Mars Science Posters 3

N

N

Scandia Coll es

60°

·

a

Nili Fossa e

· B arn ard

es Holm

S –2

Pla

niac · Vish

·

ert Gilb

nit

a Tyr

rra

Te ·I sil

i·

200

lla

ia

· Kas ab

tes

 –8

Suz by

s Da o Vallis

40°

is Vall Mad

Malea Planum el ich · M

lles

· Te rb y

eM on

He

ll

e

tes

He AR

rhen

al les

lla Sc op C ha ulu r yb dis Sco s pul us

Scy

Mon us

20°

Co ro na

xi aC ol

h ·C

Ne r

t

sl oges ·

s

s

oros R upes 80°

Jarry De

Au xi a sV all e

les

T iu V allis

O

is

di Va llis Sha lb a

sa e

Fla m m

Line refers to the max. extent of frost cover

A uqakuh-v.

us

oV

Vallis

Maja

Bosporos Rupes

Fo ss a e la ri ta s C

Na ne

sae

Sac ra Fos

e

T

tam aV alli s

es i V all

na

on te s

ha rs is

Ul

yss

M

es Pa ter a

es

up

lci Su ga s Gi

se y

i

Hu s

Ica r

Ju vent ae Ch.

a

pic

Oly m

u Lyc us S

a

us P atera

se

Er eb us

Phleg ra M o

Vall is

ira

ul

aS cop

a ni

Hype rn

W

Er id

Valli s

Pro me th

Promethei P l.

·

us

P ro m e t h ei R u p es

· Verlaine

ul

h

a in · M

op

pu sM ons

Sc

Reull

ia

During one Martian year measured at 0° longitude, various latituted. Source: MGS TES Jun 2000. - Apr. 2002.

tr

Temperature

L

no

Gu

Lu

ntes

Va llis

Foss ae

A me nthe s

Orc

itia

an Pl is

ti a ani s Pl

Oe

· McMurd o

· S ou t

· Fournier

·

Scopulus

vi

 –3900

Isidi

llis s Va

id

s

u Arn

Is

e oll aC en Ar

Vallis ing Hs

Liris V alles

s Valle

Cor onae

-85°

600

45° 180°

s su

1000

90°

sV cra Lo

56-sol months (1=Ls 270)

1400

0°

0°

uo

Cu

llis Va

7 8 9 101112 1 2 3 4 5 6

ae ss Fo

ng

*

270° Ls

VIEW OF CHRYSE PLANITIA (Viking–1)

PRESSURE [mbar] Viking–2, 47°N 10 mb Southern CO2 frost sublimated

Southern CO2-frost precipitated

0°

90°

180°

0 mb

270° Ls 0°

WIND SPEED [m/s] Viking–2, 47°N 20 dust storm season 0°

90°

180°

270° Ls 0°

0

TEMPERATURE [C°] Viking–2, 47°N

daily max .

-20 -40 -60 -80 -100 -120

dail y min .

DAILY SOLAR ENERGY AT VARIOUS LATITUDES [Wh/sol·m2]

-45°

loe Co

I

56-sol months (1=Ls 270)

-140

Change of seasons on Mars is expressed as Ls degrees (Solar Longitude: distance of the Sun from the first point of Aries). Seasonal changes are caused not only by the tilt of axis, but also by the Solar distance, which is a major factor because of the highly elliptical orbit of Mars. This plays a major role on the actual extent of the polar (frost) caps. Southern seasons are extreme: Summer is while Mars is close to to Sun (perihelion), Winter is while far from the Sun (aphelion). Northern seasons are less different: Winter is in perihelion. The basis (x axis) of the diagrams shown here is not Ls, but a 56-sol month. The southern Summer is the season for global dust storms. The almost always cloudless sky of Mars is pale pink in colour because of the dust. During the winter, CO2 is precipitated as frost, even at 50° lat. By summer, this sublimates, adding to global atmospheric pressure and making strong winds.

85°

*

m

7 8 9 101112 1 2 3 4 5 6

K 313 293 270 253 233 213 193 173 153 133

+M

*

rsu Do

a hi C Sisyp

M.

ore sC av i

· Lyell

s

n po

197 1†

*

Pa u sa tera ty Pi

i by a M. 0°

Schroeter

HUYGENS

Alphe us Co

u s Pa tera ne Am Pe ph i Pa rites t a e P ter r a a e l a a M

ia ev Br

t u Toi · Dana · D oly · J

S i s y p h i Te r r a es nt Mo

rmán ps n Ká · Philli o v ·

Wegener ·

Australe

-140

K 303 293 283 273 263 258 253 243 213 203 183 173 163 153 148 143

is all aV

· Russell

80°

· Schaeber le

· Green

hi

°C 30 20 10 0 -10 -15 -20 -30 -60 -70 -90 -100 -110 -120 -125 -130

T i si a

· Denning

· Bouguer · Lambert

· Procto r

· Roddenberry

Doaus Vallis

Planum

60°

PHOTOMAP OF MARS Showing albedo features of the surface Lambert Transversal Equivalent Azimuthal Projection Published by Eötvös Loránd University Cosmic Materials Space Research Group, Budapest, Hungary http://planetologia.elte.hu Source: Viking Orbiter Map © Henrik Hargitai 2008 ISBN HU 978-963-463-969-5

North

METEO DATA Legend

· Teisserenc de Bort

· Rabe

· Kaiser

p Sisy

· Maraldi

Fri g

· Flaugegues

Noachis Terra

· D a r w in

Planum

li Pa

60°

Dawes ·

· Le Verrier

l al sV opa c a l Pal

20°

Syrtis M a j o r tera

40°

Sa ba Ter ra

· Lohse

· Galle

Argentea

llis Va

Pa

a

DI ANTONIA

ier Perid

* Ni
2300 Patera * Meroe

Niesten

· Wirtz

ai V.

· Pollack

os M

is

· F ont an

lle Va

is

m Sa

Rup es

Bakhuysen ·

Newcomb

· Novara

· Helmholz

te Charitum Mon yre

rs me

al l

s lli

o ·N

ra

 –3500

t · Balde

al les

· Shatskiy

· Mena

· Arkhangelsky

Dzig

A rg

ra · Peta na V.

as im a ov ra Val les

·K

er

r

· Wislicenus

· Vogel · Hartwig

re Arg y nitia Pla

ley Hal

Ma

th

era V. Him

e · Hook

E vros

· Beer

+ MARS –6

· Bozkir

Na kt o

Janssen ·

V os Braz

· Jones

· Foros

arion

SCHIAPARELLI

Margaritifer Terra

1974†· Cartago

Ery th · Ostrov ma raea Fossa

m Montes du i e

m nu

S

x.

