Potential Assessment of SAR in Compact and Full Polarimetry Mode for Snow Detection

Gulab Singh, Yoshio Yamaguchi, Sang-Eun Park Gopalan Venkataraman

Niigata University, Japan IIT Bombay, India

Outline• Introduction

• SAR Measurements

• Snow monitoring methods

• Study Area: Part of Himalayan Snow and Glacier Covered Region

• Summary

[1] J. C. Souyris, et. al., “Compact polarimetry based on symmetry properties of geophysical media: The π/4 mode,” IEEETGRS, vol. 43, no. 3, pp. 634–646, Mar. 2005.

[2] R. K. Raney, “Dual polarized SAR and Stokes parameters,” IEEE GRSL., vol. 3, no. 3, pp. 317–319, Jul. 2006[3] R. K. Raney, “Hybrid-polarity SAR architecture”, IEEE TGRS, vol 45, no. 11, pp. 3397-3404, 2007.[4] P. Dubois-Fernandez, et. al., “Compact polarimetry at low frequency”, IEEE TGRS vol. 46, no. 10, pp. 3208–3221, 2008Applications in land parameters estimation over flat terrain /region[5] M. Lavalle, “Full and Compact Polarimetric Radar Interferometry for Vegetation Remote Sensing”, Ph.D. Thesis,

Université de Rennes 1, France, 2009.[6] T. L. Ainsworth, J. P. Kelly and J.-S. Lee, “Classification comparisons between dual-pol, compact polarimetric and

quad-pol SAR imagery”, ISPRS Journal of Photogrammetry and Remote Sensing, 64, pp. 464-471, 2009.[7] F.J. Charbonneau, B. Brisco, R.K. Raney, H. McNairn, et. al., “ Compact Polarimetry overview and applications

assessment”, Can. J. Remote Sensing, vol. 36, no. S2, pp. S298-S315, 2010.****************************************************************************************************************************************[8] S. R. Cloude, Polarisation: Applications in Remote Sensing. London, U.K.: Oxford Univ. Press, 2009The compact assumptions in [1],[4]-[6] do not apply to scattering from sloped terrain [2],[3],[7] ,hybrid system3-dB loss in the radar signal , mismatching the transmitter and receiver polarization basisthe system and theoretical justification issues

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out of several land parameters ……… snow……………

Introduction: previous studies

Snowfall SASE HQ19-01-2006

Snow parameters inmountain areas areparticularly sensitive tochanges in environmentalconditions.

SASE Observatory at Solang, Himachal

Timely information about snow parameters andtheir temporal and spatial variability representsa significant contribution in climatology, localweather, avalanche forecasting and for thehydropower production in high mountainousareas.

Ground-based method represents onlyexact location measurements of fieldobservations which may not berepresentative of a large area or basin.

Due to the strong spatial and timedependent dynamics of snow cover,frequent observation cycles are necessary.

Snow covered : gentle slope

Snow free : Steep Slope

Snow covered: River (Solang Nala) Bank, Himachal

23-01-2009

20-01-2009

SAR

Air/snow interface

snow

snow/ground interface

Ground

SAR interaction with snowpack

m3

m3

m3

m3

Dry snow

ε's>> ε''s

05

101520253035404550556065707580859095

0.5

1.5

2.5

3.5

4.5

5.5

6.5

7.5

8.5

9.5

10.5

11.5

12.5

13.5

14.5

Snow Wetness (Ws in %)

Pe

ne

tra

tio

n D

ep

th (

δp

in

cm

)1.27 GHz

5.6 GHz

9.6 GHzat snow density at 300 kg/m3

ε's>> ε''s

SAR measurements

Single Polarization (ERS-1/2,JERS/PALSAR, Radarsat-1/2, ASAR, TSX)

Dual -Polarization (ASAR, PALSAR, TSX, Radarsat-2)

Quad Polarization (PALSAR, TSX, Radarsat-2)

Compact Polarization (MiniSAR/Chandrayaan-1)

a few satellites are planned by leading space agencies for earth observations

Snow/ice monitoring ??

