Position on Rover
The Mastcam-Z calibration targets are positioned on the rear starboard side of the Mars 2020 Perseverance rover deck. Figure 3 shows the targets in position, imaged by the flight cameras as well as a drawing of the rover with the location of the Mastcam-Z calibration targets on the RPFA box pointed out. Table 4 lists values for distance and pointing from the camera head to the two calibration targets based on the rover CAD model.
Table 4 Distances and pointings camera to calibration targets in rover navigation frame. Remote Sensing Mast elevation convention is \(0^{\circ}\) when camera pointed horizontally and negative downwards from that. Emission angle for reflected light emitted from target towards camera is thus \(90^{\circ}\) + RSM elevation. Azimuth convention is \(0^{\circ}\) when camera pointed straight ahead and positive counterclockwise from that. These numbers are based on the rover CAD model. Final numbers for the rover as built are not yet available at the time of writing and may deviate slightly Effect of Primary-Target Embedded Magnets
The effect of the magnetic field from the magnets on the trajectory of a magnetized dust grain is a complex interplay of gravity, drag, Brownian motion and magnetic forces (Kinch et al. 2006). Patterns of dust deposition are functions of wind speed, and dust grain size, density, and magnetic properties. As such there is no simple prescription for exactly how wide the “clean” spot in the center of the color and grayscale patches will turn out to be during operations on the Martian surface. Nonetheless, wind tunnel experiments, numerical simulation, and flight experiences from the MER, Phoenix and MSL missions provide useful indications.
This accumulated experience indicates that the larger Phoenix-type magnets that are part of the Mastcam-Z calibration target will deliver a central “clean” spot with low and uniform dust deposition of at least 3 mm radius. The equivalent minimum clean spot for the smaller MER-type magnets that flew on MER and MSL is 2 mm diameter. These numbers are illustrated by Figs. 17-20.
On the Mastcam-Z calibration targets we expect the “clean” spots to be circles at least 3 mm in diameter. Using 1.1 m as a camera range and using 3 mm as the low estimate of the “clean” spot diameter, we can calculate the angular width of the “clean” spot. Clean spot width (azimuthal dimension) = 3 mm / 1.1 (m/Rad) = 2.73 mRad.
In the elevation dimension the angular width is smaller because we’re viewing the cal target at an emission angle of \(58^{\circ}\). Clean spot width (elevation dimension) = \(2.73~\mbox{mRad}\cdot \mathrm{cos}(58^{\circ}) = 2.73~\mbox{mRad}\cdot 0.53 = 1.45\) mRad.
So, viewed from Mastcam-Z the “clean” spots will appear as ellipses with (rough numbers) major axis = 2.73 mRad and minor axis = 1.45 mRad.
The resolutions attainable by Mastcam-Z span the range between 0.28 mRad/px at 26 mm focal length (widest field) to 0.067 mRad/px at 111 mm (narrowest field). At 0.28 mRad/px the magnet clean spot will be roughly 10 by 5 pixels. At 0.067 mRad/px the magnet clean spot will be roughly 40 by 20 pixels. Note that these ranges are pixels on the detector. The Bayer patterns means that only 1/4 of these will be blue pixels, 1/4 red pixels and 1/2 green pixels, so for broadband RGB filters as well as for many of the narrowband filters the number of active pixels is reduced accordingly (see Hayes et al. 2021). Operationally the calibration target will be imaged using a zoom setting that presents a reasonable compromise between number of pixels for data analysis and total data volume required. This sweet spot is not precisely known at present but will be somewhere between the two ranges given above (see Figs. 14 & 15 and Sect. 4.4).
On MSL, the cal target is severely out of focus when viewed through the right eye of the Mastcam (M100). When viewed through the left eye (M34) the smaller MSL magnets have clean spots of about 7 by 4 pixels (Figs. 18 & 19). The Mastcam-Z system will thus be a very significant improvement over MSL Mastcam in terms of number of pixels available in the clean spots.
