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Seasonal thermal inertia variations at Gale crater: Role of active surface deposition phenomena
Icarus 337 (2020) 113499
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Seasonal thermal inertia variations at Gale crater: Role of active surface deposition phenomena Vidhya Ganesh Rangarajan a, b, *, Mili Ghosh b a b
Department of Earth Sciences, Indian Institute of Technology Kanpur, Kalyanpur, Kanpur 208016, U.P, India Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India
A R T I C L E I N F O
A B S T R A C T
Keywords: Mars Thermal inertia Gale crater MSL Curiosity Surface deposition phenomena
Thermal inertia has been found to play a significant role in planetary remote sensing applications, forming the basis of potentially all major lithological discriminations of the planetary surface. It has always been visualised to serve as a distinctive thermal property of the surface with its values remaining fairly constant at least for a short time period, unless the surface undergoes extensive and rapid degradation. Thermal inertia for Mars has been previously calculated from point spectrometer, orbiter and rover measurements and they have been successfully used for surface geologic interpretations. The present study computes surface thermal inertia over 12 sols along Curiosity’s traverse at Gale crater using Thermal Infrared Imaging System (THEMIS) and Rover Environmental Monitoring Station (REMS) observations and attempts to analyse the same on a seasonal timescale. We find that surface thermal inertia at Gale follows a seasonal sinusoidal behaviour with the surface exhibiting maximum and minimum thermal inertia values during southern autumn and spring respectively, the difference between them of the order of 300 to 400 t.i.u. We have attempted here to illustrate that the seasonal behaviour seen in surface thermal inertia may be the result of active surface deposition processes localised to Gale. In this regard, we have also explored the potential role of seasonal dust and water ice deposition in contributing to these observed variations, effectively concluding that seasonal dust deposition could be a highly likely cause to which these seasonal thermal inertia changes can be attributed to.
1. Introduction Thermal inertia (TI) is a measure of the ability of the subsurface to store heat during the day and re-radiate it during the night. It can be regarded as the degree of slowness with which the temperature of a body approaches that of its surroundings. This would control the amplitude of surface temperature variations and is closely related to the thermal conductivity of the surface material (Mellon et al., 2000; Christensen et al., 2001; Putzig and Mellon, 2007). Thermal inertia plays an important role in planetary remote sensing studies. For planetary sur faces, it plays a key role in controlling the diurnal and seasonal surface temperature variations and is generally dependent on the physical properties of near-surface materials (Jakosky, 1986; Rangarajan and Ghosh, 2019). The amplitude of the diurnal temperature curve roughly approximates the thermal inertia and it can be inferred that diurnal temperature changes are minimal in areas having high thermal inertia and vice-versa. Thermal inertia represents a complex combination of particle size, composition, rock abundance, bedrock outcropping and
degree of hardening (Christensen et al., 2001; Fergason et al., 2006; Rangarajan et al., 2018). Accurate surface thermal inertia estimation is highly necessary to understand the past and present geologic processes occurring on the surface as distinct identification of rocks could help us gain an insight into how they were formed and emplaced at the study site. Thermal inertia has been widely used as a key tool in discerning the lithology and mineral assemblages present on the Martian surface and would not generally change drastically over short time periods like a few years, provided the material has not undergone extensive degradation in that period. Hence, thermal inertia has played an extensive role in forming the basis for lithological discrimination of planetary surface materials. Thermophysical measurements and mapping of Mars using orbital data started right from the Mariner and the Mars missions in the early 1970s. Measurements with the 8 to 40 μm radiometers on Mars 3 and Mars 5 (Moroz and Ksanfomaliti, 1972; Ksanfomaliti and Moroz, 1975; Moroz, 1976) led to estimation of thermal properties which were well within the range as ascertained from the 8 to 12 μm and 18 to 24 μm
* Corresponding author at: Centre for Planetary Science and Exploration/Department of Earth Sciences, Western University, London, ON, Canada. E-mail address: [email protected] (V.G. Rangarajan). https://doi.org/10.1016/j.icarus.2019.113499 Received 13 February 2019; Received in revised form 9 October 2019; Accepted 17 October 2019 Available online 31 October 2019 0019-1035/© 2019 Elsevier Inc. All rights reserved.
V.G. Rangarajan and M. Ghosh
Icarus 337 (2020) 113499
measurements from Mariner 6, Mariner 7 and Mariner 9 (Neugebauer et al., 1971; Kieffer et al., 1973). The Viking IRTM data was among the first used to study thermophysical parameters on Mars in a detailed perspective. Using these observations, Kieffer et al. (1976) provided the first thermal mapping results of the Martian surface and atmosphere. They studied the diurnal variation of surface temperature in the 20 μm channel as it provided the best temperature resolution below 170 K. Kieffer et al. (1977) further extended their work and calculated thermal inertia over the Tharsis region. Their model however did not account for effects of atmospheric IR opacity and radiometric albedo on thermal inertia computation. Mellon et al. (2000) generated thermal inertia from the Thermal Emission Spectrometer data (Christensen et al., 1992) on board the Mars Global Surveyor on a global scale at 3 km spatial reso lution. This study was the first to derive thermal inertia from single night time temperature measurements. However, their method was also based on determining that value of thermal inertia that produced model temperatures that best correlated with observed temperature measure ments. A large number of diurnal temperature cycles from a numerical thermal model considering seven parameters including albedo, thermal inertia, surface pressure, dust opacity, latitude, time of day and season were computed to generate a lookup table. Each temperature observa tion was correlated with other data (latitude, albedo, time of day, etc.) and was interpolated through this lookup table to find the best fitting thermal inertia. This single point TI derivation model was later devel oped to facilitate high resolution, accurate TI mapping from Thermal Infrared Imaging System (THEMIS) data (Fergason et al., 2006; Piatek and Moersch, 2006) and has since then been widely used for TI deri vations (Chojnacki et al., 2010; Hughes, 2012; Valdeuaza, 2016). Thermal inertia over the Gale crater has also been studied using �mezCuriosity Rover Environmental Monitoring Station (REMS) (Go Elvira et al., 2014) observations. Hamilton et al. (2014) first analysed thermophysical properties of the crater rim from REMS data of Curiosity for the first 100 sols. Martínez et al. (2014) later developed a novel method to compute surface energy budget and thermal inertia from REMS data by fitting a 1D heat conduction equation to soil and thereby simulating and analysing ground heat storage. However, a comment on seasonal variability of these components and their effect on the near surface thermal environment was missing, essentially because their study was limited to observations up to Sol 139. Since most lithological
interpretations of the Martian surface are closely linked to surface thermal inertia values (Mellon et al., 2000; Christensen et al., 2003), it is critical for us to understand any variations in thermal inertia, if any as it could directly impact the confidence with which we interpret surface lithology. The present work, therefore, aims to assess the seasonal variability of thermal inertia over Gale crater and also lists two different potential causal mechanisms that we think could be contributing to seasonal variations as observed by our study at Gale. 2. Data used The area selected for our study is the Gale crater, a possible dry lake (Hurowitz et al., 2017) on the north-western part of the Aeolis Quad rangle (Fig. 1a). Since our aim is to study seasonal variations of surface energy fluxes and thereby thermal inertia, the choice of time of study is of great importance. The time of study is chosen based on two factors – solar longitude and the movement of the MSL Curiosity rover (Grot zinger et al., 2012). In order for the study to provide an efficient rep resentation of each season, the sols are so chosen so that they lie in the mid (second month) of each three-month season period wherein peak characteristics of each season are experienced. Moreover, more accurate measurements of environmental parameters are possible when the rover is stationary as the motion and instantaneous change in location of the rover within the sol would give reason for uncertainty in location of measurement. Hence, the drive log of the Curiosity rover is used to select sols wherein the rover is stationary. From the Curiosity drive log, the rover was found to be stationary between Sol 102–111 at Point Lake, Sol 133–297 at Yellowknife Bay, Sol 440–453 at Cooperstown and Sol 609–630 at Mt. Remarkable during the spring, summer, autumn and winter seasons respectively (Fig. 1b). Table 1 provides a detailed description of the twelve sols, three each from each season chosen for this study. The Earth date – Mars date conversions and solar longitude for the chosen sols of study were obtained from Mars Climate Database v5.3. The Mars-Sun distance for each of these days was obtained from Astropixels, a publicly available planetary ephemeris data repository (http://astropixels.com/ephemeris/ephemeris.html). The Rover Environmental Monitoring Station (REMS) suite onboard
Fig. 1. (a) Study Area (Gale Crater) (b) Curiosity traverse map showing the four selected study locations – Point Lake (PL), John Klein - Yellowknife Bay (YKB), Cooperstown (CT) and Mt. Remarkable (MR). 2
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Icarus 337 (2020) 113499
Table 1 Details of the sols chosen for study. Sol 108 110 112 234 251 270 440 441 443 610 620 631
Table 2 (a) Description of the various THEMIS scenes used to assess TI at Gale crater.
Earth Date
Solar Longitude Ls(� )
Solar Distance (A.U)
Season
23/11/2012 25/11/2012 27/11/2012 30/03/2013 15/04/2013 04/05/2013 21/10/2013 22/10/2013 24/10/2013 09/04/2014 19/04/2014 30/04/2014
212 213.4 214.3 291.7 301.3 312.4 38.5 39 39.9 113.9 118.5 123.8
1.40837 1.40674 1.40516 1.41024 1.42507 1.44552 1.63936 1.64006 1.64143 1.62179 1.61254 1.60153
Spring
Sl. No.
