2nd advanced course on radar polarimetry 2013 01/24/2013 · 2nd advanced course on radar...

2nd Advanced Course on Radar Polarimetry 2013 01/24/2013

1

Surface Parameter Estimation:Basics and Advanced Concepts

2nd Advanced Course on Radar Polarimetry – 24.Jan 2013 – [email protected]

Irena HajnsekChair of Earth Observation and Remote SensingInstitute of Environmental Engineering, ETH ZürichMicrowaves and Radar Institute, DLR Oberpfaffenhofen

Slide 2

Part I: Theory and basicsIntroduction into SAR polarimetric observablesDecomposition techniques Introduction into material properties of soilModel description and inversion strategy

Part II: Exercises with L-band airborne dataRead the dataSpeckle filteringOh, Dubois and X-Bragg inversion

OverviewHH HV

VH VV

Slide 2

Slide 3

Motivation of Radar Remote Sensing

Independent from WeatherData acquisition during cloud cover, rain, ...

Radar is complementarily to opticsSensitive to dielectrical and geometrical properties of scatterers,...

Penetrates through or in volumesOverlay of scattering processes,…

Is an active systemData acquisition independent of daylight, ...

Delivers highly accurate physical productsDigital Elevation Models, Forest heights, Soil moisture maps ...

Slide 3Ethna DEM (TanDEM-X) Forest height Map

L (23cm)C (5cm)X (3cm)

35,3 km

21,3

km

Optical image

DLR Location: Oberpfaffenhofen

B:SurfaceR:Dihedral G:VolumeSAR-Bild

Drygalski glacier velocity measurement at the Antarctic Peninsula

(TerraSAR-X)

TanDEM-X dual polarisations acquisition of a Saudi

Arabien desert, montage with a Google-Earth image.

1994: Shuttle Radar Lab SIR-C/X-SAR (Brazilian Rainforest)

Slide 4

What does the Radar measure ?

Radar reflectivity (backscattered signal) of the target as a function of position.

radar transmits a pulse (travelling velocity is equal to velocity of light)

some of the energy in the radar pulse is reflected back towards the radar.

This is what the radar measures.It is known as radar backscatter o(sigma nought or sigma zero).

Slide 4


2

Slide 5Slide 5

Normalized radar cross-section (backscattering coefficient) is given by:

o (dB) = 10. Log10 (energy ratio)

whereby received energy by the sensor

“energy reflected in an isotropic way”energy ratio =

i.e.

The backscattered coefficient can be a positive number if there is a focusing of backscattered energy towards the radar

or

The backscattered coefficient can be a negative number if there is a focusing of backscattered energy way from the radar (e.g. smooth surface)

What does the Radar measure ?

Slide 6Slide 6

Backscattering Coefficient o

Levels of Radar backscatter Typical scenario

Very high backscatter (above -5 dB) man-made objects (urban)terrain slopes towards radarvery rough surfaceradar looking very steep

High backscatter (-10 dB to 0 dB) rough surfacedense vegetation (forest)

Moderate backscatter (-20 to -10 dB) medium level of vegetation agricultural crops moderately rough surfaces

Low backscatter (below -20 dB) smooth surface calm waterroad very dry terrain (sand)

Slide 7Slide 7

Commonly Used Frequency Bands

Frequency band Frequency range Application examples

VHF 300 KHz - 300 MHz foliage/ground penetration, biomass

P-Band 300 MHz - 1 GHz soil moisture, biomass, penetration

L-Band 1 GHz - 2 GHz agriculture, forestry, soil moisture

C-Band 4 GHz - 8 GHz ocean, agriculture

X-Band 8 GHz - 12 GHz agriculture, ocean, high resolution radar

Ku-Band 14 GHz - 18 GHz glaciology (snow cover mapping)

Ka-Band 27 GHz - 47 GHz high resolution radar

Slide 8

Environmental Sensing from Air and Space

Airborne measurementsHighly flexible operationCoverage of dedicated areasExperimental configurationSensor specific data formatsShort re-visit times

Slide 8

Spaceborne measurementsHighly regular observationWide area coverageHighly operational & reliableStandard product deliveryLong term observations

DLR‘s E-SAR DLR‘s TanDEM-X Mission


3

Slide 9Slide 9

HHX-band

PI-SAR / Test Site: Gifu-Japan

Distributed Scatterers:•Norm. Backscattering Cross Section σ0 ;•Texture (σ0 statistics).Deterministic Scatterers:•Radar Cross Section (RCS) ;•Phase of the SAR signal. O

bser

vatio

n Sp

ace

Appl

icat

ions

Single Pol (Channel) SAR

Classification/Segmentation (Texture);Change detection (Multi temp. analysis);Glacier velocities (Feature tracking); Mapping (Feature extraction/Bordering );Ocean wave/Wind mapping;Coherent scatterers (CSs).

