radar statistical reconnaissance of the 2016 insight landing … · 2015. 3. 17. · orbital...
Post on 24-Jan-2021
2 Views
Preview:
TRANSCRIPT
Radar Statistical Reconnaissance ofthe 2016 InSight Landing Sites
C. Grima*, D. D. Blankenship*Contact:
cyril.grima@gmail.com*Institute for Geophysics,
University of Texas at Austin, TX 78758, USA
1. RADAR STATISTICAL RECONNAISSANCE (RSR)PRESENTATION
The NASA's InSight (Interior Exploration
using Seismic Investigations, Geodesy
and Heat Transport) Lander is scheduled
for launch and Mars landing in March and
September 2016, respectively. From 16
landing sites defined in 2012 in Elysium
Planitia [1], the selection has been
narrowed to 4 ellipses in 2014 [2].
The landing site selection process is
achieved through a combination of various
orbital remote-sensing technologies (e.g.
laser altimetry, imagery, spectro-imagery,
spectroscopy, thermal sensing) to assess
the terrain relief, surface cohesiveness,
rock height and rock abundance [3].
We present a preliminary application of
the Radar Statistical Reconnaissance
(RSR) [4, 5, 6] technique to the active 20-
MHz Shallow Radar (SHARAD)
instrument [7] to support the InSight
landing site selection. The RSR can
assess, with a single instrument, new
observables for the surface properties:
surface RMS heights, surface
permittivity, and surface heterogeneity.
References. [1] Golombek M., Redmond L. et al. (2013) “Selection of
the InSight landing site: contraints plans and progress”, LPSC XLIV,
#1691 [2] Golombek M., Warner N. et al. (2014) “Final four landing
sites for the InSight geophysical lander”, LPSC XLV, #1499. [3] Ball et
al. (2007) “Planetary and entry probes”, Cambridge University Press
[4] Grima C., Kofman W., Hérique A., Orosei R., Seu R. (2012),
“Quantitative analysis of Mars surface radar and reflectivity at 20MHz”.
Icarus 220, 84-99. [5] Grima C., Schroeder D. M., Blankenship D. D.,
and Young D., “Planetary landing zone reconnaissance using ice
penetrating radar: Concept validation in Antarctica”. Planetary and
Space Science 103, 191-204. [6] Grima C., Blankenship D. D., Young
D. A., Schroeder D. M. (2014) “Surface slope control on firn density at
Thwaites Glacier, West Antarctica: Results from airborne radar
sounding“. Geophysical Research Letters 41(19), 6787-6794. [7] Croci
R. et al. (2011) “The Shallow RADar (SHARAD) onboard the NASA
MRO mission”. Proceedings of the IEEE(99):794-807 [8] Jakeman E.
(1980), “On the statistics of K-distributed noise”, J. of Physics:
Mathematical and General, 13(1), 31–48. [9] Ulaby F. T., Moore R. K.,
and Fung A. K. (1982), “Microwave Remote Sensing: Active and
Passive. Volume II: Radar Remote Sensing and Surface Scattering
and Emission Theory”, Addison-Wesley Publishing Company.
The RSR is a novel technique that estimates surface properties from surface echo amplitude
statistics [4, 5, 6].
Total Surface Power
Reflectance (Pc)
Scattering (Pn)
2. LANDING RISK ASSESSMENT
Correlation coeff. (ρ) between
the empirical amplitude
distribution and the fit. It
estimates the agreement of the
real surface pattern with the
assumption of the statistical
model (at a horizontal scale of
~20 km for SHARAD).
ρ ↗ w/ surface homogeneity
ρ ↘ w/ surface heterogeneity
(roughness and composition)
Pc/Pn ratio illustrates the
degree of cohesiveness in the
signal received at the antenna.
A high Pc/Pn ratio does not
necessarily indicates a specular
surface. Pn fades 1/h2 faster
than Pc with the propagation
distance (~300 km in altitude)
Then, a high Pc/Pn associated
with a weak Pc might indicate
very rough surfaces.
Surface Properties Relationship Roughness Horizontal Scale 2014 Landing Ellipses
Pc = - 11.7 dB
Pn = - 11.8 dB
Pc/Pn = + 0.1 dB
ρ = 97.8 %
E05
E08 Pc = - 14.7 dB
Pn = - 10.6 dB
Pc/Pn = - 4.1 dB
ρ = 97.8 %
E09 Pc = - 15.7 dB
Pn = - 13.1 dB
Pc/Pn = - 2.6 dB
ρ = 97.4 %
E17 Pc = - 16.8 dB
Pn = - 10.9 dB
Pc/Pn = - 5.9 dB
ρ = 97.6 %
CONCLUSION
The surface echoes acquired all along a survey track are gathered in
successive packets (~1000 observations = ~20 km along-track for SHARAD).
Their amplitude distribution is best-fitted with a theoretical statistical
envelope [8] providing the coherent (reflectance) and incoherent (scattering)
signal components.
Reflectance (Pc) is mainly
sensitive to surface permittivity
and deterministic structures.
Coherent & Specular
+++ Permittivity
(density, composition)
+++ Thin deposits
+ Height distribution
Scattering (Pn) is mainly
sensitive to non-deterministic
structures.
Incoherent & Diffuse
+++ Height distribution
+++ Slope distribution
+++ Void in the near-surface
+ Permittivity
~20 km
Pc=r
2e−(2 kσ
h)2
R = Fresnel coefficient
k = Wave number
D = footprint diameter
H = altitude (~300 km)
ε = Surface permittivity
σh = Surface RMS height
l = Surface correlation length
Signal components (Pc and Pn)
can be related to surface
properties through backscattering
models. We usually obtain a 2
equations and 3 unknowns
system. For instance:
From the Small Perturbation
Method and the Physical Optic
[4, 8]:
From the Small Perturbation
Method only (restricted to
surfaces with σh < 0.75 m) [4, 8]:
The horizontal scale (HS) of the roughness
parameters are not well known. σh is
thought to be measured over several
wavelengths (λ = 20 m). l is closer to the
footprint radius (3000 m). Roughness
obtained from altimetry at various HS
shows some features (black arrow) invisible
at short scales are also not visible on Pn.
i.e. HS for SHARAD might also be closer to
several decameters.
Information content about
surface properties is important in the
radar signal. It can be constraint
with the RSR.
Relative risk assessment for
insight landing ellipses considering
surface permittivity and roughness.
Figure. Final 2014 InSight landing ellipses (orientation varies with launch
window) and SHARAD orbit tracks (black line). Terrain types in different colors
(green is smooth, orange is etched, red are steep crater walls or highland
scarp). Background is THEMIS thermal mosaic (From [2]).
We extracted surface amplitude histograms
for each of the 4 final landing ellipses (red).
Surface homogeneity (from ρ) is higher
than average. Pc/Pn ratios does not match
the application domain of the SPM. As a
preliminary assessment, we propose a
relative classification of the landing
hazard for each ellipse based on the
relative Pc and Pn. By increasing order:
E05: Less risk of soft material (ε > 3).
Intermediate roughness. Possibly covered
by two distinctive units.
E09: Low permittivity but the lowest risk of
rough surface.
E08: Intermediate permittivity but risk of
higher roughness.
E17: Risk of both low density (ε < 3) and
higher roughness.
NEXTToward an absolute/quantitative
risk assessment:
IEM and fractals backscattering
models to extend the RSR to a
wider set of terrains.
Characterization of the measured
roughness horizontal scale.
Constrain the signal calibration.
top related