56-sol months (1=Ls 270)

T

Va ll

80°

Highest daytime temperatures during one Mars year [K]

1 2 3 4 5 6 7 8 9 101112

alin ek

V.

80°

a em

56-sol months (1=Ls 270)

-140

n

Pl a

· Airy

ti C a vi

im

1 2 3 4 5 6 7 8 9 101112

EL L

ia

·

ni ridia

· Mädler

· Ag · Schm assiz idt An gu s

·

ilt

56-sol months (1=Ls 270)

-140

do

· Henr y

20°

2004

· Hale

in · Sumg

60°

da

1 2 3 4 5 6 7 8 9 101112

n

LO W

· Lorika

· Bond

· Bunge

e · Eg glass Dou

·

Uzboi Vallis

·

60°

1 61 126 193 257 317 371 421 468 514 562 612 668 φ Solar Distance: 206–249 million km Sol 1 61 126 193 257 317 371 421 468 514 562 612 668 φ 80 80 Earth Distance: 54–401 million km 70 70 60 Equatorial Radius: 3396.2 km 60 50 50 Obliquity to orbit: 25°19’ (±10°) 40 40 Orbital Period: 668.59 Mars Day 30 30 20 20 (668.59 Sol) (=687 Earth Day) 10 10 Rotational Period (1 sol): 24h:37m 0 0 10 Gravity: 0.38 g 10 20 20 Length of Equator: 21 300 km 30 30 Surface Area: 144.2 million km2 40 40 50 50 Atmosphere: 95% CO2; 2,6% N2 60 60 Pressure: 6 mbar [min: 0,7–Olympus, 70 70 max: 12–Hellas) 80 80 Ls 0 30 60 90 120 150 180 210 240 270 300 330 350 Ls 0 30 60 90 120 150 180 210 240 270 300 330 350 0. Longitude: Airy-0 crater da Height Datum 3396 km radius 40 40 40 40 30°S 40 60°S il t i 0° me 20 60°N dailtime max. 20 30°N dailtime max. 20 20 20 ma Distance from Earth at Light 0 0 0 0 0 x . -20 -20 -20 -20 -20 speed : 03:02–22:19 min. -40 -40 -40 -40 Duststorm→ -40Duststorm→ Duststorm→ Duststorm→ -60 -60 -60 -60 -60 Solar Distance: 589.2 W/m2 -80 -80 -80 -80 -80 nighttime min. -100 -100 nighttime min. -100 -100 -100 nighttime min. nighttime min. nighttime min. Satellites: Phobos, Deimos -120 -120 -120 -120 -120 -140

lis

Lowest nighttime temperature [K]

llis Va

CLIMATE

T iu

’s ion ar

er

La

l Va llis

y · Ritche

m anu s Pl

OPPORTUNI TY + Me

Margaritifer Chaos

ire

H

Va lle s

· Tikhonravov

· Crommelin 0°

Lo V. ga

Nilosyrtis Men sae

r · Schöne

Indus V allis

· Gill

Pyrrhae Chaos

is N Holden irga

·

u ngarian 18 77 p/H ma

wr

l Va

South

North

MARS FACTS

He rD es h er V all

· Pasteur

A ra bia Ter ra

Iani Chaos 340°

Asrinoses Chaos

· Flamm

CASSINI

· Marth

Aureum Chaos

Vinogradov

· Luzin

· Bequerel

t

olles Nili C

Cerulli Maggini ·

Are sV a

audo · Ren

ensae

isset · Quen

· Rutherford · Radau

· Galilaei

Aurorae Chaos

u Os

akin

S

EÖTVÖS L. UNIVERSITY COSMIC MATERIALS SPACE RESEARCH GROUP BUDAPEST, HUNGARY

s Eo

40°

·R

·

Ma

es Ar

m au Th

s

eno

rte r

40°

x· Rudau

s

· Trouvelot

ma as Ch

Eos Chaos

imov · Ibrag

ro spo

b · Ba

North

ux · More

sm enia e e Fossa

· Sklodowska

V

Aram Chaos

a

erl in

ear

asia Thaum m Planu

ae ens M ia on

· Marursky · Sagan

rr

· St

He av ys ide

Par va Planum

am b

· Po

s Fo ia as

lli Va ud Sim

· Ch

· Lassel

40°

· McLaughin

Te

· Do k uc h ae v

· Br ash

pr Ca

VA LLES M A RIN ERIS

Bo

· Paks

Ganges Chasma

la nu um m Mela Plan a s Ch ra e a sm Cop sm o r r u ates C ha a hasm A iC

· Semeykin

Curie

1997

Hydraotes Chaos

Da Vinci ·

s up e Og ygis R

·

Orson Welles ·

o

rk

e

· Mutch

a C oprates Catena

20°

20°

r Ch .

d Cy

+ PATHFINDER

320°

he

A

sma

lanu m

nt

Fossae

80°

· Cla

sa os F a

s

Mensa

Protonilu sM

e Mensa nilus o r e t eu

i

t ni

a

Pl a pi Uto

LYOT

tia

D

+ VIKING–1

ris Necta

Planum Australe

·

60°

Kunowski

Nilokeras M ensae

V.

Xa

lph

La u

ius Sc. ot

Australe Ch a

Oudemans ·

Acidalia

· Timbuktu

Si

olds

Lou ros Valle

has ma

Plani

Chryse P lanitia

· Sharanov

ir P Oph

· Sto n ey

en

lis al

300°

ae oss is F rac Co

· Re yn

um

ith Sm

upes

· Mill man

· Charlier

· Richardson

Ius C

lia

· Sytin skaya

m nu

s os

1999†

es R ei

· Nans

· Wright · Trump er

+ MA R S AR L ANDER POL

g

num

· Kuiper

· Suess

· Pickerin

· Eud oxu s

er Va lle s

Liu Hsin

pes is R u

Jeans ·

p Ru

60°

d Byr n · nlei um i e · H einba · W Bu rro ugh s· Hut ton · Liai s· les

y xle Hu

COPERNICUS

Ulyx

hi

a

Kov al’sky

NEWTON

Hipparchu s· Pto lemaeus · · Li Fan

· Mendel

Chronium Planum

ne

F

os

C Cando

Tithoniae Cat ena

Planum

e sa

Ter ra S iren

Cimmeria T erra · · Campbell

T hy

c ec ·S

· lls We

rr

e· ac all

e· s u

Very ·

e ir

· Bern ard

ra

Maja

hir

Icaria Pla

ov

Te

ikh ·T

i

r · K

ch ovi tof ish

o Brah Tych

· Huggin

S

Gorgonum C haos

· Bjerknes

Dra Rossby va Va·lles s

s cti yr No Lab

us inth

· Tim oshe nko

1976

Ganges Catena

Op

asma

ssae

Luna Me nsa

· Chia

Perrotin ·

Tithonium Ch

ida

ul Nilo keras Scop

Kase i Valles Bahram Vallis

280° Heb es Ch.