Summer
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Autumn Winter
the MSL Curiosity rover could be thought of a moving surface-weather station on Mars housing instruments to measure parameters such as temperature, humidity, pressure, wind speed and UV radiation. In all, REMS is made up of seven sensors namely the Wind Sensors (WS), Ground Temperature Sensor (GTS), Ultraviolet Sensor (UVS), Pressure Sensor (PS), Humidity Sensor (HS) and the Air Temperature Sensor (ATS) mounted on one or both the booms attached to the remote sensing �mez-Elvira et al., 2014). mast (Go For the present study, in order to enable Curiosity based thermal inertia computation, surface temperature measurements from the GTS, air temperature measurements (at 1.6 m above the surface) from the ATS and surface pressure observations from the PS were used. Wind speed measurements from WS were not available for all of the chosen sols. Therefore, to present the worst-case scenario, maximum and min imum wind speeds obtained during calibration of the sensor were used. The general nature of surface were interpreted on the basis of MAST CAM, Mars Hand Lens Imager (MAHLI) and/or NAVCAM images for the corresponding selected sols of study. All these datasets were obtained freely from the PDS Geosciences Node. Satellite based thermal inertia computations allow for possibilities of observing the same location over multiple solar longitudes unlike ob servations made from Curiosity where the focus of interest changes with respect to the motion of the rover. This makes orbiter-based observa tions much more viable for seasonal studies. The THEMIS sensor on board the Mars Odyssey (Christensen et al., 2004) has been providing high resolution (100 m) thermal data for Mars and has been effectively and widely utilised for assessing the lithological and thermophysical characteristics of the Martian surface. About 30 night-time (from 3 to 7 h LMST) THEMIS scenes across different seasons were utilised for assess ing the seasonal thermal inertia variations at Gale. It is to be noted that only night time IR datasets were used in this study in order to eliminate the effects of albedo and sun heated slopes that would lead to a much better thermal contrast due to particle sizes (Fergason et al., 2006). Band 9 measurements (12.57 μm) were found to have greater signal to noise ratio and were relatively transparent to atmospheric dust and thereby, only these measurements were utilised in this study. Elevation from the Mars Orbiter Laser Altimeter (MOLA) derived DEM further blended with High Resolution Stereo Camera (HRSC) observations (McCord et al., 2007) and global bolometric albedo maps generated from TES obser vations (Christensen et al., 2001) were also utilised as additional inputs to the thermal model in order to aid thermal inertia computation. About 20 THEMIS scenes for Nili Patera and 13 scenes for Eddie crater were also used to enable alternate studies. The details of the datasets utilised for Gale, Nili Patera and Eddie crater are provided in Table 2(a), Table 2 (b) and Table 2(c) respectively.
Dataset ID
Date of Acquisition (dd/mm/ yyyy)
Solar longitude (� )
Time of acquisition – LMST (hr)
Visible dust opacity (from MCD)
I01350002 I17950012 I18262008 I34571007 I34883005 I35195003 I48275006 I49174003 I49636006 I50098003 I50585006 I51072006 I51609005 I54144002 I54606005 I55530005 I57003016 I57340002 I57989004 I58301023 I59524022 I59836013 I60435002 I60722023 I61296002 I61321002 I61346002 I62631002 I66897002 I67745003
04/04/2002 31/12/2005 26/01/2006 29/09/2009 25/10/2009 20/11/2009 01/11/2012 14/01/2013 21/02/2013 31/03/2013 10/05/2013 19/06/2013 02/08/2013 27/02/2014 06/04/2014 21/06/2014 20/10/2014 17/11/2014 09/01/2015 04/02/2015 15/05/2015 10/06/2015 29/07/2015 22/08/2015 08/10/2015 10/10/2015 12/10/2015 26/01/2016 12/01/2017 23/03/2017
352.92 349.14 2.15 346.35 359.47 12.07 198.76 244.49 268.57 292.15 315.87 338.11 0.93 95.26 112.41 149.11 217.71 235.05 268.89 284.91 342.76 356.04 20.06 31.02 52.22 53.13 54.03 100.53 297.69 337.52
3.25 4.22 4.37 3.21 3.31 3.40 4.75 4.48 4.14 3.83 3.69 3.75 3.98 5.41 5.58 5.98 6.30 6.18 5.76 5.55 5.45 5.61 6.01 6.22 6.53 6.62 6.72 6.97 6.11 5.92
0.773 0.812 0.683 0.843 0.712 0.590 0.797 1.274 1.092 0.836 0.802 0.839 0.697 0.374 0.375 0.521 1.102 1.260 1.091 0.852 0.848 0.746 0.540 0.485 0.406 0.405 0.405 0.371 0.827 0.841
Table 2 (b) Description of the various THEMIS scenes used to assess TI at Nili Patera. Sl. No.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Dataset ID
Date of Acquisition (dd/mm/ yyyy)
Solar longitude (� )
Time of acquisition – LMST (hr)
Visible dust opacity (from MCD)
I01627002 I01989006 I05347011 I06458016 I06845008 I07956012 I08293012 I09903015 I12062002 I13884004 I14196002 I17266012 I17578021 I27025013 I27312009 I27599005 I33600012 I34199006 I36882009 I54521008
27/04/2002 27/05/2002 27/02/2003 30/05/2003 01/07/2003 30/09/2003 28/10/2003 08/03/2004 02/09/2004 30/01/2005 25/02/2005 05/11/2005 30/11/2005 17/01/2008 10/02/2008 04/03/2008 11/07/2009 30/08/2009 08/04/2010 30/03/2014
4.35 18.71 144.26 193.99 213.1 270.65 287.89 1.54 82.19 358.93 165.89 318.56 332.88 18.81 29.83 40.56 301.66 329.94 74.64 109.21
3.33 3.57 5.03 5.39 5.40 4.69 4.39 4.24 5.11 5.50 5.57 4.15 4.15 4.73 4.89 5.00 3.24 3.11 3.92 5.49
0.399 0.311 0.297 0.408 0.565 0.601 0.509 0.409 0.241 0.316 0.349 0.493 0.527 0.311 0.280 0.252 0.488 0.522 0.240 0.259
suitable for this study owing to their greater temporal coverage coupled with high temporal resolution. Before utilizing the same to analyse seasonal thermal inertia variations, THEMIS derived thermal inertia were compared with high resolution thermal inertia derived using a combination of Curiosity REMS observations and a 1D heat conduction model for accuracy assessment.
3. Methodology The attempt of this work is to primarily estimate and analyse surface thermal inertia at locations along Curiosity’s traverse on a seasonal timescale from THEMIS images. THEMIS datasets are particularly 3
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Icarus 337 (2020) 113499
Table 2 (c) Description of the various THEMIS scenes used to assess TI at Eddie crater. Sl. No.
