit of ≈ 380km, the actual surface sampling is 3 x ≈ 8 km. The pixels are elongateddowntrack because the final mapping orbit of MGS is in the opposite di-rection of that originally planned, and the image motion compensationcould not be used. MGS-TES also has two broadband radiometers, whichmeasure energy in the thermal (≈ 5 – 100µm) and visible/near-infrared(≈ 0.3–3.5µm) wavelengths. The focal planes in each wavelength intervalconsist of 6 detectors arranged in a 3 by 2 array (Glotch & Christensen(2005)).

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10.2 Thermal Emission Imaging System (THEMIS)

10.2 Thermal Emission Imaging System (THEMIS)The Thermal Emission Imaging System (THEMIS) is a camera on

board the 2001 Mars Odyssey orbiter, it’s managed from the Mars SpaceFlight Facility at Arizona State University and was built by the Santa Bar-bara Remote Sensing division of Raytheon. It images Mars in the visibleand infrared parts of the electromagnetic spectrum in order to deter-mine the thermal properties of the surface and to refine the distributionof minerals on the surface of Mars as determined by the Thermal Emis-sion Spectrometer (TES). Additionally, it helps scientists to understandhow the mineralogy of Mars relates to its landforms, and it can be usedto search for thermal hotspots in the Martian subsurface (Christensenet al. (2005)).

The Mars Odyssey THEMIS instrument contains both thermal in-frared (TIR) and visible/near-infrared (VNIR) imagers. The THEMIS TIRimager consists of an uncooled 320 by 240 microbolometer array with9 spectral channels centered from 6.5 to 15 µm. Spatial sampling is 100m from the 420 km altitude circular orbit of the Mars Odyssey space-craft. An internal calibration flag and instrument response functions de-termined from prelaunch data are used to produce calibrated radianceimages. The THEMIS VNIR imager is a frame imager that consists offive spectral bands centered between 425 and 860 nm. Spatial sampling is18 m, and pixels can be coadded to decrease bandwidth (Glotch & Chris-tensen (2005)).

10.3 Mars Orbiter Laser Altimeter (MOLA)The Mars Orbiter Laser Altimeter (MOLA) was one of five instruments

on board the Mars Global Surveyor (MGS) spacecraft, which operated inMars orbit from September 1997 to November 2006. The MOLA in-strument transmitted infrared laser pulses towards Mars at a rate of 10times per second, and measured the time of flight to determine the range(distance) of the MGS spacecraft to the Martian surface. The range mea-surements resulted in precise topographic maps of Mars. The precisionmaps are applicable to studies in geophysics, geology and atmosphericcirculation. MOLA also functioned as a passive radiometer, and mea-sured the radiance of the surface of Mars at 1064 nanometers.

A laser altimeter is an instrument that measures the distance from anorbiting spacecraft to the surface of the planet or asteroid that the space-craft is orbiting. The distance is determined by measuring the complete

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10.4 Mars Orbital Camera (MOC)

round trip time of a laser pulse from the instrument to the surface of theplanet or asteroid and back to the instrument.

The distance to the object can be determined by multiplying the round-trip pulse time by the speed of light and dividing by two. With a well-known attitude and position of the instrument or spacecraft, the locationon the surface, which is illuminated by the laser pulse can be determined.The series of the laser spot, or footprint, locations provides a profile ofthe surface1.

10.4 Mars Orbital Camera (MOC)The Mars Orbiter Camera or Mars Observer Camera (MOC) was a

scientific instrument onboard the Mars Observer and Mars Global Sur-veyor spacecrafts. The camera was built by Malin Space Science Systems(MSSS) for NASA and the cost of the whole MOC scientific investigationproject was about US dollar 44 million.

Originally named Mars Observer Camera, it was selected by NASA in1986 for the Mars Observer mission, but it returned only three images ofplanet Mars before the loss of the spacecraft in 1993. A second cameraof the same specifications, renamed to Mars Orbiter Camera (MOC), wasbuilt (with assistance by California Institute of Technology) and launchedonboard the Mars Global Surveyor (MGS) spacecraft in 1996. The cam-era returned 243,668 images while in orbit around Mars, before the lossof the MGS spacecraft in 2006.

The scientific instrument consisted of three elements: a black-and-white narrow-angle camera with a spatial resolution of 1.4 metres perpixel (from an altitude of 378 km), and two wide-angle cameras (one red,the other blue) with resolution capabilities spanning 230 m per pixel to7.5 km/pixel. The narrow-angle camera provided 97,097 (roughly 40%)of the 243,668 images returned by Mars Orbiter Camera.

The narrow-angle camera was placed inside an 80cm-long cylinderwith a diameter of 40 cm, and the two wide-angle cameras were attachedabove the cylinder’s front area. All cameras were based on CCD technol-ogy and were supported by state-of-the-art 1980s electronics, including a32-bit radiation-hardened 10 MHz processor (capable of 1 million instruc-tions per second) and 12 MB of DRAM memory buffer.

In addition to taking images, the MOC instrument’s 12 MB mem-ory buffer serviced the Mars Global Surveyor’s Mars Relay antenna as

1http://tharsis.gsfc.nasa.gov/MOLA/index.php

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10.5 Context Camera (CTX)

temporary data storage for communications between Earth and landedspacecraft on Mars. For example, more than 7.6 terabits of data weretransferred to and from the Mars Exploration Rovers (Spirit and Op-portunity). The camera also enabled NASA scientists to choose suitablelanding sites for other exploration missions2.

10.5 Context Camera (CTX)The Context Camera (CTX) provides grayscale images (500 to 800

nm) with a pixel resolution up to about 6 m. CTX is designed to providecontext maps for the targeted observations of HiRISE and CRISM, and isalso used to mosaic large areas of Mars, monitor a number of locationsfor changes over time, and to acquire stereo (3D) coverage of key regionsand potential future landing sites (Malin et al. (2007)). The optics of CTXconsist of a 350 mm focal length Maksutov Cassegrain telescope with a5,064 pixel wide line array CCD. The instrument takes pictures 30 km(19 mi) wide and has enough internal memory to store an image 160 kmlong before loading it into the main computer3. The camera was built,and is operated by Malin Space Science Systems. CTX mapped 50% ofMars by February 20104.

10.6 High Resolution Imaging Science Experi-ment (HiRISE)

The High Resolution Imaging Science Experiment camera is a 0.5 mreflecting telescope, the largest ever carried on a deep space mission, andhas a resolution of 1 microradian (µrad), or 0.3 m from an altitude of 300km. In comparison, satellite images of Earth are generally available witha resolution of 0.5 m, and satellite images on Google Maps are availableto 1 m. HiRISE collects images in three color bands, 400 to 600 nm (blue-green or B-G), 550 to 850 nm (red) and 800 to 1,000 nm (near infrared orNIR)5.

Red color images are 20,264 pixels across (6 km wide), and B-G andNIR are 4,048 pixels across (1.2 km wide). HiRISE’s on-board computerreads these lines in time with the orbiter’s ground speed, and images are

2http://www.msss.com/mars-images/moc/3http://www.msss.com/mro/ctx/ctx-description.html4http://www.msss.com/all-projects/mro-ctx.php5marsoweb.nas.nasa.gov/HiRISE/instrument.html-components

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10.6 High Resolution Imaging Science Experiment (HiRISE)

potentially unlimited in length. Practically however, their length is limitedby the computer’s 28 Gigabit (Gb) memory capacity, and the nominalmaximum size is 20.000 × 40.000 pixels (800 megapixels) and 4.000 ×40.000 pixels (160 megapixels) for B-G and NIR images. Each 16.4 Gbimage is compressed to 5 Gb before transmission and release to thegeneral public on the HiRISE website in JPEG 2000 format6. To facilitatethe mapping of potential landing sites, HiRISE can produce stereo pairsof images from which topography can be calculated to an accuracy of0.25 m. HiRISE was built by Ball Aerospace and Technologies Corp.

6marsoweb.nas.nasa.gov/HiRISE

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Chapter 11

TES Data Tool

11.1 Parameters ListAlbedo (BOL.LAMBERT-ALBEDO) .

Lambertian albedo, derived from visual bolometer.See Chapter 13 Section 3 for a description of the visible bolometercalibration algorithms.

Algor Risk (RAD.QUALITY:ALGOR-RISK) .A constraint for spectral data that has a low risk of algor phase in-versions (RAD.QUALITY:ALGOR-RISK=0).See Chapter 12 for a complete description of this quality issue.

Atmospherically Corrected Emissivity .Total emissivity is deconvolved using eight spectral componentswhich represent surface, atmospheric dust, atmospheric ice, anda thermal blackbody. The atmospherically corrected emissivity iscalculated by removing the atmospheric dust and ice componentsfrom the total emissivity. The correction is only applied to singlescan observations over channels 9-35 and 65-100.

Atmospheric Opacity (ATM.NADIR-OPACITY) .Atmospheric opacities are provided as scaling factors for four spec-tral shapes (STDSHAPES) that best fit the nadir radiance spectrum.The RMS residual between the observed and four endmember mod-eled spectra is also available, given in units of 1e − 8watts cm−2

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11.1 Parameters List

steradian−1 wavenumber−1. See Chapter 13 Section 7 for moreinformation.

• Aerosol dust opacity (ATM.NADIR-OPACITY[1])• Aerosol water ice opacity (ATM.NADIR-OPACITY[2])• CO2 hot bands opacity (ATM.NADIR-OPACITY[3])• Reference surface emissivity (ATM.NADIR-OPACITY[4])• Opacity RMS (ATM.NADIR-OPACITY-RESIDUAL)

TES Team scientists have rated the quality of the derived opacityresults and provided a constraint to select only the best results(ATM.QUALITY:ATMOSPHERIC-OPACITY-RATING=0). See Chap-ter 12 for more information.

Bolometric Temperature (BOL.BOLOMETRIC-BRIGHTNESS-TEMP)Temperature observed by the thermal bolometer, assuming that thetarget is radiating as a blackbody; in units of K.See Chapter 13 Section 4 for a description of the thermal bolometercalibration algorithms.

Calibrated Bolometer (BOL.CALIBRATED-VISUAL-BOLOMETER) .Calibrated visual bolometer radiance; given in units of (watts cm−2

steradian−1). The visual bolometer operates at 0.3 - 2.7 µm.See Chapter 13 Section 3 for a description of the calibration algo-rithms.

Clear Atmosphere .TES Team scientists recommend the following constraints for queryresults of spectra with minimal atmospheric effects.Note: do not use this constraint when requesting only bolometricresults.

• ATM.QUALITY:ATMOSPHERIC-OPACITY-RATING 0 0.• ATM.NADIR-OPACITY[1] -0.03 0.2 (i.e. Dust Opacity).• ATM.NADIR-OPACITY[2] -0.03 0.1 (i.e. Water Ice Opacity).

Detector (*.DETECTOR-NUMBER) .The number of the spectrometer detector that made the observa-tion. Detectors are numbered from 1 to 6.

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Detector Mask (OBS.DETECTOR-MASK) .A spatial detector mask was applied to every TES spectral observa-tion prior to transmission to Earth. There are eight possible com-binations [0-7] of co-adding the spectra from the six detectors, forexample: mask 1 = all detectors co-added into single spectrum;mask 7 = all detectors downlinked as separate spectra.See Chapter 12 Section 6 for more information.

Downwelling Flux .Atmospheric downwelling flux due to either CO2 (667 cm−1 band)or all atmospheric aerosols; given in watts cm−2.See Chapter 13 Section 7 for more information.

Emission Angle (GEO.EMISSION-ANGLE) .Angle between the spacecraft, the target point and the surface nor-mal vector at the target. When TES is observing the surface directlybelow the spacecraft, the emission angle is close to 0 degrees.

Emissivity .Emissivity is calculated by dividing the TES calibrated radiance by ablackbody at the calculated target temperature for the observation.There are 143 radiance channels at ≈ 10 wavenumber spacing, and256 radiance channels at ≈ 5 wavenumber spacing. To restrict thereturned spectral resolution, use commas to separate a list of indi-vidual channel numbers (1,2,....,n) and/or define a range of channelnumbers (min:max). Example = 6,50:100,125,130:143.If SCAN-LENGTH is not constrained, then all available wavelengthmodes (singles and/or doubles) will be returned in the results; inthis case, the value of radiance channels [144:256] will be NULL forsingle mode data.See Wavelength entry for more information.

Emissivity Indexes and Ratios .Emissivity band depths indexes and ratios are calculated as follows(all bands given in cm-1):

• Hematite Index (PCT.HEM-INDEX) - emissivity band ratio =[360-391]/[286-296+466-476].

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• LongWave Depth Index (PCT.LW-DEPTH) - emissivity at longwavelengths 350-450.• ShortWave Depth Index (PCT.SW-DEPTH) - emissivity at long

wavelengths 1359-1401.• LongWave Ratio (PCT.LW-RATIO) - emissivity band ratio =

[425-455]/[380-390+465-475].• 450 and 530 Index - not currently available.

See Chapter 13 Section 10 for more information.

Ephemeris Time (POS.EPHEMERIS-TIME) .The time at the beginning of the observation, given in seconds since12:00 a.m. January 1, 2000. The TES mapping mission covers theET range -26495432 and 214295844.

High Gain Antenna and Solar Panel Motion (RAD.QUALITY) .The TES Team scientists have noted that the motions of either theantenna or the panels induce microphonics which adversely affectthe quality of the observed spectra. Use this constraint to obtainspectral data collected at a time when the High Gain Antenna wasnot moving.(RAD.QUALITY:HGA-MOTION=1) or when the Solar Panels werenot moving.(RAD.QUALITY:SOLAR-PANEL-MOTION=1).See Chapter 12 for a complete description of this quality issue.

Highest BOLOMETRIC Quality .TES Team scientists recommend the following constraints so thatquery results only contain the highest quality bolometric data:

• BOL.QUALITY:BOLOMETRIC-INERTIA-RATING 0 1.• BOL.QUALITY:BOLOMETER-LAMP-ANOMALY 0 0.

See Chapter 12 for more information on these fields.

Highest SPECTRAL Quality .TES Team scientists recommend the following constraints so thatquery results only contain the highest quality spectral data:

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• RAD.QUALITY:MAJOR-PHASE-INVERSION 0 0.• RAD.QUALITY:ALGOR-RISK 0 0.• OBS.DETECTOR-MASK 7 7.• RAD.TARGET-TEMPERATURE 250 300.• OBS.IMC-COUNT 0 0.

See Chapter 12 and Chapter 13 Section 8 for more information onthese fields

ICK (OBS.ICK) .The number of two-second intervals that have elapsed since thestart of the orbit. The two-second interval is the smallest time unitdefined by the TES instrument and equals the time to complete asingle length scan. There are approximately 3530 icks per TESorbit.

IMC Status (OBS.IMC-COUNT) .TES was originally designed to improve the quality of the collectedspectra by mechanically compensating for the MGS orbital motionusing Image Motion Compensation (IMC); however, due to issuesduring aerobraking, the MGS orbit was modified and the TES IMCwas infrequently used. The IMC-COUNT can be used to constraindata based on the use of the IMC:

• IMC Off selects only spectral data where OBS.IMC-COUNT =0• IMC On selects only spectral data where OBS.IMC-COUNT >

1

Incidence Angle (GEO.INCIDENCE-ANGLE) .Angle between the sun, the target point, and the surface normalvector at the target. TES day observations have an incidence anglebetween 0 and 90 degrees; TES night observations have an incidenceangle between 90 and 180 degrees.

IRTM .TES calibrated radiances are convolved to Viking IRTM band reso-lution for the five primary data channels. Original IRTM channels,with wavelength ranges, are generated from the TES (double scan)channels as follows:

• T7 (6.1 - 8.3 µm) = (184 - 284).

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• T9 (8.3 - 9.8 µm) = (154 - 234).• T11 (9.8 - 12.5 µm) = (118 - 186).• T20 (17.7 - 30.0 µm) = (36 - 90).• T15 (14.56 - 15.41 µm) = (92 - 104).

Latitude (GEO.LATITUDE) .Areocentric latitude of target point; given in degrees.All derived surface geometry values are computed on the IAU-1994Mars ellipsoid.

Local Time (GEO.LOCAL-TIME) .Local time at target, in decimal Martian hours. The Martian day isdivided into 24 equal hours.

Longitude (GEO.LONGITUDE) .Originally, TES data was released with Areocentric WEST longitudecoordinates of observed point, based on the IAU-1994 Mars ellip-soid. TES longitudes are now also available in Areocentric EASTcoordinates, based on the IAU-2000 Mars ellipsoid. All longitudesare given in degrees.To translate between IAU-1994 (west) and IAU-2000 (east) coordinatesystems:EAST-LONGITUDE = 360 - WEST-LONGITUDE + 0.271 with theappropriate adjustments made at the 0/360 boundary.