As an additional point, it can be seen in Figs. 19 & 20 that even in the center of the magnet the radiance profile is not entirely flat indicating that even the “clean” area is not entirely free of dust. The larger magnets employed in the current design are expected to more efficiently repel dust and so keep the center of the clean area cleaner than was the case on MER/MSL.
In-Flight Imaging
During Mars surface operations, the Mastcam-Z radiometric calibration targets will be regularly imaged. Typically, Mastcam-Z will acquire images of the calibration targets within 30–60 minutes of the period when the most recent sequence of images was acquired, particularly if these images include multispectral observations (i.e., the narrow-band filters on each camera). Sub-frames of Mastcam-Z images that cover the calibration targets will be returned to Earth to assist with conversion of Mastcam-Z images to reflectance. The secondary target will in most cases be imaged in the same frame, but the secondary target image data will most likely not be used in the standard ground data reduction pipeline. Rather these images will be used intermittently to perform a manual check of results from the primary-target analysis. As discussed in Sects. 4.4 and 5.2, an optimal value will be determined for the focal range to be employed when the calibration targets are imaged.
Use of Calibration Targets for Reflectance Calibration
Data Reduction
The Mastcam-Z data reduction pipeline is described in detail in the work by Hayes et al. (2021). The raw images are converted through a series of steps (bias subtraction, dark current subtraction, flatfielding, etc.) into images calibrated to radiance (W/(\(\mbox{m}^{2}\,\mbox{nm}\,\mbox{sr}\)). The radiometric calibration coefficients used to convert from Data Numbers (DN) to radiance are a function of detector temperature and also have a straightforward dependence on focal length. The coefficients are summarized for a single temperature and two focal lengths in Table 7 of Hayes et al. (2021). These radiance-calibrated images are the starting point of the reflectance (I/F) calibration.
In the absence of the martian atmosphere converting from radiance to reflectance is a simple matter of dividing by the known solar irradiance as a function of wavelength and Mars-Sun distance. Figure 2 of Hayes et al. (2021) lists a reference DN level for each filter, defined as the DN level (after bias and dark current subtraction) expected from observing in a 10 ms exposure a perfectly diffuse and white sunlit surface at zero-incidence when Mars is at perihelion (1.38 AU) ignoring atmospheric attenuation. From these reference DN levels a rough reflectance estimate can be derived knowing only the exposure time and Mars-Sun distance.
Principle of Reflectance (I/F) Calibration
As pointed out in Sect. 2.2, the irradiance on the Martian surface is significantly affected by the dusty atmosphere, may change on short timescales, and is hard to model accurately. Thus, in order to produce “tactical”, reflectance calibrated data products fast enough to inform daily commanding of the rover, we rely on images of the calibration targets.
A radiance-calibrated image of a martian scene is produced by a combination of the illumination, the reflectance of the scene, and the system spectral throughput. We can write this as:
$$ \mathit{Image}_{\mathit{RAD}} = \frac{1}{\pi } \int F \left ( \lambda \right ) \cdot r \left ( \lambda \right ) \cdot \rho \left ( \lambda \right ) \cdot d\lambda \sim \frac{F}{\pi } \int r \left ( \lambda \right ) \cdot \rho \left ( \lambda \right ) \cdot d\lambda = \frac{F}{\pi } \mathit{Image}_{\mathit{IOF}} $$
(2)
Where F(\(\lambda \)) is the spectral flux (energy per time per area perpendicular to the direction of light propagation), F is the Flux averaged over the band, r(\(\lambda \)) is the normalized system spectral response, and \(\rho \)(\(\lambda \)) is the spectral reflectance, here given as I/F. The \(\pi \) is just the conversion factor from bidirectional reflectance to I/F. The approximation of replacing the spectral flux with the average value is fundamental to our approach. For the narrow-band filters with bandwidths of a few tens of nanometers this not a large source of uncertainty. For the broad Bayer RGB filters it is more significant. Fundamentally, the IOF image is the scene reflectance folded with the camera system spectral throughput curve. Ideally one should fold also with the illumination spectrum, but since this is the unknown quantity, that is not possible – hence we approximate the illumination spectrum with an average over the band.