1 2 3 4 5 6 7 8 9 10 11 12 13
Dataset ID
Date of Acquisition (dd/mm/ yyyy)
Solar longitude (� )
Time of acquisition – LMST (hr)
Visible dust opacity (from MCD)
I05257013 I06730010 I07479026 I07841014 I08203013 I26074026 I28682005 I28969005 I32699002 I35906009 I37104006 I54381009 I61171007
20/02/2003 21/06/2003 22/08/2003 21/09/2003 20/10/2003 31/10/2007 01/06/2008 25/06/2008 28/04/2009 17/01/2010 26/04/2010 18/03/2014 28/09/2015
140.56 207.34 245.78 264.67 283.33 339.63 79.78 90.16 255.68 39.24 82.63 103.99 47.67
4.99 5.29 5.09 4.83 4.48 4.33 5.43 5.52 4.04 3.51 3.95 5.45 6.46
0.332 0.741 1.051 0.911 0.722 0.673 0.302 0.305 1.044 0.353 0.311 0.291 0.339
where H is the sensible heat flux, ρa is the atmospheric density computed from the surface pressure, Cp is the specific heat of CO2 at constant pressure (�736 J kg 1 K 1), Tg is the GTS recorded surface temperature, Ta is the ATS measured atmospheric temperature, K is the von Karman constant (¼0.4), Za is the height at which atmospheric temperature and wind speed ‘u’ are recorded (¼1.6 m), Z0 is the surface roughness and f (Rb) is a function of Bulk Richardson number Rb which is used to incorporate the effect of wind turbulence into the system (Sutton et al., 1978). The surface roughness length was assumed to vary from 0.5 to 1.5 cm based on TES measurements at Gale by H�ebrard et al. (2012). Maximum and minimum values of the sensible heat flux were obtained when the absolute difference of ground and air temperatures, surface roughness and wind speed were maximum and minimum respectively (Martínez et al., 2014; Rangarajan and Ghosh, 2018). A simple rearrangement of the surface energy budget equation (Eq. (1)) resulted in ground heat flux calculation (G) after all other compo nents were derived. Martínez et al. (2014) also provide a novel method to compute ground heat storage by analytical means (G*) by solving the 1D heat conduction equation applied to the soil. Their solution (Eq. (4)) indicates that G* can be reduced to a function of thermal inertia (I) for reasonable values of surface density (ρ), soil specific heat (Cp), depth of topmost soil layer of the numerical model (δ) and temperature Td at depth z ¼ zd where subsurface temperature may be considered to be invariant. � � λ ∂Tðz;tÞ �z¼zd 2 G* ¼ ’ ρICp Tðδ; tÞ Tð0; tÞ ∂z z¼0 � (4) δ
3.1. Estimation of thermal inertia from curiosity measurements The surface energy budget (Eq. (1)) is governed by the law of con servation of energy and has been used as an effective tool to characterise near surface thermal environments (Bonan, 2002). ð1
AÞS↓ þ L↓ ¼ L↑ þ H þ λE þ G
(1)
where A is the albedo of the surface, S↓ is the downwelling short-wave radiation, L↓ is the downwelling longwave radiation, L↑ is the upwelling longwave radiation, H is the sensible heat flux, λE is the latent heat flux and G is the heat exchange by conduction into ground. The terms on the LHS correspond to the forcing terms and those on the RHS correspond to the response terms of the radiative transfer. The availability of high resolution measurements of surface and air temperature, wind speed and surface pressure at Gale from the Curiosity REMS instrument has enabled us to accurately compute various surface energy budget components and by extension, surface thermal inertia (Martínez et al., 2014; Rangarajan and Ghosh, 2018). The estimation of downwelling shortwave radiation was performed using a comprehensive radiative transfer model developed by Haberle et al. (1993) for which the diurnal values of atmospheric dust opacity for the twelve sols under consideration were retrieved from the Mars Climate Database v5.2 (Madeleine et al., 2011). The albedo was varied from 0.20 to 0.25 in the model that proved to be a satisfactory approximation to the expected albedo values at Gale based on TES al bedo values (Pelkey and Jakosky, 2002). The magnitudes of down welling longwave radiations, however, were directly retrieved from the Mars Climate Database v5.2 for this study as they were found to be at par with previously modelled values by Martínez et al. (2014), with errors <5%. The magnitude of longwave fluxes emitted by the surface to the at mosphere can be quantified using the Stefan-Boltzmann equation (Boltzmann, 1884) (Eq. (2)) utilizing the GTS ground temperature measurements. L↑ ¼ εσ T4g
Based on the approximations of Martínez et al. (2014), volumetric heat capacity values (ρCp) between 0.8 and 1.7 � 106J m 3 K 1 with greater values for surfaces with greater rock density and a depth of 10 cm for zd were adopted. The values of Td, on the other hand, were analysed from hourly GTS measurements and their standard deviations, such that Td was higher than the minimum ground daily temperature to ensure upward heat flux from deep soil but also slightly lower than daily average ground temperature in order to provide the most accurate so €rvi, 1995; lution to the heat conduction equation at diurnal scales (Savija €rvi and Ma €a €tta €nen, 2010; Martínez et al., 2014). Savija With above approximations for ρCp, zd and Td, G* was reduced to a function of I. A direct comparison between the diurnal variations of G* and G provided means to analyse the thermal inertia of the surface as the former depends on a thermal inertia parameter while the latter doesn’t (Martínez et al., 2014). 3.2. Estimation of thermal inertia from THEMIS observations THEMIS RDR data were acquired from the THEMIS Image Explorer repository and were initially pre-processed using the THMPROC module developed by ASU. They were then converted into brightness tempera tures from Planck’s black body radiation curves. Surface thermal inertia estimations were then performed using a 7D lookup table approach based on the thermal model developed by Mellon et al. (2000) using the jENVI package in ENVI (Piatek and Moersch, 2006). The lookup table contained information on albedo, thermal inertia, surface pressure, dust opacity, latitude, longitude and time of the day for single point thermal inertia computations. Since this study is conducted on a seasonal basis, it is important to also account for any variations in atmospheric dust opacity anticipated throughout the course of different solar longitudes, particularly due to occurrence of both global and localised dust storms during the southern spring and summer. Therefore, an additional input of the atmospheric dust opacity at the time of observation of the THEMIS scene was retrieved from the Mars Climate Database Global Circulation Model and incorporated into the thermal model before surface thermal
(2)
where L↑ is the upwelling longwave radiation (W/m2), ε is the surface emissivity, σ is the Stefan-Boltzmann constant (¼5.67 � 10 8 W m 2 K 4) and Tg is the GTS ground temperature (K). The sensible heat flux is directly proportional to the temperature difference between the surface and surrounding air and inversely pro portional to a transfer resistance and can be calculated using Eq. (3). � Tg Ta � � (3) H ¼ K2 Cp u ρa fðRb Þ ln2 zz0a 4
V.G. Rangarajan and M. Ghosh
Icarus 337 (2020) 113499
inertia computation. MOLA-HRSC blended elevation and TES albedo layers were also served as additional inputs into the model. These input parameters are used to compute model based surface temperatures for specific observation scenarios and thermal inertia ranges leading to a table of model derived surface temperature values as a function of thermal inertia. The computed brightness temperatures for each THE MIS scene are then interpolated with respect to the generated table of surface temperatures and appropriate thermal inertia values are assigned.
spread across different seasons using the relation developed by Presley and Christensen (1997) between thermal conductivity and particle size. Soil classification was done based on the Indian Standard Soil Classifi cation scheme (ISSCS) (IS 1498:1970). Fig. 3 shows the particle size distribution maps prepared from the selected THEMIS observations. Previous studies by Fergason (2013), Putzig and Mellon (2007) suggest that variations in thermal inertia observations from THEMIS possibly emerge due to subsurface layering or even the incapability of the ther mal model itself to accurately represent the true nature of the seasonal radiative interaction between the surface and the atmosphere. However, if the latter were to be true, such variations should be expected at other locations on Mars as well. To test this possibility, we took up a case study comparison with two other points near the Nili Patera caldera in the Syrtis Major volcanic construct, one known to be free from major dust activity and hence little or no chances of potential layering or dust deposition phenomena (Lillis et al., 2015; Rangarajan et al., 2018). The lithological discrimination of the Nili Patera caldera indicates presence of a north western high TI unit in the interior with lower thermal inertia values on the outer flanks (Fergason et al., 2006). Two representative points, one on each unit were taken and their seasonal TI observations were plotted (Fig. 4). However, no such stark variation as observed in Gale was seen. In fact, the surface thermal inertia values remained pretty much the same, irrespective of the solar longitude of observation. An alternative study was also conducted in a region near the crater peak of the Eddie crater in the Elysium construct. This region was particularly chosen due to its high similarity with respect to Gale in terms of its near-equatorial location and similar surface albedo values (MGS TES bolometric albedos roughly ranging from 0.23 to 0.25 for both the chosen areas), possibly indicating that the two study sites might possess similar dust cover. A seasonal study of surface thermal inertia over the Eddie region, interestingly, also showed very little or no vari ation across different seasons (Fig. 5). This emphasises the notion that the variation observed at Gale might not just be a renderance of possible ambiguity in the thermal model description alone, but could point towards some potential surface pro cesses going on in the crater that might be causing these seasonal changes. The prospect of seasonal dust deposition and removal at Gale and/or possible deposition of microscopic liquid water ice or volatiles could seem likely. The role of both these processes are discussed in further sections.
4. Results 4.1. Accuracy assessment of the THEMIS thermal model The constant motion of the Curiosity rover does not allow for single point seasonal surface thermal inertia estimations. Alternatively, THE MIS orbiter thermal inertia observations acquired at different times of the year could serve as a reasonable substitute despite coarseness in spatial context of observation. It, however, is highly necessary to first assess how accurate the THEMIS thermal inertia estimations derived by our thermal model are, before relying on them to reciprocate seasonal behaviour of the surface. Table 3 shows a comparison between Curiosity derived TI and esti mated TI from THEMIS observations at similar solar longitudes from our model computations. It is seen that the THEMIS TI values are in very good agreement with Curiosity derived estimations with errors just about 10%. These values are also in very good concordance with pre vious estimations made by Martínez et al. (2014) and Vasavada et al. (2017) at Gale with errors less than around 10%. Hence, it can be safe to conclude that the thermal model could be used for accurate seasonal TI retrievals. 4.2. Seasonal behaviour of thermal inertia Thermal inertia is often regarded as a distinctive property of the surface and is expected to be constant at least for short periods of time like a year or two as not much change in the surface composition is expected. Night time thermal inertia estimations from about 25 THEMIS scenes were utilised to plot the seasonal behaviour at each of the four selected locations along Curiosity’s traverse. The seasonal thermal inertia plots for Point Lake, Yellowknife Bay, Cooperstown and Mt. Remarkable respectively are shown in Fig. 2. Contrary to the nearly constant value expected, surface thermal inertia observations show a distinct sinusoidal variation with the surfaces dis playing highest thermal inertia during the late southern autumn or early winter boundary, around Ls ¼ 80 to 90 and the least TI value during late spring (Around Ls ¼ 250 to 270) near the perihelion of the Martian orbit. The observed variations are quite large, of the order of almost 300 t.i. units. We also estimated particle sizes from five THEMIS TI images
5. Discussions We observed a distinctive seasonal sinusoidal trend in surface ther mal inertia values at four points along Curiosity’s traverse from THEMIS derived thermal inertia of the order of around 300 to 400 t.i.u, with the surface exhibiting greatest thermal inertia during the late southern autumn/ early winter and the lowest thermal inertia during late spring
Table 3 Comparison of Curiosity and THEMIS derived TI measurements based on our computations. Location
Point Lake Yellowknife Bay Cooperstown Mt. Remarkable
Curiosity
THEMIS
Sol
Ls (� )
Thermal Inertia (t.i.u)
108 110 112 234 251 270 440 441 443 610 620 631
212 213.4 214.3 291.7 301.3 312.4 38.5 39 39.9 113.9 118.5 123.8
285 280 285 460 470 495 455 395 395 285 275 290
Error in estimation (%)
Average Thermal Inertia (t.i.u)
ID
Ls (� )
Thermal Inertia (t.i.u)
283.33
I57003016
217.7
299.28
5.62
475
I66897002
297.7
463.55
2.41
415
I61296002
52.2
450.57
8.57
283.33
I55530005
149.1
314.75
11.08
5
V.G. Rangarajan and M. Ghosh
Icarus 337 (2020) 113499
Fig. 2. Seasonal THEMIS TI variations at (a) Point Lake (b) John Klein, Yellowknife Bay (c) Cooperstown (d) Mt. Remarkable.
