VIMS observations

Icarus 220 (2012) 331–338

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Water ice abundance and CO2 band strength on the saturnian satellite Phoebe from Cassini/VIMS observations Gary B. Hansen a,⇑, Emily C. Hollenbeck b, Katrin Stephan c, Sean K. Apple b, Eun-Ju Z. Shin-White b a

Department of Earth and Space Sciences, University of Washington, Box 351310, Seattle, WA 98195-1310, United States University of Washington, United States c Deutschland für Luft- und Raumfahrt, Institute of Planetary Research, Germany b

a r t i c l e

i n f o

Article history: Received 11 October 2011 Revised 2 May 2012 Accepted 3 May 2012 Available online 14 May 2012 Keywords: Saturn, Satellites Satellites, Composition Satellites, Surfaces Spectroscopy Ices, IR spectroscopy

a b s t r a c t We have studied the near-infrared spectrum of the Saturn satellite Phoebe, a distant satellite observed before Cassini’s Saturn orbit insertion, using data from the Visual and Infrared Mapping Spectrometer (VIMS) on the Cassini orbiter. We have done a critical calibration of the dataset that involves careful correction of dark artifacts. We model areally mixed water ice and non-ice (assumed segregated because of the low 3% albedo of the non-ice material) for several high and medium resolution observations of Phoebe made near closest approach. Using a Hapke roughness factor of 15°, we find ice abundances from 0.1% to over 4%. The ice grain radii vary from 1 to 10 lm. These are displayed on a projected map of Phoebe with about 50% coverage (about 33% coverage at better than 5 km spatial resolution). Detailed looks at the water ice spectral fits shows that the weak 1.05 and 1.25-lm bands are missing in most of the spectra, implying that the ice endmember is not pure ice, but has a dark material mixed with it that lowers the albedo and suppresses these bands. We made a model of ice contaminated with Phoebe-like dark material showing that a few percent of dark material lowers the albedo to 50% and suppresses the bands. The dirty ice model produces better fits to the spectra and implies that the amount of dirty ice is about 1.5 times the amount of pure ice. We have also calculated the CO2 band depth for these same observations and projected the results. The CO2 band depth varies inversely with water ice abundance. Ó 2012 Elsevier Inc. All rights reserved.

1. Introduction We are working on a project to analyze the visible and infrared spectrum of the satellites of Saturn as observed by the Visual and Infrared Mapping Spectrometer (VIMS) on the Cassini orbiter. First we are looking at observations of Phoebe, a satellite too distant to be observed by Cassini while in Saturn orbit, but was observed during a close (2071 km) encounter on 11 June 2004, 22 days before Saturn orbit insertion (Clark et al., 2005; Coradini et al., 2008; Cruikshank et al., 2008). It is in a retrograde orbit with a period of 550 days (215 RS) and is thought to be a captured object (Burns, 1986). It rotates in a prograde manner with a period of 9.27 h (Bauer et al., 2004). It has a roughly equant shape with a mean radius of 107 km and a density of 1630 kg m2 (Porco et al., 2005), which implies that it contains as much as 75% water ice or has less ice and is porous. The albedo is 6%, implying that there is very little exposure of bright water ice frost on the surface. Owen et al. (1999) telescopically measured the 2-lm band of water ice on Phoebe and modeled it as an intimate mixture of 3% ⇑ Corresponding author. E-mail address: [email protected] (G.B. Hansen). 0019-1035/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.icarus.2012.05.004

water ice having a grain size of 500 lm with carbon black. Buratti et al. (2008) modeled averaged VIMS spectra with intimately mixed models having 10–20% ice with 26-lm grain size for the icy spectra, and 40% ice with 1.4 mm grain size for the dark areas. Clark et al. (2005) shows a relative CO2 band depth map for Phoebe, Coradini et al. (2008) use a classification scheme to classify Phoebe spectra. Using a four-spectra classification they compared the band depth of CO2 to that of water ice and found that water ice and CO2 seem to be positively correlated. Cruikshank et al. (2010) discusses the position and width of the CO2 band on Phoebe compared to the other saturnian satellites and find it consistent with the position and width for pure CO2 ice. They say that the widespread CO2 on Phoebe argues for a native (endogenous) source. 2. Observations The VIMS is an imaging spectrometer that generates cubes of up to 64  64 spatial coverage and 352 wavelengths (96 wavelengths 0.35–1.05 lm for VIMS-v and 256 wavelengths 0.8–5.2 lm for VIMS-ir) with an ir spectral resolution of 14 nm below 3 lm increasing to 20 nm by 4 lm (Brown et al., 2004). The instantaneous field-of-view of the instrument in nominal mode is