MGS Altitude (GEO.SPACECRAFT-ALTITUDE) .Distance in KM from the spacecraft to the sub-spacecraft point onthe planet surface.

Phase Inversion (RAD.QUALITY:MAJOR-PHASE-INVERSION) .A constraint for spectral data that does not contain major phaseinversion (RAD.QUALITY:MAJOR-PHASE-INVERSION=0).See Chapter 12 for a complete description of this quality issue.

Pointing Angle (OBS.MIRROR-POINTING-ANGLE) .The angle the TES scanning mirror was pointing during the ob-servation, measured in degrees away from nadir. Mirror anglesbetween -90 and +65 view Mars and space.

Observation Type (OBS.OBSERVATION-TYPE) .A coarse data constraint based on the following observation criteria:

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• Surface = OBS.OBSERVATION-TYPE D D.• Warm Surface = OBS.OBSERVATION-TYPE D D and RAD.TARGET-

TEMPERATURE 250 300.• Limb = OBS.OBSERVATION-TYPE L L.• EPF = OBS.OBSERVATION-CLASSIFICATION:INTENDED-TARGET

1 1 and TES-SEQUENCE 6 6.

Orbit/Ock (OBS.ORBIT-COUNTER-KEEPER) .Sequential count of the number of orbital revolutions since orbitinsertion; unique throughout the entire MGS mission. This numberis identical to the MGS project orbit number up until the beginningof the Mapping Phase (ock=1684) when the MGS Project reset itsorbit count to 1. An approximate orbit timetable is available in thisTable 11.1.

Mars year Lsubs approx ock mgs-sclk utc24 103 1583 604699714 1999-02-2824 180 3456 617922389 1999-07-3124 270 5251 630593257 1999-12-2525 0 7188 644265740 2000-05-3125 90 9619 661428669 2000-12-1625 180 11862 677274965 2001-06-1725 270 13655 689943203 2001-11-1126 0 15591 703618986 2002-04-1826 90 18021 720781433 2002-11-0326 180 20266 736635715 2003-05-0526 270 22060 749304383 2003-09-2927 0 23994 762967648 2004-03-0527 90 26480 780532107 2004-09-2427 180 28667 795983380 2005-03-2227 270 30459 808643677 2005-08-1628 0 32395 822320324 2006-01-2128 90 34825 839486452 2006-08-0828 121 35686 845568757 2006-10-17

Table 11.1: Orbit timetable (modified from tes.asu.edu/data-tool/mars-year).

Radiance (RAD.CALIBRATED-RADIANCE) .Calibrated spectral radiance; given in units of (watts cm−2 steradian−1

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wavenumber−1).See Chapter 13, Section 2 for a description of the calibration algo-rithms.There are 143 radiance channels at ≈ 10 wavenumber spacing, and256 radiance channels at ≈ 5 wavenumber spacing. To restrict thereturned spectral resolution, use commas to separate a list of indi-vidual channel numbers (1,2,....,n) and/or define a range of channelnumbers (min:max).Example = 6,50:100,125,130:143.If SCAN-LENGTH is not constrained, then all available wavelengthmodes (singles and/or doubles) will be returned in the results; inthis case, the value of radiance channels [144:256] will be NULL forsingle mode data.See Wavelength entry for more information.

SCLK (*.SPACECRAFT-CLOCK-START-COUNT) .The value of the spacecraft clock at the beginning of the observation,given in seconds since 12:00 a.m. January 1, 1980. The TES mappingmission covers the SCLK range 604699726 and 845491040.

Spectral Mask (RAD.SPECTRAL-MASK) .A spectral mask was applied to every TES spectral observation priorto transmission to Earth.There are 21 masks defined which either select, reject, or averagethe data from each spectral sample; mask 00 selects all spectralsamples.See Chapter 12 Section 7 for more information.

Solar Longitude (Ls) (GEO.SOLAR-LONGITUDE) .Planetocentric longitude of the sun, used to measure the Martianseasons:

• 0 = start of Northern spring/Southern autumn.• 90 = start of Northern summer/Southern winter.• 180 = start of Northern autumn/Southern spring.• 270 = start of Northern winter/Southern summer.

Surface Endmember Array .Results from Bandfield’s atmospheric correction routine available asan array of the eight percentage concentrations and the RMS error.In order, the array contains:

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• PCT.PERC-BAS1: concentration of Acidalia Type surface end-member (andesite).• PCT.PERC-BAS2: concentration of Syrtis Type surface end-

member (basalt).• PCT.PERC-HEM: concentration of hematite endmember.• PCT.PERC-DUST1: concentration of low CO2 dust endmem-

ber.• PCT.PERC-DUST2: concentration of high CO2 dust endmem-

ber.• PCT.PERC-ICE1: concentration of high latitude water ice cloud

endmember.• PCT.PERC-ICE2: concentration of low latitude water ice cloud

endmember.• PCT.PERC-BB: concentration of blackbody endmember.• PCT.RMS-ERROR: RMS error between measure and modeled

spectra.


Surface Pressure (ATM.SURFACE-PRESSURE) .Atmospheric pressure at the surface of Mars, given in mbar x1000.It is calculated from the altitudes given by the MOLA 1/4 degree x1/4 degree topographic map, the hydrostatic law assuming a 10 kmscale height, and an adjustment for the seasonal CO2 sublimationcycle. Proper normalization is obtained by fitting the pressures ob-served by the Viking and Pathfinder landers.See Chapter 13 Section 7 for more information.

Target Temperature (RAD.TARGET-TEMPERATURE) .Derived temperature of the observed target; given in units of K.See Chapter 13 Section 5 for a description of the surface tempera-ture algorithm.

Temperature Profile (ATM.NADIR-TEMPERATURE-PROFILE) .Array of atmospheric temperatures from nadir observations corre-sponding to 38 pressure levels (16.58152 to 0.00159 mbars), given indegrees-K x100. The NULL value (444.4) is used either when (a) thetemperature retrieval fails, or (b) the pressure either exceeds thelocal surface pressure, or is less than 0.1 mbar.

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TES Team scientists have rated the quality of the pressure-temperatureprofile and provided a constraint to select only the best results(ATM.QUALITY:TEMPERATURE-PROFILE-RATING=0).See Chapter 12 for more information.

Thermal Inertia (BOL.BOLOMETRIC-THERMAL-INERTIA) .Thermal inertia, derived from thermal bolometer; given in unitsof (Jm−2s−1/2K−1). When selecting this field, consider applying theHighest BOLOMETRIC Quality constraint.See Chapter 13 Section 6 for a description of the thermal inertiaalgorithms.

Wavelength (OBS.SCAN-LENGTH) .Wavelength of scan; for complete wavenumber tables, organized bydetector and channel:

• single length scans (≈ 10 wavenumber spacing)• double length scans (≈ 5 wavenumber spacing)

TES Team Scientists highly recommend defining a SCAN-LENGTHconstraint if any Spectral Bands (Radiance, Emissivity, or Atmo-spherically Corrected Emissivity) are selected. Constraining SCAN-LENGTH by “None” has the effect of allowing both wavelength modes(single and/or double) to be returned when available, so it shouldonly be used ...

• ... IF the query returns only TES bolometer data;• ... or IF the query returns fields not derived from TES spectral

data;• ... or IF either SCAN-LENGTH is acceptable in the query results.

When the wavelength mode is unconstrained, consider adding SCAN-LENGTH (under Additional OBS Fields) to the Field Selection Listfor clarity during analysis1.

1http://tes.asu.edu/data-tool/glossary.html

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Chapter 12

Quality Parameters

12.1 OverviewThis section describes the bit contents of the OBS.QUALITY, RAD.QUA-

LITY, BOL.QUALITY, and ATM.QUALITY keywords, including an expla-nation for each bit and the code definitions for the bit values. The overallobservation quality is affected most by spacecraft and instrument motion;it is determined per observation made and stored in the OBS.QUALITYkeyword. The quality of the data is evaluated per detector and the resultsare available in the RAD.QUALITY keyword. Data quality is related to thesignal received from the TES instrument and the ground based calibra-tion routines. The quality value stored in BOL.QUALITY, RAD.QUALITY(bits 8-10), and ATM.QUALITY is a credibility flag for the derived thermalinertia and atmospheric products, and is based on the characteristics ofthe input data and the limitations of the models generating the products.

The remainder of this section is divided into three parts: observationquality characteristics, data quality characteristics, and derived productsquality characteristics. A brief explanation and the code for the bit valuesis given for each of the ten quality characteristics listed. The quality bitinformation can be accessed within a vanilla command using the formatQUALITY:field-name where the field names are listed in Table 12.1.

Note that the OBS.QUALITY and RAD.QUALITY keywords may con-tain up to 32 bits, and the ATM.QUALITY and BOL.QUALITY keywordsmay contain up to 16 bits. Bits currently not assigned to a particularcharacteristic are reserved for future use.

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12.2 Observation Quality

NAME PARAMETER BIT NO. QUALITY CHARACTERISTICATM.QUALITY 1,2 Pressure/Temperature Profile

3,4 Atmospheric OpacityBOL.QUALITY 1-3 Thermal Inertia, Bolometric

4 Bolometer Reference Lamp AnomolyOBS.QUALITY 1,2 High Gain Antenna Motion

3-5 Solar Panel Motion6 Algor Patch Status7 IMC Patch Status8 Momentum Desaturation Status9 Equalization Table Status

RAD.QUALITY 1 Major Phase Inversions2 Risk of Algor Phase Inversions3 Calibration Failure

4-5 Calibration Issues6,7 Spectrometer Noise

8-10 Thermal Inertia, Spectral11 Detector Mask 1 Problem

Table 12.1: Quality Bit Keywords


12.2.1 High Gain Antenna MotionThe high gain antenna (HGA) was deployed after the end of the aer-

obraking phase; notable adverse affects due to HGA movement appearin TES mapping data starting at ock 1985. Motion of the HGA inducesmicrophonics in TES and appear as noise in the TES data. Higher ratesof motion correspond to higher noise levels in the data.

General descriptions of HGA motion can be made according to thespacecraft operation periods. For early mapping orbits, ock 1985 to 3588,the HGA was continually in motion, autotracking throughout most of theorbit with a brief rewind period. For the remainder of “Nominal Map-ping” (ock 3589-5784 and 11901-15152), the HGA motion was restricted tooccur only during periods of earth contact. During the “Beta-supplement”operations phase (ock 5788-11900 and 15153 to present), the HGA motionremained in autotrack for periods of earth contact and executed complexrewind motions during two periods: straddling the descending equator

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crossing, and in the southern hemisphere dayside.Appendix: Determining High Gain Antenna and Solar Panel Motion

contains information on how the HGA motion data is obtained.Disclaimer: this bit should be used with caution as the information

source is spacecraft telemetry which is subject to dropped bytes and cannot be completely verified.

BIT CODE DEFINITION:

0 = HGA motion unknown.

1 = HGA not moving.

2 = HGA moving at 0.05 degree/second (autotrack motion).

3 = HGA moving at 0.51 degree/second (rewind motion).

12.2.2 Solar Panel MotionSimilar to the situation of the HGA, the motion of either one of the two

solar panels induces microphonics in the TES instrument that appear asnoise in the data. At the start of the mapping phase the solar panels werecontinuously in motion, autotracking and rewinding to follow the sun asMGS orbited Mars. Starting at ock 3589, orbit rates and motions werealtered to reduce the noise affects on TES; under the new sequence thesolar panels only move 3 times per orbit and remain stationary duringthe interim time periods. This “move and hold” pattern will continue untilthe end of the mission with the exception of expected periods of powerconstraints which will require continuous solar panel motion to maintainthe health of the spacecraft.

The amount of noise present in the data due to solar panel motion is anapproximately linear function of the rate of panel motion. The bit valuesreflect the variety of panel motion rates that may be used. For moreinformation on how the solar panel motion and rates are correlated withindividual TES observations, see Subsection 12.5.1.

Disclaimer: this bit should be used with caution as the informationsource is spacecraft telemetry which is subject to dropped bytes and cannot be completely verified.


0 = panel motion unknown.

1 = panels not moving.

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2 = panels moving at 0.051 degree/second (non-eclipse, autotrack mo-tion).

3 = panels moving at 0.120 degree/second (during eclipse, prior to ock3589).

4 = panels moving at 0.240 degree/second (during eclipse, starting ock3589).

5 = panels moving at 0.400 degree/second (used during aerobraking phases).

6 = panels moving and changing between non-eclipse and eclipse rates.

7 = not assigned.

12.2.3 Algor patch statusTwo patches are simultaneously loaded to correct problems in the TES

flight software involving the calculation of the sign of the spectral dataand the calculation of the location of the zero path difference (ZPD) inthe interferogram. Both of these problems are interconnected and canaffect the accuracy of the computed spectra. Better data are producedwhen the Algor flight software patches are onboard, however some datamay still be at risk for problems and can be identified from Data Qualitybit 2 (see Subsection 12.3.2).

Algor patch 2A modifies the method employed to calculate the signof the spectral data by computing the phase for more frequencies, thusimproving the phase determined for the output spectra. TES PROM flightsoftware relies upon the symmetry of the interferogram, characteristicto TES-I, to calculated ZPD. The TES-II interferogram is notably asym-metric and another method must be used to calculate ZPD; this alternatecalculation is accomplished with Patch 2B.


0 = Algor flight software patch not onboard TES.

1 = Algor flight software patch onboard TES.

12.2.4 IMC patch statusThis bit applies to TES data collected while using Image Motion Com-

pensation (IMC). The IMC software patch was used to control the direc-tion of steps taken for motion compensation as related to the spacecraft

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reference frame. For aerobrake orbits, imc moving in the forward di-rection (bit value 0) will compensate for the spacecraft orbital motion;for mapping orbits, imc moving in the reverse direction (bit value 1) willcompensate for the spacecraft orbital motion.


0 = imc moving in forward direction - IMC patch not onboard.

1 = imc moving in reverse direction - IMC patch onboard.

12.2.5 Momentum Desaturation statusNormal spacecraft operations include routine firing of mono propel-

lant thrusters for a duration of about 3 minutes to adjust the angularmomentum of the spacecraft. Any change in spacecraft motion has thepotential of introducing noise into the TES data. The amount of noisecontributed by momentum desaturation has not been established at thistime.

Disclaimer: this bit should be used with caution as the informationsource is spacecraft telemetry which is subject to dropped bytes and cannot be completely verified.


0 = autonomous angular momentum desaturation not occurring on space-craft.

1 = autonomous angular momentum desaturation occurring on space-craft.

12.2.6 Equalization tables statusThe purpose of the equalization tables is to improve the data com-

pression ratio. These tables should not affect the quality of the data. TESPROM flight software resets the equalization table values to default valuesafter every cold or warm reset. Thus when equalization table edits areloaded for use, the equalization reset patch (2C) is simultaneously loaded.Further information regarding the Equalization Tables is available in “TESSoftware Specification Document, Instrument Flight Software”.

At the time of this writing, the equalization tables have only been usedtwice: during aerobraking (ock 20-40) and during mapping (ock 8159-8312).

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12.3 Data Quality

Aerobraking: the entropy bits were reset from their default values forDetector Mask 7 to spec-entropy= 4 and reference-det= 2. Com-pression did not execute as expected, and the equalization tableswere removed.

Mapping: the entropy bits were reset from their default values for Detec-tor Mask 7 to reference-det= 2. Compression/decompression forfull spectral, full spatial resolution spectral data worked as expected;however, this compression/decompression method is incompatiblewith spectrally and/or spatially masked spectral data. All maskeddata collected with equalization tables onboard has been deleted asit should not be used for scientific analysis.


0 = equalization tables not onboard TES.

1 = equalization tables onboard TES.

12.3 Data Quality

12.3.1 Major Phase InversionsSpectra with major phase flips or other grossly inaccurate features

due to lost bits, incorrect ZPD determination, or excessive “ringing” areidentified in this bit. These are major problems with the spectra andpossible minor phase flips are not detected here. Appendix Major phaseinversions and other grossly inaccurate spectra contains more informa-tion regarding how this bit is identified.


0 = data does not contain major phase inversions

1 = data does contain phase inversions

12.3.2 Risk of Algor Phase InversionsSpectra with the possibility of inaccurate minor phase flips due to algor

problems are assigned a value of 1 in this bit. These flips may not actuallybe present or recognizable in the calibrated radiance spectra, but carefulinspection should be performed before using this data. Appendix Algorphase inversions contains more information regarding how algor phase

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12.3 Data Quality

inversions are identified.Disclaimer: this bit should be used with caution as the potential for

phase inversions has been identified and verified to the best of our ability,but some “low risk” data may actually contain phase inversions.


0 = data at low risk of algor phase inversion.