Syria P

d Ta

ri Eu

y·

he

o olst A. T

R KEPLE

et

V.

n· iso nn

om

Harm akhis

c ebe · S

Reull Va llis

40°

enius · Arrh

Pr

v. Dao-

Hellas M.

Rupes

· Cruls


14057

260°

Arsia Mons

Daed alia

er

Tempe Mensa

h.

· Martz

M

or ph e os

Niger Vallis

a ca dri a Ha ater P

Pavonis Mons

um lan is P Sol

M.

*

Mariner ·

ia

ov

um Plan

a oni Aus

· ich Sav

· M agelhaens

AtlantisChaos

um

e

Va s t it a s B o re a l i s

ti

a Pl

· Poynting

ae

e sa os eF

Nilus Chaos ra Mensa S ac

Sinai

· C olumbus Ar iadnes Colles


18229

Fortuna Fossae

Moleswor th

·

on

ss

Fesen k

20°

20°


8700 is Tholus s Thar

Müller ·

M

· Dejnev

n em

Fo

era Pat

ra Pate

Apollinaris Tholus

e um Planum Bor

Acidalia Colles

Lob

Mons Ascraeus


17780

o pe F Tem

Ac

ris Planum

s niu

um

· W illiams

Co mas Sola ·

be a La

Ura

h Qa Al-

Graff ·

s T holu

· B urton

2004

Hadley ·

na Cate

a rhen Tyr

SPIRIT+· Gusev

s niu Ura s lu o h T 4700


0
460

Pere pelki n

e

e T p T es Fom ssa

240°

Aganippe Fossa

· Igal

de Vaucouleurs

Boeddic ker

is Ma’adim Vall

an

Mensae

v.

Pl

· Marca Cobres ·

mp Te


8000 holus T nius u a r Ce

Biblis Patera

Mangala Va lles

Wien

Zephiria

rius-

a

* 20°

Reuyl

220°

Fo ss ae

um lan P s u

Du

ri

Luc

R

ae

Apollinaris Patera
3100

HERSCHEL

H

pe

200°

180°

ensa e

Gale

Lasswitz

n

Nicholson

Knobel

es

Am

te s

o az

P

orsum

160°

Aeolis M

*

Jovis

s du Me

0°

ae

e

M on

140°

120°

100°

nitia

te

m rsu Do

sa

r Ma es ib

Pla

ssa e

n la

us Oly mp

i rdi

m

s Fo

Pettit

i

it

sD enide

iu

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Coll es

ntes Mo us

Go

ys

Cer b

ctus

Eddie

us

H

· Du Matheray

nt he sM en

mpus Mo Oly n
21287

Eum

+ BEAGLE–2

El

sa e

lci

ossae Ulysses F

os sF stu

Nep e

r r ta Ta

Tra

a

Heph ae

Tarta r

s Fos

sm ha


3800

Albor Tholus

.

ssae

Lockyer


14126

e ssa Fo

·

s

C ys ium El

bl Hy

· Bak

Fossae

M

s

ae us Fo

siu m

ra Pate cria

Arcadia Planitia

Adams

Mons

iu s a un Ce r

ic u

Ely

Elysium

Baphyraskráterlánc

ani ndz Ga

ena Cat

s

Gran


4700

Galaxias F luctus

20°

*

Dia

Hecates Tholus

ssa Fo tis o e ar A scu

eron

a

Tyndall

a

niti

. M

en at

Pla

li Val Hrad

ia

Acheron Fos sae

M

40°

40°

Ach

Mie

C ba

op

Va lle s

2003†

ra ate 6700 P a
Al b s

Al

Ut

ico nV all e

a ic

Nie r

Ru b

na Ta

VIK 197 ING 6 –2 +

a hasm

a Hy perboreae Und

60°

M 1:320 000 000 1 cm = 320 km

sae Fos lus les Val nta Ta R avius ssae a Fo

40°

Esc ori al

A lb

kovic Milan

Stokes

B

C ale ore

Lomonosov

Va s t it a s B o re a l i s North

dae os Un Abal

88 18

li el

P H O T O M A P

Solis Dorsa

· Korolev

Ka

60°

80°

Sch iap ar

MARS

e Oly mpia Unda

sma

Ko nk oly

80°

Planum Boreum

Echus C ha

80°

80°

Chalcop

l ós 1877 Mik ge e Th

0° SPRING

90°

180°

SUMMER

270° Ls 0°

AUTUMN

WINTER

SOLAR DISTANCE (million km) 2

200 0°

0

2,5 0°

90°

180°

270° Ls

0°

VIEW OF UTOPIA PLANITIA (Viking–2)

European Mars Science and Exploration Conference: Mars Express & ExoMars ESTEC, Noordwijk, The Netherlands, 12 - 16 November, 2007

MICROWAVE TOMOGRAPHY APPROACH FOR UPPER LAYERS SUBSURFACE EXPLORATION VIA GPR Francesco Soldovieri, Giancarlo Prisco Istituto per il Rilevamento Elettromagnetico dell’Ambiente, Consiglio Nazionale delle Ricerche Via Diocleziano 328, 80124 Napoli, ph: +39 081 5704945, [email protected]

S.E. Hamran Forsvarets ForskningsInstitutt-FFI, P.O. Boax 2027 Kieller, Norway.