Fig. 3. Particle size distribution maps derived from selected THEMIS thermal inertia images.
6
V.G. Rangarajan and M. Ghosh
Icarus 337 (2020) 113499
Fig. 4. (a) THEMIS Day Time IR mosaic of the Nili Patera caldera (b) Seasonal TI variation at point A (c) Seasonal TI variation at point B.
Thermal Iner!a (t.i.u)
Loca!on A 800 700 600 500 400 300 200 100 0
R² = 0.6989
0
40
80
120
160
200
240
280
320
360
Solar Longitude (°)
Thermal Iner!a (t.i.u)
Loca!on B 800 700 600 500 400 300 200 100 0
R² = 0.6237
0
40
80
120
160
200
240
280
320
360
Solar Longitude (°)
Fig. 5. (a) THEMIS Day Time IR mosaic of Eddie crater (b) Seasonal TI variation at location A (c) Seasonal TI variation at location B.
very close to Mars’s perihelion (Fig. 2). Such changes in thermal inertia had previously attributed to inefficiency of the 1D thermal model being used to estimate surface thermal inertia from orbiter observations (Fergason, 2013). However, a case study over the Nili Patera caldera in the Syrtis Major region revealed no significant seasonal change in
orbiter thermal inertia (Fig. 4) which indicated the variations at Gale might as well be a result of some localised surface process that needs to be understood further. Two potential causes for the observed seasonal TI trend were hypothesized: (i) seasonal dust deposition (ii) seasonal water ice deposition. 7
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5.1. Seasonal dust deposition
5.2. Seasonal water-ice deposition
It is seen from Fig. 2 that the periods where maximum thermal inertia is displayed by the surface coincides with the seasons least affected by local or global dust storm activity i.e. autumn/ winter. It may be possible that during the dust storm season (i.e. southern spring and summer), a thin layer of dust is deposited on the surface thereby obscuring and covering the bed rock lying underneath. Dust or any other fine grained soil matter do not have as much capability as large hard rocks to store heat, thereby reducing the thermal inertia of the top layer of the surface. As heavy gusts of winds continue to blow through the end of summer into autumn, the deposited dust is slowly removed by the wind and the underlying bedrock is exposed, thereby increasing the observed thermal inertia of the top surface layer. The fact that thermal inertia variations are not seen in a practically dust free environment like the Syrtis Major construct (Fig. 4) could also support this idea. There have also been observations from the Mars Hand Lens Imager (MAHLI) of seasonal dust deposition activity from aeolian suspension on the sensors of the Curiosity instrument (Fig. 6) as well as on natural surfaces along Curiosity’s traverse (Edgett and Newsom, 2018). A first impression of identifying such dust deposition would suggest that we look into surface albedo variations. However, albedo derived from visible imagery, especially in the southern summer and spring might not be that pristine, majorly due to the thick atmospheric dust cover as reflected by the high dust opacity observations in these two seasons. We therefore resorted to estimating particle sizes from five THEMIS TI images spread across different seasons (Fig. 3). The maps show the maximum % cover of fine grained soil in I49174003 (Ls ¼ 244.49� ) which gradually reduces until it becomes almost negli gible in I54144002 (Ls ¼ 95.26� ) with the surface being covered with coarser soil grains which could further strengthen the possibility of occurrence of this seasonal dust deposition phenomenon. The interesting observation of hardly any thermal inertia variation on a seasonal scale in a region south-east of the Eddie crater peak region (Fig. 5), which presumably has a similar dust cover and location (nearequatorial) as that of Gale could possibly indicate that this seasonal deposition phenomena is not seen in all dusty equatorial craters. Consequently, the localisation of this process to Gale needs to be further studied and understood in greater detail.
Another potential cause for these seasonal thermal inertia variations could be the possibility of seasonal water ice deposition on the surface. It has been known that in autumn/ winter, at higher latitudes in Mars, water condenses from the atmosphere to the surface as frost. Subse quently, in the spring/summer seasons, this frost sublimates back into the atmosphere (Audouard et al., 2014). The idea of changes in surface moisture due to potential seasonal water ice deposition being a potential cause is drawn from the fact that as the surface/ soil becomes moist, the water molecules trapped in between the various soil particles (either physically or chemically bonded) tend to absorb a larger quantity of heat and thereby tend to slow down the process of heat getting re-radiated back to the atmosphere. This could tend to increase the surface ther mal inertia values when moisture content is higher. Higher TI values for wet soils have also been observed in previous studies (Matsushima et al., �l and Kereszturi (2017) and Martínez et al. (2016) 2012). Studies by Pa suggest the possibility of frost formation on the Martian surface. Fig. 7 describes the plot of relative humidity for the first 1000 sols of Curiosity which clearly indicates a distinct seasonal variation very much similar to the variation of surface thermal inertia we have observed at Gale (Fig. 2). The possibility of water ice deposition actually causing observed seasonal TI variations is very much likely too as temperatures necessary for deliquescence do exist at Gale (Martínez et al., 2016). However, it could always be helpful if any potential alteration in surface chemical composition albeit to a very minor extent due to the seasonal water ice deposition can be identified by hyperspectral imagery such as CRISM/ OMEGA that could give this possibility significant noteworthiness and strength. 6. Conclusions Seasonal changes in thermal inertia could greatly lead to ambiguous geologic interpretations of the properties of the surface. There always lies a possibility of incorrect lithological and mineralogical discrimina tions if thermal inertia values are being utilised randomly without giving much importance to the time/ season in which the datasets were ac quired. Based on our results, we conclude that there may be some active localised surface deposition phenomena, mostly dominated by dust occurring at Gale that could be causing such large surface thermal inertia variations, as observed in our study. However, despite significant evidence of potential seasonal dust deposition activity, there are still
Fig. 6. MAHLI images of the UV sensor acquired on (a) Sol 36 (b) Sol 1314 (Source: PDS Geosciences Node). 8
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Fig. 7. Seasonal Relative Humidity variations at Gale for the first 1000 sols (Martínez et al., 2016).
some glaring questions to be answered, particularly regarding the thickness of dust cover necessary in order to facilitate such large thermal inertia variations as observed at Gale. Alternatively, the possibility of seasonal water ice deposition which could affect the surface moisture content and hence the surface thermal inertia was also investigated. Recent studies also point towards the possibility of occurrence of microscopic liquid water and likely frost events at higher latitudes. There have been several questions regarding possibility of such events at equatorial latitudes. However, the way in which seasonal variations in relative humidity observations (Fig. 7) show exceptional similarity to the nature of seasonal thermal inertia variations observed at Gale (Fig. 2) could also point towards its potential role in causing such changes. One important conclusion that can be derived from the comparative analyses at Eddie is that such seasonal thermal inertia variations do not occur at all Gale-like sites. Although Eddie is similar to Gale in terms of its near-equatorial location and similar average dust cover, its smaller diameter and crater depth may be altering the nature of how local wind and dust storm patterns operate as compared to how they occur at Gale, which could be key to understanding why we don’t see traces of dust deposition activity at Eddie. The lack of high resolution observations of relative humidity or water/ice opacity at Eddie as we have for Gale further makes it difficult to ascertain the presence of any water ice deposition activity there. If however there is water ice deposition at Eddie, it is most likely that it may not be strong enough to cause an appreciable change in surface thermal inertia. It might also be noteworthy to also identify other sites along the equatorial belt in the future where such stark variations in thermal inertia is seen and assess the degree of impact that this phenomena might actually have in affecting interpretations of existing lithological maps of different units on Mars.