1 = data at high risk of algor phase inversion

12.3.3 Calibration IssuesThe spectral calibration algorithm requries space and blackbody ref-

erence observations to complete calibration successfully. These refer-ence observations are systematically collected with the data. In the eventthat either reference observation is unavailable, the RAD.CALIBRATED-RADIANCE fields are filled with “N/A”. The RAD.QUALITY:CALIBRATION-FAILURE allows the user to select data which have failed the calibrationalgorithm (value of 1) due to the lack of the required reference observa-tions.


0 = radiance calibration successful

1 = radiance calibration failed

The remaining bits are currently undefined and reserved for future use.

12.3.4 Spectrometer NoiseThe value of this bit is a representation of the noise level in the data

due to the performance of the spectrometer over time. To completelycharacterize the noise levels in a particular observation, this bit shouldbe used in conjunction with other quality bits related to noise inducingfactors, such as HGA or solar panel motion. Spectrometer noise is cal-culated from the standard deviation of a 10-ick set of space observationsmade at least once a day expressly for this purpose. The noise level cal-culated is applied to all data collected between this and the next 10-ickspace observation. Appendix Determining Spectrometer Noise containsmore information regarding how the spectrometer noise is calculated forthis bit. The bit value 0 is used for all aerobraking orbits and for mappingorbits where the necessary space observations are not available.


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12.3 Data Quality

0 = spectrometer noise not calculated.

1 = spectrometer noise at nominal levels.

2 = spectrometer noise at anomalously high levels.

3 = not assigned.

12.3.5 Detector Mask 1 ProblemSpectra affected by the onboard detector (spatial) mask problem are

assigned a value of 1 in this bit. In March, 2000 the TES Team identifieda problem occurring during onboard processing and associated with theuse of Detector Mask 1; use of the mask was suspended at that time.This problem is known to affect 0.5% of the surface spectra collectedduring ock 1723 through 6439. The TES Team strongly suggests thatusers select only spectra unaffected by this problem, until a method isdevised to mathematically correct the problem.


0 = spectrum not affected.

1 = spectrum affected by the detector mask 1 problem.

12.3.6 Bolometer Reference Lamp AnomalyCalibration of the visual bolometer data requires regular sampling

of one of the two internal reference lamps (see Chapter 13, Section 3for details). If the reference lamp looks are unavailable for a significantperiod of time, the calibration may be adversly affected and calibrateddata products should be used with caution.

As a result of upload errors, visual bolometer reference lamp looksare unavailable for several hundred orbits collected during July-August,2001. The reference lamp gap spanning ocks 12064 to 12688 is markedwith this bit set to the value of 1; calibrated data products are available,however, they should be used with caution.


0 = reference lamp looks routinely sampled.

1 = reference lamp looks missing.

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12.4 Derived Products Quality


12.4.1 Thermal Inertia, Spectral and BolometricThe quality of the derived thermal inertia products is rated and stored

in bits 1-3 of the BOL.QUALITY word and bits 8-10 of the RAD.QUALITYword for the bolometric and spectral values, respectively. The ratings arebased on percentage uncertainties in the modeled thermal inertia assum-ing the instrument noise levels stated in the design specifications. Theuncertainty levels are estimated from the partial derivative of log-inertiawith respect to temperature, and the ranges associated with each ratingare listed in the Bit Code Definition below. Other sources contributing tothe total uncertainty of the thermal inertia values are described in DOC-UMENT/PROCESS Section 6.0. Thermal inertia values rated as lowestquality (5 to 7) could not be accurately derived due to the reason given inthe Bit Code Definition.

BIT CODE DEFINITION (for both Spectral and Bolometric bits):

0 = best quality (estimated instrument-noise uncertainty < 1%).

1 = good quality (estimated instrument-noise uncertainty 1− 5%).

2 = medium quality (estimated instrument-noise uncertainty 5− 20%).

3 = low quality (estimated instrument-noise uncertainty > 20%).

4 = not assigned.

5 = lowest quality - observed temperature outside of model-predictedrange.

6 = lowest quality - no model temperature variation as a function of ther-mal inertia.

7 = lowest quality - thermal inertia value not computed due to lack ofnecessary data.

12.4.2 Pressure-Temperature ProfileThe quality of the derived atmospheric temperature profile is rated

and stored in bits 1-2 of the ATM.QUALITY word. Currently, only thenadir value ratings of good (0) or not available (3) are in use. These are

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applied based on the input values of the pressure and temperature bound-ary conditions: the effective surface temperature (CO2-CONTINUUM-TEMP) must be between 130 and 320 K; the atmospheric temperature(NADIR-TEMPERATURE-PROFILE) for pressure levels greater than 0.1mbar must be between 100 and 300 K. Other sources contributing to thetotal uncertainty of the pressure-temperature profile values are describedin DOCUMENT/PROCESS, Section 7.1.


0 = nadir values are good.

1 = nadir values are questionable (not used).

2 = nadir values are bad (not used).

3 = nadir values are not available.

12.4.3 Atmospheric OpacityThe quality of the derived atmospheric opacity value is rated and stored

in bits 3-4 of the ATM.QUALITY word. The ratings are based on thethermal contrast between the atmosphere and surface, and the physicalmeaning of the NADIR-OPACITY results. Opacity values are rated asgood (0) if all of the following conditions are met:

• thermal contrast is high (CO2-CONTINUUM-TEMP > 220 K).

• radiative transfer model opacities and fitted spectral shape opacityvalues agree (NADIR-OPACITY-RESIDUAL < 0.1).

• derived opacity components are realistic (dust: NADIR-OPACITY[1]>= -0.05), (water-ice: NADIR-OPACITY[2] > -0.05), (CO2 hot andisotope bands: 0.05 > NADIR-OPACITY[3] > -0.01).

Opacity values are rated as questionable (1) if any of the above conditionsare not met. The opacity calculation also depends on the availability of thecorresponding atmospheric temperature profile; thus, opacity values arerated as not available (3) if QUALITY:TEMPERATURE-PROFILE-RATINGis greater than zero. Other sources contributing to the total uncertaintyof the atmospheric opacity values are described in Chapter 13, Subsection7.2.


0 = opacity values are good.

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12.5 Appendices Quality Parameters

1 = opacity values are questionable.

2 = opacity values are bad (not used).

3 = opacity values are not available.


12.5.1 Determining High Gain Antenna and Solar PanelMotion

The high gain antenna (HGA) motion status recorded in the first twobits of the OBS.QUALITY keyword is obtained by combining rate andmotion information from the HGA with the time of each TES observa-tion. The HGA rate values were obtained from personal communicationwith spacecraft engineers (Stuart Spath, Lockheed Martin Astronautics,August 1999) as they are not recorded in the spacecraft telemetry. TheHGA motion information is obtained from different sources dependenton how the HGA commands were handled by the spacecraft team.

During “Nominal Mapping” operations (ock 1985-5784 and 11901-15152),the HGA motion is determined from the values encoded in the spacecrafttelemetry channels. Channel F-0621 defines the HGA Gimbal Drive Elec-tronics (GDE) elevation motor status as “moving” or “not moving” at spe-cific times, sampled at regular intervals. Channel F-0622 defines the HGAGDE elevation motor direction as “forward”, corresponding to autotrackmotion, or “reverse”, corresponding to rewind motion. Used together,the motion and direction information from these two telemetry channelscompletely define the status of the HGA.

During “Beta-supplement” operations (ocks 5788-11900, 15153-21833,and 25685-30733), the HGA direction is controlled by commands withinthe Spacecraft Command Files which are uploaded to the spacecraft ev-ery 3-4 days. The command code SAHACE begins HGA autotrack motion;the command code SALHTA begins a specific HGA rewind maneuver. Bycombining the time sequential command codes (for direction) with thespacecraft telemetry from Channel F-0621 (for motion) the status of theHGA is again completely defined.

The solar panel motion status is determined in a similar manner. Themotion of each solar panel, SAM and SAP, is defined independently in sep-arate spacecraft telemetry channels, F-0801 and F-0821 respectively. Ineach channel the elevation motor status is given as “moving” or “not mov-ing” at specific times. Again the rate of solar panel motion is not recorded

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in the spacecraft telemetry and was obtained through communication withthe spacecraft engineers. The solar panel rate varies over the course ofthe mission, and also throughout the course of an orbit. During a singleorbit, two rates are used: a faster rate during solar eclipse periods, anda slower rate for autotrack motion during non-eclipse periods. From thepoint of view of the spacecraft, the time of solar eclipse varies by orbitand must be obtained from the heliocentric surface occultation beginning(SOCCSB) and end (SOCCSE) time entries in the Orbit Propagation andTiming Geometry (OPTG) file.

To fully assign the solar panel bit value in the quality word, the teleme-try motion information, the known rates, and the solar eclipse times mustbe combined with each TES observation and associated time. Because thesolar panels communicate telemetry only at specific time intervals whichmay or may not correspond with each TES observation time, some logicalinterpolation was applied to determine the value of these bits. For exam-ple, TES observations obtained at a time that falls between a telemetryrecord showing motion during an eclipse period and a record showingmotion during a non-eclipse period (or vise versa) would be tagged withthe bit value 6 corresponding to panel motion during a transition period.

12.5.2 Determining Phase InversionsMajor phase inversions and other grossly inaccurate spectra

An algorithm detects major phase flips or other grossly inaccuratefeatures due to lost bits, incorrect ZPD determination, or excessive “ring-ing”. These are spectral problems that are clearly identified when thespectrum is plotted, but may not be noticed otherwise. The algorithmchecks for these problem spectra using two methods: specific thresholdsand derivatives.

The threshold checks that uncalibrated radiance values are within spe-cific thresholds in several wavelength regions: 200-220 cm−1 (value range-10 to 3); 645-680 cm−1 (value range -80 to 1); and 1610-1650cm-1 (valuerange -12 to 7). If any spectral channel lies outside this range of values,the spectrum is determined to be bad and a value of 1 is assigned. Thederivative check takes the derivative of the spectrum from 200-530 cm−1

and 800-1200 cm−1. If the absolute value of any derivative throughoutthis range is >15, then the spectrum is assigned a value of 1 indicating aproblem with the spectrum.

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Algor phase inversions

Algor phase inversions are due to low temperature contrast betweenthe sensor and the target, and it occurs because the phase of the spectrumis interpolated between a number of points in the spectrum. In spectralregions where the measured voltage is near 0, it becomes impossible tointerpolate this value exactly. This only occurs at the shorter wavelengths(< 850 cm−1) and has not been observed at longer wavelengths.

The algorithm that checks for possible minor phase flips due to algorproblems is fairly strait forward. The uncalibrated radiance spectrum isscanned between 850 and 1400 cm-1 for values with an absolute valueof less than 1. This is an arbitrary threshold where the phase flips havebeen known to occur. Where the entire range is either above 1 or be-low -1, the phase flips are assumed to not be present and the spectrumis assigned a quality value of 0. If any spectral sample in the spectralrange inspected is within these bounds, then the spectrum is assigned abit value of 1 indicating that the likelihood of minor phase flips is probable.

12.5.3 Determining Spectrometer NoiseThe spectrometer noise recorded here is a representation of the re-

sults from a study to monitor the health of the instrument over the courseof the mission. For this study, 10-ick space observations are routinelycollected at least once a day. The raw radiance for the 10-ick set is av-eraged together and the standard deviation is calculated from 3 selectedwavelength ranges: 300-400 cm−1, 900-1000 cm−1, and 1500-1600 cm−1.Finally, the average value of the standard deviation in these three rangesis used to define “nominal” or “anomalously high” levels of spectrometernoise. For single length scans, standard deviations of 0.00 to 0.28 are con-sidered nominal spectrometer noise; for double length scans, standarddeviations of 0.00 to 0.40 are considered nominal spectrometer noise.

The space observations used in this study are collected during periodswhen neither the high gain antenna nor solar panels are moving, sinceboth induce increased noise in the spectrometer. A strategy to specif-ically target data collection in periods of non-motion for spectrometernoise analysis has been in effect since ock 3589; before this time spaceobservations collected during periods of antenna or panel motion mayhave been used if no others were available1.

1http://tes.asu.edu/mgst/index/quality.txt

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12.6 Detector Mask

12.6 Detector MaskThe TES onboard Data Editor aggregates the spectral data from the

six detectors, in order to reduce the total amount of data to be downlinked.Data editing is performed according to the Detector and Spectral Masksspecified by the sequence. Detector masks are applied prior to spectralmasks.

The detector mask is applied to combine the data in the spatial do-main. Depending on the mask selected, data from different detectors arecombined (added). This computation is done in-place, with the data fromhigher numbered spectral buffers being added into the lower numberedbuffers. After application of the detector mask one or more spectralbuffers will contain data, while one or more buffers will be empty.

The detectors make up an array three detectors wide by two detectorstall. Detectors are numbered from left to right across the array, startingwith 1 in the upper left, ending with 6 in the lower right. There are eightdetector masks.

Mask 0: no data.

Mask 1: all detectors co-added.

Mask 2: no data detectors 1,3,4,6; detectors 2 and 5 co-added.

Mask 3: no data detectors 1,3,4,6; detector 2; detector 5.

Mask 4: detectors 1 and 3 co-added; detectors 2 and 5 co-added; detectors4 and 6 co-added.

Mask 5: detectors 1 and 4 co-added; detectors 2 and 5 co-added; detectors3 and 6 co-added.

Mask 6: detectors 1 and 4 co-added; detector 2; detector 5; detectors 3and 6 co-added.

Mask 7: data from all detectors - no co-adding2.2http://tes.asu.edu/mgst/index/detmask.txt

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12.7 Spectra Mask

12.7 Spectra MaskThe TES onboard Data Editor aggregates the spectral data from the

six detectors, in order to reduce the total amount of data to be downlinked.Data editing is performed according to the Detector and Spectral Masksspecified by the sequence. Detector masks are applied prior to spectralmasks.

Spectral masks are applied to combine data in the spectral domain.The data from each spectral sample may either be selected/rejected, oraveraged with the data from adjacent samples. The selection or averageoption is determined by a bit in the spectral mask itself.

The detectors make up an array three detectors wide by two detectorstall. Detectors are numbered from left to right across the array, startingwith 1 in the upper left, ending with 6 in the lower right. Each detec-tor has an associated spectral buffer with the same number. There aretwo spectral masks associated with the data acquired by the central andperipheral detectors respectively. The center mask is used on spectralbuffers 2 and 5, while the periphery mask is used on spectral buffers1,3,4, and 6.

At the end of all additions, each data point is divided by the total num-ber of samples used to generate it. In this division any failed detectorsare taken into account so that the final numbers are an average of thedata from active detectors only. The spectral data is then scanned andshifted appropriately so that the highest value is represented in a 12 bitwide number (sign bit plus 11 bit mantissa).

The amount of shifting is the exponent which is also included in thedownlinked packet. Data editing is all done “in-place” with the edited datareplacing the original values in the spectral buffers. The TES onboardData Editor keeps track of the final lengths of the six spectral buffersafter editing.

TES has 19 spectral masks loaded onboard at all times (residing inPROM). The names of these 19 masks are listed below with a brief de-scription of the mask (the bits are defined in specmask.tab); for any av-eraging mask, the value is returned in the last band of those averagedtogether.

Mask 00 - Full-Select: All bands selected.

Mask 01 - Half-Select: Every second band selected.

Mask 02 - Third-Select: Every third band selected.

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12.7 Spectra Mask

Mask 03 - Quarter-Select: Every fourth band selected.

Mask 04 - Eighth-Select: Every eighth band selected.

Mask 05 - Full-Average: All bands averaged, single value returned.

Mask 06 - Half-Average: Every two bands averaged together.

Mask 07 - Third-Average: Every three bands averaged together.

Mask 08 - Quarter-Average: Every four bands averaged together.

Mask 09 - Eighth-Average: Every eight bands averaged together.

Mask 10 - Half+CO2-Select: <500.0 cm−1 select every second band; 500.0to 850.0 cm−1 select every band; >850.0 cm−1 select every secondband.

Mask 11 - Third+CO2-Select: <500.0 cm−1 select every third band; 500.0to 850.0 cm−1 select every band; >850.0 cm−1 select every thirdband.

Mask 12 - Quarter+CO2-Select: <500.0 cm−1 select every fourth band;500.0 to 850.0 cm−1 select every band; >850.0 cm−1 select everyfourth band.

Mask 13 - Half+CO2-Average: <500.0 cm−1 average every two bands;500.0 to 850.0 cm−1 average every band; >850.0 cm−1 average everytwo bands.

Mask 14 - Third+CO2-Average: <500.0 cm−1 average every three bands;500.0 to 850.0 cm−1 average every band; >850.0 cm−1 average everythree bands.

Mask 15 - Quarter+CO2-Average: <500.0 cm−1 average every four bands;500.0 to 850.0 cm−1 average every band; >850.0 cm−1 average everyfour bands.

