The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) is a Visible and Near-Infrared (VNIR) imaging spectrometer onboard Mars Reconnaissance Orbiter (MRO), in orbit since 2006. It was built and tested by the Johns Hopkins University Applied Physics Laboratory under the supervision of principal investigator Scott Murchie. The observations enable to have mineralogy information of the martian surface at a spatial resolution of ~20 to ~200 m/px.
Data description
The CRISM instrument has two acquisition modes:
The targeted mode (multiangular pointing)
The instrument tracks the targets and takes 11 hyperspectral images (544 bands from 362 to 3920 nm) at different emission angles due to the rotation of the detector at ± 70°: 10 hyperspectral images taken at different emission angles before and after the central image corresponding to the close nadir image (image #07). The 10 hyperspectral multi-angular observations are reduced to a factor 10 compared to the spatial resolution of the central image. According to the spatial resolution of the central image, four product types are associated to this acquisition mode. If the central image is sampled at 20 m/px, the associated product is a Full Resolution Targeted observation (FRT). By reducing the spatial resolution of the central image by a factor 2, the spatial resolution is set at 40 m/px and the associated products are Half Resolution Short (HRS) and Half Resolution Long (HRL) observations. An HRL sampled surface is twice as long as an HRS observation. Only the central image #07 is processed by MarsSI for the mineralogy identification. ATO observations are Along-Track Oversampled products resulting in incresing of the resolution up to 3 m/px.
| Types of targeted observations | |||
| FRT: 18 m/px | HRS: 36 m/px | HRL: 36 m/px | ATO: 3-12 m/px | 
|   |   |   |   | 
The survey mode (nadir pointing)
This mode was designed to estimate key locations for further analysis with the targeted mode as it covers wider areas and produces lower spatial resolution data than the targeted mode. The instrument acquires multispectral images using fixed pointing where the emission angle is set at 0° (with specific bands chosen over the 544 spectral bands, in order to identify principal minerals). There are different types of observations using this acquisition mode:
- Multispectral Survey (MSP): 200 m/px and 55 channels
- Hyperspectral Survey (HSP): 200 m/px and 154 channels
- Multispectral Window (MSW): 100 m/px
Downloading and processing CRISM data
- From the “Maps” tab, zoom-in on your region of interest, you can display the THEMIS mosaic for more precision, and then display the CRISM layer corresponding either to the targeted or survey mode, or You get all CRISM stamps that appear in red or pink. You can use the “Select” button to choose the stamp you desire. You can choose several stamps by clicking on several stamps, or by dragging your mouse to select adjacent stamps. You can also use the “Search” button for more options.
- To add your desired stamps to your cart click on “Add” in the “Cart” window: you can notice in the “Cart” window that different data were added: the TRDR files and the DDR files in Short (VNIR) and Long (IR) wavelength ranges, “S” and “L”. Only the “L” cubes are used in the CRISM processing and are needed for further processing in MarsSI, but you can also download the “S” cubes for your own use. Then, go to the “Workspace” tab.
- Your CRISM TRDR and DDR “L” files appear in the window “Data to process”. If not, you may already have processed them yourself, or someone else may have processed them, then they may already be in the “Data processed” window. If the data have not been processed, you will have to go download and process the TRDR and DDR cubes by clicking on the “Process” button in the “Data to process” button.
Pipeline
1) The TRDR cube containing the reflectance values in the infrared range will be processed using an implementation of the CAT pipeline (CRISM Analysis Tools, an add-on to IDL/ENVI available from the CRISM team (Murchie et al., 2007; Pelkey et al., 2007)). The cube will be corrected for the observation geometry (reflectance divided by the cosine of the incidence angle, available in the associated DDR cube) and for absorptions due to atmospheric CO2 using the CAT algorithm developed for CRISM based on the ‘volcano-scan’ approach (McGuire et al., 2009).

FRT00003E12_07_IF166L_TRR3_CAT_corr.img (false color composition)
2) The targeted TRDR cube corrected for atmospheric absorption and incidence angle will then be processed through a custom pipeline to remove column-dependant noise and enhance spectral features of small spatial extent: this pipeline basically divides the whole spectral data  in  the  cube  by  a  median  spectrum  computed  from  half of  the  lines  (those  in  the middle)  of  the  cube,  on  a  column-by-column basis: a procedure we dub ‘ratioing’. The output  ratioed  cube  is  dubbed medianratio.  A  by-product  of  the  procedure  is  the creation of a transposed cube where columns and lines are switched, but which is only saved for the pipeline processing and should not be used as a standalone product. This   procedure   dramatically   reduces   detector   noise   (mostly   column-dependant), corrects  for  most  residual  atmospheric  absorptions  (CO2 gas  and/or  water  vapor/ice)and  enhances  small  spectral  features  in  ratioed  spectra.  However,  there  are  also  caveats  to  this  ratioing  procedure,  such  as  the  risk  to  introduce  artifacts  in  ratioed spectra  if  the  median  spectra  used  for  division  itself  was  not  blank  but  had  spectral  features: in such cases, the ratioed spectra will show inverted spectral features (eg., an absorption in median spectra will yield a positive peak in ratioed spectra) which will be  artifacts  and  must  not  be  interpreted  as  meaningful  data.  Still, for  typical  CRISM  data, acquired  over  terrain  with  spectral  features  of  low  spatial  extent  (a  few  times  smaller  than the cube spatial extent), the median spectra will be nearly featureless and allow for reasonably straightforward detection of meaningful spectral features in ratioed spectra.

