Subsurface scattering of SAR signals assessed using GPR

Matthew B. CharltonSchool of Geography

The University of Nottingham

Identification of duricrusts using remote sensing in the Libyan Sahara to facilitate location of archaeological

sites and palaeo-environmental reconstruction.

AIMAssess why SAR detection of such sites is variable.

In particular, determine the role of subsurface scattering using GPR.

Duricrusts3 types found in interdunes

• Calcretes• Silcretes• Gypcretes

Protect underlying sediments, forming inverted relief

Preserve sedimentary contexts otherwise removed by deflation processes

Duricrust

Lacustrine sediments

Dune sand

Archaeology

Hand axes

Burials

Rock Art

Optical: Enhanced Thematic Mapper

Calcretes can be discriminated from multispectral data

3 km

L-band Radar

Landsat ETM JERS-1

JERS detects some (not all) silcretes invisible in TM imagery...

C-band Radar

Landsat TM JERS-1 Radarsat

Other silica-rich duricrusts evident in Radarsat fine beam mode image, but not in JERS or TM

Factors influencing backscatter from duricrusts

• SAR system: incidence angle, frequency, orbit, etc.• Surface roughness variation.• Dielectric constant.• Subsurface scattering.

Methods

• Acquire SAR data (ERS, JERS, RADARSAT, SIR-C).• Acquire ETM imagery.• Identify sites (NSGP1, UBR1, UL1).

12.80 12.95 13.10

26.80

26.65

26.50

12.80 12.95 13.10

26.80

26.65

26.50

Longitude (decimal degrees)

Lat

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JERS-1

RADARSAT

Site Locations (Lat / Lon) (decimal degrees):NSGP1: 26.59865N 13.06504EUBR1: 26.68181N 12.84430EUL1: 26.71133N 12.90280E

Libyan Fezzan

NSGP1: thin gravel crustUBR1: thinner calcrete / silcrete crustUL1: thick calcrete / gypsum crust

Methods

• Acquire SAR data (ERS, JERS, RADARSAT, SIR-C).• Acquire ETM imagery.• Identify sites (NSGP1, UBR1, UL1).• Field survey of surface and subsurface:

• Surface roughness (10m profiles at 0.0084m resolution)• Dielectric constant (Thetaprobe)• Local topographic surveys• Surface samples (texture, etc.)• GPR profiles (30-75m, 450 and 900 MHz, CMP)• Pits (structure and samples)

Analysis

• Process SAR data (georegister, convert to backscatter).• Use surface properties to run simplified IEM.• Compare estimated and observed backscatter.• Basic interpretation and processing of GPR data, plus determination of penetration depth, attenuation, and scattering.

IEM Inputs

Dielectric Constant= 2.5 - 4.1

Average value: 2.88

No significant variation

Roughness Parameters

NSGP1 = 0.53L = 47.68

UBR1 = 0.66L = 35.87

UL1 = 0.24L = 20.53

Exponential and gaussian functions

• Sites smooth at L- & C-band• High variability at shorter profile lengths• Drift and periodicity in correlograms

Frequency1.275 GHz5.3 GHz

Incidence Angle10-50°

JERS: 35.21°RADARSAT: 36.9°

Estimated and Observed BackscatterBackscatter (dB):

JERS

Predicted: -34.12Observed: -0.52

Predicted: -30.95Observed: 2.60

Predicted: -37.70Observed: 4.33

Backscatter (dB): RADARSAT

Predicted: -27.44Observed: -22.39

Predicted: -23.87Observed: -21.64

Predicted: -31.67Observed: -23.86

Backscatter (dB): JERS

Predicted: -34.12Observed: -0.52

Predicted: -30.95Observed: 2.60

Predicted: -37.70Observed: 4.33

Backscatter (dB): RADARSAT

Predicted: -27.44Observed: -22.39

Predicted: -23.87Observed: -21.64

Predicted: -31.67Observed: -23.86

• Backscatter coefficient is underestimated. • Patterns are wrong for JERS:

Predicted and RADARSAT:

UBR1 > NSGP1 > UL1

JERS

UL1 > UBR1 > NSGP1

Estimated and Observed Backscatter

IEM Prediction Error

• Assumption of constant incidence angle.• Assumption of constant range direction.• Dielectric constant.• Drift and periodicity in correlograms results in high L.• Trade-off between and L.• Variability in roughness parameters.• Subsurface scattering not accounted for in simplified IEM.

Ground-penetrating radar surveys

PulseEKKO 1000A GPR 450 & 900 MHz 0.02 - 0.05 m Step Size 30 - 75 m Profiles Ground-coupled 80ns Time Window 10 ps Sampling Interval CMP surveys

Basic Processing• trace edit• dewow• time zero• topographic

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Horizontal Position (m) 6.0 22.0 37.5

Horizontal Position (m) 0.0 15.0 30.0

NSGP1(Constant Gain = 50)

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900 MHz GPR Profiles

UL1(Constant Gain = 50)

NSGP1 dominated by horizontal layering, ringing and limited penetration.