Mask 16 - Half+H2O+CO2-Select: <310.0 cm−1 select every band; 310.0to <500.0 cm−1 select every second band; 500.0 to 850.0 cm-1 selectevery band; >850.0 cm-1 select every second band.

Mask 17 - Half+H2O+CO2-Average: <310.0 cm−1 average every band;310.0 to <500.0 cm-1 average every two bands; 500.0 to 850.0 cm−1

average every band; >850.0 cm−1 average every two bands3.

3http://tes.asu.edu/mgst/index/specmask.txt

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Chapter 13

Mars Global Surveyor TES(Christensen et al. (2001))

13.1 OverviewThe Thermal Emission Spectrometer (TES) investigation is designed

to study the surface and atmosphere of Mars using thermal infrared (IR)spectroscopy, together with broadband thermal and solar reflectance ra-diometry. The specific objectives of the TES experiment are:

1. to determine and map the composition of surface minerals, rocks,and ices;

2. to study the composition, particle size, and spatial and temporal dis-tribution of atmospheric dust;

3. to locate water-ice and CO2 condensate clouds and determine theirtemperature, height, and condensate abundance;

4. to study the growth, retreat, and total energy balance of the polarcap deposits;

5. to measure the thermophysical properties of the martian surfacematerials;

6. to characterize the thermal structure and dynamics of the atmo-sphere.

The TES instrument consists of three sub-sections, the primary one beinga Michelson interferometer that produces spectra from 1700 to 200 cm−1

(≈ 6 to 50 µm), at a spectral sampling of either ≈ 5 or ≈ 10 cm−1. The

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13.1 Overview

instrument cycle time, including collection of the interferogram, mirrorflyback, and electronic reset, is 2 sec for 10 cm−1 (“single scan”) opera-tion, and 4 sec for 5 cm−1 (“double scan”) operation. The interferometerincludes a visible interferometer with a monochromatic source that isused to generate fringes which control the linear drive servo and deter-mine position in the interferogram. This system uses two redundant neonlamps that produce an emission line at 703.2 nm for fringe generationand a continuum that is used for a quasi-white-light source for determi-nation of zero path difference.

The TES instrument returns 143 points in single-scan or 286 points indouble-scan mode. The starting spectral sample point can be determinedby ground command. In single-scan mode the default PROM sequencefor Detector 2 begins at 148.6 cm−1 and ends at 1655.9 cm−1. This spectralrange was used throughout the aerobraking and Science Phasing Orbits.However, the first five spectra samples in single-scan mode (first 10 indouble-scan mode) have very low instrument response and a very lowsignal-to-noise ratio. Therefore, beginning with the mapping orbits thestarting sample in single-scan mode will be changed to 201.6 cm−1 (Det.2), with an ending sample of 1708.9 cm−1. The single-scan data are storedin a 148-point array beginning at 148.6 cm−1 (Det. 2), in which either thefirst five (mapping phase) or last five (aerobraking phase) samples are setto zero or null. The double-scan data are stored in a 296-point array withcorresponding offsets and null values.

The finite size and off-axis position of the six detectors results in self-apodization and a spectral shift that is a function of both distance fromthe axis and optical frequency. The resulting full-width half-maximum(FWHM) value is ≈ 12.5cm−1 for 10 cm−1 sampling at 200 cm−1 and 15.4cm−1 at 1650 cm−1. For the corner detectors and at the highest frequency(shortest wavelength) there is a significant departure from the ideal linewidth, giving a worst-case degradation of a FWHM of ≈ 24cm−1. Becauseall of the response functions have the same area there is no loss in signalwhen viewing a smooth continuum scene like Mars. However, there willbe a slight loss in contrast of narrow spectral features due to broaden-ing of the spectral width. Because the self-apodization is considerable, thedata are used without further apodization. Separate fast fourier transform(FFT) algorithms are used for the center and edge detectors in order topartially compensate for the different spectral shifts introduced into thesedetectors. These offsets are discussed in Subsection 13.2.4.

A pointing mirror capable of rotating 360◦ provides views to space,both limbs, and to internal, full-aperture thermal and visible calibrationtargets, as well as image motion compensation. In addition to the spec-

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13.2 Spectrometer calibration

trometer, the instrument has bore sighted bolometric thermal radiance(4.5 to ≈ 100µm) and solar reflectance (0.3 to 2.7 µm) channels. Each in-strument sub-section has six instantaneous fields of view (IFOV) of ≈ 8.5mrad that provide a contiguous strip three elements wide with a spatialresolution designed to be 3 km from the final MGS mapping orbit altitudeof 350 km. The outputs from all TES channels are digitized at 16 bits, pro-cessed, and formatted before being sent to the spacecraft Payload DataSubsystem (PDS). The outputs of the interferometer receive the followingprocessing within the instrument before transfer to the PDS:

1. selectable apodization;

2. Fast Fourier Transformation (FFT) of data from all six interferom-eter channels;

3. correction for gain and offsets;

4. data editing and aggregation;

5. data compression;

6. formatting for the PDS.

A separate 1.5 cm diameter reflecting telescope, collimated with the maintelescope and using the same pointing mirror, is used for the thermal andvisible bolometer channels. These channels have similar 3x2 arrays ofdetectors, that are bore sighted with the spectrometer array. The opticalsystem consists of a single off-axis paraboloidal mirror operating at f/8.A reflecting resonant fork chopper operating at 30 Hz is used to separatethe solar reflectance and thermal emission bands.


13.2.1 Spectrometer algorithm overviewThe measured spectra can be characterized at each wavenumber by

the equation:Vt = (Rt − Ri) ∗ IRF

where,

• Vt is the voltage generated by the TES looking at a target,

• Rt is the radiance of the target,

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• Ri is the radiance of the instrument,

• IRF is the instrument response function.

The radiance of the target can be determined from the above equa-tion once the instrument radiance and the response function are known.These parameters are determined using observations of space and theinternal reference surface at planned time intervals. These observationsgive two equations of the form:

Vr = (Rr − Ri) ∗ IRFVs = (Rs − Ri) ∗ IRF

where Vr and Vs are the measured voltages viewing space and refer-ence respectively, Rr is derived from the measured temperature of the ref-erence surface, and Rs is the radiance of space (≈ 0Wcm−2str−1/cm−1).These equations can be solved for the two unknown values, Ri and IRF,giving:

Ri = (Vs ∗ Rr − Vr ∗ Rs)/(Vs − Vr)IRF = Vr/(Rr − Ri)

Or the equivalent:

IRF = Vs/(Rs − Ri)These computed values are then used to compute the radiance of the

planet using:Rp = (Vp/IRF ) + Ri

13.2.2 Spectrometer algorithm versionThe simultaneous determination of IRF and Ri requires Space (S) and

Reference surface (R) observations spaced closely in time. Typically theseare acquired as consecutive or interleaved observations that are termed“SR-pairs”. The IRF is assumed to vary slowly, whereas Ri can varythroughout the orbit. Thus, the SR-pairs are only acquired several timesper orbit to determine IRF, whereas Space observations are acquired ap-proximately every 3-5 minutes to determine Ri.

It is necessary for the calibration that the required subsets of all theparameters are also available. For example, the spectral values for theplanet acquired from detector 5 can only be calibrated if all other pa-rameters are also available for detector 5. Similarly, single-scan planet

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observations are calibrated using single-scan S and R observations, anddouble-scan planet observations require double-scan S and R observa-tions.

During the aerobraking and Science Phasing Orbits the thermal stateof the TES was not stable. For example, during each spacecraft roll theSun could directly illuminate the reference surface. Therefore, it was notpossible to use long-term averages of IRF and Ri to reduce the noise levelpresent in a single determination of these parameters. In this version ofthe algorithm the bounding values of IRF and Ri are simply interpolatedto determine Rp . The instrument response was not averaged over multi-ple SR-pairs, nor was the Ri term smoothed to reduce noise.

During the Extended Mission Phase, specifically starting at TES ock12581, the orientation of the MGS spacecraft was pitched 16 degrees asa fuel saving measure. Due to this new mapping configuration, spaceobservations could no longer be acquired at the -90 pointing angle, andwere instead collected at the +74 pointing angle. This new angle gener-ates a slightly different field of view which adds a non-zero componentto Rs as a function of wavenumber and detector. The radiance differenceis assumed to be constant and has been incorporated into the Rs termfor all space observations not collected at -90; the radiance constants areprovided in the DATA/S74CORDS and DATA/S74CORSS files. Wherespace observations are available at the -90 pointing angle, the Rs term isunchanged and the resulting calibrated radiances are identical to thoseobtained using the previous calibration algorithm (version 002D).

The following sequence of operations was carried out for spectral cal-ibration:

1. Read the data associated with all the observations under considera-tion.

2. Find all of the single and double scan SR-pairs and Space observa-tions (S) in the given set of observations.

3. At each SR-pair, compute the radiance of the instrument (Ri), theIRF, and the temperature of the instrument (Ti) (for reference only).For each detector:

• Average the voltage of all the Space observations having thesame scan length. This is Vs.• Average the voltage of all the reference observations having

the same scan length. This is Vr . Average the reference sur-face thermistor temperatures (aux-temp[1-3]) to find the aver-

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age temperature of the reference surface for this SR-pair. Thisis Tr .• Compute the radiance of the reference surface (Rr) at temper-

ature Tr using the Planck blackbody radiance function.• Compute the radiance of space (Rs) at the temperature of space

(3K), using the Planck radiance function.• Compute the radiance of the instrument by substituting the

calculated values in the equation:

Ri = (Vs ∗ Rr − Vr ∗ Rs)/(Vs − Vr)

• Compute the instrument brightness temperature (Ti) at eachspectral sample by inverting the blackbody radiance functionwith radiance Ri.• Take the average of the instrument brightness temperatures

from spectral samples 50 through 90 (single scan; samples 100-180 double scan), to determine a single best-fit value of Ti. Thisis the temperature of this particular detector and is used forinformation only.• Compute IRF using the equation:

IRF = Vs/(Rs − Ri)

If IRF equals zero or inf for a particular spectral sample, thenaverage the two neighboring spectral samples to compute anIRF value for that spectral sample.• In order to calibrate spectrally masked planet data it is neces-

sary to compute a spectrally averaged Ri and IRF. To do this Vrand Vs are averaged over the spectral range averaged on-boardby the TES instrument. Rr and Rs are computed over this spec-tral range and used with the averaged Vr and Vs to compute anaveraged Ri and IRF for each spectral mask. These values arestored separately for use in the planet data calibration.

4. Store the computed values of IRF, Ri, and Ti into one packet, tagit as an SR-pair with its starting sclk-time and pool it among othersimilar packets for SR-pairs and Space observations in ascendingorder of their sclk-time. This pool is called the IRF-pool.

5. Replicate the first SR-pair as an additional SR-pair in the beginningof the given set of observations.

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6. Replicate the last SR-pair as an additional SR-pair at the end of thegiven set of observations.

7. At each Space observation, compute Ri. For each detector:

• Average the voltage of all the Space spectra in a given set ofconsecutive spectra having the same scan length. This is Vs.• Compute radiance of space (Rs) at temperature of space (3K)

using the Planck radiance function.• Search in the IRF-pool to find the closest SR-pair in each di-

rection. Interpolate over sclk-time between the two boundingSR-pairs to compute the IRF of this Space observation.• Compute the radiance of the instrument using the value of IRF

in the equation:Ri = Rs − (Vs/IRF )

• Compute the instrument brightness temperature (Ti) at eachspectral sample by inverting the blackbody radiance functionwith radiance Ri.• Take the average of the instrument brightness temperatures

from spectral samples 50 through 90 (single scan; samples 100to 180 double scan) to determine a value of Ti (used for infor-mation only).• Store the computed values of Ti and Ri for this Space observa-

tion into one packet. Tag this packet as an S with its startingsclk-time and pool it in the IRF-pool in ascending order of itssclk-time.• For spectrally-masked planet data, a spectrally averaged Ri is

computed using the interpolated values of the spectrally aver-aged IRF computed and stored for the bounding S,R pairs.

8. At each planet observation, determine IRF and Ri and compute Rp .For each detector:

• Interpolate over sclk-time between the IRF values of the twobounding SR observations to determine the IRF at this planetobservation.• Interpolate over sclk-time between the Ri values of the two

bounding SR or S points to determine Ri at this planet obser-vation.

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• If this observation had a spectral mask other than full spectralresolution, average the Ri and IRF corresponding to the mask.• Use IRF and Ri to compute Rp using the equation:

Rp = (Vp/IRF ) + Ri.

• Spectrally masked planet data are calibrated using the appro-priate spectrally averaged IRF and Ri. For spectral masks thataveraged only two spectral samples the calculated calibrated ra-diance is stored for both spectral samples. For spectral masksthat averaged more than two samples the calibrated radianceis stored at the sample closest to the mid-point of the spectralmask and the calibrated radiance of all other samples in themask are set to zero. In Version 002D the data collected usingspectral masks that include spectral samples 144-148 (singlescan) (≈ 1655 − 1709cm−1) have not been calibrated and thecalibrated radiance is not stored in the TES database. Subse-quent versions will include these calibrated data.

9. Write the calibrated spectra to the database.

13.2.3 Precision and accuracyThe TES spectrometer has a noise equivalent spectral radiance near

1.2x10− 8Wcm−2str−1/cm−1. This corresponds to a signal-to-noise ratio(SNR) of 490 at 1000 cm−1 (10 µm) viewing a 270K scene. Absoluteradiometric accuracy was estimated from pre-launch data to be betterthan 4x10− 8Wcm−2str−1/cm−1.

13.2.4 Wavenumber sample position and spectral line shapeIn an ideal interferometer with an on-axis point detector, the spectral

samples are uniformly distributed in wavenumber, and the full-width, halfmaximum (FWHM) of each sample is simply determined by the opticaldisplacement of the Michelson mirror. The TES uses a neon bulb with aline at 0.7032 µm in the visible interferometer to sample the IR interfer-ometer. The ideal sample spacing of the interferometer is given by:Sample spacing =

1(0.7032x10−4cm) ∗Npts

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where Npts is the number of points in the FFT.For a large detector, the two beams of the interferometer are not

in phase over the entire areal extent of the detector, producing “self-apodization”, or widening of the instrument line shape. In addition, thepath length of the rays traveling to the off-axis portion of each detectoris decreased relative to the optical axis rays by a factor of cos ø, where øis the angle of the off-axis ray. As a result, the mirror must move fartherto produce interference of the off-axis rays, producing a shift of the cen-ter frequency of each spectral sample to a higher apparent wavelength(lower wavenumber) than its true spectral position. All six detectors areoffset from the optical axis, producing separate shifts in the spectral lineposition, shape, and modulation efficiency of each detector.

The TES flight software processes the interferogram data with primefactors FFTs that use a different number of points for the center and edgedetectors respectively. These FFT’s were selected to produce a slightlydifferent spacing that partially compensates for the different spectral off-sets due to self-apodization between the edge and center detectors. Thenumber of points and sample spacing is given in Table 13.1.

Edge Detectors (1,3,4,6)Name Single Scan Double Scan

Npts in FFT 1350 cm−1 2700 cm−1

Sample Spacing 10.53 cm−1 5.267 cm−1

Sample 1 Position (ideal) 147.47 cm−1 147.47 cm−1

Sample 148 (single; 296 double) 1695.95 cm−1 1701.22 cm−1

Center Detectors (2,5)Npts in FFT 1344 cm−1 2688 cm−1

Sample Spacing 10.58 cm−1 5.290 cm−1

Sample 1 Position (ideal) 148.13 cm−1 148.13 cm−1

Sample 148 (single; 296 double) 1703.52 cm−1 1708.81 cm−1

Table 13.1: The sample spacing used to compute the sample position ofthe archived data is computed in full digital precision using 0.7032 µ inequation of Sample spacing

A numerical model has been developed by Co-Investigator StillmanChase to model the self-apodization effects and to determine the truespectral position, FWHM, and spectral line shape of each sample. Inter-ferogram data of Mars were collected immediately after Mars orbit in-sertion, and the atmospheric CO2 data were used to verify Chase’s model

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13.3 Visible bolometer calibration

of line shape and position. Because the focal plane is symmetric in thecross-track direction (e.g. detectors 1 and 3 are symmetrically located rel-ative to the optical axis), the position and FWHM are identical for detectorpairs 1 and 3 and detector pairs 4 and 6. The sample position offset wascalculated for each detector, taking into account the actual prime factorFFT used for each detector. Examples of the offset and the actual sampleposition calculated with this offset and the actual prime factors FFT usedfor the double-scan samples 1 and 296 are given in Table 13.2.

Name Edge Detectors (1,3) Center Detector (2)Sample 1 Self-Apodization Offset 1.19 cm−1 0.44 cm−1

Sample 296 Self-Apodization 14.00 cm−1 5.45 cm−1

Sample 1 (actual) 148.66 cm−1 148.57 cm−1

Sample 296 (actual) 1715.22 cm−1 1714.26 cm−1

Table 13.2: Double Scan Self-Apodization


13.3.1 Visible blometer algorithm overviewThe in-flight calibration of the TES visible bolometer is performed for

each detector in the following stages.