surface energy budget components. The authors also thank Dr. Fred Espanek for providing with the yearly planetary ephemeris data for Mars in the Astropixels website. The authors acknowledge the support by the Indian Space Research Organization, Department of Space through the scheme ISRO/SSPO/MOM-AO/2016-17 and grant number: 19013/28/ 2016-Sec.2 to conduct this research. Immense help provided by Dr. German Martínez and Dr. Jennifer Piatek during the course of this work is also highly acknowledged. The authors also wish to thank the editors and the two reviewers for their insightful reviews that have greatly helped restructure the manuscript. References Audouard, J., Poulet, F., Vincendon, M., Milliken, R.E., Jouglet, D., Bibring, J.-P., Gondet, B., Langevin, Y., 2014. Water in the Martian regolith from OMEGA/Mars express: Mars 3 μm absorption and water mobility. Journal of Geophysical Research: Planets 119, 1969–1989. https://doi.org/10.1002/2014JE004649. Boltzmann, L., 1884. Ableitung des Stefan’schen Gesetzes, betreffend die Abh€ angigkeit der W€ armestrahlung von der Temperatur aus der electromagnetischen Lichttheorie. Ann. Phys. 258, 291–294. https://doi.org/10.1002/andp.18842580616. Bonan, G., 2002. Surface Energy Fluxes, Ecological Climatology: Concepts and Applications, First edition. Cambridge University Press, pp. 209–247. Chojnacki, M., Moersch, J.E., Burr, D.M., 2010. Climbing and falling dunes in Valles Marineris, Mars. Geophys. Res. Lett. 37 https://doi.org/10.1029/2009GL042263. Christensen, P.R., Anderson, D.L., Chase, S.C., Clark, R.N., Kieffer, H.H., Malin, M.C., Pearl, J.C., Carpenter, J., Bandiera, N., Brown, F.G., Silverman, S., 1992. Thermal emission spectrometer experiment: Mars observer mission. J. Geophys. Res. 97, 7719. https://doi.org/10.1029/92JE00453. 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, R.L., Jakosky, B.M., Mellon, M.T., Pearl, J.C., Conrath, B.J., Smith, M.D., Clancy, R.T., Kuzmin, R.O., Roush, T., Mehall, G.L., Gorelick, N., Bender, K., Murray, K., Dason, S., Greene, E., Silverman, S., Greenfield, M., 2001. Mars global surveyor thermal emission spectrometer experiment: investigation description and surface science results. Journal of Geophysical Research: Planets 106, 23823–23871. https://doi.org/ 10.1029/2000JE001370. Christensen, P.R., Bandfield, J.L., Bell, J.F.I.I.I., Gorelick, N., Hamilton, V.E., Ivanov, A., Jakosky, B.M., Kieffer, H.H., Lane, M.D., Malin, M.C., McConnochie, T., McEwen, A. S., McSween Jr., H.Y., Mehall, G.L., Moersch, J.E., Nealson, K.H., Rice Jr., J.W., Richardson, M.I., Ruff, S.W., Smith, M.D., Titus, T.N., Wyatt, M.B., 2003. Morphology and composition of the surface of Mars: Mars odyssey THEMIS results. Science 300, 2056–2061. https://doi.org/10.1126/science.1080885. Christensen, P.R., Jakosky, B.M., Kieffer, H.H., Malin, M.C., Jr, H.Y.M., Nealson, K., Mehall, G.L., Silverman, S.H., Ferry, S., Caplinger, M., Ravine, M., 2001. In: Russell, C.T. (Ed.), The thermal emission imaging system (Themis) for the Mars 2001
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Seasonal thermal inertia variations at Gale crater: Role of active surface deposition phenomena Vidhya Ganesh Rangarajan a, b, *, Mili Ghosh b a b
Department of Earth Sciences, Indian Institute of Technology Kanpur, Kalyanpur, Kanpur 208016, U.P, India Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India
A R T I C L E I N F O
A B S T R A C T
Keywords: Mars Thermal inertia Gale crater MSL Curiosity Surface deposition phenomena
Thermal inertia has been found to play a significant role in planetary remote sensing applications, forming the basis of potentially all major lithological discriminations of the planetary surface. It has always been visualised to serve as a distinctive thermal property of the surface with its values remaining fairly constant at least for a short time period, unless the surface undergoes extensive and rapid degradation. Thermal inertia for Mars has been previously calculated from point spectrometer, orbiter and rover measurements and they have been successfully used for surface geologic interpretations. The present study computes surface thermal inertia over 12 sols along Curiosity’s traverse at Gale crater using Thermal Infrared Imaging System (THEMIS) and Rover Environmental Monitoring Station (REMS) observations and attempts to analyse the same on a seasonal timescale. We find that surface thermal inertia at Gale follows a seasonal sinusoidal behaviour with the surface exhibiting maximum and minimum thermal inertia values during southern autumn and spring respectively, the difference between them of the order of 300 to 400 t.i.u. We have attempted here to illustrate that the seasonal behaviour seen in surface thermal inertia may be the result of active surface deposition processes localised to Gale. In this regard, we have also explored the potential role of seasonal dust and water ice deposition in contributing to these observed variations, effectively concluding that seasonal dust deposition could be a highly likely cause to which these seasonal thermal inertia changes can be attributed to.
1. Introduction Thermal inertia (TI) is a measure of the ability of the subsurface to store heat during the day and re-radiate it during the night. It can be regarded as the degree of slowness with which the temperature of a body approaches that of its surroundings. This would control the amplitude of surface temperature variations and is closely related to the thermal conductivity of the surface material (Mellon et al., 2000; Christensen et al., 2001; Putzig and Mellon, 2007). Thermal inertia plays an important role in planetary remote sensing studies. For planetary sur faces, it plays a key role in controlling the diurnal and seasonal surface temperature variations and is generally dependent on the physical properties of near-surface materials (Jakosky, 1986; Rangarajan and Ghosh, 2019). The amplitude of the diurnal temperature curve roughly approximates the thermal inertia and it can be inferred that diurnal temperature changes are minimal in areas having high thermal inertia and vice-versa. Thermal inertia represents a complex combination of particle size, composition, rock abundance, bedrock outcropping and
degree of hardening (Christensen et al., 2001; Fergason et al., 2006; Rangarajan et al., 2018). Accurate surface thermal inertia estimation is highly necessary to understand the past and present geologic processes occurring on the surface as distinct identification of rocks could help us gain an insight into how they were formed and emplaced at the study site. Thermal inertia has been widely used as a key tool in discerning the lithology and mineral assemblages present on the Martian surface and would not generally change drastically over short time periods like a few years, provided the material has not undergone extensive degradation in that period. Hence, thermal inertia has played an extensive role in forming the basis for lithological discrimination of planetary surface materials. Thermophysical measurements and mapping of Mars using orbital data started right from the Mariner and the Mars missions in the early 1970s. Measurements with the 8 to 40 μm radiometers on Mars 3 and Mars 5 (Moroz and Ksanfomaliti, 1972; Ksanfomaliti and Moroz, 1975; Moroz, 1976) led to estimation of thermal properties which were well within the range as ascertained from the 8 to 12 μm and 18 to 24 μm
* Corresponding author at: Centre for Planetary Science and Exploration/Department of Earth Sciences, Western University, London, ON, Canada. E-mail address: [email protected] (V.G. Rangarajan). https://doi.org/10.1016/j.icarus.2019.113499 Received 13 February 2019; Received in revised form 9 October 2019; Accepted 17 October 2019 Available online 31 October 2019 0019-1035/© 2019 Elsevier Inc. All rights reserved.
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measurements from Mariner 6, Mariner 7 and Mariner 9 (Neugebauer et al., 1971; Kieffer et al., 1973). The Viking IRTM data was among the first used to study thermophysical parameters on Mars in a detailed perspective. Using these observations, Kieffer et al. (1976) provided the first thermal mapping results of the Martian surface and atmosphere. They studied the diurnal variation of surface temperature in the 20 μm channel as it provided the best temperature resolution below 170 K. Kieffer et al. (1977) further extended their work and calculated thermal inertia over the Tharsis region. Their model however did not account for effects of atmospheric IR opacity and radiometric albedo on thermal inertia computation. Mellon et al. (2000) generated thermal inertia from the Thermal Emission Spectrometer data (Christensen et al., 1992) on board the Mars Global Surveyor on a global scale at 3 km spatial reso lution. This study was the first to derive thermal inertia from single night time temperature measurements. However, their method was also based on determining that value of thermal inertia that produced model temperatures that best correlated with observed temperature measure ments. A large number of diurnal temperature cycles from a numerical thermal model considering seven parameters including albedo, thermal inertia, surface pressure, dust opacity, latitude, time of day and season were computed to generate a lookup table. Each temperature observa tion was correlated with other data (latitude, albedo, time of day, etc.) and was interpolated through this lookup table to find the best fitting thermal inertia. This single point TI derivation model was later devel oped to facilitate high resolution, accurate TI mapping from Thermal Infrared Imaging System (THEMIS) data (Fergason et al., 2006; Piatek and Moersch, 2006) and has since then been widely used for TI deri vations (Chojnacki et al., 2010; Hughes, 2012; Valdeuaza, 2016). Thermal inertia over the Gale crater has also been studied using �mezCuriosity Rover Environmental Monitoring Station (REMS) (Go Elvira et al., 2014) observations. Hamilton et al. (2014) first analysed thermophysical properties of the crater rim from REMS data of Curiosity for the first 100 sols. Martínez et al. (2014) later developed a novel method to compute surface energy budget and thermal inertia from REMS data by fitting a 1D heat conduction equation to soil and thereby simulating and analysing ground heat storage. However, a comment on seasonal variability of these components and their effect on the near surface thermal environment was missing, essentially because their study was limited to observations up to Sol 139. Since most lithological
interpretations of the Martian surface are closely linked to surface thermal inertia values (Mellon et al., 2000; Christensen et al., 2003), it is critical for us to understand any variations in thermal inertia, if any as it could directly impact the confidence with which we interpret surface lithology. The present work, therefore, aims to assess the seasonal variability of thermal inertia over Gale crater and also lists two different potential causal mechanisms that we think could be contributing to seasonal variations as observed by our study at Gale. 2. Data used The area selected for our study is the Gale crater, a possible dry lake (Hurowitz et al., 2017) on the north-western part of the Aeolis Quad rangle (Fig. 1a). Since our aim is to study seasonal variations of surface energy fluxes and thereby thermal inertia, the choice of time of study is of great importance. The time of study is chosen based on two factors – solar longitude and the movement of the MSL Curiosity rover (Grot zinger et al., 2012). In order for the study to provide an efficient rep resentation of each season, the sols are so chosen so that they lie in the mid (second month) of each three-month season period wherein peak characteristics of each season are experienced. Moreover, more accurate measurements of environmental parameters are possible when the rover is stationary as the motion and instantaneous change in location of the rover within the sol would give reason for uncertainty in location of measurement. Hence, the drive log of the Curiosity rover is used to select sols wherein the rover is stationary. From the Curiosity drive log, the rover was found to be stationary between Sol 102–111 at Point Lake, Sol 133–297 at Yellowknife Bay, Sol 440–453 at Cooperstown and Sol 609–630 at Mt. Remarkable during the spring, summer, autumn and winter seasons respectively (Fig. 1b). Table 1 provides a detailed description of the twelve sols, three each from each season chosen for this study. The Earth date – Mars date conversions and solar longitude for the chosen sols of study were obtained from Mars Climate Database v5.3. The Mars-Sun distance for each of these days was obtained from Astropixels, a publicly available planetary ephemeris data repository (http://astropixels.com/ephemeris/ephemeris.html). The Rover Environmental Monitoring Station (REMS) suite onboard
Fig. 1. (a) Study Area (Gale Crater) (b) Curiosity traverse map showing the four selected study locations – Point Lake (PL), John Klein - Yellowknife Bay (YKB), Cooperstown (CT) and Mt. Remarkable (MR). 2
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Table 1 Details of the sols chosen for study. Sol 108 110 112 234 251 270 440 441 443 610 620 631
Table 2 (a) Description of the various THEMIS scenes used to assess TI at Gale crater.
