International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
wavelength) polarimetric images of both lunar poles down to
80? latitude, plus a handful of image strips closer to the equator.
The Level 1 (unprojected, range-azimuth geometry) images
total 32 GB. Map-projected products include 18 GB of
individual images (Level 2) and 1 GB of mosaics (Level 3).
These products are uncontrolled and unrectified (i.e., projected
onto a sphere, so parallax distortions are uncorrected), and
contain less than the full set of polarization information. The M*
obtained nearly complete global coverage at 140 m/pixel with
85 spectral bands and targeted coverage at 70 m/pixel with 260
bands (Boardman et al. 2011). The full dataset includes 0.75 TB
of Level 0 (raw) data, 2.12 TB of Level 1B (calibrated radiance)
data and 1.78 TB of Level 2 (reflectance) data. The Level 1B
and 2 data are selenoreferenced (ie, latitude-longitude
coordinates of each pixel are provided) based on control to LRO
altimetry but are unprojected. These Mini-RF and M' products
can be obtained from the NASA PDS at http://pds-
geosciences.wustl.edu/ missions/chandrayaanl/.
2.25 LRO: The primary cartographic instruments are the
Lunar Orbiter Laser Altimeter (LOLA), LROC, and Mini-RF
(Chin et al. 2007). Secondary instruments providing important
compositional data at lower resolution include the Diviner
Lunar Radiometer Experiment (DLRE) and Lyman Alpha
Mapping Project (LAMP). LOLA, Mini-RF, and DLRE data are
publically available through the PDS Geosciences Node
(http://pds-geosciences.wustl.edu/missions/Iro/), whereas LROC
and LAMP data are hosted by the PDS Imaging Node
(http://pds-imaging.jpl.nasa.gov/volumes/lro.html).
The 5-spot design and 10 Hz pulse rate of LOLA (Smith et al.
2009) allow it to gather significantly more data than previous
altimeters. At the time of writing, LOLA has made 2.2 billion
laser shots (G. Neumann, written comm. 2012) and collected
5.5 billion valid range measurements (http://imbrium.mit.edu/
BROWSE/LOLA RDR/. Archived data include raw (EDR) and
processed (RDR) tables of point-by-point measurements, raster
DTMs (GDR) at grid spacings ranging from 4 to 512
pixels/degree (i.e., 7.5 km to 60 m/pixel), and spherical
harmonic coefficients (SHDR).
LROC (Robinson et al. 2010) consists of wide (WAC) and
narrow-angle (NAC) cameras. WAC uses a pushframe design
with 90° total crosstrack field of view and color coverage in 5
visible and 2 ultraviolet bands over 60° field of view. The
nominal ground sample distance is 100 m/pixel in the infrared
and ~400 m in the UV. NAC consists of a pair of identical
pushbroom cameras with wide spectral sensitivity, 0.5 m/pixel
nominal ground sample distance, and 5 km total swath width.
The wide swath of WAC allows for useful stereo sidelap with
neighboring orbit coverage, while NAC stereopairs are obtained
by rolling the spacecraft to image the same target on different
orbits. To date, the LROC team has delivered 222.9 TB of data
to the PDS, consisting of 74.3 TB of raw (EDR) and 148.6 TB
of calibrated (CDR) products (http://www .lroc.asu.edu/news/
index.php ?/archives/541-LROC-9th-PDS-Release.html). This
dataset includes 667,572 EDRs and 667,572 CDRs of which
about 2/3 are NAC images. The WAC images provide global
monochrome, color, and stereo coverage. NAC images cover
only a few percent of the Moon, but include complete coverage
of both poles and about 1200 stereopairs (M. Robinson, written
comm. 2012). High level products (RDRs) on the LROC team
site (http://wms.lroc.asu.edu/lroc/rdr_product_select) include an
uncontrolled 100 m/pixel WAC global mosaic and DTM
(Scholten et al. 2011), regional color WAC mosaics,
uncontrolled polar and local NAC mosaics, and NAC DTMs.