Slide 10

Polarimetric SAR

Slide 10

VVVH

HVHH

SSSS]S[

HH

VV

HV

VHFirst Order Parameters:

Norm. Backscattering Cross Section σ0 ;Intensity (Cross Section σ0) ratios;Polarimetric phase differences.

Second Order Parameters:Polarimetric (inter channel) coherences.O

bser

vatio

n Sp

ace

Slide 11Slide 11

X-band

PI-SAR / Test Site: Gifu-Japan

R:HH-VV G:HV+VH B:HH+VV

First Order Parameters:Norm. Backscattering Cross Section σ0 ;Intensity (Cross Section σ0) ratios;Polarimetric phase differences.

Second Order Parameters:Polarimetric (inter channel) coherences.O

bser

vatio

n Sp

ace

Appl

icat

ions

Soil moisture/roughness estimation;Wettland/Veg. characterisation/mapping; Snow & Ice mapping (type classification);Ship & Oil spill detection; Classification/Segmentation (Pol based).

VVVH

HVHH

SSSS]S[

Polarimetric SAR

Slide 12Slide 12

)r(E)r(E

)r(S)r(S)r(S)r(S

r)riexp(

)r(E)r(E

iv

ih

VVVH

HVHHsv

sh

)r(E)r(E

)r(E sv

shs

hv

Scattered Field

In the far zone region

( )

2x2 Complex Scattering Matrix

|'r||r| |r|

Incident (Plane) Wave

(Jones Vector Representation)

)r(E)r(E

)r(E iv

ihi

hv


Mapping of the 2-dim incident vector into the 2-dim scattered vector)(rEihv

)(rEshv

Scatterer

Transforms the incident

into the scattered wave

The Polarimetric Scattering Problem


4

Slide 13Slide 13

)r(E)r(E

)r(S)r(S)r(S)r(S

r)riexp(

)r(E)r(E

iv

ih

VVVH

HVHHsv

sh

)r(E)r(E

)r(E sv

shs

hv

Scattered Field

In the far zone region

( )

2x2 Complex Scattering Matrix

|'r||r| |r|

Incident (Plane) Wave


)r(E)r(E

)r(E iv

ihi

hv


Mapping of the 2-dim incident vector into the 2-dim scattered vector)(rEihv

)(rEshv

1: Changes the polarisation state of the incident wave

2: Changes the degree of polarisation of the incident wave

Scatterer

Transforms the Incident

into the scattered wave

The Polarimetric Scattering Problem

Slide 14

Coherent Scattering Matrix

Slide 14

iv

ih

VVVH

HVHHsv

sh

EE

SSSS

rri

EE )exp(

)exp(||)exp(||)exp(||)exp(||)exp(][

VVVVVHVH

HVHVHHHH

iSiSiSiS

rriS

[S] is independent on the polarisation of the incident wave !!!

… also known as the Jones Matrix in the bistatic and Sinclair Matrix in the monostatic case

IJS

Complex Scattering Amplitudes:

=f(Frequency,Scattering Geometry)

and depends only on the physical and geometrical properties of the scatterer

||)(exp||)(exp||)(exp||)exp()exp(][

VVVVVHVH

VVHVHVVVHHHHVV

SiSiSiS

ririS

Seven Parameters: 4 Amplitudes & 3 PhasesAbsolute PhaseFactor

Total Scattered Power: 2VV

2VH

2HV

2HH SSSSSSTraceSSpanTP ||||||||)]][([])([

Slide 15

Bi- & Mono-Static Measurement of the Scattering Matrix

Slide 15

T = 1/PRF

VVVH

HVHH

SSSS

V

H

V

H

V

H

V

H

H V VH Tx

RxT

H

V

H

V

R

R T

H

V

HH

HV

VH

VV

Slide 16Slide 16

VH VV

HH HV

Azi

mut

h

E-SAR / Test Site: Oberpfafenhoffen

Scattering Amplitude Images


5

Slide 17

Backscattering (FSA & BSA)

Slide 17

||)(exp||)(exp||)(exp||)exp()exp(][

VVVVXXXX

VVXXXXVVHHHHVV

SiSiSiS

ririS

Five Parameters: 3 Amplitudes & 2 PhasesAbsolute PhaseFactor

In the case of monostatic backscattering from reciprocal scatterers:

BSAXX

BSAVH

BSAHV SSS )( FSA

XXFSA

VHFSAHV SSS Reciprocity Theorem

VVXX

XXHH

SSSS

S][

VV

XX

XX

HH

L4

SSSS

k

0S2

SSSS

21k

XX

VVHH

VVHH

P4

VV

XX

HH

L3

SS2

Sk

XX

VVHH

VVHH

P3

S2SSSS

21k

3-dim Lexicographic (L) and Pauli (P) Scattering Vectors:

Note: The factor is required to keep the vector norm of invariant L3k

2

The scattering problem can be addressed in the 3-dim complex space:

Slide 18

Partial Scatterers

Slide 18

Deterministic Scatterers Partial Scatterers

Completely described by [S] Cannot be described by a single [S]

• Change the polarisation state of the wave • Change the polarisation state of the wave

• Do not change the degree of polarisation and also change the degree of polarisation

Scatterers with Space or Time VariabilityPoint Scatterers

MonochromaticIncident Wave

MonochromaticScattered Wave

Depolarisationdescribed by second order statistics

Slide 19Slide 19

2

2

2

3

||4)(2)(2)(2|)(|))(()(2))((|)(|

:][

HVVVHHHVVVHHHV

HVVVHHVVHHVVHHVVHH

HVVVHHVVHHVVHHVVHH

SSSSSSSSSSSSSSSSSSSSSSSSS

T

Covariance & Coherency Matrices in Backscattering

Covariance Matrix [C]:

LL kkC 333 :][

2

2

2

3

||22||22

2||:][

VVHVVVHHVV

VVHVHVHHHV

VVHHHVHHHH

SSSSSSSSSS

SSSSSC

Coherency Matrix [T]:

PP kkT 333 :][

Pauli Scattering Vector:

TXXVVHHVVHHP SSSSSk 221

3

Lexicographic Scattering Vector:

TVVXXHHL SSSk ]2[4

and are by definition 3x3 hermitian positive semi-definite matrices &contain in general 9 independent parameters

][][ 33 TC

Slide 20Slide 20

Scattering Polarimetry

INTERPRETATION OF SCATTERING MECHANISMS

• Directly from the Scattering Matrix

• Model based


6

Slide 21

Interpretation of Scattering Mechanisms

Slide 21

VVHV

HVHH

SSSS

S][ TxxVVHHVVHHP S2SSSS2

1k

PP

kk1e

XX

VVHH

VVHH

P3

2

1

PP S2

SSSS

k21

ii

ik

k1e

expsinsinexpcossin

expcos

Scattering Matrix: Scattering Vector:

Unitary Representation:

Parameterisation of in terms of five angles:e 321 ,,,,

Slide 22Slide 22

001

10000

00

001

i000i000i

e

3

2

1

cossinsincos

cossinsincos

expexp

exp


e00

001e

cossinsincos' e

10000

e

cossinsincos

'

Point Reduction Theorem:

Change of Scattering Mechanism:

characteristicorientationphase

Slide 23Slide 23


010

001

100001010

100010001

e'

02121

001

1000212102121

100010001

e'

001

10000

00

001e

cossin

sincos

cossinsincos'

H-DipolScatterer

Dihedral Scatterer

=90º and β=0º

=45º and β=0º

HV

VVHH

VVHH

P S2SSSS

k21

001

e

02121

001

1000212102121

100010001

e'V-Dipol

Scatterer

=45º and β=180º

where

Slide 24Slide 24

Scattering Processes: Fresnel Scattering

2

2

sincossincos

HR

2

2

sincossincos

VR

V

H

RR

S0

0][

Fresnel Reflection Coefficients:

Scattering Matrix:

… where e is the dielectric constant of the surface

and

HH

VV

= 20.


7

Slide 25Slide 25

Scattering Processes: Bragg Scattering

2

22

sincos)]sin1()[sin1(

VR

Bragg Scattering Coefficients:

Scattering Matrix:

… where is the dielectric constant of the surface

V

H

RR

S0

0][

2

2

sincossincos

HR and

VV

HH

= 20.

Slide 26Slide 26

Scattering Processes: Dihedral Scattering

0 001 0 1 0[ ]

0 000 1 0HS HS HTHT

iiVS VS VTVT

R R RRS

R R R eRe

2

2

cos sin

cos sinS

HS

S

R

2

2

cos sin

cos sinS S

VS

S S

R

2

2

cos(90 ) sin (90 )

cos(90 ) sin (90 )T

HT

T

R

2

2

cos(90 ) sin (90 )

cos(90 ) sin (90 )S S

VT

S S

R

Fresnel Coefficients:

Scattering Matrix:

and

and

HH

VV

= 20.