Earth Date
Solar Longitude Ls(� )
Solar Distance (A.U)
Season
23/11/2012 25/11/2012 27/11/2012 30/03/2013 15/04/2013 04/05/2013 21/10/2013 22/10/2013 24/10/2013 09/04/2014 19/04/2014 30/04/2014
212 213.4 214.3 291.7 301.3 312.4 38.5 39 39.9 113.9 118.5 123.8
1.40837 1.40674 1.40516 1.41024 1.42507 1.44552 1.63936 1.64006 1.64143 1.62179 1.61254 1.60153
Spring
Sl. No.
Summer
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Autumn Winter
the MSL Curiosity rover could be thought of a moving surface-weather station on Mars housing instruments to measure parameters such as temperature, humidity, pressure, wind speed and UV radiation. In all, REMS is made up of seven sensors namely the Wind Sensors (WS), Ground Temperature Sensor (GTS), Ultraviolet Sensor (UVS), Pressure Sensor (PS), Humidity Sensor (HS) and the Air Temperature Sensor (ATS) mounted on one or both the booms attached to the remote sensing �mez-Elvira et al., 2014). mast (Go For the present study, in order to enable Curiosity based thermal inertia computation, surface temperature measurements from the GTS, air temperature measurements (at 1.6 m above the surface) from the ATS and surface pressure observations from the PS were used. Wind speed measurements from WS were not available for all of the chosen sols. Therefore, to present the worst-case scenario, maximum and min imum wind speeds obtained during calibration of the sensor were used. The general nature of surface were interpreted on the basis of MAST CAM, Mars Hand Lens Imager (MAHLI) and/or NAVCAM images for the corresponding selected sols of study. All these datasets were obtained freely from the PDS Geosciences Node. Satellite based thermal inertia computations allow for possibilities of observing the same location over multiple solar longitudes unlike ob servations made from Curiosity where the focus of interest changes with respect to the motion of the rover. This makes orbiter-based observa tions much more viable for seasonal studies. The THEMIS sensor on board the Mars Odyssey (Christensen et al., 2004) has been providing high resolution (100 m) thermal data for Mars and has been effectively and widely utilised for assessing the lithological and thermophysical characteristics of the Martian surface. About 30 night-time (from 3 to 7 h LMST) THEMIS scenes across different seasons were utilised for assess ing the seasonal thermal inertia variations at Gale. It is to be noted that only night time IR datasets were used in this study in order to eliminate the effects of albedo and sun heated slopes that would lead to a much better thermal contrast due to particle sizes (Fergason et al., 2006). Band 9 measurements (12.57 μm) were found to have greater signal to noise ratio and were relatively transparent to atmospheric dust and thereby, only these measurements were utilised in this study. Elevation from the Mars Orbiter Laser Altimeter (MOLA) derived DEM further blended with High Resolution Stereo Camera (HRSC) observations (McCord et al., 2007) and global bolometric albedo maps generated from TES obser vations (Christensen et al., 2001) were also utilised as additional inputs to the thermal model in order to aid thermal inertia computation. About 20 THEMIS scenes for Nili Patera and 13 scenes for Eddie crater were also used to enable alternate studies. The details of the datasets utilised for Gale, Nili Patera and Eddie crater are provided in Table 2(a), Table 2 (b) and Table 2(c) respectively.
Dataset ID
Date of Acquisition (dd/mm/ yyyy)
Solar longitude (� )
Time of acquisition – LMST (hr)
Visible dust opacity (from MCD)
I01350002 I17950012 I18262008 I34571007 I34883005 I35195003 I48275006 I49174003 I49636006 I50098003 I50585006 I51072006 I51609005 I54144002 I54606005 I55530005 I57003016 I57340002 I57989004 I58301023 I59524022 I59836013 I60435002 I60722023 I61296002 I61321002 I61346002 I62631002 I66897002 I67745003
04/04/2002 31/12/2005 26/01/2006 29/09/2009 25/10/2009 20/11/2009 01/11/2012 14/01/2013 21/02/2013 31/03/2013 10/05/2013 19/06/2013 02/08/2013 27/02/2014 06/04/2014 21/06/2014 20/10/2014 17/11/2014 09/01/2015 04/02/2015 15/05/2015 10/06/2015 29/07/2015 22/08/2015 08/10/2015 10/10/2015 12/10/2015 26/01/2016 12/01/2017 23/03/2017
352.92 349.14 2.15 346.35 359.47 12.07 198.76 244.49 268.57 292.15 315.87 338.11 0.93 95.26 112.41 149.11 217.71 235.05 268.89 284.91 342.76 356.04 20.06 31.02 52.22 53.13 54.03 100.53 297.69 337.52
3.25 4.22 4.37 3.21 3.31 3.40 4.75 4.48 4.14 3.83 3.69 3.75 3.98 5.41 5.58 5.98 6.30 6.18 5.76 5.55 5.45 5.61 6.01 6.22 6.53 6.62 6.72 6.97 6.11 5.92
0.773 0.812 0.683 0.843 0.712 0.590 0.797 1.274 1.092 0.836 0.802 0.839 0.697 0.374 0.375 0.521 1.102 1.260 1.091 0.852 0.848 0.746 0.540 0.485 0.406 0.405 0.405 0.371 0.827 0.841
Table 2 (b) Description of the various THEMIS scenes used to assess TI at Nili Patera. Sl. No.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Dataset ID
Date of Acquisition (dd/mm/ yyyy)
Solar longitude (� )
Time of acquisition – LMST (hr)
Visible dust opacity (from MCD)
I01627002 I01989006 I05347011 I06458016 I06845008 I07956012 I08293012 I09903015 I12062002 I13884004 I14196002 I17266012 I17578021 I27025013 I27312009 I27599005 I33600012 I34199006 I36882009 I54521008
27/04/2002 27/05/2002 27/02/2003 30/05/2003 01/07/2003 30/09/2003 28/10/2003 08/03/2004 02/09/2004 30/01/2005 25/02/2005 05/11/2005 30/11/2005 17/01/2008 10/02/2008 04/03/2008 11/07/2009 30/08/2009 08/04/2010 30/03/2014
4.35 18.71 144.26 193.99 213.1 270.65 287.89 1.54 82.19 358.93 165.89 318.56 332.88 18.81 29.83 40.56 301.66 329.94 74.64 109.21
3.33 3.57 5.03 5.39 5.40 4.69 4.39 4.24 5.11 5.50 5.57 4.15 4.15 4.73 4.89 5.00 3.24 3.11 3.92 5.49
0.399 0.311 0.297 0.408 0.565 0.601 0.509 0.409 0.241 0.316 0.349 0.493 0.527 0.311 0.280 0.252 0.488 0.522 0.240 0.259
suitable for this study owing to their greater temporal coverage coupled with high temporal resolution. Before utilizing the same to analyse seasonal thermal inertia variations, THEMIS derived thermal inertia were compared with high resolution thermal inertia derived using a combination of Curiosity REMS observations and a 1D heat conduction model for accuracy assessment.
3. Methodology The attempt of this work is to primarily estimate and analyse surface thermal inertia at locations along Curiosity’s traverse on a seasonal timescale from THEMIS images. THEMIS datasets are particularly 3
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Table 2 (c) Description of the various THEMIS scenes used to assess TI at Eddie crater. Sl. No.