1. Use observations of the internal TES reference calibration lamp andspace to determine the instrument response function (IRF) and thezero-level radiance (background).

2. Convert each target observation to calibrated radiance using the IRFand background.

3. Compute the Lambert albedo using the calibrated radiance, the Sun-Mars distance, and the incidence angle.

13.3.2 Visible blometer algorithm version1. Read the data associated with all the observations to be calibrated.

For each observation this includes:

sclk-time: Spacecraft Clock Time.

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pnt-view: Pointing Angle View.det-mask: Detector mask.scan-len: Scan length.solar-distance: Solar distance.aux-temps(1-3): Temperature of the reference surface.temps(1): Temperature of the detector array.detector: Detector Number.vbol: Raw voltages from visible bolometer.incidence: Solar incidence angle at the target.latitude: Latitude of the target.

2. Separate the data into single and double scan modes. Each mode iscalibrated separately.

3. Sort the data on ascending sclk-time.

4. Find all distinct groups of space observations (S) in the given setof observations. Between each set of bounding calibration lampobservations (collected at two day intervals) for each detector:

• Find the mode value of all Space observations in this set. Thisis called the background. This method eliminates erroneouslyhigh visible bolometer observations due to scattering off of theTES pointing mirror when the Space view was pointing within20-40 degrees of the Sun.• Store the background value with the beginning sclk-time of

this set in one packet.

5. Find all distinct groups of consecutive visible bolometer referenceobservations (REFAn) within the given set of observations, where nrefers to the calibration lamp number 1 or 2. At each REFAn foreach detector:

• Compute the IRF for each internal lamp observation:– Average the vbol for all consecutive observations within

this set of lamp observations.– Correct vbol for the background signal by subtracting the

background from the nearest Space observation to give thelamp-voltage.

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– Average the temperature of the detector array (temps[1])to yield detector temperature (det-temp) in ◦C.

– Average the three reference surface temperatures (aux-temps[1-3]) to yield lamp temperature (lamp-temp).

– Choose the lamp absolute radiance at 28.2 ◦C (lamp-absolute)for this lamp and scan length (single or double) using thevalues in the TES Calibration Report.

– Select the ∂RL/∂T from the for the lamp that was observed.– Compute the actual lamp radiance (RL)(TL)cal for each de-

tector at the lamp temperature using lamp-absolute, thevariation in lamp radiance (RL) with temperature (∂RL/∂T)from the TES Calibration Report, and the difference (∆TL)between lamp-temp and the internal lamp absolute calibra-tion temperature (28.2◦). The lamp radiance equation is:lamp-radiance = lamp-absolute + (∂RL/∂T * (lamp-temp -28.2)))

– Compute IRF using the equation: IRF = lamp-voltage /lamp-radiance

• Store the IRF, the background voltage, and the detector tem-perature in one packet. Tag it as a REFAn packet and pool itamong other REFAn packets in ascending order of their be-ginning sclk-time.

6. Replicate the first REFAn as an additional REFAn in the beginningof the given set.

7. Replicate the last REFAn as an additional REFAn at the end of thegiven set.

8. At each target (planet) observation, compute the calibrated visiblebolometer radiance (cal-vbol). The visible bolometer response func-tion must be corrected at each planet observation to account forchanges in the detector temperature between the lamp and planetobservations. In order to avoid discrete jumps at each lamp observa-tion, it is necessary to interpolate IRF and the detector temperaturebetween successive lamp views. This baseline IRF is then correctedto the actual IRF at each planet observation using ∂f/∂TD , ∂2f/∂T2

D, along with the coefficients in the TES Calibration Report and thedetector temperature difference between the baseline lamp obser-vations and the planet observations. This is done by: Search the

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pool of REFAn observations to find the two bounding observations.For each detector:

• Interpolate linearly on sclk-time between this observation andtwo bounding REFAn times to compute the baseline IRF.• Interpolate linearly on sclk-time between this observation and

two bounding REFAn times to get the baseline detector.• Determine ∂IRF/∂T and ∂2IRF/∂T2 using the calibration coef-

ficients determined pre-launch and the equations:

∂IRF/∂T = 3∗af∗(detector−temp)2+2∗bf∗detector−temp+cf

∂2IRF/∂T2 = 6 ∗ af ∗ detector − temp + 2 ∗ bfwhere,af = ’alpha’ of the visual bolometer from the TES CalibrationReport. bf = ’beta’ of the visual bolometer from the TES Cali-bration Report. cf = ’chi’ of the visual bolometer from the TESCalibration Report.• Get the actual detector temperature (temps[1]) for this obser-

vation.• Calculate ∆T by subtracting the baseline detector temperature

value from the actual value.• Correct IRF for this detector temperature value by the equation:

CorrIRF = IRF + ∂IRF/∂T ∗∆T + ∂2IRF/∂T2 ∗∆T2/2!

• Compute cal-vbol using the equation: cal-vbol = (vbol - back-ground) / Corr-IRF

9. Compute Lambert albedo.

• Extract incidence angle and solar distance from the database.Convert solar-distance to Astronomical Units.• Compute albedo using the equation: lambert-alb = cal-vbol /

((Sun-absolute / solar-distance2) * cos(incidence-angle)) whereSun-absolute is the solar radiance at 1 A.U. integrated over theTES visible bolometer relative spectral response, and is equalto 1.666x10−2Wcm−2str−1.Note: The cos(incidence-angle) in the denominator can lead todivision by small numbers (including zero), generating highlyinaccurate values for the albedo. To avoid this problem, theLambert albedo is not computed for incidence angles >88◦.

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10. Write cal-vbol and lambert-alb to the database.

13.3.3 Precision and accuracyThe precision, zero-level offset, and absolute accuracy of the in-flight

calibration was determined using data from cruise (test tes-c2a and tes-c9a) and orbits P3 through P460.

The in-flight precision (noise level) of the calibrated radiance measure-ments was determined using observations of deep space acquired awayfrom Mars during spacecraft rolls prior to and after periapsis.. The in-ternal lamp was not used because its temperature increases if left on foran extended period of time, which changes its brightness level. The dataused were acquired on orbits P95 through P100 (no data were availablefor orbit P99) at a Mars-Sun distance of 2.068*108 km (1.382 A.U.). Onlyobservations well away from Mars, selected by constraining the height ofthe tangent point of the observation to be >2000 km above the martiansurface, were included. The sigma values of the calibrated radiance ofthe space observations are given in Table 13.3.

Detector Sigma (Radiance) Mean Zero-level (Radiance)(x10−6Wcm−2str−1) (x10−6Wcm−2str−1)

1 3.62 0.9142 3.74 1.033 3.77 1.074 3.73 0.6765 3.67 0.9426 3.59 1.00

Table 13.3: Calibrated radiance

The 1σ variation in the zero-level radiance is≈ 3.75x10−6Wcm−2str−1

for all six detectors. This value is consistent with the variation in the in-ternal lamp brightness measured pre-flight (1 − 6x10 − 6Wcm−2str−1).A Lambertian surface with a reflectivity of 1.0 would have a radiance of8.718x10−3Wcm−2str−1 at the Mars-Sun distance of these observations,measured at normal incidence angle. The 1σ precision of the visiblebolometer calibrated radiance corresponds to a noise-equivalent delta re-flectivity (NE∆R) of 0.0004, and is equivalent to an SNR of 2100 for asurface with unit reflectivity.

The zero-level radiance as a function of time is determined by the cal-ibration algorithm using periodic observations of space and the internal

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lamps to correct for detector response and offset drifts. Table 13.3 givesthe mean zero-level radiance of the space observations. This radiance isa factor of nearly four lower than the 1σ variation of the data, indicatingthat there are no measurable systematic biases introduced into the data byincorrectly removing the variations in detector response and lamp bright-ness with time and temperature. In addition, no systematic offsets, trends,or discrete changes in value at space or lamp observations were observedin the calibrated radiance of space. From these data it is concluded thatthe calibration algorithm is accurately accounting for variations in detec-tor response and lamp brightness with time and temperature at the noiselevel of the instrument. The 3σ accuracy of the zero-level radiance isapproximately ±1 ∗ 10−5Wcm−2str−1 for all six detectors, consistent withthe values in Table 13.3.

The data given in Table 13.3 were acquired of a black target (space)with zero signal and therefore do not provide a measure of the true ab-solute calibration for bright surfaces. This can only be determined byobservations of a bright source with known radiance. No surfaces ofknown brightness exist on Mars to verify the absolute radiance. In ad-dition, because the internal calibration lamps are used in the calibration,they do not provide an independent test of the absolute radiance. How-ever, it is possible to estimate changes in the lamp output with time bycomparing the measured lamp voltage, corrected for background, withthe pre-flight measurements as a function of lamp and detector temper-ature.

The pre-flight thermal vacuum tests (albm tests) and the in-flight datafrom cruise (tests tes-c2 and tes-c9, and orbits 12, 15, 95-98, 100, 222, and460) indicate a 0− ≈ 3% increase in the measured signal for detectortemperatures of 10-15◦C, and an increase of ≈ 3 − 6% near 0◦C relativeto the pre-flight measurements. This change can be due to a combinationof:

1. a change in the alignment of the lamp relative to the detectors;

2. an increase in lamp 1 brightness;

3. a change in the chopper alignment or timing;

4. an increase in detector response.

Of these, a change in alignment is least likely because no decrease inlamp signal was observed for any detector. A possible change in lamp1 brightness was investigated using the ratio of lamp 1 to lamp 2 for

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13.4 Thermal blometer calibration

pre-flight and in-flight data. Lamp 2 was observed once during cruise(test tes-c9) and once in orbit early in the mission (orbit P59). The lampratio, adjusted for lamp temperature, is unchanged for detectors 3 and 6,is ≈ 1% higher for detectors 1, 4, and 5, and is ≈ 1% lower for detector2. The change in lamp 1 relative to lamp 2, averaged for all detectors, is≈ 0.7%, and is essentially constant with temperature. Based on experienceat SBRS on the Galileo PPR instrument, the stability of these lamps isestimated to be ±0.5% on a long-term (years) basis and ±0.15% on ashort term (hours) basis. The ratio of the two TES lamps is consistentwith these stability values. Furthermore, both lamps would have to haveincreased in brightness to account for changes in the lamp 1 signal levels.It is therefore concluded that the changes in lamp 1 signal level are notassociated with changes in lamp output.

It is more likely that either the detector response with temperaturehas varied in flight, which would account for both the variations betweendetectors and the relatively large changes over temperature, or that thechopper alignment or timing has changed slightly. Neither of these caseswill affect the absolute calibration because the detector views both Marsand the lamps with the same chopper and detector characteristics. Indeed,the on-board calibration lamps are specifically intended to remove theseeffects. It is concluded that the absolute calibration is most likely ≈ 1%relative to the pre-flight calibration of the internal lamps. The relativeaccuracy from orbit P15 to P460 is ≈ 0.5%.


13.4.1 Thermal bolometer algorithm overviewThe measured integrated radiance can be characterized by the follow-

ing equation:

Vt = (Rt − Ri) ∗ IRFwhere,Vt is the voltage generated by the TES looking at a target. Rt is

the integrated radiance of the target. Ri is the integrated radiance ofthe instrument. IRF is the instrument response function. The inte-grated radiance of the target is determined from the above equation oncethe instrument radiance and the response function are known. Theseparameters are determined using observations of space and the internalreference surface at planned time intervals. These observations give two

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equations of the form:

Vr = (Rr − Ri) ∗ IRF

Vs = (Rs − Ri) ∗ IRFwhere Vr and Vs are the measured voltages viewing space and ref-

erence respectively, Rr is derived from the measured temperature ofthe reference surface, and Rs is the integrated radiance of space (≈0Wcm−2str−1). These equations can be solved for the two unknown val-ues, Ri and IRF, giving:


IRF = Vr/(Rr − Ri)or:

IRF = Vs/(Rs − Ri)These computed values then are used to compute the radiance of the

planet:Rp = (Vp/IRF ) + Ri

13.4.2 Thermal bolometer algorithm versionThe simultaneous determination of IRF and Ri requires space (S) and

reference surface (R) observations spaced closely in time. Typically theseare acquired as consecutive or interleaved observations that are termed“SR-pairs”. The IRF is assumed to vary slowly, whereas Ri can varythroughout the orbit. Thus, the SR-pairs are only acquired several timesper orbit to determine IRF, whereas Space observations are acquired ap-proximately every 3-5 minutes to determine Ri.

Prior to calibration, the weighted integrated radiance as a functionof scene temperature is computed by convolving the instrument relativespectral response with the blackbody radiance at each wavenumber from0 through 2500 with a step of 2 wavenumbers. The relative spectral re-sponse of the TES thermal bolometer was determined pre-launch and isgiven in the TES Calibration Report. The integrated radiance is com-puted for temperature values from 60K through 400K with a step of 0.01degrees. A look-up table consisting of two columns: temperature andweighted integrated radiance, is stored in a separate file, and is used toconvert brightness temperature (TB) to radiance and radiance to TB.

The following sequence of operations is carried out for spectral cali-bration:

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1. Read the data associated with all the observations under considera-tion.

2. Find all of the single and double scan SR-pairs and Space observa-tions (S) in the given set of observations.

3. At each SR-pair, compute the temperature of the instrument (Ti) andIRF. For each detector:

• Average the voltage of all the Space observations having thesame scan length. This is Vs.• Average the voltage of all the reference observations having

the same scan length. This is Vr . Average the reference sur-face thermistor temperatures (aux-temp[1-3]) to find the aver-age temperature of the reference surface for this SR-pair. Thisis Tr .• Compute the radiance of the reference surface (Rr) at temper-

ature Tr using the TB-to-radiance look-up table.• Assume the radiance of space (Rs) to be equal to zero.• Compute IRF using the equation:

IRF = Vs/(Rs − Ri)

• Compute the integrated radiance of the instrument by substi-tuting the calculated values in the equation:


• Compute the instrument brightness temperature of the instru-ment (Ti) from the radiance Ri using the radiance-to-brightnesstemperature look-up table.

4. Store the computed values of IRF and Ti into one packet, tag it as anSR-pair with its starting sclk-time and pool it among other similarpackets for SR-pairs and Space observations in ascending order oftheir sclk-time. This pool is called the IRF-pool.

5. Replicate the first SR-pair as an additional SR-pair in the beginningof the given set of observations.

6. Replicate the last SR-pair as an additional SR-pair at the end of thegiven set of observations.

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13.5 Surface temperature determination

7. At each set of Space observations, compute Vs. Vs is used to computethe radiance of the instrument (Ri) in the planet caibration using theequation:

Ri = Rs − (Vs/IRF )For each detector:

• Average the voltage of all the Space observations in a given setof consecutive observations having the same scan length. Thisis Vs.• Store the value of Vs for this Space observation into one packet,

tag this packet as an S with its starting sclk-time, and pool it inascending order of its sclk-time.

8. At each planet observation compute Rp. For each detector:

• Interpolate linearly over sclk-time between the IRF values atthe two bounding SR or S observations to determine the IRFat this planet observation.• Interpolate linearly over sclk-time between the Vs values at the

two bounding SR or S observations to compute Vs at this planetobservation.• Compute Rp from IRF and Vs using:

Rp = (Vp/IRF ) + Ri.

Replacing Ri in this equation with Ri = Rs - (Vs / IRF ) gives:

Rp = Rs + (Vp − Vs)/IRF

• Convert Rp to the brightness temperature of the planet (Tp)using the radiance-to-brightness temperature look-up table.

9. Write the brightness temperature to the database.

13.5 Surface temperature determinationA simple algorithm was performed on each TES spectrum in order

to estimate the effective surface kinetic temperature using the TES spec-trometer data. The primary use of this temperature is for emissivity de-termination where only a first-order estimate of thesurface temperature

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13.5 Surface temperature determination

is required. No attempt is made to model mixtures of surface materialsat different kinetic temperatures, nor to remove atmospheric effects.

This algorithm is based on the desire to use the entire spectrum tolocate the region with the highest emissivity, where the brightness tem-perature will provide the closest approximation to the surface kinetic tem-perature. In practice, both in laboratory measurements and in TES data,the short-wavelength region (<8 µm) often has the highest emissivity. Un-fortunately, at low temperatures (<≈ 225K), the short-wavelength regionof the spectrum has significant noise and measurements in this spectralregion are unreliable. Thus, it is necessary to have a flexible algorithmthat uses the best available data to estimate surface temperature.