When imaging the calibration target, the radiance from any given color and grayscale can be written in the same form as Eq. (2) above:
$$ \mathit{CT}_{\mathit{RAD}} = \frac{1}{\pi } \int F \left ( \lambda \right ) \cdot r \left ( \lambda \right ) \cdot \rho \left ( \lambda \right ) \cdot d\lambda \sim \frac{F}{\pi } \int r \left ( \lambda \right ) \cdot \rho \left ( \lambda \right ) \cdot d\lambda = \frac{F}{\pi } \mathit{CT}_{\mathit{IOF}} $$
(3)
In this case, the I/F value of each calibration target material (material reflectance folded with the camera system spectral throughput curve) is known from pre-flight characterization (see Sect. 4). In Eq. (3), therefore, the flux F is the only unknown. This quantity can therefore be isolated and used in Eq. (2):
$$ \frac{\mathit{Image}_{\mathit{RAD}}}{\mathit{CT}_{\mathit{RAD}}} \sim \frac{\mathit{Image}_{\mathit{IOF}}}{\mathit{CT}_{\mathit{IOF}}} < => \mathit{Image}_{\mathit{IOF}} \sim \mathit{CT}_{\mathit{IOF}} \cdot \frac{\mathit{Image}_{\mathit{RAD}}}{\mathit{CT}_{\mathit{RAD}}} $$
(4)
Equation (4) is the basic equation behind the tactical I/F calibration procedure using the calibration target. In practice, as described in Sect. 5.4.3, there are several calibration target materials, each with their own pre-flight-characterized reflectance value and so we perform a linear fit, effectively averaging over several instances of Eq. (4), one for each reference material.
Note that multiplicative terms in the radiance calibration (DN-to-radiance coefficients) divide out of Eq. (4) to the extent that they are identical between the calibration target image and the image of a martian scene. Thus, potential radiance calibration problems such as drift in detector sensitivity etc. will not, to first order, affect the I/F calibration. Note also, that the method does not rely on similarity in illumination geometry between calibration target image and the image being calibrated. The calibration target reflectances are known in the specific illumination and observation geometry of the calibration target image (from the pre-flight reflectance model as described in Sect. 4). From this, the solar flux is known. The I/F calibrated image is then an expression of the I/F of the imaged scene in the specific illumination and observation geometry of that image.
Note also, since I/F (unlike R∗) is fundamentally defined relative to flux (energy per area normal to direction of propagation), rather than irradiance (energy per area impinging on the surface), the derived I/F is strictly correct, no matter the specific solar incidence of the observed surface. Of course, in order to convert to R∗ and know the albedo of the surface, one needs to know the angle of incidence (see Eq. (1) for relation between R∗ and I/F).
Practical Implementation of the I/F Calibration
For each calibration target image, Regions of Interest (ROI’s) will be selected for analysis. The selection will happen by a semi-automated process. First, a predefined pattern is automatically overlain on the image using known pointings and zoom settings combined with minor adjustment by an image-alignment algorithm. Second, these patterns will be manually checked by a calibration pipeline operator and adjusted if necessary. The relatively dust-free central spots of the eight color and grayscale patches will be selected as will illuminated regions of the four grayscale rings. In addition, shadowed regions of the grayscale rings will be selected.
The observed mean radiances from selected ROI’s in direct sunlight are then plotted as a function of known substrate reflectance values in the same illumination and viewing geometry. Figure 21 & 22 show examples of such radiance-reflectance plots based on calibration target images acquired during ATLO. For more examples see e.g. Bell et al. (2006).
With a clean calibration target the observed ROI mean radiance values fall on a straight line through the origin in such a radiance-reflectance plot and the slope of this line is a measure of the irradiance at this wavelength (determined by the filter used for acquisition of this particular image).