Introduction The possibility to exploit Ground Penetrating Radar (GPR) in space exploration is well recognized as it can be inferred by the research activity in Marsis subsurface explorations with sensors on satellite platforms and the development of GPR systems for in-situ exploration [1]. In particular, attention is focused towards lander and rover platforms for in situ diagnostics of the first layers of the subsurface where Ground Penetrating Radar (GPR) is one of the instrumentations of the package. GPR is usually exploited in a configuration, where the receiving and transmitting antennas are separated by a small fixed offset and are moved very close to or in contact with the ground-interface. A time domain trace is collected and for each antenna’s position, then the traces are joined and processed in order to visualize the radargram. The interpretation of the radargram in order to achieve information about the scene is usually exploited on the basis of the operator’s expertise and on the a priori information. In the last years, microwave-tomography based techniques have become an increasing popular interpretational tool over for groundpenetrating radar applications. By recasting data processing as an inverse scattering problem [2-4] the interpretation of the ‘image’ can be improved and, in addition because the microwavetomography technique exploits a more refined model of the electromagnetic scattering phenomenon, this can help in the understanding of crucial aspects of a specific problem at a much deeper interpretational level.[4, 5].

Microwave tomography A microwave tomographic algorithm based on the Born Approximation (BA) [4, 6, 7] is here described. The adoption of the BA allows us to recast the problem as the inversion of a linear, integral relationship connecting the measured scattered field with an unknown contrast function. The geometry of the problem is presented in figure 1 and is concerned with a half-space scenario and two-dimensional case. The adopted measurement configuration is multi-bistatic/multi-frequency. The scattered field is given as the ‘difference’ between the total field and the unperturbed field Einc. The total field is the field reflected by the soil when buried objects are present, whereas the unperturbed field is the field reflected by the soil when the objects are absent and, therefore, it accounts for reflection/transmission at the air/soil interface and other reflections due to buried layers when these are accounted for in the reference scenario assumed for the model. The targets are invariant along the y-axis and their cross-section is assumed to be included in a rectangular investigation domain. The unknowns of the problem are the relative dielectric permittivity profile and the conductivity profile inside D. Under BA, the relationship between the unknown contrast function and the scattered field data is provided by the integral equation [4, 6, 7]: r r r r ε object r ' contrast function 2

Es ( xs , ω ) = k s ∫ Ge ( xs , ω , r ')Einc ( xs , ω , r ′)χ (r ′)dr ′

( )

χ (r ') =

εb

D

−1

The ‘unknown’ in the inversion problem is the contrast function, which accounts for the difference between the dielectric permittivity/conductivity of the objects and the soil. Thus, the result of the reconstruction is a spatial map of the modulus of the contrast Geometry of the problem function within the region under investigation. Ge(·) is Green’s function, Einc is the incident field and ks is the wave-number in the in the soil. The linear model allows us to analyse the reconstruction capabilities of the solution algorithm in terms of the spatial variations of the retrieved ‘unknown’ target object and, ultimately, the achievable resolution limits as well as the spatial and frequency sampling that has to be adopted in the survey criteria [6, 7]. The linear integral relation is inverted thanks to the Singular Value Decomposition (SVD) tool that allows to achieve the stability of the solution. The datum of the inversion algorithm is the field scattered by the buried object in the frequency domain while the raw-data are collected in time-domain and accounts for the total field; thus a pre-processing of the measurement is necessary. The first step is to “gate” the first part of all the time domain traces, which corresponds to erase the direct and surface wave contributions; this step roughly provides an estimation of the scattered field. After the choice of the time-zero, the data are Fourier transformed in frequency domain and finally they are processed by the inversion algorithm.

Inversion scheme

r

χ (r ′)

Es

Pre-processing of the data collected in time domain

Measured data

Scattered field data in frequency domain

Inversion of the linear integral equation TSVD scheme

Retrieved contrast function

Numerical and experimental imaging results We present a numerical example and the result of the processing of experimental data. In all the cases below, it is adopted the multi-monostatic measurement configuration with the measurements collected along at the air/soil interface. In the model, the incident field source is assumed as a time-harmonic filamentary y-directed electric current radiating in a frequency band. Geometry

Normalized modulus of χ

Radargram

Parameter

Value

Relative dielectric permittivity of the soil

4

Relative dielectric permittivity of the water

80

Conductivity of the soil

0.005 S/m

Conductivity of the water

0.5 S/m

Spatial step of the measurements

0.05m

Measurement domain

2 m (41 points spaced by 0.05 m)

Frequency band

100-1000 MHz

Frequency step

20 MHz (46 freq. exploited in the inversion)

Investigation domain

2 m (horizontal)x(1-3m) depth

The measurements presented in this poster was collected in front of two glaciers located at Svalbard at 78.55 degrees north. The annual mean temperature in the area is -6.3 degrees centigrade and the permafrost depth is estimated to be 100 meter. The measurements were done in April before melting had begun. GPR measurements were done with a Mala impulse radar system using 500 and 800 MHz with shielded antennas, see figure. The GPR data displayed to the left below were processed by first moving start time, DC-removal (de-wow) and applying gain as a function of depth. -8

x 10

1800

2

1600

3

1400 4

1200

t [s]

5

1000

6

800

7

600

8

400

2 4 6

155

160

165

170

175

180 185 x [m]

190

195

200

205

200

9

2 meter thick sediment layer

z [m]

1

0

10 150

160 -8

x 10

170

180 x [m]

190

200

Radargram

210

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Normalized modulus of χ

2

z [m]

3 4

t [s]

5 6

2 4 6

7

5

10

15

20

25

8

20 meter thick ice

Layering inside the ice

30 x [m]

35

40

45

50

9 10 0

10

20

30 x [m]

40

50

References [1] Hamran S.E. , Berger T., Hanssen L., Øyan M.J., Ciarletti V., Corbel C. , Plettemeier D., “A prototype for the WISDOM GPR on the ExoMars mission “, Proc. of IWAGPR2007, Napoli, June , (2007). [2] A. Brancaccio, G. Leone, F. Soldovieri, R. Pierri, “Localization of interfaces embedded in a half-space by a linear inverse scattering algorithm”, IEEE Transactions on Geoscience and Remote Sensing, Oct. 2007. [3] A. Brancaccio, G. Leone, F. Soldovieri, R. Pierri , “Subsurface localization of interfaces”, Proc. of IWAGPR2007, Napoli, Italy, June 2007. [4] G. Leone, G., F. Soldovieri., Analysis of the distorted Born approximation for subsurface reconstruction: truncation and uncertainties effect, IEEE Trans. Geoscience and Remote Sensing, 41, 66-74, (2003). [5] Pierri R., Liseno A., Solimene R., Soldovieri F., Beyond physical optics SVD shape reconstruction of metallic cylinders, IEEE Trans. Antennas and Propagation, vol. 54, 655-665, (2006). [6] Soldovieri F., Persico R., Leone G., Frequency diversity in a linear inversion algorithm for GPR prospecting , Subsurface Sensing Technologies and Applications Journal, Special Issue GPR2004, vol.6, 25-42, (2005). [7] Persico R., Soldovieri F., Reconstruction of a slab embedded in a three layered medium from multifrequency data under Born approximation, Journal of the Optical Society of America, Pt. A, vol 21, 35-45, (2004).