1 2 3 4 5 6 7 8 9 10 11 12 13
Dataset ID
Date of Acquisition (dd/mm/ yyyy)
Solar longitude (� )
Time of acquisition – LMST (hr)
Visible dust opacity (from MCD)
I05257013 I06730010 I07479026 I07841014 I08203013 I26074026 I28682005 I28969005 I32699002 I35906009 I37104006 I54381009 I61171007
20/02/2003 21/06/2003 22/08/2003 21/09/2003 20/10/2003 31/10/2007 01/06/2008 25/06/2008 28/04/2009 17/01/2010 26/04/2010 18/03/2014 28/09/2015
140.56 207.34 245.78 264.67 283.33 339.63 79.78 90.16 255.68 39.24 82.63 103.99 47.67
4.99 5.29 5.09 4.83 4.48 4.33 5.43 5.52 4.04 3.51 3.95 5.45 6.46
0.332 0.741 1.051 0.911 0.722 0.673 0.302 0.305 1.044 0.353 0.311 0.291 0.339
where H is the sensible heat flux, ρa is the atmospheric density computed from the surface pressure, Cp is the specific heat of CO2 at constant pressure (�736 J kg 1 K 1), Tg is the GTS recorded surface temperature, Ta is the ATS measured atmospheric temperature, K is the von Karman constant (¼0.4), Za is the height at which atmospheric temperature and wind speed ‘u’ are recorded (¼1.6 m), Z0 is the surface roughness and f (Rb) is a function of Bulk Richardson number Rb which is used to incorporate the effect of wind turbulence into the system (Sutton et al., 1978). The surface roughness length was assumed to vary from 0.5 to 1.5 cm based on TES measurements at Gale by H�ebrard et al. (2012). Maximum and minimum values of the sensible heat flux were obtained when the absolute difference of ground and air temperatures, surface roughness and wind speed were maximum and minimum respectively (Martínez et al., 2014; Rangarajan and Ghosh, 2018). A simple rearrangement of the surface energy budget equation (Eq. (1)) resulted in ground heat flux calculation (G) after all other compo nents were derived. Martínez et al. (2014) also provide a novel method to compute ground heat storage by analytical means (G*) by solving the 1D heat conduction equation applied to the soil. Their solution (Eq. (4)) indicates that G* can be reduced to a function of thermal inertia (I) for reasonable values of surface density (ρ), soil specific heat (Cp), depth of topmost soil layer of the numerical model (δ) and temperature Td at depth z ¼ zd where subsurface temperature may be considered to be invariant. � � λ ∂Tðz;tÞ �z¼zd 2 G* ¼ ’ ρICp Tðδ; tÞ Tð0; tÞ ∂z z¼0 � (4) δ
3.1. Estimation of thermal inertia from curiosity measurements The surface energy budget (Eq. (1)) is governed by the law of con servation of energy and has been used as an effective tool to characterise near surface thermal environments (Bonan, 2002). ð1
AÞS↓ þ L↓ ¼ L↑ þ H þ λE þ G
(1)
where A is the albedo of the surface, S↓ is the downwelling short-wave radiation, L↓ is the downwelling longwave radiation, L↑ is the upwelling longwave radiation, H is the sensible heat flux, λE is the latent heat flux and G is the heat exchange by conduction into ground. The terms on the LHS correspond to the forcing terms and those on the RHS correspond to the response terms of the radiative transfer. The availability of high resolution measurements of surface and air temperature, wind speed and surface pressure at Gale from the Curiosity REMS instrument has enabled us to accurately compute various surface energy budget components and by extension, surface thermal inertia (Martínez et al., 2014; Rangarajan and Ghosh, 2018). The estimation of downwelling shortwave radiation was performed using a comprehensive radiative transfer model developed by Haberle et al. (1993) for which the diurnal values of atmospheric dust opacity for the twelve sols under consideration were retrieved from the Mars Climate Database v5.2 (Madeleine et al., 2011). The albedo was varied from 0.20 to 0.25 in the model that proved to be a satisfactory approximation to the expected albedo values at Gale based on TES al bedo values (Pelkey and Jakosky, 2002). The magnitudes of down welling longwave radiations, however, were directly retrieved from the Mars Climate Database v5.2 for this study as they were found to be at par with previously modelled values by Martínez et al. (2014), with errors <5%. The magnitude of longwave fluxes emitted by the surface to the at mosphere can be quantified using the Stefan-Boltzmann equation (Boltzmann, 1884) (Eq. (2)) utilizing the GTS ground temperature measurements. L↑ ¼ εσ T4g
Based on the approximations of Martínez et al. (2014), volumetric heat capacity values (ρCp) between 0.8 and 1.7 � 106J m 3 K 1 with greater values for surfaces with greater rock density and a depth of 10 cm for zd were adopted. The values of Td, on the other hand, were analysed from hourly GTS measurements and their standard deviations, such that Td was higher than the minimum ground daily temperature to ensure upward heat flux from deep soil but also slightly lower than daily average ground temperature in order to provide the most accurate so €rvi, 1995; lution to the heat conduction equation at diurnal scales (Savija €rvi and Ma €a €tta €nen, 2010; Martínez et al., 2014). Savija With above approximations for ρCp, zd and Td, G* was reduced to a function of I. A direct comparison between the diurnal variations of G* and G provided means to analyse the thermal inertia of the surface as the former depends on a thermal inertia parameter while the latter doesn’t (Martínez et al., 2014). 3.2. Estimation of thermal inertia from THEMIS observations THEMIS RDR data were acquired from the THEMIS Image Explorer repository and were initially pre-processed using the THMPROC module developed by ASU. They were then converted into brightness tempera tures from Planck’s black body radiation curves. Surface thermal inertia estimations were then performed using a 7D lookup table approach based on the thermal model developed by Mellon et al. (2000) using the jENVI package in ENVI (Piatek and Moersch, 2006). The lookup table contained information on albedo, thermal inertia, surface pressure, dust opacity, latitude, longitude and time of the day for single point thermal inertia computations. Since this study is conducted on a seasonal basis, it is important to also account for any variations in atmospheric dust opacity anticipated throughout the course of different solar longitudes, particularly due to occurrence of both global and localised dust storms during the southern spring and summer. Therefore, an additional input of the atmospheric dust opacity at the time of observation of the THEMIS scene was retrieved from the Mars Climate Database Global Circulation Model and incorporated into the thermal model before surface thermal
(2)
where L↑ is the upwelling longwave radiation (W/m2), ε is the surface emissivity, σ is the Stefan-Boltzmann constant (¼5.67 � 10 8 W m 2 K 4) and Tg is the GTS ground temperature (K). The sensible heat flux is directly proportional to the temperature difference between the surface and surrounding air and inversely pro portional to a transfer resistance and can be calculated using Eq. (3). � Tg Ta � � (3) H ¼ K2 Cp u ρa fðRb Þ ln2 zz0a 4
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Icarus 337 (2020) 113499
inertia computation. MOLA-HRSC blended elevation and TES albedo layers were also served as additional inputs into the model. These input parameters are used to compute model based surface temperatures for specific observation scenarios and thermal inertia ranges leading to a table of model derived surface temperature values as a function of thermal inertia. The computed brightness temperatures for each THE MIS scene are then interpolated with respect to the generated table of surface temperatures and appropriate thermal inertia values are assigned.
spread across different seasons using the relation developed by Presley and Christensen (1997) between thermal conductivity and particle size. Soil classification was done based on the Indian Standard Soil Classifi cation scheme (ISSCS) (IS 1498:1970). Fig. 3 shows the particle size distribution maps prepared from the selected THEMIS observations. Previous studies by Fergason (2013), Putzig and Mellon (2007) suggest that variations in thermal inertia observations from THEMIS possibly emerge due to subsurface layering or even the incapability of the ther mal model itself to accurately represent the true nature of the seasonal radiative interaction between the surface and the atmosphere. However, if the latter were to be true, such variations should be expected at other locations on Mars as well. To test this possibility, we took up a case study comparison with two other points near the Nili Patera caldera in the Syrtis Major volcanic construct, one known to be free from major dust activity and hence little or no chances of potential layering or dust deposition phenomena (Lillis et al., 2015; Rangarajan et al., 2018). The lithological discrimination of the Nili Patera caldera indicates presence of a north western high TI unit in the interior with lower thermal inertia values on the outer flanks (Fergason et al., 2006). Two representative points, one on each unit were taken and their seasonal TI observations were plotted (Fig. 4). However, no such stark variation as observed in Gale was seen. In fact, the surface thermal inertia values remained pretty much the same, irrespective of the solar longitude of observation. An alternative study was also conducted in a region near the crater peak of the Eddie crater in the Elysium construct. This region was particularly chosen due to its high similarity with respect to Gale in terms of its near-equatorial location and similar surface albedo values (MGS TES bolometric albedos roughly ranging from 0.23 to 0.25 for both the chosen areas), possibly indicating that the two study sites might possess similar dust cover. A seasonal study of surface thermal inertia over the Eddie region, interestingly, also showed very little or no vari ation across different seasons (Fig. 5). This emphasises the notion that the variation observed at Gale might not just be a renderance of possible ambiguity in the thermal model description alone, but could point towards some potential surface pro cesses going on in the crater that might be causing these seasonal changes. The prospect of seasonal dust deposition and removal at Gale and/or possible deposition of microscopic liquid water ice or volatiles could seem likely. The role of both these processes are discussed in further sections.