(Hybrid C-L)

• With the quad polarization capabilities, newergeneration spaceborne SAR sensors areexpected to lead significant improvements ineasily snow identification based on microwavescattering mechanisms

24-05-2010 AVNIR-2 06-06-2010 PALSAR

• Is SAR acquisition in quad polarizationadvantageous as compared to SAR acquisitionin single, dual and hybrid polarization formonitoring snow cover in mountainous area(Himalayas)?

Date Sensor Polarization Off-nadir

angle (0)

Orbit pass GMT

(hh:mm:ss)

Himalayan

Regions

19/05/2007 ENVISAT-ASAR HH+VV 39.1– 42.8 Descending 04:35:58 Badrinath

10/11/2007 ENVISAT-ASAR HH+VV 39.1– 42.8 Descending 04:37:42 Badrinath

12/05/2007 ALOS-PALSAR Quad-Pol 21.5 Ascending 17:04:40 Badrinath

12/11/2007 ALOS-PALSAR Quad-Pol 21.5 Ascending 17:04:31 Badrinath

22/05/2009 ALOS-PALSAR Quad-Pol 23.1 Ascending 17:13:13 Siachen

ENVISAT-ASAR APS and ALOS-PALSAR SLC data

Siachen

Badrinath

N

126cm – 886cm

Snow Monitoring Methods

Based on

-Single Polarization (temporal changes) -Dual –Polarization (Pol. ratio) -Quad Polarization -Compact (Hybrid CL)Polarization

SAR measurements

PALSAR Backscatter Response

σ0

~10 times lower

Problem with single/Dual Pol. SAR data for snow mapping

AVNIR-2 (06-05-07) Snow Map (ASAR) Snow Map (PALSAR) Snow map (PALSAR)

NO

YES

Generate Polarization Fraction value image

ALOS PALSAR Quad Polarization SLC Data

Multi-Looked (6×1) in (Azimuth × Range)

and make Coherency Matrix (T3)

(HV≈VH)

Generate Eigenvalues Image (λ1, λ2, λ3)

PF >=0.55 && Normalized λ3<0.015

Snow Area

Non-snow feasible Area

Extract Scattering Matrix(S)

Polarimetric Speckle Filtering

13

10321

3

PF

Snow Detection Algorithm

(SDA)

Problem with Single/Dual Pol. SAR Data for Snow Mapping - Resolved by Quad Pol.

SDA based Snow Map

Non-snow

feasible Area Snow Cover Area

Discrimination of snow from

other Bragg scattering dominant

surface may be problematic.

L-band fully polarimetric SAR is

not able to detect shallow-depth

snow

snow cover (magenta) derived

from PALSAR (26-05-11),

overlaid to AVNIR-2 (24-05-11)

Agassizhorn region,

Bernese Alps, Switzerland

Study Area(snow and glacier covered terrain)

Part of Indian Himalaya (place of ice )

Siachen Glacier area Standing snow

1.2-8.8 m (low-high altitude)

SWE Product of AMSR-E

Feb., 2007 Aug., 2007

Length ~73 km

FP vs CPFP [C16] [C9] (monosatic) (refl. sym.)[C5]

m-δ CP[J4] refl. & rot. sym. *C’5]

SDA

Tx=LHC, Rx=H,V

[1]-[8]

FP vs CPFP [C16] [C9] (monosatic) (refl. sym.)[C5]

m-δ CP[J4] refl. & rot. sym. *C’5]

Tx=LHC, Rx=H,VPolSARPro ver. 4.1.5 (ESA)

ver. 2.0

SDA

[1]-[8]

[C9] [C5](SHV=SVH) (SHHS*HV ≈ S VVS*VH ≈0)

(SHHS*HV ≈ S VVS*VH ≈0)