0.2

0.3

0.4

0.5

0.6

Parameter

Value

Relative dielectric permittivity of the soil

5.76

Conductivity of the soil

0.005 S/m

0.7

0.8

Spatial step of the measurements

0.1m

Measurement domain

5 m (51 points spaced by 0.1 m)

Frequency band

100-700 MHz

Frequency step

10 MHz (61 freq. exploited in the inversion)

Investigation domain

5 m (horizontal)x(0.1-6.1m) depth

0.9

55

THE NEW MARS CLIMATE DATABASE (version 4.2) E. Millour, F. Forget, F. González-Galindo, A. Spiga, S. Lebonnois, K. Dassas, Laboratoire de Météorologie Dynamique du CNRS, IPSL, France, S.R. Lewis, L. Montabone, Department of Physics and Astronomy, The Open University, UK, P.L. Read, Atmospheric, Oceanic & Planetary Physics, University of Oxford, UK, F. Lefèvre, F. Montmessin, Service d’Aéronomie, IPSL, France, M.A. López-Valverde, G. Gilli, Instituto de Astrofísica de Andalucía, Spain, F. Montmessin, F. Lefèvre, Service d’Aéronomie, CNRS, France, M.-C. Desjean, CNES, France, J.-P. Huot, European Space Research and Technology Centre, European Space Agency, Netherlands

What is the Mars Climate Database?

New features in version 4.2 of the MCD

• The Mars Climate Database (MCD) is a combination of datafiles and software containing many statistics and predictions of Martian environment. The database has been built from outputs of numerical simulations of Mars’ climate and atmospheric circulation using a General Circulation Model (GCM) developed at the Laboratoire de Métérologie Dynamique du CNRS (France) in collaboration with the Open University (UK), the Oxford University (UK) and the Instituto de Astrofisica de Andalucia (Spain) with support from the European Space Agency (ESA) and the Centre National d’Etudes Spatiales (CNES, France). • The database was originally developed for mission design (re-entry studies) but it is also a convenient tool for many other scientific studies such as modeling, data processing and interpretation, ...

Why a model-based climate database? • The Martian environment is highly variable. In spite of the new observations available from Mars Global Surveyor, Mars Express and now, Mars Reconnaissance Orbiter, it remains difficult to predict the climatic conditions on Mars at any time and any locations from available observational data. This is especially true for climate variables which are not directly observed (e.g.: winds). • Martian GCMs have been extensively validated using available observational data and we believe that they represent the current best knowledge of the state of the Martian atmosphere given the observations and the physical laws which govern the atmospheric environment and surface conditions on the planet.

• Improved access software. The main Fortran program to use to retrieve and process database files is now “call_mcd”; it includes all the features of its predecessor (seasonal interpolation, choice of multiple vertical coordinates, the possibility to specify input dates as Earth or Mars dates, etc…) and more: • RMS day to day standard deviations are now given pressure-wise (as in previous versions of the MCD) and altitude-wise. • A new “high resolution mode” has been implemented, which generalizes and extends the extraction of accurate surface pressure at a resolution of 1/32 of a degree.

The high resolution mode • The GCM horizontal longitude×latitude computational grid is 5.675°×3.75°. • By combining high resolution (32 pixels/degree) MOLA topography and Viking Lander 1 pressure records (used as a reference to correct the atmospheric mass) with GCM surface pressure, a corrected high resolution surface pressure may be derived (as is done in our “pres0” utility). • The high resolution surface pressure can be used to reconstruct the vertical atmospheric pressure distribution and, within the restriction of the procedure, yield high resolution values of atmospheric variables.

B Models can be used to extrapolate observations

What are the main features taken into account in this climate database? • The MCD includes 4 different dust scenarios in order to better represent the range of variability of the Martian atmosphere due to the amount and distribution of suspended dust. • The MCD extends into the thermosphere, up to ~350 km (and more), since the GCM it is derived from includes a thermosphere model above ~100 km. Data corresponding to 3 Extreme Ultra Violet (EUV) scenarios, which account for various states of the solar cycle (minimum, average and maximum), are thus supplied. • Much more than just the main meteorological variables are supplied, as the GCM includes a full water cycle model as well as a chemistry model.

Illustrative example: MCD data along Opportunity’s entry

Altitude (km)

MCD v.4.2 scenarios: Dust storm Warm (“dusty”) MY24 Cold (“clear”) Opportunity Entry profile (retrieved by Paul Withers)

LEFT: Comparison between Opportunity entry profile, retrieved by Paul Withers and mean MCD profiles obtained for various dust scenarios. Note that Mars Express and MGS measurements show that the atmosphere was then dustier than usual. LOWER LEFT: Same (MY24) temperature profile topped with the three Solar EUV inputs. BELOW: Some MCD (MY24) predictions of species Mixing Ratios along Opportunity’s entry trajectory.

Sections of atmospheric temperature above Valles Marineris, in the early afternoon of Northern Hemisphere Spring equinox, using MCD low (left) and high (right) resolution modes. Note that there is more than an order of magnitude between horizontal and vertical scales in these sections; what appear as sharp spikes are in fact much smoother, as plots using commensurate axes would show.

Accuracy of Mars Climate Database data The MCD has been validated using observational data from many available sources: Mars Global Surveyor (TES, Radio Science, accelerometer), Mars Express (SPICAM, PFS, OMEGA, MaRS), Viking Landers, Pathfinder, MER.

Example n°1: TES atmospheric temperatures

Temperature (K)

Altitude (km)

The Thermal Emission Spectrometer (TES) onboard Mars Global Surveyor has nearly continuously monitored the Martian Atmosphere for almost 3 Martian years, yielding detailed information on the local and seasonal evolution of atmospheric conditions on Mars. O3 Dust

Ice Cloud !