4. Results 4.1. Accuracy assessment of the THEMIS thermal model The constant motion of the Curiosity rover does not allow for single point seasonal surface thermal inertia estimations. Alternatively, THE MIS orbiter thermal inertia observations acquired at different times of the year could serve as a reasonable substitute despite coarseness in spatial context of observation. It, however, is highly necessary to first assess how accurate the THEMIS thermal inertia estimations derived by our thermal model are, before relying on them to reciprocate seasonal behaviour of the surface. Table 3 shows a comparison between Curiosity derived TI and esti mated TI from THEMIS observations at similar solar longitudes from our model computations. It is seen that the THEMIS TI values are in very good agreement with Curiosity derived estimations with errors just about 10%. These values are also in very good concordance with pre vious estimations made by Martínez et al. (2014) and Vasavada et al. (2017) at Gale with errors less than around 10%. Hence, it can be safe to conclude that the thermal model could be used for accurate seasonal TI retrievals. 4.2. Seasonal behaviour of thermal inertia Thermal inertia is often regarded as a distinctive property of the surface and is expected to be constant at least for short periods of time like a year or two as not much change in the surface composition is expected. Night time thermal inertia estimations from about 25 THEMIS scenes were utilised to plot the seasonal behaviour at each of the four selected locations along Curiosity’s traverse. The seasonal thermal inertia plots for Point Lake, Yellowknife Bay, Cooperstown and Mt. Remarkable respectively are shown in Fig. 2. Contrary to the nearly constant value expected, surface thermal inertia observations show a distinct sinusoidal variation with the surfaces dis playing highest thermal inertia during the late southern autumn or early winter boundary, around Ls ¼ 80 to 90 and the least TI value during late spring (Around Ls ¼ 250 to 270) near the perihelion of the Martian orbit. The observed variations are quite large, of the order of almost 300 t.i. units. We also estimated particle sizes from five THEMIS TI images
5. Discussions We observed a distinctive seasonal sinusoidal trend in surface ther mal inertia values at four points along Curiosity’s traverse from THEMIS derived thermal inertia of the order of around 300 to 400 t.i.u, with the surface exhibiting greatest thermal inertia during the late southern autumn/ early winter and the lowest thermal inertia during late spring
Table 3 Comparison of Curiosity and THEMIS derived TI measurements based on our computations. Location
Point Lake Yellowknife Bay Cooperstown Mt. Remarkable
Curiosity
THEMIS
Sol
Ls (� )
Thermal Inertia (t.i.u)
108 110 112 234 251 270 440 441 443 610 620 631
212 213.4 214.3 291.7 301.3 312.4 38.5 39 39.9 113.9 118.5 123.8
285 280 285 460 470 495 455 395 395 285 275 290
Error in estimation (%)
Average Thermal Inertia (t.i.u)
ID
Ls (� )
Thermal Inertia (t.i.u)
283.33
I57003016
217.7
299.28
5.62
475
I66897002
297.7
463.55
2.41
415
I61296002
52.2
450.57
8.57
283.33
I55530005
149.1
314.75
11.08
5
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Icarus 337 (2020) 113499
Fig. 2. Seasonal THEMIS TI variations at (a) Point Lake (b) John Klein, Yellowknife Bay (c) Cooperstown (d) Mt. Remarkable.
Fig. 3. Particle size distribution maps derived from selected THEMIS thermal inertia images.
6
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Fig. 4. (a) THEMIS Day Time IR mosaic of the Nili Patera caldera (b) Seasonal TI variation at point A (c) Seasonal TI variation at point B.
Thermal Iner!a (t.i.u)
Loca!on A 800 700 600 500 400 300 200 100 0
R² = 0.6989
0
40
80
120
160
200
240
280
320
360
Solar Longitude (°)
Thermal Iner!a (t.i.u)
Loca!on B 800 700 600 500 400 300 200 100 0
R² = 0.6237
0
40
80
120
160
200
240
280
320
360
Solar Longitude (°)
Fig. 5. (a) THEMIS Day Time IR mosaic of Eddie crater (b) Seasonal TI variation at location A (c) Seasonal TI variation at location B.
very close to Mars’s perihelion (Fig. 2). Such changes in thermal inertia had previously attributed to inefficiency of the 1D thermal model being used to estimate surface thermal inertia from orbiter observations (Fergason, 2013). However, a case study over the Nili Patera caldera in the Syrtis Major region revealed no significant seasonal change in
orbiter thermal inertia (Fig. 4) which indicated the variations at Gale might as well be a result of some localised surface process that needs to be understood further. Two potential causes for the observed seasonal TI trend were hypothesized: (i) seasonal dust deposition (ii) seasonal water ice deposition. 7
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5.1. Seasonal dust deposition
5.2. Seasonal water-ice deposition
It is seen from Fig. 2 that the periods where maximum thermal inertia is displayed by the surface coincides with the seasons least affected by local or global dust storm activity i.e. autumn/ winter. It may be possible that during the dust storm season (i.e. southern spring and summer), a thin layer of dust is deposited on the surface thereby obscuring and covering the bed rock lying underneath. Dust or any other fine grained soil matter do not have as much capability as large hard rocks to store heat, thereby reducing the thermal inertia of the top layer of the surface. As heavy gusts of winds continue to blow through the end of summer into autumn, the deposited dust is slowly removed by the wind and the underlying bedrock is exposed, thereby increasing the observed thermal inertia of the top surface layer. The fact that thermal inertia variations are not seen in a practically dust free environment like the Syrtis Major construct (Fig. 4) could also support this idea. There have also been observations from the Mars Hand Lens Imager (MAHLI) of seasonal dust deposition activity from aeolian suspension on the sensors of the Curiosity instrument (Fig. 6) as well as on natural surfaces along Curiosity’s traverse (Edgett and Newsom, 2018). A first impression of identifying such dust deposition would suggest that we look into surface albedo variations. However, albedo derived from visible imagery, especially in the southern summer and spring might not be that pristine, majorly due to the thick atmospheric dust cover as reflected by the high dust opacity observations in these two seasons. We therefore resorted to estimating particle sizes from five THEMIS TI images spread across different seasons (Fig. 3). The maps show the maximum % cover of fine grained soil in I49174003 (Ls ¼ 244.49� ) which gradually reduces until it becomes almost negli gible in I54144002 (Ls ¼ 95.26� ) with the surface being covered with coarser soil grains which could further strengthen the possibility of occurrence of this seasonal dust deposition phenomenon. The interesting observation of hardly any thermal inertia variation on a seasonal scale in a region south-east of the Eddie crater peak region (Fig. 5), which presumably has a similar dust cover and location (nearequatorial) as that of Gale could possibly indicate that this seasonal deposition phenomena is not seen in all dusty equatorial craters. Consequently, the localisation of this process to Gale needs to be further studied and understood in greater detail.
Another potential cause for these seasonal thermal inertia variations could be the possibility of seasonal water ice deposition on the surface. It has been known that in autumn/ winter, at higher latitudes in Mars, water condenses from the atmosphere to the surface as frost. Subse quently, in the spring/summer seasons, this frost sublimates back into the atmosphere (Audouard et al., 2014). The idea of changes in surface moisture due to potential seasonal water ice deposition being a potential cause is drawn from the fact that as the surface/ soil becomes moist, the water molecules trapped in between the various soil particles (either physically or chemically bonded) tend to absorb a larger quantity of heat and thereby tend to slow down the process of heat getting re-radiated back to the atmosphere. This could tend to increase the surface ther mal inertia values when moisture content is higher. Higher TI values for wet soils have also been observed in previous studies (Matsushima et al., �l and Kereszturi (2017) and Martínez et al. (2016) 2012). Studies by Pa suggest the possibility of frost formation on the Martian surface. Fig. 7 describes the plot of relative humidity for the first 1000 sols of Curiosity which clearly indicates a distinct seasonal variation very much similar to the variation of surface thermal inertia we have observed at Gale (Fig. 2). The possibility of water ice deposition actually causing observed seasonal TI variations is very much likely too as temperatures necessary for deliquescence do exist at Gale (Martínez et al., 2016). However, it could always be helpful if any potential alteration in surface chemical composition albeit to a very minor extent due to the seasonal water ice deposition can be identified by hyperspectral imagery such as CRISM/ OMEGA that could give this possibility significant noteworthiness and strength. 6. Conclusions Seasonal changes in thermal inertia could greatly lead to ambiguous geologic interpretations of the properties of the surface. There always lies a possibility of incorrect lithological and mineralogical discrimina tions if thermal inertia values are being utilised randomly without giving much importance to the time/ season in which the datasets were ac quired. Based on our results, we conclude that there may be some active localised surface deposition phenomena, mostly dominated by dust occurring at Gale that could be causing such large surface thermal inertia variations, as observed in our study. However, despite significant evidence of potential seasonal dust deposition activity, there are still
Fig. 6. MAHLI images of the UV sensor acquired on (a) Sol 36 (b) Sol 1314 (Source: PDS Geosciences Node). 8
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Icarus 337 (2020) 113499
Fig. 7. Seasonal Relative Humidity variations at Gale for the first 1000 sols (Martínez et al., 2016).
some glaring questions to be answered, particularly regarding the thickness of dust cover necessary in order to facilitate such large thermal inertia variations as observed at Gale. Alternatively, the possibility of seasonal water ice deposition which could affect the surface moisture content and hence the surface thermal inertia was also investigated. Recent studies also point towards the possibility of occurrence of microscopic liquid water and likely frost events at higher latitudes. There have been several questions regarding possibility of such events at equatorial latitudes. However, the way in which seasonal variations in relative humidity observations (Fig. 7) show exceptional similarity to the nature of seasonal thermal inertia variations observed at Gale (Fig. 2) could also point towards its potential role in causing such changes. One important conclusion that can be derived from the comparative analyses at Eddie is that such seasonal thermal inertia variations do not occur at all Gale-like sites. Although Eddie is similar to Gale in terms of its near-equatorial location and similar average dust cover, its smaller diameter and crater depth may be altering the nature of how local wind and dust storm patterns operate as compared to how they occur at Gale, which could be key to understanding why we don’t see traces of dust deposition activity at Eddie. The lack of high resolution observations of relative humidity or water/ice opacity at Eddie as we have for Gale further makes it difficult to ascertain the presence of any water ice deposition activity there. If however there is water ice deposition at Eddie, it is most likely that it may not be strong enough to cause an appreciable change in surface thermal inertia. It might also be noteworthy to also identify other sites along the equatorial belt in the future where such stark variations in thermal inertia is seen and assess the degree of impact that this phenomena might actually have in affecting interpretations of existing lithological maps of different units on Mars.
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