Pauli Decomposition

Hello again, and welcome to another week of Arctic Resolution.

Last post, we explored the new 2016 RADARSAT-2 Acquisitions we obtained over the Expedition Fiord region of Axel Heiberg Island, Nunavut.  I showed you derived Circular Polarization Ratio (CPR) images of our study area.  This week is similar.  Since producing the CPR maps, I have compiled a set of Pauli decomposition images for our site.

What is a Pauli decomposition?  Pauli decompositions are used to show scattering mechanisms affecting the roughness of a surface.  In this way, they are very similar to CPR images; albeit more qualitatively interpreted.  CPR is calculated as a single integer, in which surfaces can be categories as smooth (CPR <<1), moderately rough (CPR ~0.5-1), or blocky (CPR >1).

In contrast, Pauli decompositions are more interpretive.  They are represented as RGB composite images, with the colour red being derived from the HH and VV bands: |HH-VV|^2; green from the HV band: |2HV|^2; blue also from HH and VV: |HH+VV|^2.  Recall that we are using quad-polarimetric radar, which takes advantage of RADARSAT-2's capability of transmitting and receiving horizontal and vertical signals.

What does this ultimately mean?  Areas of the images that feature more red highlight areas of double-bounce scattering, like blocky surfaces or ice.  Green areas show multiple-bounce scattering, which are rougher surfaces.  Blue shows single-bounce radar, which will indicate smoother surfaces.  Of course, these images are composites, so very few places will show solely one colour, with lots of purples and yellows indicating multiple types of radar return.  This fact exemplifies that Pauli decompositions are qualitatively interpreted; they look pretty, but caution is advised when characterizing surfaces.

Like the last post, the images are terrain corrected in SNAP, and uploaded into ArcGIS.  Unlike the previous CPR images, we did not use PolSARPro at any step during the processing.  All of the processing was performed in SNAP, as follows:

• Radiometric calibration
• Multilooking (we went with 4 azimuth looks this time, instead of 6)
• Pauli decomposition
• Terrain correction (Range-Doppler)
• Export to ArcGIS

For some reason, though, some of our images look silly!  Instead of looking like a regular Pauli decomposition, a few took on very bright pinks, blues, and greens.

Pauli decomposition produces an RGB composite image, with purple, yellowish and greenish colours most common.  The bright pink and aqua is the result of a stretching issue.

I was able to fix this problem by setting NoData values to 0 in each of the images, which removed pixels that were offsetting the histograms used in stretching the images.  I haven't stretched the images on the same scale, yet, but you can see they look much better.

Pauli decomposition of 2016 RADARSAT-2 acquisitions, masking out areas of 0 elevation (i.e., water).  Images are not mosaiced or stretched on the same scale.  

Pauli decomposition of 2016 RADARSAT-2 acquisitions, including water.  Images are not mosaiced or stretched on the same scale.

Comparing differences between the water-inclusive and water-exclusive compliations, as well as colour tone discrepancies among images within the same compilations, shows the importance that image stretching plays when we interpret these images.  Remember, since Pauli decompositions are qualitatively interpreted, consistency is very important.

A preliminary assessment, though, shows that our large salt dome is yellow, which suggests that this area is rough, with a combination of double-bounce scattering (red band) and multiple bounce scattering (green band).  This is consistent with the high CPR signature seen previously.

RADARSAT-2 Data and Products (c) MacDonald, Dettwiler and Associates, Ltd. (2016) - All Rights Reserved. RADARSAT is an official trademark of the Canadian Space Agency.

HiRISE Images!

And now for something completely different.

This past month I've been working on another project outside of my Arctic radar thesis work: my first planetary mission experience!  I've had the pleasure to be involved a 2-week imaging campaign for HiRISE - the highest resolution imager in Mars orbit.  Many of you have heard me mention that I've been working on HiRISE lately, so here is some explanation of what that means.

HiRISE (High Resolution Imaging Science Experiment) is an instrument on the Mars Reconaissance Orbiter (MRO), which has been in orbit since 2006.  As the MRO orbits Mars, HiRISE takes very high resolution images (~25-30cm/pixel) of the surface of Mars.  These images are used for geological mapping, interpreting surface features, and even exploring both the science and safety of candidate landing sites for future Mars missions.