H2O Vapor

Atmospheric variations included in the MCD ‰ Year to year variability and dust content variations : Simulation of years with three different solar Extreme UltraViolet (EUV) inputs as well as with different dust content were done, corresponding to: ƒ A baseline scenario MY24 (Mars Year 24), based on assimilation of TES observations in 1999-2001. ƒ Two scenarios which bracket reality: a clear (cold) and a dusty (warm) one. ƒ A global dust storm scenario to represent conditions during such events. ‰ Seasonal cycle : In the MCD are stored 12 “typical” days (average over 30° of Ls) around the year. Values at a given date are obtained by interpolation. ‰ Diurnal cycle : Environmental data are stored 12 times per day; interpolation is used to evaluate values of variables at a given time of day. ‰ Day to day variability (e.g. representation of transient waves): Within a month, statistics of variations of meteorological variables are stored in the form of their standard deviations and EOF components.

• Left & middle plots: Distributions of binned (using 1K bins) temperature differences (at 106 Pa pressure level) between MCD MY24 predictions and TES (2pm or 2am) measurements over Mars Years 24 and 25 (up to Ls=180, i.e. before the global dust storm) and for latitudes ranging from 50°S to 50°N. Displayed MEAN and RMS values are computed from the obtained histograms and the curves correspond to normal distributions of same MEAN and RMS. • Right plot: Same distributions evaluated this time between different MCD scenarios (cold, baseline MY24 and warm).

Example n°2: Surface pressure at Viking Lander 2 site

Data that the v4.2 MCD provides ‰ Mean values of variables: (stored at 12 local times of a typical day for each of 12 months) • Atmospheric density, pressure, temperature and winds (horizontal and vertical), • Surface pressure and temperature, CO2 ice cover, • Atmospheric turbulent kinetic energy, • Thermal and solar radiative fluxes, • Dust column opacity and mass mixing ratio, • [H2O] vapor and [H2O] ice (columns and mixing ratios), • [CO], [O], [O2], [N2], [CO2], [H2] and [O3] volume mixing ratios, • Air specific heat capacity, viscosity and molecular gas constant R.

‰ Variability of meteorological variables: Various tools are provided to reconstruct variabilities Perturbations may be added as: • Large scale perturbations, using Empirical Orthogonal Functions (EOFs) derived from the GCM runs. • Small scale perturbations, by adding a gravity wave of user-defined wavelength. Standard deviations of main meteorological variables are given for: • Surface temperature, surface pressure, dust opacity. • Atmospheric density, pressure, temperature and winds. These RMS day to day variabilities are given both pressure-wise and altitude-wise.

Obtaining and using the database • For intensive and precise work: You will need the database DVD-ROM, which contains the data files (in NetCDF format) and access software (which does all the post-processing to include and account for sub-grid scales, day-to-day variations of the Martian atmosphere, etc…) as well as the lighter standalone high resolution surface pressure predictor “pres0”.

• Switching from the baseline MY24 scenario to the Dust Storm scenario enables to recover the change in behavior recorded by Viking Lander 2 during the 1977 global dust storm.

• Surface pressure cycle over a Martian year, as predicted by the MCD MY24 scenario at Viking Lander 2 site, with an envelope of twice its standard deviation, compared to the recorded values.

The online Mars Climate Database • For moderate needs: You should use the World Wide Web site: http://www-mars.lmd.jussieu.fr which gives access to: • All scenarios and variables. • A choice between 3 different vertical coordinates (pressure levels, altitude above areoid or above surface). • A wide range of output formats: Images (gif or postscript files), NetCDF data files, various formats of plain text files.

The software is written in Fortran 77; works on Unix and Linux and can be ported to Windows. IDL, Matlab, Scilab, C and C++ interfaces to the MCD are also provided.

• Computations of user defined variables (average, min or max values,…).

Contact [email protected] and/or [email protected] for a free copy.

• An Earth date to Mars date (value of solar longitude Ls) converter.

Planet Mars

Mars, the red planet, is the most Earth-like of all t he planets; it too has polar ice caps t hat grow and recede with t he change of the seasons. It also has markings t hat appear to be similar to water channels on Eart h. The Martian soil is composed mostly of clay rich in iron. T he oxidation of t he Martian soil causes the reddish coloration of the planet. The process described above is that of rusting. So Mars is like a car. When a car has been exposed to t he elements (rain, air, winter’s salty streets etc.) its body begins to rust, and that’s what happened to Mars. Imagine a rusty planet in our solar system! The temperature on Mars can be very, very cold. On one of its best summer days, temperatures can reach 20°C in certain areas; however, colder temperatures are more common with night -time temperatures of 140°C below zero.

How heavy would you be on Mars? If you weigh 34 kg (75 lb) you would weigh 12 kg (28 lb) on Mars. To calculate your weight, all you have to do is multiply your weight (kilos or pounds) by 0.38. How old would you be on Mars? If you are 10 years old t his year you would be 6 years old on Mars.

Quality Assessment of ExoMars PanCam 3D Reconstruction Gerhard Paar1, David P. Barnes3, Jürgen Oberst2, Andrew Griffiths4, Peter Rueffer7, Andrew J. Coates4, J.J.- Peter Muller4, Yang Gao5, Ralf Jaumann2, Ron Li6 1Institute

of Digital Image Processing, JOANNEUM RESEARCH (JR), Graz, Austria. [email protected] www.joanneum.at/dib Aerospace Center, Institute of Planetary Research,Rutherfordstr. 2, D-12489 Berlin, Germany. juergen.oberst/[email protected] www.dlr.de/berlin of Computer Science, University of Wales, Aberystwyth, SY23 3DB, UK. [email protected] www.aber.ac.uk/compsci/Research/robots Space Science Laboratory (MSSL), University College London, Holmbury St. Mary, Dorking, RH5 6 NT, UK. www.mssl.ucl.ac.uk/www_plasma 5SurreySpace Centre, University of Surrey, Guildford, GU2 7XH, UK. [email protected] www.ee.surrey.ac.uk/SSC/ 6Mapping and GIS Laboratory, CEEGS, The Ohio State University, 2070 Neil Avenue, Columbus, OH 43210-1275. [email protected] shoreline.ceegs.ohio-state.edu/ 7Technical Univ. Braunschweig, Hans-Sommer Str. 66, D-38106 Braunschweig. [email protected] http://www.ida.ing.tu-bs.de/ 2German