1. Convert the calibrated radiance to brightness temperature at eachwavenumber assuming that:

• the emissivity is unity (temp. = TB);• the emissivity is 0.97 and dividing the calibrated radiance by this

value before determining the brightness temperature (temp. =TB’). Filter the brightness temperatures using a unity-weightfilter 7 samples wide to reduce noise effects.

2. Find the maximum brightness temperature over the sample rangesfrom:

• 300 to 1350 cm−1, excluding the region from 500 cm−1 to 800cm−1 where atmospheric CO2 has strong absorptions. Thisrange was selected to include both the long and short wave-length portions of the spectrum, and to include the wavenum-ber typically with the highest brightness temperature (≈ 1300cm−1)as determined by both the Mariner 9 IRIS and the preliminaryTES data.• 300 to 500 cm−1 only. This range covers only the long wave-

length portion of the spectrum.

3. If TB is ≥ T2 (225 K), set Tsurface to TB; if TB’ is ≤ T1 (215 K)set Tsurface to TB’. Otherwise, provide a smooth transition betweenthese to cases by setting Tsurface to weighted average of TB and TB’.Weighting is determined by:

Weight1 = 1− ((T2 − TB)/(T2 − T1))

Weight2 = 1− ((TB′ − T1)/(T2 − T1))If Weight1 or Weight2 < 0, then they are set to 0.

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13.6 Thermal inertia determination

4. Finally:

Tsurface = ((TB ∗Weight1)+ (TB′ ∗Weight2))/(Weight1 +Weight2)

13.6 Thermal inertia determinationThe approach is to match TES temperature observations against ther-

mal model predictions of the surface temperature and brightness tem-perature, which are functions of thermal inertia and several other fac-tors. Two fields of thermal inertia values are available in the TES dataset:“spectral-thermal-inertia” is derived using the surface kinetic temperatureestimated from TES spectral radiance measurements in the 20 micronspectral region; “bolometric-thermal-inertia” is derived using the plan-etary brightness temperature measured by the thermal bolometer. Ageneral summary of the model is provided below, however, users shouldobtain the reference mentioned above for full details and discussion ofthis derivation method.

The numerical thermal model used is similar to that of Haberle &Jakosky (1991), with several enhancements to account for seasonal ef-fects and most of the factors affecting surface temperature variability(e.g. solar heating, thermal radiation to space, carbon dioxide condensa-tion, atmospheric thermal re-radiation, etc.). This model is used to pre-dict surface kinetic and planetary brightness temperatures as a functionof seven parameters: local time of day, season, latitude, thermal inertia,albedo, surface pressure, and atmospheric dust opacity. Using Mars ap-propriate ranges for each parameter, model results are precomputed andstored in a lookup table to be matched with the TES observed tempera-tures and correlated parameters. The lookup table presents each possiblecombination of the latter four parameters (thermal inertia, albedo, dustopacity, and surface pressure) binned per every 5 degrees of latitude andaccessed through the diurnal and seasonal surface temperatures. Duringprocessing, any necessary interpolating between lookup table values em-ploys the fit (quadratic, cubic spline, linear, or log) most appropriate foreach parameter.

Each thermal inertia value is determined by matching observed valuesfor the remaining six thermal model parameters and surface temperatureto the lookup tables. TES observed surface temperatures and the corre-lating time and position parameters are available directly from the TESdataset, while the remaining three parameter values are determined fromother sources. Albedo values are obtained from published maps with 1/4

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13.7 Atmospheric product determination

degree resolution and 65% surface coverage, based on TES observations.Surface pressure is estimated from 1 degree resolution elevation mapspublished by MGS-MOLA, and from a seasonal pressure model. Dustopacity is estimated to be 0.1 at infrared wavelengths normalized to 6.1mb.

Three major sources of uncertainty have been recognized: instrumentnoise, the thermal model, and lookup-table interpolation. Each has beenquantitatively evaluated for a representative nighttime surface at 180 Kto establish that the maximum total uncertainty from all three sources is6.0% and 16.9% for the bolometric and spectral thermal inertia, respec-tively. The first, uncertainty due to instrument noise in TES measuredtemperature values, is the primary contribution to the above estimates. Arough estimate of this uncertainty has been used to assign a quality ratingfor each thermal inertia value available. The second, uncertainty due tothe thermal model performance, has been evaluated through compari-son of model results with analytical solutions. The third, uncertainty dueto interpolation error, is determined by comparison of direct calculationresults with interpolated results. Thermal model performance and inter-polation error contribute < 2% each to the total uncertainty. Additionaluncertainty from the remaining input values, (e.g. dust opacity, albedo,etc.), may contribute to the total uncertainty.


13.7.1 Atmospheric temperatureThe approach uses known CO2 transmittances to find the atmospheric

temperature profile that best fits the observed thermal emission measure-ments of the CO2 absorption band complex, centered at 667 cm−1 (15µm). Results, sampled at 38 pressure levels, are in the “nadir-temperature-profile” field of the ATM table, along with the other ancillary parametersdiscussed here.

As on previous Mars missions, the nadir-view TES CO2 absorptionband measurements are useful for the retrieval of atmospheric thermalstructure because of the variation of opacity across the band. Near theband center (667 cm−1), the atmosphere is most opaque and measure-ments at these frequencies provide information about the upper atmo-spheric levels. In the band wings (≈ 624 and ≈ 708 cm−1), the atmosphereis nearly transparent and measurements at these frequencies provide in-formation about the near-surface levels. In this algorithm it is assumed

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that there is no scattering and that local thermodynamic equilibrium ex-ists at all atmospheric levels. TES (nadir) measured spectral radiance isthe sum of the atmosphere attenuated surface emission plus the emissionfrom the integrated atmospheric column. Therefore, by accounting forthe surface component with boundary conditions, a temperature profilecan be quantitatively derived from the atmospheric component. The tem-perature profile is obtained by inverting the atmospheric spectrum usingan algorithm that includes filtering to reduce the inherent sensitivity toinstrument noise.

The TES spectral data used in this analysis are averages of spectrafrom all six detectors collected while the instrument was nadir looking(i.e. <82 degree emission angle), and are limited to measurements takenbetween 625.32 and 710.14 cm−1. Values for three boundary conditionsare required to model the surface emission component: surface emis-sivity, surface pressure, and effective surface temperature. Surface emis-sivity is assumed to be unity for the CO2 absorption band wavelengths.Surface pressure is calculated using the hydrostatic law (assuming 10 kmscale height) and surface elevations from the MGS-MOLA 1/4 degree res-olution topographic maps (averaged over the entire 6-detector footprintof TES).

Surface pressure adjustments are made for the seasonal CO2 cycle,but not for local time of day. An effective surface temperature is calculatedby defining a continuum for the 667 cm−1 CO2 band as the interpolatedaverage brightness temperature between the two spectral bands: 507.89 -529.05 cm−1 and 804 - 825.31 cm−1. Surface pressure and mean contin-uum temperature values used in each atmospheric temperature retrievalare available in the fields “surface-pressure” and “co2-continuum-temp” re-spectively.

Four sources of uncertainty contribute to the total error associatedwith this algorithm: instrument noise, uncertainty in surface temperatureand pressure, assumption of a unit surface emissivity and omission ofaerosol opacity, and uncertainty in CO2 absorption coefficients. Propaga-tion of instrument noise, estimated at about 2.0x10−8Wcm−2str−1/cm−1,results in retrieved temperature errors of 2-4 K. Errors from estimates ofsurface pressure and temperature will affect the lower atmospheric levels,but are negligible above one-pressure scale height. Errors resulting fromthe assumption of unit surface emissivity and neglect of aerosol opacityare partially compensated by use of the calculated effective surface tem-perature continuum. Uncertainties in the calculated CO2 absorption coef-ficients may contribute systematic atmospheric temperature errors of upto 2 K. Two fields in the ATM table are useful for monitoring the quality of

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each atmospheric temperature profile: the “temperature-profile-residual”gives the RMS difference between the final calculated and observed radi-ance values.

13.7.2 Atmospheric optical depthThe approach is to match TES spectral observations against atmo-

spheric radiative transfer models that account for contributions fromaerosol optical depth, surface temperature, atmospheric temperature, andnon-unit surface emissivity. Aerosol optical depth includes contributionsfrom both dust and water ice. The resulting contribution to the atmo-spheric optical depth from each component is stored in the ATM tablearray field “nadir-opacity”, and the spectral shapes used in the retrievalcalculation are available in DATA/STDSHAPE.TAB.

Dust and water ice aerosol optical depth can be retrieved from TESnadir observations because each spectrum has spectrally-distinct contri-butions from the surface emissivity, the aerosol optical depth, the surfacetemperature, and the atmospheric temperature. Two assumptions aremade: the dust aerosol is well mixed with the CO2 gas, and atmosphericaerosols are non-scattering. The water ice aerosol is not assumed to bewell-mixed. The water condensation level is computed based on the re-trieved temperature profile, and water-ice aerosol is restricted to the atmo-sphere above the condensation level. The contribution from CO2 hot andisotope bands is estimated using a fixed optical depth value of 0.025, andthe surface emissivity is estimated using a value from a latitude-longitudelook-up table (map). The dust and water-ice spectral shapes were care-fully chosen to be globally representative of Martian conditions.

The measured radiance spectrum from TES is matched against a se-ries of radiative transfer solutions that combine the effects of the aerosoloptical depth, the atmospheric temperature profile (“nadir-temperature-profile”, retrieved as described in the previous section), surface emissivity,and the CO2 hot and isotope bands. The surface temperature is varied inthe retrieval in a self-consistent way along with aerosol optical depth. Dustand water-ice aerosol optical depth are varied until the retrieval arrivesat a best-fit solution that minimizes the least-squares difference betweenthe modeled radiance spectrum and the TES observed spectrum. Thesolution is stored in “nadir-opacity”, and the rms residual from the fit isstored in “nadir-opacity-residual”.

Because aerosol scattering is neglected, the retrieved dust and water-ice optical depth should be interpreted as an absorption optical depth, not

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the full extinction optical depth (which includes scattering). Numerical ex-periments show that a reasonable estimation of the full extinction opticaldepth can be obtained by multiplying the dust absorption optical depth bya constant factor of 1.3, and the water ice absorption optical depth by 1.5.Three major sources of uncertainty contribute error to the solutions de-rived by this method: instrument noise, derived surface and atmospherictemperatures, and the assumption of fixed spectral endmember shapes.The first two sources can each add uncertainties of 0.02 in aerosol opacityfor spectra with a typical daytime atmospheric-surface thermal contrastof 20-40 K. The uncertainty caused by instrument noise is measured perspectral channel, and thus is minimized since the retrieval uses a largespectral range (many TES channels). The uncertainty caused by tempera-ture estimates can be further minimized by restricting the use of aerosolopacities to only those with high thermal contrast (users should selecton “co2-continuum-temp” > 220K). The uncertainty caused by the chosenspectral shapes is estimated to be no greater than 0.03 in aerosol opticaldepth. Including all sources, the total uncertainty for any one aerosolopacity retrieval is estimated to be no greater than 0.05; overall qualityof each aerosol opacity retrieved is available in the “quality:atmospheric-opacity-rating” field.

13.7.3 Downwelling fluxDownwelling flux is calculated using the same radiative transfer algo-

rithms employed in the temperature and aerosol opacity retrievals. Com-putation of the total downwelling flux as seen from a point on the surfaceinvolves integration over height, angle, and frequency. The integrationover height is performed from the top of the atmosphere down to thesurface. The angular integration is performed over all solid angles in the“upward” hemisphere as viewed from the surface. The integration overfrequency is performed over the TES spectral range.

Results are provided divided into two parts:

1. the contribution from the CO2 gas alone (“co2-downwelling-flux”)is available for all TES measurements for which the atmospherictemperature retrieval is successful;

2. the total downwelling flux (“total-downwelling-flux”) is evaluated forthose cases where the aerosol retrieval is also successful, and there-fore, is restricted to daytime observations only.

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13.8 Data quality/anomalies

The total downwelling flux includes contributions from both CO2 gas andaerosols, with the former typically accounting for an estimated 60− 80%of the total calculated flux.

13.8 Data quality/anomalies

13.8.1 Spectral ringingThe TES spectra occasionally exhibit a high frequency “ringing” in

which the amplitude of the spectrum oscillates from one spectral pointto the next. This ringing has been found to occur when there is alarge change in scene temperature from one observation to the next.These large temperature variations frequently occur during observationsof Mars acquired at large distances where there is a significant change inposition on the planet between successive observations. The TES analogelectronics are designed to keep the DC (base) level of interferogramcentered at zero volts. However, when the radiance of the scene changes,the base level of the interferogram changes and there is a finite timerequired for the electronics to compensate. The frequency of the TESinterferogram information band is 10-100 Hz, so the electronics are de-signed to pass all information within this band to avoid filtering out signalinformation. Therefore, the base correction electronics are designed tohave a time constant >0.1 seconds to avoid altering the true interfero-gram spectral information. There are only 0.2 seconds between the endof one interferogram and the start of the next, so if the scene changestemperature rapidly between observations, then the electronics will nothave sufficient time to fully recenter the base level of the interferogrambefore the start of the next scan. In this case the interferogram will stillbe settling (or rising) toward the base level during the first 0.1-0.2 secondsof the interferogram scan. This settling results in a discontinuity at thebeginning of the scan due to the fact that the first point is significantlygreater (or less) than zero. Because this spike occurs at the beginning ofthe scan, it always produces a sine wave at the highest possible frequencyin the transformed spectrum. As a result, a sine wave with a point-to-point variation is superimposed on the data. The interferogram baselevel can be increasing or decreasing if the temperature of the currentscan is higher or lower respectively than the previous scan. Therefore,the phase of the superimposed sine wave can vary by 180◦.

An algorithm has been developed to artificially remove the spectralringing by transforming the spectral data back to frequency and remov-

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13.9 Global mineral distributions on Mars (Bandfield (2002))

ing the end points of the interferogram. However, this approximationlowers the spectral resolution of the data, and has not been applied to thecalibrated spectra on the TES CD-ROMS in this release. A more sophis-ticated approach is being developed using the measured time constant ofthe TES electronics to model the settling of the interferogram toward thebase level. This correction will be applied in later releases of the TESdata.

Up to 80% of the low resolution data acquired away from periapsis canshow significant ringing. Even data that do not exhibit a visible ringingcan have higher than expected power in the highest frequency, suggestingthat some “ringing” is present. The ringing effect should be significantlyreduced when the TES is operated in the planned mode during mapping.

13.8.2 Spectrometer non-zero background calibrated ra-diance

In-flight observations indicate that a small, systematic calibration offsetwith a magnitude of ≈ 1x10− 7Wcm−2str−1/cm−1 is present in the TESdata. This error is primarily due to slight variations in the instrumentbackground energy between observations taken of space at an angle of–90◦ aft (0◦ = nadir) for calibration and those viewing the planet at angleother than –90◦. This error is not significant for surface observations attemperatures above ≈ 240K. However, for observations of the polar capsand the atmosphere above the limbs, where the radiance is low, this errorcan be significant.

Observations have been collected to characterize the variation in thecalibration offset with pointing mirror angle and instrument temperatures.Models are being developed to account for this effect. This correction willbe applied in later releases of the TES data.

13.9 Global mineral distributions on Mars (Band-field (2002))

13.9.1 TES instrument and data set overviewThe TES instrument is a Fourier Transform Michelson Interferometer

that covers the wavelength range from 1700 to 200 cm−1 (≈ 6 to 50 µm)at 10 or 5 cm−1 sampling. The instrument also contains bore-sightedthermal (5–100 µm) and visible/near-infrared (0.3–3.5 µm) bolometers.

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The focal planes in each wavelength consist of three cross-track and twoalong-track detectors with an instantaneous field of view of ≈ 8.5 mrad.The TES instrument uses a pointing mirror that allows for limited tar-geting capability, limb observations, image motion compensation (IMC),emission phase function measurements, and periodic calibration by ob-serving space and an internal reference surface. The final 2 hour, ≈ 380km altitude mapping orbit provides a surface sampling of 3 x ≈ 8 km.The elongated pixel dimension is due to the final mapping orbit of MGS,which begins its orbit at 0200 local time (LT) rather than the intended1400 LT because of damage to the solar panel that required lower aero-braking rates. Spacecraft direction relative to the surface is reversed, andIMC does not produce adequate results when stepping the mirror in thedirection opposite that originally intended. As a result, spatial sampling issmeared in the along-track direction.

A linear response function for each of the six detectors is derived pe-riodically from observations of space and an internal reference surfaceof known emissivity and temperature. Three scans each of space and thereference surface are taken and averaged for each detector. The linearresponse function allows for a simple conversion from raw spectra intocalibrated radiance. All spectra used in this study were converted intoapparent emissivity by dividing out a Planck curve of the highest bright-ness temperature within a band of 50 cm−1. This brightness temperatureis also assumed to be the surface temperature.

