GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice...

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GISMO Simulation Study • Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used for space-borne phase history data simulation Data processing steps Results analysis Modifications for airborne simulation

Transcript of GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice...

Page 1: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

GISMO Simulation Study

• Objective

• Key instrument and geometry parameters

• Surface and base DEMs

• Ice mass reflection and refraction modeling

• Algorithms used for space-borne phase history data simulation

• Data processing steps

• Results analysis

• Modifications for airborne simulation

Page 2: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Objective

• to perform analysis to validate the interferometric ice sounding technique for measuring the ice mass thickness in polar areas using a P-band space-borne SAR

• Analysis approach– Generate phase history data– Process the data into SLC data and interferograms– Band pass filtering to extract the basal contribution and

to derive the ice thickness from both surface and base interferogram

Page 3: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Key instrument and geometry parameters

• Platform Height: 600 km• Center Frequency: 430 MHz • Chirp Bandwidth: 6 MHz• Pulse Length: 20 us• PRF: 2 kHz• Antenna Length: 12.5 m• Antenna Boresight Angle: 1.5o

• Baseline: 45 m

Page 4: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Surface and base DEMs (Greenland)

Surface DEM Base DEM

200 km

200 km

Page 5: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Ice mass reflection and refraction modeling

n1=1

n2 =1.8

n3 =3(for rocks)

1

2

basal DEM(land or water)

surface DEM

ice mass

S

A

B

C

Fig. 1 ice mass reflection and refraction model

H

D

h

d

s

xb

xs

Page 6: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Space-borne SAR phase history data simulation

• Reflectivity map calculation for both reference and slave antennas

• Phase history data generation

Page 7: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Reflectivity map calculation

1

2

basal DEM(n3: land or sea water)

surface DEM (n1)

ice mass (n2)

S(sensor)

A

B

C

ground range grids

Fig. 2 Implementation of reflectivity map calculation

S2

(sensor) B(baseline)

All quantities: slant range, incidence angle, refraction angle and reflection coefficients, are calculated at each ground range grid. A slant range grid will lie between two neighboring ground range grids. The reflectivity coefficient for each slant range grid is calculated through interpolation of these two neighboring ground range bins.  

When calculating the reflection from the basal, we still start from the ground range grid on the surface. The refraction vector may or may not hit exactly the ground range grids. Bilinear interpolation is therefore used to calculate the refraction pointing vector from each surface ground grid to the basal. At each surface ground range grid the basal reflection coefficient and the slant range from the sensor to the basal are calculated.  

All the calculations for the second orbit are the same as for the reference orbit except the interferometric phase, which is the result of the non-zero baseline and DEMs, is added to the secondary reflectivity map for both surface and basal calculations.

Page 8: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Phase history simulation

• Inverse chirp scaling

Phase history data

HSAR(f) SLC data

Reflectivity map

H-1SAR(f) Phase history data

Page 9: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Data Processing

• SAR processor (Vexcel’s FOCUS)

SLC data• IFSAR processor (Vexcel’s RAMS2)

Interferograms• Interferometric ice sounding processing

– Band-pass filtering to extract the basal interferogram

– Derive surface and base topography

Page 10: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Results Analysis

• Interferogram with both surface and basal contributions

• Interferogram spectrum analysis • Extracted basal interferogram using band-pass

filter• Comparison between the true and derived ice mass

thickness

Page 11: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Results Analysis ……

• DEMS in slant range geometry

echo delay caused by the ice thickness at nadir

(a) surface DEM (b) basal DEM

148.8 km (ground range) 148.8 km

137.5

km

Page 12: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Results Analysis ……

• Amplitude images of the phase history data

Page 13: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Results Analysis ……

• Amplitude images of the SLC data

Page 14: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Results Analysis ……

• 80-azimuth-look interferogram 2

Slant range 11.8 km ( ground range 70 km )

Azimuth (130 km)

0

Page 15: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Results Analysis ……

• Interferogram range spectrum

-35

-30

-25

-20

-15

-10

-5

0

-0.01 -0.008 -0.006 -0.004 -0.002 0 0.002 0.004 0.006 0.008 0.01

Frequency (1 / m )

Sp

ec

tra

l A

mp

litu

de

(d

B)

The peak at 0 frequency represents the surface contribution and the peaks at the right side are from base contribution.