Each of the 8 color and grayscale patches and each of the 4 grayscale rings generate a data point on the plot. A least-squares fit is performed to determine the slope of the best-fit line and derive a value for the irradiance, which is effectively a translation from units of radiance to units of reflectance. This procedure is similar to the procedure employed for MER Pancam (Bell et al. 2003) and MSL Mastcam (Bell et al. 2017).
Diffuse Sky Light
The procedure described in Sects. 5.4.3 and 5.4.4 assumes the illumination to be fully directional, i.e. all light arriving from the direction of the sun. In reality, on Mars, a significant fraction of the light is scattered light from the atmosphere. The fraction of the total irradiance that is diffuse skylight varies from ∼20%-30% around noon under relatively low atmospheric dust loading to essentially 100% under dust storm conditions (Kinch et al. 2015). This complicates derivation of I/F in two ways: First, Since the reflectance of the calibration target materials depends on illumination geometry, it may be slightly different for diffuse sky illumination than for direct solar illumination. Second, since any measure of reflectance must be defined relative to an illumination and observation geometry, strictly the derived I/F of the martian scene will be a reflectance under the specific combination of direct sunlight and diffuse sky illumination that was present when the image was acquired.
The way we handle this problem in the calibration pipeline is by approximating the illumination as consisting of two components: One directional, from the Sun, one diffuse, arriving equally from the entire sky hemisphere. For each calibration target material, the model returns two values of R∗: One directional for the relevant geometry, and one hemispherical, found from an average over all angles of incidence and azimuth. The shadowed regions of the grayscale rings are then modeled as only illuminated by the diffuse component, while all other surfaces are illuminated by both the diffuse and the directional component. In this way the model will derive two free parameters describing the total irradiance: J = JDirectional + JDiffuse. Since the calibration target materials have fairly uniform R∗ for incidence angles less than \({\sim} 45^{\circ}\) (see Sect. 4.2), and since most imaging will happen with the sun relatively high in the sky, in most cases the calibration target reflectance estimate will not be radically different between the directional and the diffuse component. Problematic situations may occur at low sun, when the diffuse skylight is very far from uniformly distributed across the sky, especially in a forward-scattering geometry, when many of the calibration target materials have strong reflectance peaks (see Sect. 4.2).
Conceptually, the presence of the diffuse component of illumination presents another problem in interpreting the I/F calibrated images. The solar flux F is an inherently directional quantity. With a diffuse component present, the illumination must instead be expressed in terms of irradiance J = F ⋅ cos(i). In this formulation, the angle of incidence of the sunlight on the surfaces in the image matter for the interpreted I/F value and so this value is now only strictly valid for surfaces with the same angle of incidence as the calibration target (essentially, horizontal surfaces). Another way of expressing this conceptual problem: With purely directional illumination the relation between irradiance and angle of incidence is well understood. With a diffuse component present that in reality is not evenly distributed across the sky, the irradiance on any surface that is not level with the calibration target is not that well-known, which will introduce extra uncertainty in interpreting the derived I/F values when applied to non-horizontal surfaces.
Dust Correction
Dust is always present in the martian atmosphere. As soon as the Perseverance rover has landed the rover deck, including Mastcam-Z calibration targets, will begin accumulating a layer of aeolian dust. In addition, as discussed in Sect. 2.2, an appreciable amount of material is likely to be deposited during the landing process as the skycrane retrorockets mobilize surface materials. Notably, this component may have different color properties from the aeolian dust. Because of the overlying dust, the reflectance properties of the calibration target materials will begin changing immediately after landing and will no longer be fully described by the pre-flight characterization (Sect. 4). This represents a significant problem for reflectance calibration. We plan to handle the problem of dust deposition by two separate methods. First, through the built-in sweep magnets, that will significantly reduce dust deposition on sections of the primary calibration target. Second, by a dust-correction algorithm that has been used successfully on MER (Kinch et al. 2015) and MSL (Bell et al. 2017).