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Table 1 Details of the Cassini–VIMS observations of Phoebe used for modeling. Observation name

Number of cubes averaged or mosaicked

PHOEBE017

7

PHOEBE015 PHOEBE049 PHOEBE018 PHOEBE013 PHOEBE030 PHOEBE040 PHOEBE043

11 2 6 2 4 7 6

Observation time relative to closest approach (h)

Pixel scale (km)

0.0 to +0.5

1.3  1.3; 2.7  5.4 6.3  6.3 7.4  14.8 11  11 7.9  15.8 19.3  38.6 37.4  74.8 40.3  80.6

0.5 +1.0 1.0 1.5 4.0 7.0 7.5

0.5  0.5 mrad (providing 1-km spatial resolution at the Phoebe closest approach), with the 64  64 image constructed over time with the two-dimensional movement of a mirror for the ir channel and a one-dimensional mirror on the vis channel. High spatial resolution modes are available that divide the IFOV into two parts (0.50  0.25 mrad) in the ir and nine parts (0.17  0.17 mrad) in the vis. The original wavelength list has been modified in the region around 4.25 lm (Cruikshank et al., 2010). There is a small offset between the optical axis of the vis and ir cameras, and this combined with often mixed modes (nominal vs. high resolution) makes it very difficult to combine vis and ir observations, so this paper describes only results from the ir camera. In an earlier modeling exercise, we used an easily-combined vis-ir cube for modeling (Hansen et al., 2009). All the Phoebe close-approach observations (10 h before to 14 h after) were carefully calibrated, in particular using the unstable dark correction methods we have devised. This involves dividing the spectral image at each (known) anomalous channel by a scaled

average of two or four adjacent channels, ideally making a flat image with obvious dark anomalies that can be removed, as described in Section 8 of McCord et al. (2008). The signal level for Phoebe observations is so small that linear approximations work well at almost all wavelengths. There is also a thermal tail that depresses the darks beyond 4 lm that is added back in. Eight of the observations were then selected for modeling and projection, listed in Table 1 and shown in Fig. 1 in the approximate order of highest to lowest spatial resolution. The first five are ridealongs with the Imaging Science Subsystem (ISS) during the close approach phase. Ridealongs with the Composite Infrared Spectrometer (CIRS) are distorted due to motion during the observation. Those and the VIMS controlled observations would add very little to the observations here because they are bracketed in time by the ISS observations and cover the same surface regions. The first four observations were mosaicked by inspection; since the VIMS is nearly a framing camera, the overlaps between images are obvious and require at most a small scaling of the image. These first four observations also needed to have radiation spikes removed, where we used a modified despiking routine from our Galileo Near-Infrared Mapping Spectrometer (NIMS) studies of the Galilean satellites (e.g., Hansen and McCord, 2008a). The other observations had multiple similar observations that were despiked by averaging.

3. Water ice modeling We model the spectra with water ice and non-ice components, assuming linear mixing (segregated ice and non-ice terrains), which is possible considering the 3% albedo of the dark materials similar to Iapetus. The models of Spencer (1987) and Spencer and Denk (2010) imply that a forcing is required to generate the

Fig. 1. VIMS images of Phoebe at 0.8-lm from the eight observations used for the modeling. (a) PHOEBE017; (b) PHOEBE015; (c) PHOEBE049; (d) PHOEBE018; (e) PHOEBE013; (f) PHOEBE030; (g) PHOEBE040; (h) PHOEBE043.