FRT00003E12_07_IF166L_TRR3_CAT_corr_medianratio.img (false color composition)
3) The  penultimate  step  of  the  pipeline  computes  so-called  ‘spectral  parameters’  or ‘spectral  criteria’  such  as  band  depths  or  combination  of  band  depths,  as  initially implemented  in  the  CAT  for  multispectral  CRISM  data (Pelkey  et  al.,  2007),  or  other parameters  based  on  analysis  of  the  shape  of  the  spectra.  We  take  advantage  of  the higher  number  of  spectral  channels  in  hyperspectral  targeted  CRISM  observations compared  to  original  CAT  multispectral  parameters:  signal-to-noise  is  improved  by using medians of spectral channels and specificity of criteria is improved by combining two or more criteria. The definition of a subset of the custom criteria available in MarsSI is  given  in  Thollot  et  al.  (2012)  and  the  remaining  can be  made  available  on  request (pending publication in a future paper). The resulting data cube, dubbed hyparam (for hyperspectral  parameters),  has  the  same  spatial  dimensions  as  the  TRDR  but  each  band  in  the  spectral  dimension  contains  the  result  of  the computing  of  a  spectral criterion designed for detection of one or several spectral features from the medianratio  cube resulting from the previous step. The hyparam  cube can be used in parallel with spectral  data  in  IDL/ENVI  to  compare  the  spatial  mapping  of  spectral  criterion  with actual spectra.

FRT00003E12_07_IF166L_TRR3_CAT_corr_medianratio_hyparam.img (olivine index)
4) Finally, the pipeline projects the hyparam cube (adding the _p suffix) for use in a GIS in combination with other datasets (DTMs, CTX or HiRISE, etc.).

FRT00003E12_07_IF166L_TRR3_CAT_corr_medianratio_hyparam_p_equir.img (olivine index)
Data names
The CRISM naming convention is the following:
(ClassType)(ObsID)_(Counter)_(Activity)(SensorID)_(Filetype).(EXT)
- ClassType: FRT, HRS, HRL, ATO, MSP, HSP, MSW, ...
- ObsID: observation ID
- Counter: image number of the targeted sequence (only the n°7 is used in MarsSI). In the survey mode, this corresponds to 1
- Activity: subtype of product: IF stands for reflectance data (I/F unit), DE for ancillary data (e.g. latitude, longitude, incidence, emission, phase angle
- SensorID: S detector (Short wavelenghts) or L (Long wavelenghts)
- Filetype: type of dataset: TRR3 are calibrated data cubes, DDR1 companion data cube containing the ancillary data
- EXT: extension of the data (img, lbl, ...)
Ex: FRT000A82E_07_IF164L_TRR3.IMG correponds to calibrated   data   cube   containing   the  reflectance data (I/F unit) in the IR range (from 1002 to 3920 nm) of the central  image (corresponding to the “07” image of the targeted sequence) of an FRT  observation.
Working with CRISM targeted parameters
Please consider these products with a critical eye. Despite using labels describing specific mineral (or other) detection, they are the result of spectral analysis results that are *USUALLY* typical of such minerals presence, and is presented as an evidence, but not a definitive proof of such. Be wary to not overinterpret instrument artefacts and other false positive as real results!
The hyparam cube produced by MarsSI contains computed spectral parameters. They account on each pixel for the likeness between the observation spectrum and the theoretical spectrum of a mineral specie, but can also give information on the position of absorption band, intensity of one particuliar band, etc. This cube is used to map spectral features: please keep in mind that the spectral parameters mapping does not represent mineralogical maps; to infer the true mineralogy of one location, the associated spectra (in corr or corr_medianratio  cubes) should be studied carefully.