3Department 4Mullard

The Pasteur payload on the ESA ExoMars Rover 2013 is designed to search

Scope for evidence of extant or extinct life either on or down to ~2 m below the surface of Mars. It will be equipped with a panoramic imaging system (PanCam, [1]) for visual characterization of the rover’s surroundings and remote detection of potential sample sites. PanCam consists of two wide angle multispectral cameras each with a Wide Angle Camera (WAC), with a field-ofview (FOV) still under design, separated by 0.5 m stereo base length, and a mono-scopic camera (High Resolution Camera, HRC) currently designed to have an 8° FOV, both mounted on a shared pan-tilt unit (Fig. 1). PanCam is the primary context providing system. The quality of its data products is crucial for the scientific output of the mission. The current design is therefore undergone a review on data usability by evaluating the quality of the main results: a) 3D reconstruction and its representation means like digital terrain models (DTM) and virtual views (Fig. 2) b) Panorama Mosaics. Main influences on 3d reconstruction & panorama formation quality are: (1) design & Software parameters, such as stereo base length, Field-of-View, number of camera pixels, compression method & ratio, radiometric resolution, and the availability of different wavelengths (2) geometric & radiometric properties of the scene: Viewing direction incidence angle, presence of texture, occlusions, multiple elevations (Fig. 3), dynamic range and albedo variability are such factors. Most crucial for stereo-based 3D reconstruction is certainly the scene distance, which has quadratic reciprocal influence on reconstruction accuracy (Fig. 4).

Fig. 2: Virtual view of 11 MER PanCam stereo reconstruction fusion with bounding boxes of individual point clouds

ExoMars PanCam WAC Reconstruction Accuracy (distance) depending on Stereo Base and Distance (Matching accuracy: 0.3 pixel; 0.6 mrad / Pixel) Distance [mm] 1000

Distance Accuracy [mm]

Quality Drivers

Fig. 1: PanCam Layout

Stereo Base [mm]:

100

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7000

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7,2208884 16,195327 28,726077 44,79403 64,380215 87,465794 114,03207 177,53254 697,69186

500

2,9325072 6,5370281 11,577967 18,052221 25,956694

35,2883

46,043963 71,815191 285,08017

1000 1,5350349 3,3397521 5,8650144 9,1100421 13,074056 17,756279 23,155935 36,104442 143,63038

Fig. 3: Multiple Elevations from MER Stereo (exaggerated)

Fig. 4: Expected Stereo reconstruction accuracy dependent on camera base and scene distance

The PanCam Team has access to some experimental sites

Evaluation such as a Planetary Analogue Terrain (PAT) at the University Procedure of Wales (UWA, Fig. 5)), and a 1:10 scaled mockup at & Criteria Joanneum Research (JR, Fig. 6), with well-defined

geometrical objects. Using PanCam – analogue optics and viewing parameters (e.g. consumer cameras and re-sampling the images to their nominal resolution and FOV) realistic imaging conditions can be realized in a straightforward manner. The images are compressed-decompressed (Fig. 7) and processed using 3d stereo reconstruction software available within the Team [2]. Evaluation starts with a visual check of the results, to be categorized as usable - restricted usability - or unusable by experts. Other criteria are DTM noise determined on planar objects, edge sharpness on objects with known shape, and stereo matching success rate (i.e. scene coverage).

Image patch: 400 * 300 pixels

Fig. 5: ExoMars Concept-E model rover in PAT Laboratory at the University of Wales, Aberystwyth

Fig. 6: 1:10 terrain mockup and artificial objects at Joanneum Research, Graz. Camera – scene distance 2m simulated

Fig. 7: Compression effect: 8*3 bits/pixel (top) vs. 0.1*3 bits / pixel (bottom)

An example for effects caused by variations of crucial

Preliminary parameters such as viewing distance is displayed on Figure 9. Compression tests showed that a Results & Outlook compression ratio of below 1/8 is realistic for the

Fig. 8: 3D vision processing Draft: On-ground data production chain (top left), visualization and planning (bottom left), workshare within PanCam 3D vision Team (right)

standard use of stereo images in operations planning. Midterm tests contain a numeric evaluation of DTM accuracy, the usage of the UWA PAT as well as synthetic imagery (Fig. 10), and panorama mosaic resolution. Further tests will include the impact of toein angle on scene coverage, fusion between HRC and WAC, and the potential to combine images from different positions of the rover to one unique 3D reconstruction. An integrated processing & evaluation chain is under development by the PanCam 3D vision Team (Fig. 8), making intense use of Beagle 2 vision experience (Fig. 11).

50 cm

Simul. Dist. 2m

Simul. Dist. 5m

Simul. Dist. 10 m

Simul. Dist. 20 m

Fig. 9: Stereo reconstruction quality depending on distance between camera and scene. Images were taken on 1:10 mockup with simulated Beagle2 WAC optical parameters, stereo base 5 cm and distances 20cm, 50cm, 1m, 2m, respectively. Top: Stereo pairs. Bottom: Vrml views of reconstructed terrain using a triangle mesh generated directly from disparity images

References [1] Griffiths, A.D., Coates, A.J., Jaumann, R., Michaelis, H., Paar, G., Barnes, D.P., Josset, J.L. and the PanCam Team. Context for the ESA ExoMars rover: the Panoramic Camera (PanCam) instrument. International Journal of Astrobiology, doi:10.1017/S1473550406003387. [2] Paar, G., Griffiths, A.D., Barnes, D.P., Coates, A.J., and Bauer, A (2005). The Beagle 2 Camera Heritage for Pasteur. Geophysical Research Abstracts, Vol. 7, 06815. http://www.cosis.net/ abstracts/EGU05/06815/EGU05-J-06815.pdf

http://www.aiaa.org/spaceops2004archive/downloads/papers/SPACE2004sp-template00389F.pdf

Fig. 10: PANGU Mars landscape Simulation

Fig. 11: l: Operations planning using DTM embedded in robot arm CAD mode. r: Verification of operations plan on mockup (From Beagle2 Development)

Unraveling the Mysteries of Mars By Dr. Steven Lee

Figure 1

Figure 2

For as long as humans have looked up into the night sky, the planet Mars has sparked imaginations. For thousands of years, it was merely a bloodred star wandering across the heavens. In early cultures, it conjured up images of war and bloodshed. Egyptians named it Har décher, “the Red One.” The Babylonians called it Nergal, “the Star of Death.” The Greeks considered it Ares, “the Fiery One,” and to Romans it was the god of war, called Mars.