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lag deposit on Iapetus, but the low albedo of Phoebe implies that a considerable lag has built up everywhere. Images of Phoebe indicate a large contrast between the bright spots that cover a small percent of the surface and the surrounding dark material (Porco et al., 2005). The bright regions are typically no brighter than 30% or so, implying that the ice is ‘‘dirty’’, an effect we will investigate later. This work is developed from a similar study that is ongoing for the Galilean satellites, particularly Ganymede (Hansen and McCord, 2004, 2008b, 2009; Hansen, 2007). We derive mixing ratios and water ice grain sizes that are mapped over the surface of Phoebe at various spatial resolutions. The navigation data for Phoebe was not available from the VIMS pipeline, so we estimated the lighting geometry of the combined and averaged cubes by inspection by knowing the phase angle and assuming a spherical object. This geometry was modified for roughness using the Hapke roughness formulation (Hapke, 1984) using 15° average slope. This was determined in an earlier modeling exercise (Hansen et al., 2009) where we corrected excessive ice scaling factors near the terminator by testing various average slopes. The water ice optical constants used were for 150–160 K for 2.8–100 lm (Hansen and McCord, 2004) and 110 K for 1– 2.8 lm (Grundy and Schmitt, 1998). Water ice bidirectional reflection (BDR) models (calculated using Mie code (Hansen, 2009) and discrete-ordinates scattering code (Stamnes et al., 1988)) for 10 grain sizes were generated for the calculated geometries and sampled to the VIMS wavelengths. A Phoebe non-ice spectrum was estimated from averaging several dark pixels from one of the observations and subtracting a small amount of water ice. This was scaled by a constant, and by a fixed scaling function of wavelength (see Figs. 7 and 15 of Hansen and McCord (2004)) to a power to account for photometric variations. The fitting function (Hansen and McCord, 2008b; Hansen, 2007) varies non-ice scaling and scaling function power, and water ice scaling and grain size to fit the measured spectra in the least-squares sense:

model ¼ usf  nispectrum  nuscf

musp

þ wisf  icemodelðgsÞ

333

(a)

(b)

ð1Þ

where usf is the uniform scaling factor, nuscf is the non-uniform scaling function, nusp is the non-uniform scaling power, nispectrum is the non-ice spectrum, wisf is the ice scaling factor, and icemodel(gs) is the ice BDR at the given lighting geometry and grain size gs. Example fits for two spectra, one ice-rich and one ice-poor from one of the high-resolution observations, are shown in Fig. 2 with their least-squares cost (the square root of the sum of the residuals between data and model squared; 0.01 is good). 4. Pure ice results Every pixel of every observation was modeled, yielding four parameters and an error of fit. The modeling results were put into an equidistant cylindrical projection, the same as the Cassini camera (Imaging Science Subsystem or ISS) mosaic produced in 2005 (Porco et al., 2005; Fig. 3a). Since much of the data overlapped in the final mosaics, there were two ways to display the data: superposed, with the highest spatial resolution on top; and averaged, where the data in the layers is used in a weighted average with high resolution data weighted more heavily. The weighted average results for water ice abundance and grain size are shown in Fig. 3b and c, for comparison with the ISS mosaic. The ice abundance is displayed in Fig. 3b on a logarithmic scale and the high values correlate roughly to the bright crater regions in the image, including the brighter area at 40N and between 0 and 330W, and the general bright area east of 330W and south of 60S. The maximum abundance is about 4% and the minimum is less than 0.1%. The grain radius of the ice is displayed in Fig. 3c, using the same techniques of weighted averaging and plotted with a logarithmic

Fig. 2. The model fits for two locations on Phoebe, ice-rich (a) and ice-poor (b), The VIMS data is in a black line and the model fit is a gray line. The grain size, mixing ratio and least-squares (LS) fit are given in the legends.