As was described in Sect. 5.2, because of the magnetic properties of martian aeolian dust, the strong magnets built in to the primary calibration target are expected to very significantly reduce dust deposition in the central spots of the eight circular color and grayscale patches. Extensive experience from MER, Phoenix and MSL indicates that these spots will remain relatively dust-free. They will not be entirely clean, as some non-magnetic dust will accumulate both during landing, as the magnetic properties of ground-derived materials deposited during landing are not known, and later, since there is a small non-magnetic component of the aeolian dust. The four central grayscale rings are expected to accumulate significantly more dust as the mission progresses.
In order to correct for the presence of dust we will employ a method described in full detail by Kinch et al. (2015), validated in laboratory studies by Johnson et al. (2006) and in-flight on MER (Kinch et al. 2015) and MSL (Bell et al. 2017). The dust correction procedure fits the observed radiance values to an analytical two-layer scattering model based on the work of Hapke (1993, Sect. 9.D.2). The model treats single-scattering events in full detail and uses a two-stream formalism (e.g., Zdunkowski et al. 2007) to treat multiple-scattering events. Below we will go into some detail about the dust correction procedure, to some extent repeating and paraphrasing material found in Kinch et al. (2015). For full detail that work should be consulted.
The Problem
The presence of dust on the calibration target is easily observed in calibration target images as a reduction in contrast between the bright and the dark surfaces. The dustier the target gets, the more the observed radiance from all materials will approach the same value. Thus, by employing a suitable mathematical model, an estimate of dust thickness can be derived from each single calibration target image. Using even very simple models a rough but reasonable estimate can be derived frame-by-frame and in the absence of any information about dust color properties.
The harder problem is to understand what effect the dust has on the reflectance value, and thus on the derived irradiance – the quantity needed to perform the reflectance calibration. The presence of dust can be detected and the amount estimated from the reduction in contrast, but from single frames it is very hard to tell the difference between bright dust under relative low irradiance and darker dust under brighter irradiance. In Sect. 5.5.2, we describe the analytical two-layer reflectance model used in our dust correction algorithm, while Sect. 5.5.3 describes the method used to derive a reflectance dust spectrum.
Dust Model
The choice of model description is a balance between having a model complex enough to adequately reproduce the reflectance behavior of the dusty calibration target while simple enough and relying on a small enough number of parameters that it lends itself to routine application of a best-fit procedure for parameter determination. The model we use to describe directional scattering from a dust-covered calibration target surface is essentially the same as that given by Hapke (1993, Sect. 9.D.2) for reflectance of a double-layer of particulate material, except we have rewritten the equations so that the lower layer is not described as a layer of particulates but rather described as a single entity with reflectance properties given by our calibration target reflectance model (see Sect. 4). For the diffuse illumination component (see Sect. 5.4.4) we use the diffusive-reflectance model also given by Hapke (1993) Eq. 9.14 based on the two-stream formalism (e.g., Zdunkowski et al. 2007). At the highest level, the model can be written as:
$$\begin{aligned} \mathit{CT}_{\mathit{RAD}} =& \frac{{J}_{\mathit{Direct}}}{\pi } R_{\mathit{Direct}}^{*} \left ( \boldsymbol{\tau }_{\boldsymbol{M}}, \boldsymbol{w}_{\boldsymbol{M}}, \mathit{Material}, \mathit{Filter}, i, e, g \right ) \\ &{} + \frac{{J}_{\mathit{Diffuse}}}{\pi } R_{\mathit{Diffuse}}^{*} \left ( \boldsymbol{\tau }_{\boldsymbol{M}}, \boldsymbol{w}_{\boldsymbol{M}}, \mathit{Material}, \mathit{Filter} \right ) \end{aligned}$$
(5)
The full equations are given in Kinch et al. (2015) and will not be repeated here.