The data used in this study were from the mapping orbit data set upto orbit 5317 (ock 7000, Ls 104◦–352◦). The orbit range was restricted dueto an instrument anomaly that grew progressively worse past this period.This anomaly causes a sporadic minor feature to appear in spectral data at≈ 1000cm−1. The cause of this anomaly is unclear at this time, though itis similar to other features that are correlated with spacecraft vibrations.Data were limited to spectra of surface temperatures >250 K, dust extinc-tions of <0.18 (1075 cm−1 opacity of ≈ 0.3), water ice extinctions of <0.1(800 cm−1 opacity of ≈ 0.15), and emission angles of <30◦. In addition, anumber of quality parameters in the TES database were used to restrictanomalous data such as spectra containing phase inversions due to lostbits. Only 10 cm−1 data were used for this study, which represents > 99%of the data collected for the orbit range used.

The emissivity spectra were binned and averaged into a global mapof 1 pixel per degree. This was done for two reasons:

1. Averaged spectral data contain a lower random and systematic noiselevel.

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2. Processing time required to deconvolve the TES spectra is reducedby up to several orders of magnitude from about a year to severalhours.

Some concern may be raised in averaging spectra from two contrast-ing atmospheric conditions such as a spectrum measured during a duststorm and a spectrum measured over the aphelion water ice cloud belt.However, data were restricted to prevent such extreme atmospheric con-ditions, and atmospheric components have been demonstrated to combinein an extremely linear manner.

13.9.2 Algorithm descriptionIt has been demonstrated that the thermal infrared spectrum of a

mixed surface may be closely modeled using a linear combination ofthe end-member spectra weighted by the aerial concentration of eachend-member. The deconvolution provides a linear least squares fit of themeasured spectra using combinations of the endmember spectra, and theweightings represent the component areal abundances.

The linear model may be extended to the surface atmosphere inter-action in the thermal infrared for Mars. Atmospheric correction andmineral abundance determination are performed simultaneously usingthe Deconvolution Method. This method assumes that each TES appar-ent emissivity spectrum may be modeled as a linear combination of bothsurface and atmospheric end-members. The resulting concentrations ofthe surface component end-members may be normalized to retrieve theirareal concentrations and the atmospheric concentrations represent ex-tinctions that may be used with the retrieved temperature profile fromthe 667 cm−1 CO2 band to retrieve atmospheric opacities.

The least squares fit algorithm used here is an iterative program thatsuccessively removes surface component concentrations that are less thanzero and therefore unrealistic. This iterative process may find only a lo-cal, rather than global, minimum. Although this raises the possibility ofinaccuracies, we have applied this method extensively and found no ad-verse or systematic defects due to the iterative process. Spectra were fitto the TES 10 cm−1 spectral channels 9–35 and 65–110, correspondingto 233–508 cm−1 and 825–1301 cm−1, respectively. The extreme low andhigh wave number portions of the spectrum were cut out because theycontain higher random and systematic noise levels and are marked bynearly continuous water vapor and CO2 absorptions. The 508–825 cm−1

spectral region was cut to remove the 667 cm−1 CO2 absorption, which

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contains numerous sharp absorptions with opacities >>1.Atmospheric dust and water ice spectral end-members were isolated

using the target transformation and endmember recovery techniques.About 10 spectral shapes each of both atmospheric water ice and dustwere isolated from individual data subsets. These atmospheric shapescould be categorized into two groups each for water ice and dust, andthe retrieved spectra in each category was averaged to obtain the end-members used for this analysis. The dust end-members represent peri-ods of high and low opacity, and the two water ice endmembers representsmall and large particle size distributions that have been observed in theTES data set. Negative concentrations are allowed in the algorithm forthe atmospheric end-members to allow for conditions outside of the rangethat is covered by the end-members. For example, if a period of data col-lection is characterized by lower dust opacities than represented betweenthe two atmospheric dust end-members, the algorithm may extrapolateto this condition by using negative concentrations of the high-opacity at-mospheric dust end-member and positive concentrations of the low atmo-spheric dust end-member. Negative concentrations of both end-memberswere never required to fit the TES spectra, and physical reality was notviolated by the model. Though the atmospheric dust endmembers con-tain minor CO2 and water vapor absorption bands, an additional syntheticwater vapor and a synthetic CO2 spectrum formulated for the Martianatmosphere were added to better cover the range of conditions coveredby the TES instrument at Mars. This allows for better coverage of thevariable intensities of dust, water vapor, and CO2 signatures, which can beindependent and not completely modeled using only two spectral shapes.

13.9.3 End-member setMineral end-members were selected to cover a broad range of compo-

sitions (Table 13.4), and the majority was selected from the Arizona StateUniversity spectral library. Two glass spectra were included: a high-Sipotassium glass similar to obsidian and a quenched basaltic glass. Al-though it is desirable to include many compositions to avoid making a pri-ori assumptions, it is also necessary to limit the number of end-membersto keep the least squares fit solution as stable as possible. When the num-ber of linearly independent spectral end-members equals the number ofspectral channels, the system is determined and the fit will be perfectregardless of the end-member set and the spectrum being fit. However,

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unless the number of end-members approaches the number of spectralchannels, it is extremely unlikely for the algorithm to fit a spectrum withan arbitrary set of mineral spectra unrelated to those actually present.To balance these two opposing conditions, 32 minerals and glasses wereselected in addition to 6 atmospheric endmembers and a blackbody to fitthe 73 spectral channels of TES data used in this analysis. End-memberswere selected to emphasize igneous and sedimentary compositions.

Several minerals were intentionally not included in the end-memberset. Many meta morphic compositions, such as kyanite, garnet, and wol-lastonite, are not included in the end-member set as there is little evi-dence for the presence of these minerals on the Martian surface. Onlyone amphibole, an actinolite, was included in the end-member set. Highconcentrations of amphiboles have not been widely expected on the sur-face, nor has there been much indication of amphiboles from spectraland other data sets. Actinolite does not cover the variety of amphibolesby any measure, but the variety was limited to constrain the total numberof end-members. Though actinolite, a metamorphic mineral, may notnecessarily be the most likely amphibole present, its spectral signatureis similar to its related minerals. As presented below, there is no signof the presence of actinolite, and as a result, there is little evidence foramphiboles in general.

An additional end-member not included was pigeonite, which has beenwidely expected to exist on the Martian surface in high abundances. Thequality of the sample for use as a coarse particulate spectral standard ismarginal. For this study it was decided not to include this sample, as itsinclusion in previous attempts to deconvolve TES spectra has not signifi-cantly changed the results of this analysis or the quality of spectral fitting.Previous studies have noted that Martian meteorite samples with highamounts of pigeonite are not consistent with TES surface spectra.

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End-Member Category Name ASU Library NumberQuartz quartz 136

Potassium feldspar microcline 95Plagioclase albite 4

oligoclase 15andesine 79

labradorite 10bytownite 76anorthite 123

Amphibole actinolite 26Low-Ca pyroxene enstatite 51

bronzite 58High-Ca pyroxene diopside 69

augite 57augite 134

hedenbergite 59Olivine forsterite 5

fayalite 167Sheet silicate/high-Si glass Si-K glass -

biotite 80muscovite 17chlorite 9

serpentine 24nontronite 151Fe-smectite 157

illite 93Low-Si glass basaltic glass -

Oxide hematite 30Sulfate anhydrite 103

gypsum 100Carbonate calcite 114

dolomite 142Atmosphere low-opacity dust -

high-opacity dust -water ice (small) -water ice (large) -

Synth. CO2 -Synth. Water Vapor -

Table 13.4: End-Members Used for Deconvolution of TES Emissivity DataSets

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13.10 Martian Global Surface mineralogy (Bandfield (2003))

13.10 Martian Global Surface mineralogy (Band-field (2003))

One of the primary goals of the Thermal Emission Spectrometer ex-periment on the Mars Global Surveyor is to determine the mineralogy ofthe Martian surface. This information has been used to place constraintson the range of igneous processes present. The extent of water relatedprocesses on Mars has been significantly constrained by the TES exper-iment. The mineralogy of the Martian dust has also been refined usingTES data. These mineralogical results have made a significant contribu-tion towards determining the development of Mars, complimenting otherdatasets and existing information. Mineralogical determination requiresthe isolation of surface emissivity from the measured radiance and a va-riety of techniques have been developed for this purpose. Additionally,deconvolution techniques have been developed and extensively tested toseparate the individual mineral contributions to the surface emissivity.These methods and techniques have produced a variety of data productsthat are currently available through http://tes.asu.edu/.

13.10.1 Multiple emission angle observationsMultiple emission observation data have been used to separate sur-

face and atmosphere radiative contributions using a correlated-K gas ab-sorption determination and a plane-parallel radiative transfer atmosphericmodel. Bandfield and Smith produced surface emissivity and dust aerosolopacity spectral shapes as well as refined surface temperatures and dustopacities. Low albedo region surface emissivities were recovered with in-creased wavelength coverage over previous retrievals. Moderate to highalbedo surface spectra were also recovered for the first time. Moderateto high albedo surface spectra provide sensitivities unique from otherchemical and spectroscopic measurements and are able to provide usefulnew constraints on the mineralogy of the dust and soil. These spectra arediagnostic of fine-particulates with plagioclase and/or zeolites and minoramounts of bound or adsorbed water. Small amounts (≈ 2 − 3%) of car-bonates appear to be present as well, serving as a sink for atmosphericCO2.

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13.10.2 Nadir observationsAtmospheric Correction: Bandfield et al. noted that TES equivalent emis-

sivity data can be closely approximated by a linear combination ofsurface and atmosphere aerosol components. This property wasutilized to develop an atmospheric correction algorithm that deter-mined atmospheric contributions from a linear least-squares fit ofsurface and atmosphere components. A second surfaceatmosphereseparation method was developed which utilizes the non-correlationof the derivative of the atmosphere and surface components to de-termine the amplitude of their contributions to the spectra. Thereis close agreement between these two methods under a variety ofatmospheric conditions and surfaces as well as with the multipleemission method mentioned above. These results are also consis-tent with ratioed data.

Spectral Units: Most TES spectra can be closely approximated using 8spectral shapes; 2 atmospheric dust, 2 atmospheric water-ice, andfour surface emissivity spectra. The four surface spectral typesmatch those of basaltic, andesitic, and hematite mineralogies as wellas fine-particulates (approximated by a blackbody spectrum in thewavelength regions used). Mars does not display massive amountsof compositional diversity and > 99% of the surface can be charac-terized by the basaltic, andesitic, and surface dust shapes.

Basalt and Andesite: Mars displays a global dichotomy in surface min-eralogy. Basaltic surfaces are almost entirely restricted to older,heavily cratered terrain with several local exposures in the northernlowlands. A second surface composition is indicative of a basalticandesite to andesite mineralogy. This surface is present in signifi-cant quantities throughout most low-albedo regions and the highestconcentrations are located in the northern lowlands, particularly thecircum-polar sand seas. The formation mechanism behind the sil-ica component present is subject to debate between researchers whofind large quantities of basaltic andesite/andesite difficult to produceand researchers who find difficulties with the occurrence and distri-bution of an altered basaltic surface. For simplicity, this surface willbe referred to as “andesitic” and, as the references may indicate, Ido not pretend to have no preferred formation mechanism.

Other Spectral Units: Spectral diversity is present at local scales at leastto the 3 km sampling of the TES instrument and a number of dis-

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coveries are the subject of ongoing studies. Grey hematite hasbeen located in several regions within layered deposits within SinusMeridiani, Aram Chaos, and Valles Marineris. Many of the possibleformation mechanisms for these materials require liquid water insome amount, though a volcanic origin is also possible. Surface ex-posures of olivine have been located in Nili Fossae, Ganges Chasma,and other locations. The geologic context of the exposures is cur-rently being examined. TES data is continuing to provide evidenceof additional local exposures of unique mineralogies, including or-thopyroxenite, a possible ash deposit, and other spectrally unique(though the associated mineralogy remains unidentified) surfaces.

Data Products: A purpose of this abstract is to make many of the dataproducts produced by publicly available. These products include thefollowing:

• Surface emissivity cubes at 1, 2, and 4 pixels per degree (ppd)in ISIS and ENVI file types.• Mineral maps at 1, 2, and 4 ppd in ISIS and ENVI file types .• Labeled and unlabeled mineral concentration images draped

over shaded MOLA topography at 4 ppd.• An ascii file containing the 7 canonical TES endmembers, in-

cluding atmospheric, basaltic, hematite, and andesitic emissivityspectral shapes.• An ascii file containing high and low albedo surface emissivities

and dust opacity spectra derived from TES multiple emissionangle observations.

13.10.3 ConclusionsThe TES investigation has fundamentally changed and enhanced our

understanding of the development of the Martian surface and conditionspresent throughout its history, though perhaps more questions have beenraised than answered. The higher spatial resolution of the Mars OdysseyThermal Emission Imaging System and the combined spatial resolutionand unique wavelength coverage of the Compact Reconnaissance Spec-trometer for Mars on the Mars Reconnaissance Orbiter will help providea geologic context for these mineralogies as well as complimentary spec-tral information. The global scale spectral remote sensing of Mars willcontinue to reveal the unique development of Mars.

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Chapter 14

TES/THEMIS Glossary

absorption band: .the wavelength range over which electromagnetic energy is ab-sorbed by a material (e.g., a mineral, rock, or atmosphere) and hasan emissivity less than 1.0.

aerobraking: .the process of reducing the initial elliptical orbit of a spacecraftaround a planet to a nearly circular orbit by dipping the spacecraftinto the uppermost atmosphere at the closest approach to the planet(periapsis). The drag created on the spacecraft during each passprogressively reduces the distance between the spacecraft and theplanet at the farthest point in the orbit (apoapsis) until the orbitbecomes nearly circular.

albedo: .ratio of the amount of electromagnetic energy reflected by a surfaceto the amount of energy incident upon it.

anhydrite: .a sulfate mineral with the chemical formula of CaSO4.

apatite: .a phospate mineral with the chemical formula of Ca5(PO4)3(F,Cl,OH).

blackbody: .an ideal material that absorbs all radiant energy incident upon it andemits radiant energy at the maximum possible rate per unit area ateach wavelength for any given temperature. A blackbody has unitemissivity (emissivity of 1.0 across the entire spectrum).

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calcite: .a carbonate mineral with the chemical formula of CaCO3.

chromite: .an oxide mineral with the chemical formula of FeCr2O4.

detector: .a component within the instrument (e.g., TES, mini-TES, or THEMIS)that converts electromagnetic radiation into a recorded signal.

diurnal: .daily.

dolomite: .a carbonate mineral with the chemical formula of CaMg(CO3)2.

electromagnetic radiation: .energy propagated in the form of an advancing interaction betweenelectric and magnetic fields, always moving at the speed of light.

electromagnetic spectrum: continuous sequence of electomagnetic en-ergy arranged according to wavelength or frequency.

emission: .the process by which a body radiates electromagnetic energy.

emissivity: .the ratio of radiant energy from a material to that from a blackbodyat the same kinetic temperature. Materials may have wavelength-dependent emissivities between 0 and 1.0. (approximately the in-verse of reflectance).

extended mission: .a period of continued data acquisition following the end of the nom-inal, or mapping, mission.

franklinite: .an oxide mineral with the chemical formula of (Zn, Fe,Mn)(Fe,Mn)2O4.

frequency: .the number of wave oscillations per unit time or the number ofwavelengths that pass a point per unit time.

full spatial resolution: .data returned from all six TES detectors.

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full spectral resolution: .data returned from TES that include all 143 spectral data points (at10 cm−1 sampling).

goethite: .an oxide mineral with the chemical formula of α− FeO ∗OH .

gypsum: .a sulfate mineral with the chemical formula of CaSO4 ∗ 2H2O.

halite: .a chloride mineral with the chemical formula of NaCl. Halite iscommonly known as table salt.

hematite: .an oxide mineral with the chemical formula of Fe2O3.

ilmenite: .an oxide mineral with the chemical formula of FeTiO3.

incident energy: .electromagnetic radiation impinging on a surface.

infrared: .region of the electromagnetic spectrum that includes wavelengthsfrom 0.7 to 1000 microns.

kinetic temperature: .internal temperature of an object determined by random molecularmotion; measured with a contact thermometer.

kutnahorite: .a carbonate mineral with the chemical formula of CaMn(CO3)2.

magnesite: .a carbonate mineral with the chemical formula of MgCO3.

magnetite: .an oxide mineral with the chemical formula of Fe3O4.

mapping mission: .the phase of the mission after aerobraking (or after orbit insertionif aerobraking doesn’t take place) during which the spacecraft ac-quires the data planned for the mission (also sometimes called the“nominal mission”).