The details of how the different parameters can be used are as follows (as in april 2018):
| Band number | Parameter | Detections (non exhaustive) | Details | 
| 1 | OLINDEX2 | Olivine, pyroxenes, FeMg clays | Better identifies the 1 micron olivine absorption by accounting for spectral slope. Also corrects for the false identification of olivine due to high albedo. Incorrectly maps hydrated regions as olivine-rich due to the associated decrease in reflectance beyond 2-microns. Also, like OLINDEX, this parameter falsely identifies pyroxenes as olivine. future work will be done to correct both the false identifications of hydration and pyroxene as olivine-rich. | 
| 2 | BD1900R | H2O bound | Find the 1.90 micron H2O band depth | 
| 3 | BD1980 | Sulfites | Find the 1.98 micron sulfite band depth | 
| 4 | BD2200 | Al-OH bound | Find the 2.20 micron Al-OH band depth | 
| 5 | Doub2200 | Find the 2.22 and 2.28 micron doublet band depth | |
| 6 | BD2230 | Find the 2.23 micron band depth (K. Lichtenberg) | |
| 7 | BD2500 | Carbonates | Find the 2.50 micron band depth | 
| 8 | Jar_index | Jarosite | Find the 2.205-2.272 micron 'W' exact shape | 
| 9 | Kaol_index | Kaolinite | Find the 2.16-2.20 asymetric kaolinite band | 
| 10 | Opal_index | Opal | Find the 2.20-2.26 asymetric Opal band | 
| 11 | Fe_smec_index | Fe clays | Find the 2.29 and 2.40 Fe smectite band | 
| 12 | Sulf_index | Sulfates | Find the 1.94-2.0 and 2.40-2.44 non-hydrates sulfates bands | 
| 13 | BD2232m | (Ferri)Copiapite / Fe sulfates | Find the 2.232 band of part. dehydr. | 
| 14 | Hydr-salt_index | Bassanite, gypsum, carnallite | Find the 1.77, 1.92-1.98, 2.49 bands | 
| 15 | BD2205m | Kaolinite, montmorillonite, opal | Find the 2.205 band | 
| 16 | BD2205Left | Kaolinite | Find the asymetric left 2.205 band | 
| 17 | BD2205Right | Opal | Find the asymetric right 2.205 band | 
| 18 | BD2285m | Fe smectites | Find the 2.285 band | 
| 19 | BD1922m | Hydrated minerals | Find the 1.922 band | 
| 20 | BD1849m | Jarosite | Find the 1.849 band | 
| 21 | BD2106m | Sulfates | Find the 2.106 band | 
| 22 | BD2268m | Jarosite | Find the 2.268 band | 
| 23 | BD3837m | Carbonates | Find the 3.8-3.9 band | 
| 24 | POS221 | Find the exact wavelength of minimum of 2.21 band at 2.1855-2.2252 | |
| 25 | POS227 | Find the exact wavelength of minimum of 2.27 band at 2.2583-2.3046 | |
| 26 | BD1047m | Iron oxydes | Find the 1.0472 band of iron oxides relative to linear fit 1.3423-2.1261 | 
| 27 | Hydr_FeMg_clay_index | Hydrated FeMg TOT clays | Find the 1.4; 1.9; 2.3; 2.4 bands | 
| 28 | Si-OH_index | Hydrated glass | Find the 1.4; 1.9; 2.2 bands | 
| 29 | BD2265narrow | Jarosite even within mixtures | Find the 2.265 band | 
| 30 | 2205to2278 | Get 2205/2278 ratio in doublet when both bands present | |
| 31 | BD1060poly2 | Iron oxydes | Find the 1.0603 band of iron oxides relative to 2nd order polynomial fit 1.3423-1.8292-2.6021 | 
| 32 | BD1080SEC | Iron oxydes | Find the 1.0800 band of iron oxides relative to 2nd order polynomial fit 1.3423,MAX(2.1525;2.2517)@2.203,2.6021 | 
| 33 | Chlor_index | Chlorite | Find the 2.25 and 2.34 bands | 
| 34 | BD1380-1440 | OH bound | Find the 1.4 OH band | 
| 35 | POS14xx | Find the exact wavelength of minimum of 1.4 band at (1.3752)1.3818-1.4409(1.4474) | |
| 36 | BD1908m | Hydrated minerals | Find the 1.91 band | 
| 37 | BD1961m | Hydrated minerals | Find the 1.96 band | 
| 38 | 191to196 | Si-OH hydrated minerals | Find the 1.91/1.96 ratio in hydrated minerals, esp. Si-OH bearing | 
| 39 | POS19xx | Find the exact wavelength of minimum of 1.9 band at (1.895)1.902-1.981(1.987) | |
| 40 | BD2272m_2179 | Jarosite | Find the 2.272 band of jarosite anchored at 2.18 instead of 2.09 | 
| 41 | Olivine_index | Olivine | Find the 1.080, 1.257, 1.369 and 1.467 bands | 
| 42 | BD1760m | Hydrated minerals | Find the 1.76 band | 
| 43 | FeMg-OH | FeMg-OH bounds | Find the 2.3 and 2.4 bands | 
| 44 | BD2106+2139_poly2 | Monohydrated sulfates | Find the average of 2.106 & 2.139 band of monoh. sulf. relative to 2nd order polynomial fit 1.856-2.311-2.490 | 
References:
- CRISM instrument description website: http://crism.jhuapl.edu/instrument/design/overview.php
- McGuire P. C., e t al. (2009), An improvement to the volcano - scan algorithm for atmospheric correction of CRISM and OMEGA spectral data, Planet. Space Sci., 57(7), 809 - 815.
- Murchie, S., et al. (2007), Compact reconnaissance Imaging Spectrometer for Mars (CRISM) on Mars Rec onnaissance Orbiter (MRO), J. Geophys. Res., 112(E5).