[Cꞌ5]

SDACP [J4] =

Degree of polarization

Relative Phase[1]-[8]

ζ0HV/ζ0

HHPF-λ3 approach (CP)PF-λ3 approach (FP) PF-λ3 approach(FP-RS)

m-δ approach

Data

Approach

Dual-Pol

σ0HV/σ0

HH

CP

m-δ

CP

PF-λ3

FP-RS

PF-λ3

FP

PF-λ3

Non-snow feasible area

(%)

71.38 67.21 56.56 45.52 40.98

Snow area (%) 28.62 32.79 43.44 54.48 59.02

FP : Full Polarimetry

FP-RS : Full Polarimetry with Reflection Symmetry condition

CP : Compact Polarimetry

Non- snow feasible area

22-05-2009 SD126-886 cm (low-high altitude)

Summary • Importance of snow studies

• PALSAR backscattering coefficient response for variousfeatures

• Comparisons between single, dual, compact and quadpolarization data for snow detection

• Identification of suitable polarimetric descriptors fordiscriminating the snowpack– PF and normalized λ3

• Results with single polarization SAR (C-&L-band) for snow discriminationnot good.

• Results with dual polarization SAR measurements better than single pol.But it does not care of unwanted topographic distorted area.

• Full polarimetry SAR technique SDA has produced promising results.

• SDA

……takes care of unwanted topographic distorted area

...... suitable for CP too.

****CP shows capability ̴ 15% less than FP****

Summary

Quad Pol.>Compact Pol.>Dual Pol.>Single Pol.

PF-λ3 > m-δ > σ0HV/σ0

HH

Snow Practicability

m-δ Decomposition3-CSPD (Pseudo)3-CSPD (True)

May 22, 2009

0

10000

20000

30000

40000

50000

60000

70000

80000

-3.14 -1.57 0 1.57 3.14

Odd-bounce Scatterers

Peak

Even-bounce Scatterers

Peak

Volume scatterers

Peak

m-δ approachζ0HV/ζ0

HHPF-λ3 approach

Left Circular Transmission (LC)

Rotation: Anti - Clockwise

Wave Vector: H + j • V

Scattering Vector:HH + j • HV 1

j • VV + HV 2

LH

=

LV

H - j V

2

H - j V

2

H +j V

2

ReceptionH

V

Recep

tio

n

Transmission

Badrinath Region

ASAR

SNOW FREE SNOW COVER

Wet Snow Cover in month of May 2006

SNOW FREE SNOW COVER

Wet Snow Cover in month of September 2006

Badrinath Region

ASAR

PF Images

FP ̴3% SCA more than CP

RSI based snow cover mapping

-35

-30

-25

-20

1 31 61 91 121 151

Bac

ksca

tter

ing

Co

effi

cien

t (i

n d

B)

Distance in Pixels

HV VH

-25

-20

-15

-10

-5

0

1 51 101 151 201

HV VH

DEBRIS COVERED GLACIER

SNOW COVERED AREA

Distance in Pixels

Bac

ksca

tter

ing

Co

effi

cie

nt

(in

dB

)Himalayan Region

PALSAR image on 12-05-2007

Symmetry Test (Noise test)

FRA

Slope

Mapover part

of Himalaya

Penetration Depth at Snow Cover

Terrain

Penetration depth can be written with some approximations

ε’ >> ε” as

Dry Snow = ice particles + air + no liquid water

εds' = 1 + 1.7 ρs + 0.7 ρs2

(Tiuri et al. 1984)

εds ' = 1 + 1.5995 ρs + 1.861 ρs3

for ρs <0.45 gm/cm3

(Matzler 1996)

εds" = εice" ( 0.52ρs + 0.62 ρs2 )

where εice" = 0.008 (Matzler 1988)

Wet Snow = ice particles + air + contents of liquid water

Ice

Liquid waterAir Grain Boundary

(Matzler 1987)

f0= 10 GHz