Artist rendition of the Mars Reconnaissance Orbiter.
Credit: NASA

HiRISE imaging cycles are two weeks long, but before the actual imaging happens, we need to do a lot of prep work.  Planning of this cycle (#259) was led by Dr. Livio Tornabene at University of Western Ontario (a science team member of, and a former targeting specialist for HiRISE) and included a team consisting of post-doc Eric Pilles, fellow M.Sc candidate Arya Bina, and myself (Elise Harrington, if you need reminding of who's blog you are reading). In essence, we performed the role of CIPP during this cycle.  CIPP stands for "Co-I of the Pay Period" and is the person responsible for the scientific priorities of the mission.  Together, we decided what types of features on Mars we wanted to image, where the images should be placed (mostly based on previous science team and public targeted suggestions), how big the images should be, and what image settings or resolution would be most effective for the objective outlined by the suggestor.  For a given cycle, we work in conjunction with the HiTS (i.e. a HiRISE Targeting Specialist) to maximize and optimize the images we take within data, orbit, and instrument health constraints.  The HiTS is responsible for making sure our science plan is both viable and achievable with respect to our restrictions (e.g. instrument health, orbit path, flight rules, data volume that can be sent back to Earth, etc.) and generates the final commands that are sent to NASA/JPL to radiate to the spacecraft.

Oh man, did we ever have a lot of constraints this mission!  We had quite a few challenges placed before us right from the start.  First of all, we were not able to roll the spacecraft most of the time.  When I write "roll" I literally mean that the Mars Reconnaissance Orbiter would be performing a partial barrel roll in space.  Without this capability we can't turn to take images off our orbital track, and are only able to plan a few images that were not directly below MRO.  Images taken directly below spacecrafts are referred to as nadir.  Google eloquently defines nadir as "the point on the celestial sphere directly below an observer".  Remember that term, because it applies to radar imaging, too!

So, why were we roll-restricted?  Because of the present position of Mars, the Sun, and Earth, the solar panels and communication antenna are arranged in such a way, that rotating would cause the antenna to hit the solar panels.  "Stop hitting yourself, MRO!"

Another constraint we have, is that we are in a CRISM cold-cycle.  CRISM, like HiRISE, is another instrument on the MRO.  CRISM (Compact Reconnaissance Imaging Spectrometer for Mars), a spectrometer, measures different wavelengths of light and infrared light that are passively bouncing off of, or being reflected or emitted from, the surface of Mars.  Spectrometetry is super useful for trying to detect water (chemically trapped in rocks) and different types of minerals remotely.  However, if you are familiar with infrared saunas, you'll know that some infrared wavelengths are radiant heat!  This means that heat from other sources will mess up CRISM's work in trying to read the spectral signatures in the infrared range.  The CRISM cooling system has been aging ungracefully, and can only be used sparingly, so the MRO team devised CRISM cold cycles where CRISM gets the lion's share of prime imaging opportunities.  Too bad for us.

The coloured patch in the centre of the map, and the right image is an example of what CRISM data look like.  The different colours show possible relative abundances of different minerals.  This example shows relative abundances of phyllosilicate clay minerals in Mawrth Vallis.  Clays are water-bearing minerals, and are important in studying the past habitability of Mars. Because of this clay abundance, Mawrth Vallis is one of many candidate landing sites for the upcoming ExoMars mission.  Credit: NASA/JPL/JHUAPL

Finally, each cycle HiRISE has an allotted amount of downlink.  This is the amount of data we are able to send back to Earth during our cycle.  We need to share total downlink with the other instruments.  This cycle we have ~200 GB, which is about half of what we originally expect. Remember that odd configuration that would cause MRO to hit itself if it rolled?  Well, it was a specifically unique time in which it also impacted our data volume. Oh well, c'est la vie.  

To make matters worse, it was also the peak of dust storm season on Mars.  We were careful to monitor the global dust activity to makesure we don't accidentally take pictures that are hazy. Fortunately, no serious dust storms have kicked up so far.

Despite all these restrictions, we've been doing really well! Below are more of the images we have taken thus far.  Bare in mind that these are not true-colour images, i.e. the colours aren't what you would see if you were looking down at the surface of Mars with your own eyes. Instead, they are false-colour infrared images, meaning we are using infrared wavelengths to highlight some distinct rocks or sediments that you wouldn't see as clearly otherwise.  We've taken a lot of great images so far, with many more to come this week!

This is the first image I planned, and was the second image we took this cycle. Here there is two or more types of 
exposed bedrock in Terra Sabaea.   NASA/JPL/University of Arizona.

The second image that I personally planned, this time north of Hellas Planitia.  
It captures contacts between different types of bedrock, overlain by sand dunes.  
We are far enough north of Hellas Planitia to not be affected by the dust storms.  NASA/JPL/University of Arizona.

All the images we have acquired this cycle will be publicly available on the HiRISE website in the next couple weeks.  You can see a few more that we've taken on the CPSX Twitter and Facebook.