Page 16: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

2

Slant range 11.8 km ( ground range 70 km )

Azimuth (130 km)

0

Results Analysis ……

• Band-pass filtered interferogram

Page 17: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Results Analysis ……

Comparison between the true and derived ice mass thickness

+2500 m

+2137 m

2850 m

1714 m

ground range 70 kmground range 70 km

Page 18: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Results Analysis ……

• Comparison between true and extracted basal interferograms

true basal interferogram extracted basal interferogram from band-pass filtering

Page 19: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Modifications for airborne simulation

• Airborne platform with air turbulence• Varying PRF• Interferometric mode with 2+ receiving antennas• Inverse chirp-scaling modifications for varying

PRF and sensor velocity• Airborne SAR processor

Page 20: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Air Turbulence Simulation

• Along track: x = xa • sin(2fxat + xa)

+ xe • sin(2fxet + xe)

• Horizontal: y = ya • sin(2fyat + ya)

+ ye • sin(2fyet + ye)

• Vertical: z = za • sin(2fzat + za)

+ ze • sin(2fzet + ze)

Page 21: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Varying PRF Simulation

• PRF = PRFn + PRF • sin(2fPRFt + PRF)

Page 22: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Interferometric mode with 2+ receiving antennas

• Current space-borne Scatter: repeat pass mode• Future airborne Scatter:

– Repeat pass mode

– Single pass mode• One transmitting/receiving with others receiving

• Ping-Pong mode ?

Page 23: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Inverse chirp-scaling for varying PRF and varying sensor speed

• The phase history data created from the inverse chirp scaling algorithm apply to a staright line path and uniform along track spacing.

• The data need to be interpolated for a curved path, varying PRF and sensor speed

Page 24: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Airborne SAR processor

• Modify Vexcel’s current fast-back-projection space-borne spotlight SAR processor to be able to process the simulated airborne stripMap SAR data

Page 25: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

SAR Tomography Potentials of GISMO

• SAR tomography background• SAR tomography simulation• Results of E-SAR tomography tests• GISMO potentials for SAR

tomography applications

Page 26: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

SAR Tomography Background• Conventional SAR Imaging

Idealized Straight Flight Paths

Ground Reference Point

Page 27: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Multi-Pass SAR Imaging

Synthetic Elevation Aperture

Ground Reference Point

Synthetic Aperture

Page 28: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Simulated Results • Straight & Parallel Paths

40 41 42 43

44 45 46 47

48 49 50

Grazing Angle

Illumination

Illumination

19.2 m

19.2 m

Image Formation

Plane

Page 29: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Simulated Results • Straight & Parallel Paths

40 41 42 43

44 45 46 47

48 49 50

Grazing Angle Illumination

Coherent Sum

19.2 m

19.2 m

Backprojection Plane

Image Formation

Plane

Page 30: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Vexcel’s tomography research

Classified

Page 31: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Tomography results from E-SAR

• L-band• Nominal orbit altitude: 3600 m• Number of flights: 14• Total vertical aperture: 280 m• Vertical resolution: 3.5 m

Page 32: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

E-SAR Height/azimuth slice tomogram

Page 33: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

E-SAR Height/azimuth slice tomograms

Using MUltiple SIgnal Classification algorithm (MUSIC) with pre-assumed one or five scatterers

Page 34: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

GISMO’s Potentials for Tomography Applications

• One flight track– track altitude : 10 km

– 4 ~ 6 receiving antenna elements

– total aperture: 20 m

• Multiple flights– Assume 10 or more flights

– Total 40 ~ 60 measuremes

– total aperture: 400 m

H (flight height)

1

2

Baseline

D (ice thickness)

Page 35: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

GISMO’s Potentialsfor airborne case

Ground range (look angle)

Ice thickness0 km

(0o)

1 km(5.7o)

2 km(11.3o)

3 km (16.7o)

4 km(21.8o)

100 m 10.8o 6.4o 4.2o 3.0o 2.3o

500 m 23.4o 18.3o 14.4o 11.5o 9.4o

1000 m 32.0o 26.7o 22.3o 18.6o 15.7o

2000 m 42.6o 37.2o 32.3o 28.1o 24.4o

Angular separations between the surface and base return

Total Baseline / Angular resolution :

20 m / 2o (single pass)

400 m / 0.1o (repeat pass)

Page 36: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

GISMO’s Potentialsfor space-borne case

with 600 km orbit

Ground range (look angle)

Ice thickness10 km(0.95o)

30 km(2.86o)

50 km(4.76o)

75 km (7.12o)

100 km(9.46o)

100 m 0.74o 0.33o 0.2o 0.14o 0.1o

500 m 2.3o 1.38o 0.93o 0.66o 0.5o

1000 m 3.5o 2.41o 1.73o 1.26o 0.97o

2000 m 5.4o 4.0o 3.09o 2.34o 1.85o

Angular separations between the surface and base return

Total Baseline / Angular resolution :

45 m / 0.89o (single pass)

1000 m / 0.04o (repeat pass)

Page 37: GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

SAR Tomographic Ice Sounding

• Would repeat pass SAR tomographic ice sounding WORK ???

• Probably basal returns are still correlated even though the surface returns may corrupt the surface components of the tomogram.