In 1610, Galileo Galilei turned his newly invented telescope on Mars, seeing only a tiny reddish disk. Christiaan Huygens sketched the first crude maps of the planet in 1659 (Figure 1), noting that the visible dark and bright markings drifted across the disk in about 24 hours, indicating the length of the Martian day was similar to Earth’s. By the 18th century, astronomers were routinely mapping clouds, polar caps, and surface markings that seemed to change with the seasons. Dark markings were assumed to be oceans and seas; similar assumptions were applied to features on the Moon. In 1877, Italian astronomer Giovanni Schiaparelli drew maps showing a global network of interconnected dark lines (Figure 2). His term canali—Italian for channels—was mistranslated into English as “canals,” leading to a “life on Mars” hypothesis that held sway for many decades. An annual “wave of darkening,” in which the dark surface features became larger, darker, and better defined in the spring and summer seasons, was assumed to result from the springtime growth of vegetation nourished by water collected from the retreating polar ice caps. The supposed canals were taken as evidence of an advanced Martian civilization’s global-scale engineering project, designed to carry the springtime melt from the polar caps to irrigate forests and fields in the temperate regions. Since the mid-1960s, observations by spacecraft have allowed Earthbound scientists to begin unraveling the mysteries of the Red Planet and to finally put many old myths to rest. In 1965, the Mariner 4 spacecraft zipped within 6,200 miles

Figure 3 THE RED ARROW IN THE MARINER 4 IMAGE (ABOVE) POINTS TO THE LOCATION OF THE MARS GLOBAL SURVEYOR IMAGE (RIGHT). THE 1999 IMAGE SHOWS FEATURES ONLY 10 FEET (3 METERS) ACROSS—AN IMPROVEMENT IN RESOLUTION OF ABOUT 400 TIMES.

(10,000 km) of Mars and radioed back a scant 22 images collected over half an hour. This small sampling revealed a lunarlike surface scarred with impact craters (Figure 3) and sheathed in a thin carbon dioxide atmosphere. Scientific interest in the geological and possible biological history of Mars waned—it was assumed Mars was as “dead” as the Moon. The planned missions to Mars continued, however, and in 1971 Mariner 9 became the first spacecraft to orbit another planet. Over the next year, 7,329 images allowed the entire surface to be mapped. To the delight of scientists, a host of fantastic landscapes was revealed. Mars became known as a world of great extremes. Its features include the largest known volcano in the solar system at about 360 miles across (600 km) and 15 miles high (25 km)—nearly three times taller than Mount Everest; a canyon system 2,500 miles long (4,000 km), hundreds of miles across, and up to 4 miles deep (7 km)—on Earth, it would span North America; and surfaces that range from those being slowly covered by dust to those whipped by intense dust storms (Figure 4, before and during a dust storm).

Figure 4 In 1976, the Viking missions (two orbiters as well as landers in two locations) yielded several years of both surface and global observations. The Viking landers carried out the first “biology experiments” on the surface. Although the somewhat ambiguous results are still debated, the scientific consensus is that the tests revealed exotic chemical processes

rather than biological activity. Recent years have seen ongoing exploration of Mars. In 1997, the Pathfinder mission delivered the first roving vehicle to the Martian surface, while the Mars Global Surveyor (arriving 1997) and Mars Odyssey (arriving 2001) missions are continuing the orbital reconnaissance with unprecedented spy-cameralike images of the surface (Figure 3). From the accumulated spacecraft observations, it has become obvious that Mars is a very dynamic world but one very different than previously imagined. At 4,222 miles (6,794 km) Mars is about half the diameter of Earth, and the surface gravity is 38 percent of Earth’s. The Martian day lasts 24 hours and 37 minutes (Huygens was close in his estimation!), but the year is 687 days long. The rotation axis is tilted 25.1 degrees (similar to Earth’s 23.5 degrees), but each Martian season is about twice as long as the terrestrial counterpart. The atmosphere is very different—composed of 95 percent carbon dioxide and with a surface pressure less than one percent that found at sea level on Earth (equivalent to flying in an aircraft at an altitude of about 80,000 feet). On the surface, the daytime high temperatures rarely climb above the freezing point of water but plunge to at least -220 degrees F (-140 degrees C) every night. In the winter, the polar regions become cold enough to freeze carbon dioxide out of the atmosphere causing dry ice “snow” to fall! Under current conditions, liquid water cannot exist for long on the surface and will either freeze or quickly evaporate. Only about a hair’s thickness of water vapor exists in the Martian atmosphere, but Hubble Space Telescope observations have revealed that is enough to form widespread bands of water-ice clouds at some times of the year (Figure 5). The Mars Global Surveyor and Mars Odyssey missions are busy revising many of the “common wisdom” aspects of Mars that were largely based on the Mariner and Viking results. Near-surface winds seem to be more effective than once thought, forming enormous sand dunes in many regions. Huge tornadolike dust

devils sweep dust from the surface to high in the atmosphere. Most intriguing is the evidence for recent gullies, perhaps carved by running water; to a geologist, “recent” could be 10,000 years ago or yesterday. Several hundred examples of these gullies have been discovered (Figure 6), supporting the idea that subsurface aquifers containing liquid water or brines may exist in many areas. The life-on-Mars pendulum is swinging into the maybe camp once again. Many of the missions planned for the coming decade will attempt to “follow the water” in search of environments that may have once supported—or may still support—microbial life. In early 2004, two Mars Exploration Rovers will act as robotic field geologists, roaming for several months and examining two separate landing sites. Arriving in 2006, the Mars Reconnaissance Orbiter will expand on the legacy of orbital exploration. Later in the decade, a “smart lander” may place one or more rovers within driving distance of a fresh gully, perhaps determining if near-surface deposits of water are present. There are plans for a mission—perhaps as early as 2013—that will return a few hundred grams of Martian soil and rock samples for detailed analyses in laboratories on Earth. At that point, the Golden Age of Mars exploration will be nearly complete and serious plans can begin for human missions. Stay tuned because the Red Planet is slowly but surely revealing its secrets, and many fantastic new discoveries are sure to come in the months and years ahead.

Figure 5

Dr. Steven Lee is DMNS curator of planetary science. He also serves as a coinvestigator on the Mars Color Imager, a camera system slated to be launched on the Mars Reconnaissance Orbiter in 2005.

Figure 6