Of the quantities listed in the above equation, material (i.e. calibration target substrate), filter, and the angles i, e, g, are all specified by the conditions of the measurement. The four remaining quantities, listed in bold above, are the diffuse and directional components of the irradiance, J, the optical depth of dust deposited on the calibration target, \(\tau _{M}\), and the single-scattering albedo of the dust, wM. Both \(\tau _{M}\) and wM should be thought of as proxies within the limited framework of the scattering model for the true microphysical optical depth and single-scattering albedo. Both parameters are sensitive e.g. to assumptions about the phase function of single-scattering events and more generally the mathematical radiative-transfer formalism is based on assumptions about widely separated dust grains that are manifestly not true in this application. The subscript M is intended as a reminder that the relation between this model parameter and the true microphysical quantity is far from straightforward (see e.g. Shepard and Helfenstein (2007)). Our goal here is not to derive microphysical quantities of deposited dust, but rather to accurately reproduce the reflectance of a dusty calibration target surface.
In the implementation (see Sect. 5.5.4.) three quantities are derived for each image, these are the irradiance, split into a direct component from the Sun (JDirect) and a diffuse sky component (JDiffuse), and the model dust optical depth (\(\tau _{\mathrm{M}}\)). The last quantity, the model dust single-scattering albedo (wM), must be specified. This quantity is discussed in Sect. 5.5.3 below.
Dust Spectrum
As described in Sects. 5.5.1 and 5.5.2, the model dust single-scattering albedo cannot be disentangled from the irradiance based on single image frames and so must be specified in order to derive irradiance. In Kinch et al. (2015) a procedure is outlined for deriving this dust single-scattering albedo from a simultaneous analysis of a large number of calibration target images acquired over many sols with varying thicknesses of deposited dust. The idea is to look for correlation between model optical thickness of deposited dust and derived irradiance. If the model dust single-scattering albedo is too low, the derived irradiances will be observed to (artificially) increase as the calibration target gets dustier. Conversely, if the model dust single-scattering albedo is too high, the derived irradiances will be observed to (artificially) decrease as the calibration target gets dustier. The correct model dust single-scattering albedo is the value that causes no correlation between deposited dust thickness and derived irradiance.
This procedure was successfully employed for data from the MER Spirit and Opportunity rovers (Kinch et al. 2015) as well as for the Curiosity rover (Bell et al. 2017). The derived dust spectra for all three missions are shown in Fig. 23. Note that these values are derived independently for each filter on each mission. The results are very consistent in terms of derived dust spectra. For the early mission, we will employ a dust reflectance spectrum based on a smoothed average of the values derived in these earlier missions. Later on, as data accumulates, we will be able to derive a dust spectrum directly from the Mastcam-Z dataset (in fact, two dust spectra, since it is not obvious that non-magnetic dust in the magnetically-protected patch centers will have the same color properties as dust that accumulates on the grayscale rings).
Dust Correction Implementation
Figure 24, reproduced from Kinch et al. (2015), shows an example of the implementation of this model to an L4(601 nm) Pancam calibration target image from sol 180 of MER Spirit. The left frame shows radiances observed from the dusty target as a function of model clean substrate reflectances. The reduced contrast can be seen as the relative difference between bright and dark substrates is reduced in the observation (y-axis) relative to the model (x-axis) and the data points do not fall on the predicted straight line through the origin. After fitting to the dust model, the right frame shows the same observed data (y-axis) now plotted relative to the dust model prediction for the reflectances. The linear relation is recovered. Note the presence of the shadowed data points (open circles) in both figures, falling on a separate straight line in the right frame, indicative of the level of diffuse sky illumination.
The model derives for each image independently values for the directional and diffuse irradiance together with a value for the optical thickness of deposited dust. Since we may expect the dust thickness to be different in the magnetically-protected color and grayscale patches from the value on the grayscale rings we may perform the fit independently for patches and rings and compare derived irradiance estimates. The irradiance would be expected to be identical for patches and rings, so the comparison can be a check of the quality of the data. Alternatively, we may construct a fit to all extracted ROI’s with two free parameters for two different dust thickness values but only one free parameter for the derived irradiance, and thus force the irradiance estimate to be consistent between patches and rings.