Mini-RF on LRO (Nozette et al. 2010; Raney et al. 2011) is
substantially more capable than the Chandrayaan-1 Forerunner,
adding a second wavelength (X band, 4.2 cm), a 7.5 m/pixel
zoom mode, and capabilities for interferometry and bistatic
observations. Between the beginning of operations in 2009 July
and the failure of the transmitter in 2010 December, the
instrument obtained near-complete S-band zoom coverage of
both poles to about 70°, with both eastward and westward look
and illumination directions, as well as ~70% coverage from
80?—90? N in X-baseline mode. About 6096 of the mid to low
latitudes were also imaged, mainly in S-zoom mode. The PDS
archive includes raw (PDR) data, Level 1 calibrated but
unprojected (CDR) images, and Level 2 map-projected (CDR-
MAP) single images but no mosaics to date. The volume of
Level 1 CDR products is 9.9 TB. The other products contain
equivalent information but are larger because of redundancy
and non-data pixels. The dataset is roughly 3 orders of magni-
tude larger than was anticipated when Mini-RF was added to
the LRO payload as a technology demonstration in 2005.
2.3 Controlled Cartographic Products
The archives described in the previous section include
numerous cartographic products, as well as geometrically raw
(unprojected) observations, but the majority of these
cartographic datasets are uncontrolled or at best semicontrolled.
The number of controlled mosaics, DTMs, and other map
products is much smaller. Foremost among the controlled
products is the LOLA altimetric dataset, for which orbit
trajectories and ground point coordinates have been adjusted to
maximize the consistency of elevations where altimetric profiles
cross (Mazarico et al. 2011) or where dense gridded data
products already exist, such as at the poles (Mazarico et al.
2012). Given the dense sampling of LOLA, and the ability of its
5-spot pattern to measure local slopes as well as elevations, this
dataset provides the best current reference for other mapping
data. The Chang’e-1 LAM data are also being corrected by
crossover analysis (Hu et al. 2011), though so far on a local
rather than global basis. Most encouragingly, studies exploring
the potential of mutual adjustment of the altimetry from
different missions (Iz et al. 2011; Shum et al. 2012) have
recently been initiated.
Imaging data are generally controlled using the altimetric
products as a reference. Examples of controlled image products
include the adjusted selenoreferencing of M’ data based on
LOLA (Boardman et al. 2011) and the production of polar (Lee
et al. 2012) and region-of-interest (Rosiek et al. 2012)
controlled mosaics of LROC NAC images. The polar mosaics
are thought to be — in numbers of pixels — the largest controlled
extraterrestial map products ever made, covering the lunar polar
caps from 85.5? to the pole at 1 m/pixel. Much of the latter
work has been sponsored by the NASA Lunar Mapping and
Modeling Project (LMMP; Noble et al. 2009; data accessible
through the LMMP Portal http://Immp.nasa.gov/). A variety of
controlled DTMs and derived products such as orthoimages
have also been made. These include DTMs from Chandrayaan-1
TMC images (Radhadevi et al. 2011), Chang'e-1 CCD images
(Liu et al. 2009), and LROC NAC images (see Beyer et al. 2011
for an overview of the multiple groups producing such DTMs
under both LROC team and LMMP sponsorship, and Rosiek et
al. 2012 and Burns et al. 2012 for more recent summaries).
Radargrammetric analysis of LRO and Chandrayaan-1 Mini-RF
images has yielded controlled DTMs (Kirk et al. 2011) and
production of controlled polar mosaics is currently under way
(Kirk et al. 2012).
3. UNIFYING THE CURRENT DATA
To obtain maximum value for science and exploration, the lunar
remote sensing data discussed here must be co-registered in a
common coordinate reference frame. Only such an effort will
ensure the proper calibration, registration, and error analysis of
the data, which in turn will permit the full comparative and
synergistic use of the datasets. The summary in this section
describes the steps needed to ensure the development and
unification of these high-value lunar data products.
Topographic, imaging, and spectral data from all lunar missions
need to be brought together into a common frame via ses
control solutions (e.g., photogrammetric, radargrammetric, iE
altimetric crossover adjustments). This rigorous process S
allow for the merging and registration necessary to generate i
most accurate, highest resolution global DTM. Such a OR.
can then be used to support photometric calibration and ort e
rectification of the datasets. Once the images are brought into à
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