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mosaic: .composite image made by piecing together individual images cov-ering adjacent areas.

near infrared: .electromagnetic radiation ranging from 0.7 to ≈ 3 microns in wave-length

nominal: .as expected or planned; normal.

orbit: .path of a satellite around a body (e.g., Mars), under the influence ofgravity.

photon: .minimum discrete quantity of radiant energy.

picture element (pixel): .in a digital image, the area on the ground represented by each digitalnumber.

reflectance: .ratio of the radiant energy reflected by a body to the energy incidenton it (approximately the inverse of emissivity).

reflectivity: .the ability of a surface to reflect incident energy (e.g., light-coloredsurfaces tend to have greater reflectivity than dark-colored surfaces).

resolution (spatial resolution): .ability to separate closely spaced objects in an image or phtograph.

reststrahlen band: .in the thermal infrared region, refers to absorption of energy as afunction of (e.g.,) silica content.

rhodochrosite: .a carbonate mineral with the chemical formula of MnCO3.

rutile: .an oxide mineral with the chemical formula of TiO2.

siderite: .a carbonate mineral with the chemical formula of FeCO3.

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smithsonite: .a carbonate mineral with the chemical formula of ZnCO3.

solid solution: .a single crystalline mineral phase which may range in compositionanywhere between two endmember compositions. For example anMg-Fe solid solution carbonate must contain both Mg and Fe, butthe composition may range in chemistry anywhere between (but notincluding) magnesite and siderite.

spatial mask: .reduction of data returned from TES in which 1) two or more de-tectors have been averaged together, 2) not all six detectors werecollected, or 3) a combination of 1) and 2). Spatial (and spectral)masks are used to reduce the amount of data returned to Earth.

spectral mask: .reduction of data returned from TES in which not all 143 spectraldata points (at 10 cm-1 sampling) are collected. This may be ac-complished by 1) rejection of a data point from collection, or 2)averaging of adjacent spectral data points.

spectrometer: .a device for measuring the intensity of radiation absorbed, reflected,or emitted by a material as a function of wavelength.

spectrum: .a plot of energy versus frequency or wavelength of light.

sylvite: .a chloride mineral with the chemical formula of KCl.

thermal inertia: .measure of the response of a material to temperature changes withina substance.

thermal infrared: .electromagnetic radiation ranging from ≈ 3− 50 microns in wave-length.

visible light: .electromagnetic radiation ranging from 0.4 to 0.7 microns in wave-length that is detectable by the human eye.

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wavelength: .distance between successive wave crests (or other equivalent points)in a harmonic wave (Sabins (1987)).

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Bibliography

BAKER, VR, STROM, RG, GULICK, VC, KARGEL, JS, KOMATSU, G., & KALE, VS.1991. Ancient oceans, ice sheets and the hydrological cycle on Mars.

BANDFIELD, J.L. 2002. Global mineral distributions on Mars. Journal ofGeophysical Research. E. Planets, 107, 9–1.

BANDFIELD, J.L. 2003. Martian global surface mineralogy from the thermalemission spectrometer: Surface emissibity, mineral map, and spectralendmember data products. Journal of Geophysical Research. E. Plan-ets, 287(AZ 85287-6305), 4.

BERNSTEIN, M.P., CRUIKSHANK, D.P., & SANDFORD, S.A. 2005. Near-infraredlaboratory spectra of solid H2O/CO2 and CH3OH/CO2 ice mixtures.Icarus, 179(2), 527–534.

BILLS, B.G. 1999. Obliquity-oblateness feedback on Mars. Journal of geo-physical research, 104(E12), 30773–30.

BYLANDER, J., DUTY, T., & DELSING, P. 2005. Current measurement by real-time counting of single electrons. Nature, 434(7031), 361–364.

CARR, M.H., & HEAD III, J.W. 2010. Geologic history of Mars. Earth andPlanetary Science Letters, 294(3-4), 185–203.

CHRISTENSEN, PR, BANDFIELD, JL, CLARK, RN, EDGETT, KS, HAMILTON, VE,HOEFEN, T., KIEFFER, HH, KUZMIN, RO, LANE, MD, MALIN, MC, et al.2000. Detection of crystalline hematite mineralization on Mars bythe Thermal Emission Spectrometer- Evidence for near-surface water.Journal of Geophysical Research, 105(E4), 9623–9642.

CHRISTENSEN, P.R., BANDFIELD, J.L., HAMILTON, V.E., RUFF, S.W., KIEFFER,H.H., TITUS, T.N., MALIN, M.C., MORRIS, R.V., LANE, M.D., CLARK, RL, et al.2001. Mars Global Surveyor Thermal Emission Spectrometer experi-ment: investigation description and surface science results. Journal ofGeophysical Research, 106(E10), 23823–23.

178

BIBLIOGRAPHY

CHRISTENSEN, PR, MCSWEEN, HY, BANDFIELD, JL, RUFF, SW, ROGERS, AD,HAMILTON, VE, GORELICK, N., WYATT, MB, JAKOSKY, BM, KIEFFER, HH,et al. 2005. Addendum: Evidence for magmatic evolution and diversityon Mars from infrared observations. Nature, 436(7052), 882–882.

CHRISTENSEN, PR, ENGLE, E., ANWAR, S., DICKENSHIED, S., NOSS, D., GORE-LICK, N., & WEISS-MALIK, M. 2009. JMARS-A Planetary GIS. 1, 06.

COLAPRETE, A., & JAKOSKY, B.M. 1998. Ice flow and rock glaciers on Mars.Journal of geophysical research, 103(E3), 5897–5909.

ENGLE, E. 2010. Tutorial JMARS (Java Mission-planning and Analysis forRemote Sensing). Mars, 8, 324–388.

FORGET, F., HABERLE, RM, MONTMESSIN, F., LEVRARD, B., & HEAD, JW. 2006.Formation of glaciers on Mars by atmospheric precipitation at highobliquity. Science, 311(5759), 368.

GERLACH, S. 2007. The LabPlot Handbook. Icarus, 103(8), 201–270.

GLOTCH, T.D., & CHRISTENSEN, P.R. 2005. Geologic and mineralogic map-ping of Aram Chaos: Evidence for a water-rich history. J. Geophys. Res,110, 21.

GLOTCH, T.D., BANDFIELD, J.L., CHRISTENSEN, P.R., CALVIN, W.M., MCLEN-NAN, S.M., CLARK, B.C., ROGERS, A.D., & SQUYRES, S.W. 2006. Mineralogyof the light-toned outcrop at Meridiani Planum as seen by the MiniatureThermal Emission Spectrometer and implications for its formation. J.Geophys. Res, 111, 1–14.

HABERLE, R.M., & JAKOSKY, B.M. 1991. Atmospheric effects on the remotedetermination of thermal inertia on Mars. Icarus, 90(2), 187–204.

HAMILTON, V.E., WYATT, M.B., MCSWEEN JR, H.Y., & CHRISTENSEN, P.R. 2001.Analysis of terrestrial and Martian volcanic compositions using thermalemission spectroscopy, 2, Application to Martian surface spectra fromthe Mars Global Surveyor Thermal Emission Spectrometer. Journal ofGeophysical Research-Part E-Planets, 106(7), 14733.

HARTMANN, W.K., & NEUKUM, G. 2001. Cratering chronology and theevolution of Mars. Space Science Reviews, 96(1), 165–194.

HAUBER, E., VAN GASSELT, S., IVANOV, B., WERNER, S., HEAD, J.W., NEUKUM,G., JAUMANN, R., GREELEY, R., MITCHELL, K.L., MULLER, P., et al. 2005.

179

BIBLIOGRAPHY

Discovery of a flank caldera and very young glacial activity at HecatesTholus, Mars. Nature, 434(7031), 356–361.

HAYWARD, R.K., MULLINS, K.F., FENTON, L.K., HARE, T.M., TITUS, T.N.,BOURKE, M.C., COLAPRETE, A., & CHRISTENSEN, P.R. 2007. Mars globaldigital dune database and initial science results. J. Geophys. Res, 112.

HEAD, J.W. 2006. Interplanetary correlation: themes in the geologicalhistory of terrestrial planetary bodies. Vernadsky–Brown Microsympo-sium, 46, 24.

HEAD, J.W., & MARCHANT, D.R. 2003. Cold-based mountain glaciers onMars: western Arsia Mons. Geology, 31(7), 641.

HEAD, J.W., MUSTARD, J.F., KRESLAVSKY, M.A., MILLIKEN, R.E., & MARCHANT,D.R. 2003. Recent ice ages on Mars. Nature, 426(6968), 797–802.

HEAD, JW, NEUKUM, G., JAUMANN, R., HIESINGER, H., HAUBER, E., CARR,M., MASSON, P., FOING, B., HOFFMANN, H., KRESLAVSKY, M., et al. 2005.Tropical to mid-latitude snow and ice accumulation, flow and glaciationon Mars. Nature, 434(7031), 346–351.

HEAD, JW, MARCHANT, DR, AGNEW, MC, FASSETT, CI, & KRESLAVSKY, MA.2006. Extensive valley glacier deposits in the northern mid-latitudes ofMars: Evidence for Late Amazonian obliquity-driven climate change.Earth and Planetary Science Letters, 241(3-4), 663–671.

HEAD III, J.W., MARCHANT, D.R., & GHATAN, G.J. 2004. Glacial depositson the rim of a Hesperian-Amazonian outflow channel source trough:Mangala Valles, Mars. Geophys. Res. Lett, 31, 10701.

HOEFEN, T.M., CLARK, R.N., BANDFIELD, J.L., SMITH, M.D., PEARL, J.C., &CHRISTENSEN, P.R. 2003. Discovery of olivine in the Nili Fossae regionof Mars. Science, 302(5645), 627.

JAKOSKY, B.M., & HABERLE, R.M. 1992. The seasonal behavior of water onMars. Mars, 1, 969–1016.

JAMES, P.B., KIEFFER, H.H., & PAIGE, D.A. 1992. The seasonal cycle ofcarbon dioxide on Mars. Mars, 1, 934–968.

KARGEL, J.S., BAKER, V.R., BEGÉT, J.E., LOCKWOOD, J.F., PÉWÉ, T.L., SHAW,J.S., & STROM, R.G. 1995. Evidence of ancient continental glaciation inthe Martian northern plains. Journal of geophysical research, 100(E3),5351–5368.

180

BIBLIOGRAPHY

KRESS, A.M., & HEAD, J.W. 2008. Ring-mold craters in lineated valley filland lobate debris aprons on Mars: Evidence for subsurface glacial ice.Geophys. Res. Lett, 35, 23.

KRUSKAL, W.H., & WALLIS, W.A. 1952. Use of ranks in one-criterion vari-ance analysis. Journal of the American statistical Association, 583–621.

LASKAR, J., CORREIA, ACM, GASTINEAU, M., JOUTEL, F., LEVRARD, B., & ROBU-TEL, P. 2004. Long term evolution and chaotic diffusion of the insolationquantities of Mars. Icarus, 170(2), 343–364.

LEVY, J.S., HEAD, J.W., & MARCHANT, D.R. 2007. Lineated valley fill and lobatedebris apron stratigraphy in Nilosyrtis Mensae, Mars: Evidence forphases of glacial modification of the dichotomy boundary. J. Geophys.Res, 112, 1029.

LEVY, J.S., HEAD, J.W., & MARCHANT, D.R. 2009. Concentric crater fill inUtopia Planitia: History and interaction between glacial. Icarus, 202(2),462–476.

MADELEINE, J.B., FORGET, F., HEAD, J.W., LEVRARD, B., MONTMESSIN, F., &MILLOUR, E. 2009. Amazonian northern mid-latitude glaciation on Mars:A proposed climate scenario. Icarus, 203(2), 390–405.

MALIN, M.C., BELL, J.F., CANTOR, B.A., CAPLINGER, M.A., CALVIN, W.M.,CLANCY, R.T., EDGETT, K.S., EDWARDS, L., HABERLE, R.M., JAMES, P.B.,et al. 2007. Context camera investigation on board the Mars Recon-naissance Orbiter. J. Geophys. Res, 112, 25.

MARCHANT, D.R., & HEAD III, J.W. 2007. Antarctic dry valleys: Microclimatezonation, variable geomorphic processes, and implications for assessingclimate change on Mars. Icarus, 192(1), 187–222.

MCEWEN, A.S., ELIASON, E.M., BERGSTROM, J.W., BRIDGES, N.T., HANSEN, C.J.,DELAMERE, W.A., GRANT, J.A., GULICK, V.C., HERKENHOFF, K.E., KESZTHE-LYI, L., et al. 2007. Mars reconnaissance orbiter’s high resolution imag-ing science experiment (hirise). J. Geophys. Res, 112, 14.

MELLON, M.T., JAKOSKY, B.M., KIEFFER, H.H., & CHRISTENSEN, P.R. 2000.High-resolution thermal inertia mapping from the Mars global sur-veyor thermal emission spectrometer. Icarus, 159(2), 322–346.

MELLON, M.T., ARVIDSON, R.E., MARLOW, J.J., PHILLIPS, R.J., & ASPHAUG, E.2008. Periglacial landforms at the Phoenix landing site and the north-ern plains of Mars. J. Geophys. Res, 113.

181

BIBLIOGRAPHY

MORGAN, G.A., HEAD III, J.W., & MARCHANT, D.R. 2009. Lineated valleyfill (LVF) and lobate debris aprons (LDA) in the Deuteronilus Mensaenorthern dichotomy boundary region, Mars: Constraints on the extent,age and episodicity of Amazonian glacial events. Icarus, 202(1), 22–38.

NEUKUM, G., JAUMANN, R., HOFFMANN, H., HAUBER, E., HEAD, JW,BASILEVSKY, AT, IVANOV, BA, WERNER, SC, VAN GASSELT, S., MURRAY, JB,et al. 2004. Recent and episodic volcanic and glacial activity on Marsrevealed by the High Resolution Stereo Camera. Nature, 432(7020),971–979.

PACIFICI, A., KOMATSU, G., & PONDRELLI, M. 2009. Geological evolutionof Ares Vallis on Mars: Formation by multiple events of catastrophicflooding, glacial and periglacial processes. Icarus, 202(1), 60–77.

PHILLIPS, R.J., DAVIS, B.J., TANAKA, K.L., BYRNE, S., MELLON, M.T., PUTZIG,N.E., HABERLE, R.M., KAHRE, M.A., CAMPBELL, B.A., CARTER, L.M., et al.2011. Massive CO2 Ice Deposits Sequestered in the South Polar Lay-ered Deposits of Mars. Science (New York, NY), 2(2), 437–455.

QUIRICO, E., & SCHMITT, B. 1997. Near-Infrared Spectroscopy of SimpleHydrocarbons and Carbon Oxides Diluted in Solid N2and as Pure Ices:Implications for Triton and Pluto* 1. Icarus, 127(2), 354–378.

ROSSI, AP, CHICARRO, AF, PACIFICI, A., PONDRELLI, M., HELBERT, J.,BENKHOFF, J., ZEGERS, T., FOING, B., & NEUKUM, G. 2006. WidespreadPeriglacial Landforms in Thaumasia Highland, Mars. 37, 1568.

RUBINCAM, D.P. 1990. Mars: Change in Axial Tilt Due to Climate? Science,248(4956), 720.

RUBINCAM, D.P. 1993. The obliquity of Mars and “climate friction”. Journalof geophysical research, 98(E6), 10827–10.

RUFF, S.W., & CHRISTENSEN, P.R. 2002. Bright and dark regions on Mars:Particle size and mineralogical characteristics based on Thermal Emis-sion Spectrometer data. J. Geophys. Res, 107(10.1029).

SABINS, FF. 1987. Remote sensing, principles and interpretation. 134(4),274–298.

SHEAN, D.E., HEAD, J.W., & MARCHANT, D.R. 2005. Origin and evolutionof a cold-based tropical mountain glacier on Mars: The Pavonis Monsfan-shaped deposit. J. Geophys. Res, 110(E05001).

182

BIBLIOGRAPHY

SHEAN, D.E., HEAD, J.W., FASTOOK, J.L., & MARCHANT, D.R. 2007. Recentglaciation at high elevations on Arsia Mons, Mars: Implications for theformation and evolution of large tropical mountain glaciers. J. Geophys.Res, 112, 3004.

SIMMONS, S.L., BAZYLINSKI, D.A., & EDWARDS, K.J. 2006. South-seekingmagnetotactic bacteria in the Northern Hemisphere. Science, 311(5759),371.

SOARE, R.J., & OSINSKI, G.R. 2009. Stratigraphical evidence of late Amazo-nian periglaciation and glaciation in the Astapus Colles region of Mars.Icarus, 202(1), 17–21.

SQUYRES, SW, CLIFFORD, SM, KUZ’MIN, RO, ZIMBELMAN, JR, & COSTARD,FM. 1992. Ice in the Martian regolith. Mars, 1, 523–554.

WARD, W.R. 1992. Long-term orbital and spin dynamics of Mars. Mars,1, 298–320.

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