UL1 dominated by horizontal layering (more layers), greater layer roughness, greater penetration and significant numbers of diffractions of varying size.

GPR signal scattering: NSGP1

Layer reflection at sand-crust interface of variable depth.

Reflection depends on depth of burial, layer roughness, and nature of materials.

Complicated by laminations and proximity to direct arrivals.

0.45 m

0.10 m

GPR signal scattering: UL1

Layer reflection at sand-crust interface of variable depth.

Layer reflection at different crust interfaces (powder, massive, inhomogeneous).

Multiple scattering from inhomogeneities (cracks, voids, etc.).

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Horizontal Position (m) 6.0 22.0 37.5

Horizontal Position (m) 0.0 15.0 30.0

NSGP1(Constant Gain = 50)

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40.0

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900 MHz GPR Profiles

UL1(Constant Gain = 50)

• GPR response demonstrates that signal scattering varies between sites in a way that depends on the nature of the duricrust.• UL1 would appear to have higher subsurface scattering potential than NSGP1.

Backscatter (dB): JERS

Predicted: -34.12Observed: -0.52

Predicted: -30.95Observed: 2.60

Predicted: -37.70Observed: 4.33

Backscatter (dB): RADARSAT

Predicted: -27.44Observed: -22.39

Predicted: -23.87Observed: -21.64

Predicted: -31.67Observed: -23.86

• C-band has limited penetration and greater sensitivity to variability in surface roughness. Conforms to modelled pattern.

• L-band is less sensitive to limited surface roughness. With greater signal penetration, subsurface complexity becomes more important. NSGP1 has a less complex subsurface and therefore a lower observed backscatter compared to UL1.

Re-interpreting Backscatter

Quantifying Subsurface Scattering

Maximum penetration depth (from ungained images, assuming velocity of 0.177mns-1):NSGP1: 0.8mUL1: 1.2m

Attenuation calculation (from ungained images, assuming velocity of 0.177mns-1):• Convert to Instantaneous Amplitude.• Calculate Mean Trace.

Mean Instantaneous Amplitude Trace NSGP1

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Depth (m)

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Mean Instantaneous Amplitude Trace UL1

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Quantifying Subsurface Scattering

Attenuation calculation (from ungained images, assuming velocity of 0.177mns-1):• Convert to instantaneous amplitude.• Calculate mean trace to give attenuation curve (Grandjean et al., 2001).• Convert to dB: (where CF = -93.98)

• Calculate dB difference between two points of known depth using (Farr et al., 1986):

• To avoid direct arrivals attenuation was calculated between 0.5-1.5m.

CFnDN /)(log10 210

0

)12(20

)1()2( 00

zz

zz

Maximum penetration depth (from ungained images, assuming velocity of 0.177mns-1):NSGP1: 0.8mUL1: 1.2m

Quantifying Subsurface Scattering

Attenuation results:• Very similar between sites:

• NSGP1: 1.04 dB/m• UL1: 1.15 dB/m

• Relatively high for dry sand (salt content?)

• Subject to averaging error and local fluctuations in instantaneous amplitude at depth.

• Confirm that simplicity of NSGP1 GPR data is due to lack of reflections, not excessive attenuation.

Quantifying Subsurface ScatteringPreliminary assessment of subsurface scattering in upper

metre

Descriptive statistics (using pit traces):• Standard deviation of amplitudes does not work.• Nor does coefficient of variation (NSGP1 = 1.38; UL1 = 0.99).• Due to greater contrast between high (direct arrivals, reflections, scattering) and low amplitude (no reflections) zones in NSGP1 (more of UL1 has similar higher magnitude values).

RMSE:• Subtract mean pit trace from individual pit traces and calculate RMSE.• Where there is greater scattering there should be greater error.

• Analysis confirms greater scattering at UL1 and shows intra-site variability.• Such a value could be scaled to produce an equivalent to RMS surface height.

Pit UL1 NSGP11 3620 24392 3747 12773 4444 1538

Quantifying Subsurface ScatteringPreliminary assessment of subsurface scattering in upper

metre

Layer roughness:• Previous analysis considered all sources of scattering.• For the most simple inclusion in IEM layer roughness can be calculated from GPR data:

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Horizontal Position (m) 0.0 15.0 30.0

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UL1(Constant Gain = 50)

Has yet to be done. Ignores other sources of scattering. Complex task in multi-layered environments.

• Variations in surface roughness do not explain observed backscatter differences.

• C-band conforms to model results; L-band does not.

• GPR confirms considerable subsurface layer and volume scattering potential at two sites.

• Scattering is related to the nature of the duricrust (layering, nodules, voids, cracks, depth of sand overlay).

• In arid environments, at low frequencies, subsurface scattering may become more important in determining backscatter.

• Further work to (1) assess SAR penetration, (2) assess scattering dependency on subsurface complexity, and (3) develop GPR techniques to derive subsurface scattering parameters.

Conclusions

CREDITS

Field WorkDr. Nick Brooks (University of East Anglia)

Dr. Kevin White (University of Reading)

Pit PhotographsToby Savage