stretch. The grain size is indeterminate in regions of <0.01% ice (see the ice poor spectrum that has only the weakest hint of water ice bands). Where the abundance is larger the radii are 5–10 lm in the northern bright spot, and 1–2 lm in the southern/south polar bright regions. Many of the ice-poor regions default to 10-lm radius. The map of error in fit is fairly uniform, with expected deviations where the signal is poor (in the poles) and better (in the small cubes that are averages of several identical cubes). The 0.015 error of the icy example fit (Fig. 2a) is an upper bound for the errors in the combined cube. The longitude coverage from these eight observations leaves gaps from 0 to 60 and 150 to 210W. There are time gaps in the Phoebe observations that will make filling these spots with all but the lowest resolution data difficult. This very low resolution data would map as one or two pixels across a latitude meridian and would not aid in interpreting the data. 5. Improvements to model The water ice on the Galilean satellites is segregated spatially from the darker non-ice materials by a well known thermal process that depends on the temperature of the different materials (for the Galilean satellites (Spencer, 1987), pure water snow has temperatures under full Sun of 120–130 K while the segregated dark materials reach 160 K). The low albedo materials (1–3% albedo) on

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(a)

60N

3.2

30N

1.0

0 0.32 30S 0.1

60S 90S

0.032 180

(c)

Water ice abundance (%)

90N

150

120

90

60

30

0

330

300

270

240

210

180

90N

10.0

60N

7.1

30N

5.0 3.2

0

2.0 30S 1.4 60S 1.0

Water ice grain radius (µm)

(b)

90S 180

150

120

90

60

30

0

330

300

270

240

210

180

Fig. 3. Water ice results mapped on Phoebe. (a) Photomosaic of Phoebe on the same projection as the maps in (b) and (c) courtesy of Deutschland für Luft- und Raumfahrt (Porco et al., 2005). (b) Map of water ice abundance in percent with a logarithmic stretch. The average of each pixel in the map is weighted from the various observations by their resolution. (c) Map of water ice grain radius with a logarithmic stretch. The grain size tends to large sizes (>10 lm) where the abundance is small. The average is weighted in the same way as the abundance map.

Iapetus are shown to have been thermally segregated into regions that reach 130 K (Denk et al., 2010; Spencer and Denk, 2010). The high contrast between bright and dark areas on Hyperion and Phoebe implies that thermal segregation processes are at work there as well. The albedo of Phoebe was determined to be 8% by Voyager measurements (Simonelli et al., 1999), and this leads to an equilibrium noon temperature of 127 K. For a fast rotator, depending on the thermal inertia, the noon temperature would be cooler, but the Voyager measurements include icy regions, implying that the dark material is even darker (Porco et al. (2005) estimate about 7%, and the VIMS measurements are consistent with 5%). Therefore, we expect a maximum dark material temperature of 120–125 K. Flasar et al. (2005) measured peak

temperatures of 113 K using the Cassini Composite Infrared Spectrometer, but the subsolar point was never measured, and the maximum is model dependent. Since they did not account for mixed hot and cold terrains (dark and icy, consistent with the sloped brightness temperature spectra that are displayed), and cold shadowed regions at large phase angles, its likely that their measured temperatures are lower bounds for the actual surface temperatures for the sunlit dark material. But the bright material need not be pure water ice, and in fact is likely to be a slightly dirty ice with an albedo of 30–50%. Porco et al. (2005) estimate that the icy areas do not exceed 30% albedo and are not pure ice. This is evident from the overall albedo of extended bright areas such as on the trailing hemisphere of Iapetus

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Fig. 4. The measured Phoebe I/F at 90° phase (fine dashed line, with small amount of water ice subtracted) was multiplied by 2 to approximate the hemispherical albedo (solid line). This is compared with the computed albedo of a layer of 10-lm grains of carbon black (see text).

(39% according to Spencer and Denk (2010)), and from the lack of weak bands of water ice near 1.04 and 1.25 lm in the near-infrared spectra. These bands are the first to disappear when you mix water ice with dark substances (e.g., Clark, 1981; Wiscombe and Warren, 1980b). The dirty water ice has a full Sun temperature at Saturn’s solar distance of less than 110 K (pure ice is less than 80 K), cold enough to act as a trap for water molecules compared to the much warmer dark material. We ran radiative transfer models to show how much dark material needs to be mixed into the ice to remove these bands (depending on the unconstrained material opacity, a few percent mass ratio) and calculate a new area fraction of icy material compared to the pure ice models we have done for Phoebe. Such modeling, even for these segregated terrains, is complicated because the amount of dark material in the ice can vary from zero to a few percent and still be an effective trap for water molecules. With a large amount of mixed dark material the albedo becomes too similar to the dark material to provide for net accumulation of ice, but with anything providing albedos of 30% or greater, including nearly pure ice will cause net accumulation of water on the icy regions (isolated locations on Phoebe do show