Error Estimates
The pre-flight calibration of the Mastcam-Z flight instrument and resulting estimates of the uncertainty on calibrated data products is described in detail in Hayes et al. (2021). In Sects. 3.1.1 and 4.1.7 of that work, it is demonstrated that reflectance-calibrated Mastcam-Z images agree with spectra of calibration target materials as well as a number of laboratory-characterized rock samples to within 5% RMS in all cases – in many cases significantly better. This is under controlled laboratory conditions, though. The martian environment will introduce further sources of uncertainty such as
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Changes to irradiance in the time between acquisition of the image and acquisition of the associated calibration target image
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Uncertainties introduced by the presence of dust on the target (see Sect. 5.5)
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Uncertainties associated with the angular distribution of diffuse illumination from the sky (see Sect. 5.4)
Other contributions to the uncertainty are associated with additive terms in the radiance calibration (bias subtraction) and with uncertainty of the calibration target reflectance model (Sect. 4). On the other hand, I/F products will to first order be unaffected by uncertainties or drift in the multiplicative terms of the radiance-calibration pipeline (see Sect. 5.4.2)
Especially the uncertainties associated with the martian environment are hard to quantify and work will be ongoing in-flight to assess these uncertainties by comparison of in-flight calibration target images to pre-flight spectra as well as by observation of other targets of relatively well-known reflectance (SuperCam calibration target, rover deck, thick surface dust deposits, targets also observed with e.g. the SuperCam passive spectrometer).
The main goal of the calibration target is for rapid tactical calibration to influence the next sols activities. Our expectation is that later, after more detailed in-flight verification, more detailed model development, and more detailed image processing, higher-fidelity I/F-calibrated images can be produced enabling longer-term science output from the investigation (e.g. Wellington et al. 2017).
Cross-Comparison Between Primary and Secondary Targets
The analysis described in Sect. 5.4 and 5.5 may be employed also for the secondary calibration target as an extra independent verification. At times the secondary target will be in shadow, and irradiance on the secondary target will be different from that experienced by the primary target. When the secondary target is in direct sunlight, primary target patches and rings, and horizontal secondary target surfaces should all result in different derived dust thicknesses but the same irradiance value.
In addition to the use for calibration the dust-correction procedure will derive a history of dust thickness on the calibration targets in which seasonal variation in dust lifting and deposition can be tracked. Also, comparison between the magnetically-protected regions on the patches and the grayscale rings and secondary target surfaces will yield an estimate of the fraction of the aeolian dust grain population that is non-magnetizable. A separate derived estimate of dust thickness on the vertical surfaces of the secondary target can be used to analyze the relative importance of different dust deposition processes (gravitational settling versus Brownian motion random walk or electrostatic attraction).
Cross-Comparison with SuperCam
The SuperCam instrument light gathering telescope (Wiens et al. 2020, this issue, Maurice et al. 2020, this issue) is mounted on the rover mast above the Mastcam-Z. The SuperCam calibration target (Manrique et al. 2020, this issue) is on the rover deck above and behind the RPFA-box and the Mastcam-Z calibration targets (see Fig. 3). The SuperCam calibration target contains a set of five magnetically-protected optical patches analogous to the patches on the Mastcam-Z primary target intended for calibration of the SuperCam passive visible-near-infrared spectroscopy mode and the SuperCam remote micro-imager (RMI). Four of these patches reproduce materials present in the Mastcam-Z target (AluWhite98 and Lucideon Cyan, Green and Red) while the fifth is painted with an IR-black paint (Aeroglaze Z307).
The SuperCam calibration target is visible to the Mastcam-Z and the patches can be imaged using Mastcam-Z. Likewise, the Mastcam-Z calibration targets may be observed with the SuperCam passive and RMI modes, although with some focus limitations. These targets allow a dedicated cross-calibration program between Mastcam-Z and SuperCam passive and RMI observations to assure internal consistency, as has been done between the analogous Mastcam and ChemCam instruments on the MSL rover Curiosity (Johnson et al. 2015).