335

Fig. 6. Albedo models of ice dirtied by Phoebe dark material. The solid line is the albedo spectrum pure 5-lm water ice and the broken lines represent greater and greater amounts of dark material intermixed, depending on the mass ratio and the assumed grain radius of the dark material. The 1.04 and 1.25-lm water ice bands are not visible in the darkest two models.

the short-wave ice bands, and are therefore purer ice than typical). Models of the areal fraction of icy material will have large uncertainties because of this effect. 6. Dirty ice models We started by generating single-scattering properties (extinction cross-section, single-scattering albedo, and asymmetry) for Phoebe dark material. You could also go one step further and estimate optical constants, for which you have to assume a grain size. You also have to assume a grain size to mix the single-scattering properties with water ice, but this becomes just another free parameter: small opaque particles vs. large less opaque particles. We used the optical constants for carbon black (Tomaselli et al., 1981), run through Mie code to generate single scattering quantities for several grain sizes, as a guide to the similarly dark Phoebe non-ice material. We estimated starting values and ran them through a two-stream albedo model (Wiscombe and Warren, 1980a) and iterated (mainly the single-scattering albedo) until the output albedo matched the measured Phoebe non-ice spectrum times two (Figs. 4 and 5). The estimated absorption cross section for Phoebe material is much less than carbon black except in the region near 3 lm (Fig. 5). We then mixed small amounts of the Phoebe non-ice with water ice, using r = 5 lm (in the middle of the range given by Fig. 3c, r = 1–10 lm). To calculate the mixed single-scattering quantities, we need to assume a grain size for the dark material, and used r = 1, 2, 5, and 10 lm. It turns out that the results are redundant, such that 2% of 2 lm gives the same spectrum as 5% of 5 lm, etc. depending only on the effective opacity of the dark particles. The modeling results are plotted in Fig. 6. Note that the 1.04- and 1.25-lm ice bands are missing in the model calculated with 5% dark component in 2 lm grains (45% albedo) and are mostly gone in the model with 2% dark component in 2 lm grains (60% albedo), which we decided to use for further calculations. The spectrum of the mixtures are only slightly different beyond 3 lm from pure ice. 7. Comparison with VIMS data

Fig. 5. Calculated optical cross sections for Phoebe dark material particles compared to those for carbon black. Shown are the absorption cross section and the asymmetry for both materials.

Using the 5-lm ice mixed with 2% 2-lm dark material as the bright component and pure dark material as the dark component, we modeled the four brightest (iciest) pixels of the PHOEBE049

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(a)

(b)

(c)

(d)

Fig. 7. Comparison of water ice fits on four icy pixels from the PHOEBE049 observation. Each of the four spectra shows the data (thin black line), pure water ice fit (thick light gray line) and dirty water ice fit using 5-lm water ice with 2% of 2-lm dark material model shown in Fig. 6 (thick dark gray line). The dirty ice model fits better in the shortwave end because it lacks the water ice bands, but, from 1.5 to 3.0 lm, fits worse in the icier models (a) and (b) and better in the less icy models in (c) and (d).

observation (Fig. 1c) taken 1 h after closest approach at a 5  10 km spatial resolution (Fig. 7). These pixels are centered near 37N, 343W on Phoebe. The previous pure ice models used non-uniform scaling of the non-ice component (see Eq. (1)), but we use only uniform scaling in these fits (remove the nuscf term in Eq. (1)). Also, the pure ice component in these models has a variable grain radius of 3–4 lm, while the dirty ice model has only

a 5-lm icy component (which has no effects on the fits). The ice models are all bidirectional reflectance models, and so are directly quantitatively comparable. These four fits were done by computational least-squares. The water ice spatial percentage using the original (Section 4) models was (23, 5) 3.5%; (22, 5) 2.1%; (23, 4) 1.8%; and (22, 4) 1.7% (Fig. 7a–d, with a simpler model gives 3.6%, 2.3%, 2.1%, 1.5%, respectively). The dirty ice model generally fits the

Fig. 8. Some areas on the polar regions of Phoebe have purer ice than elsewhere. Shown is an average of several pixels in the PHOEBE017 observation, and a model fit using 22% Phoebe dark material and 5% pure ice mixture with grain radii of 1.4 and 17 lm.

Fig. 9. Nine-pixel spectral average from the PHOEBE015 observation with a 20% band-depth CO2 band fit. The plotted spectra use the updated wavelength list (Cruikshank et al., 2010).

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90N

0.4

0.3 30N

0.25 0.2

0

0.15 30S 0.1 60S

CO2 band depth

0.35

60N

0.05 0

90S 180

150

120

90

60

30

0

330

300

270

240

210

180

Fig. 10. Map of CO2 band depth on Phoebe showing widespread levels of 15–20% with peaks of 30% or more. The noise level is such that most fits were made on 3  3 or 5  5 spectral averages.

1.5- and 2.0-lm water ice bands as well or better than the pure ice model, and also in the 0.8–1.4-lm region, where the dirty ice model has weak or absent bands like the data. In the 2.5-lm region, the pure model fits better in the (23, 5) spectra while the dirty ice model fits better in the other three spectra. The fit is generally good beyond 2.7 lm for both models except for matching the large Fresnel peak in the data at 3.1 lm, which implies a possibly dirtier ice with a different grain size. 8. Water ice modeling discussion

CO2 Band depth

The incorporation of a dirty ice model (a few percent of Phoebe dark material mixed with ice) as the bright member of spatially segregated spectra generally improves the fit over pure ice models. The bright component area ratio of the dirty ice is about 1.6 times that of the pure ice, averaged over the four pixels in Fig. 7 (1.62 ± 0.25 using the pure ice abundances in Fig. 7 and or 1.66 ± 0.12 using the original pure ice abundances), so the maximum icy fraction is now about 6.5% rather than 4%. For mixtures with more dark material down to 30% albedo (the level implied by imaging observations) this ratio would be about 3 (since 60% albedo ice gives a ratio of 1.6  1/0.6 then 30% albedo gives 1/ 0.3  3), giving a maximum area of 12%. Purer ice is seen on

Water ice abundance Fig. 11. Correlation of CO2 band depth with water ice abundance. The scatter plot shows a slightly decreasing CO2 band depth with increasing water ice abundance (r = 0.133). A boxcar median of the data is shown as a thin black line (r = 0.423), which is carried out only until water ice abundance = 0.020 because of excessive scatter beyond that point. The best linear fit to that median is shown as a thick black line.

isolated polar regions of Phoebe (Fig. 8, from the PHOEBE017 observation), though these regions need not be pure ice as modeled (less dirty ice will also show the short-wave bands (Fig. 6)). The segregation model Spencer and Denk (2010) develop for Iapetus requires a supply of dark material, that eventually forms a lag deep enough to stay pure under the effects of micrometeorite gardening. Ice will not stick to pure dark material over long periods, although the forcing at 123 K is not as large as the forcing on the Galilean satellites at 140–160 K. The process is probably not the same for Phoebe, but it is possible that gardening resistant lags can form even from an originally mixed regolith as may have occurred. The Iapetus bright areas in Spencer and Denk (2010) are mixed dark material and ice which is stable because the volatility of ice at 120–130 K is not large, so the sinking of warm dark particles is slow enough that impact gardening should keep them optically active.

9. CO2 band-depth The CO2 band at 4.25 lm on Phoebe is modeled by a shape from averaged data to determine the percentage of absorption. If the average shape is inverted and normalized so that 1 corresponds to the center of the band and 0 to the continuation of the continuum at the center, then the model for the measured data would be (cont  1:0  bd  normshape), where cont and bd are determined by fitting. The data is averaged in 3  3 or 5  5 blocks to improve the fits. A typical fit at 20% band-depth is shown in Fig. 9, using the revised wavelengths in this region (Cruikshank et al., 2010). The CO2 band-depths are measured on the same eight observations as the water ice modeling and mapped on the Phoebe global map in the same way. Fig. 10 shows a spatial-resolution weighted average CO2 band-depth showing a background level of 12–15% with local increases to 30–35% or more. This is more or less consistent with the non-quantitative image in Clark et al. (2005). The places where the band-depth is larger correlate with less water ice as illustrated in Fig. 11 that shows a weak linear relationship between water ice abundance and CO2 band depth (with a Pearson correlation of 0.133 for the raw data and 0.423 for the median data). Coradini et al. (2008) show that CO2 is not correlated with water ice or is weakly positively correlated, but the data in their Figs. 9 and 10 is as scattered as ours and shows the same trend if the iciest spectrum is considered an outlier. It is interesting to compare this data to that from Iapetus, where the CO2 band is found deepest in mixed terrains, and shallower in ice-rich or non-ice-rich regions (Pinilla-Alonso et al., 2011). All the Phoebe pixels are mixed, with a maximum of 10% dirty ice, so the VIMS data have very little to say about CO2 in the dirty ice component,

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unlike the Iapetus data, which has pixels filled with non-ice and dirty ice. The observed CO2 is associated with the dark material on the Galilean satellites Callisto and Ganymede (Hibbitts et al., 2000, 2003) and that is the case here since the ice covers only a few percent of any pixel. The ice is generally fine grained enough on the saturnian satellites that there is significant reflection at 4.25 lm that can show absorption by included CO2, unlike for the Galilean satellites. In any case, most of the observed CO2 is located in 120 K dark material and must be adsorbed (Hibbitts and Szanyi, 2007) rather than free CO2 ice (despite the similarity in band position and width), which is not stable above about 80 K. 10. Conclusions We have modeled the water ice as a segregated component on the saturnian satellite Phoebe using infrared spectra from Cassini– VIMS. The spatial resolution of VIMS (not much less than 5–10 km for most of the observations) means that most of the bright peaks and ridges are not resolved, and our measurements imply that icerich terrains cover at most 6.5% of any pixel. We have shown that the spectra from one ice-rich region on Phoebe were consistent with a bright component of dirty ice, with a few percent of dark material, and having an albedo of about 60%. Areas of purer and higher albedo ice are found in isolated places, and lower albedo mixtures could be successfully used as well. In all cases of bright pixels with a large percentage of ice, the ice grain radius is 5– 10 lm. We also measured the band depth of the CO2 band near 4.25 lm found on Phoebe. Here we found a background level of 15% with local increases to 35% or more. The band depth is weakly anti-correlated with water ice abundance. These results are roughly consistent with previous work. Acknowledgments This work was performed with the support of the NASA Cassini Data Analysis Program. The manuscript was improved by comments from Steve Wood and two anonymous reviewers. References Bauer, J.M., Buratti, B.J., Simonelli, D.P., Owen, W.M., 2004. Recovering the rotational light curve of Phoebe. Astrophys. J. 610, L60–L67. Brown, R.H. et al., 2004. The Cassini Visual and Infrared Mapping Spectrometer (VIMS) investigation. Space Sci. Rev. 115, 111–168. Buratti, B.J., Soderlund, K., Bauer, J., Mosher, J.A., Hicks, M.D., Simonelli, D.P., Jaumann, R., Clark, R.N., Brown, R.H., Cruikshank, D.P., Momary, T., 2008. Infrared (0.83–5.1 lm) photometry of Phoebe from the Cassini visual infrared mapping spectrometer. Icarus 193, 309–322. Burns, J.A., 1986. The evolution of satellite orbits. In: Burns, J.A., Mathews, M.S. (Eds.), Satellites. Univ Arizona Press, Tucson, pp. 117–158. Clark, R.N., 1981. The spectral reflectance of water–mineral mixtures at low temperatures. J. Geophys. Res. 86, 3074–3086. Clark, R.N. et al., 2005. Compositional maps of Saturn’s moon Phoebe from imaging spectroscopy. Nature 435, 66–69. Coradini, A. et al., 2008. Identification of spectral units on Phoebe. Icarus 193, 233– 251.

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