Soil (S)

Trunk (T)

Slide 27Slide 27

Dihedral vs Volume Scattering (L- /P-Band E-SAR – Kryklan/Sweden)

08biosar0201x1_t11 L-band, hh,hv,vv 08biosar0302x1_t12 P-band, hh,hv,vv

tree trunkpower line

tree crown

range direction

Slide 28Slide 28

dbba

S][

c2baba

21k

10000

R3

cossinsincos

:)(

kRRRk 333

)]([)]([)]([),,(

cossin

sincos:)(

001

0R3

10000

R3

cossinsincos

:)(

Scattering Processes: Volume Scattering

20

0

20

Rotation about x-axisRotation about y-axisRotation about z-axis

),,(),,(][ kkT

Coherency Matrix:

Random Volume


8

Slide 29Slide 29


z

y

x

000000

][

11L4V

rii ))/((

0

23z

23y

23x

zyxi ds

2ls2ls2ls2

8lllL /// )/()/()/(

/)(

zyxzyx l

1l1

l1LLL ::::

1m

2m11L11LA

r

r

ry

rxP

)()(:

ll

LLm

x

y

y

x :

1LLL zyx

2l

2l

2l

43V zyxwhere is the particle volume and

with

Principal Polarisability Matrix:

Particle Anisotropy: Particle Shape Ratio:

m0

1m

1m oblate spheroids

prolate spheroids

Slide 30Slide 30


T3

T3

T3333 RRRPRRRP )]([)]([)]([][)]([)]([)]([),,]([

dddpppPT )()()(),,]([][

b000b0001

aT][)(

)(2PP

2P

A7A6221Ab

50b0 .

Special Case:Random Volume

p( )

-

p( )

-

p( )

- / 2 / 2

where

Coherency Matrix:

where )(),(),( ppp are the pdf’s for the corresponding angle distributions

Slide 31Slide 31


A=0.01

A=0.20

A=0.40

A=0.60

A=0.80

A=1.00

H/a Loci for varying particle

shape and width of distribution

Prolate Particles

oriented volume random volume

dipole

sphere

-dipole

randomness

Slide 32

[ S ]

COHERENTDECOMPOSITION

E. KROGAGER(1990)

W.L. CAMERON(1990)

[ K ]

[ T ] [ C ]

TARGETDICHOTOMY

EIGENVECTORS BASEDDECOMPOSITION

EIGENVECTORS / EIGENVALUES ANALYSIS&

MODEL BASED DECOMPOSITION

EIGENVECTORS / EIGENVALUES ANALYSISENTROPY / ANISOTROPY

S.R. CLOUDE - E. POTTIER(1996-1997)

A.J. FREEMAN(1992)

W.A. HOLM(1988)

S.R. CLOUDE(1985)

J.J. VAN ZYL(1992)

AZIMUTHAL SYMMETRY

J.R. HUYNEN(1970)

R.M. BARNES(1988)

Decomposition Theorems

MODEL BASEDDECOMPOSITION


9

Slide 33Slide 33


• Pauli Matrix Decomp.

• Model Based Decomp.

• Eigenvector Decomp.

Slide 34

Pauli Matrices Decomposition

Slide 34

00

0110

1001

1001

][i

idcba

baidcidcba

SSSS

SVVVH

HVHH

1001

aSa][

1001

bSb][

0110

cSc ][

0ii0

dSd ][

Single Scattering:

Dihedral Scattering ( … rotated by /2 about the LOS )

Dihedral Scattering:

Transforms all polarisation states into their orthogonal states (disappears in backscattering)

VVHH SS

VVHH SS

Slide 35Slide 35

Azi

mut

h


HH

C-band 5 cm

SIR-C / Test Site: Kudara,Russia

VV HVSlide 36Slide 36


HH

L-band

23 cm


VV HV

Azi

mut

h


10

Slide 37Slide 37

Pauli Images

C-band L-band


HH+VV

HH-VV

2HV

RGB-Coding:

Slide 38Slide 38


• Pauli Matrice Decomp.



Slide 39

Eigenvector Decomposition

Slide 39

Coherence Matrix: P3P33 kkT

:][

Diagonalisation: 1333 UUT ]][][[][

333231

232221

131211

3][eeeeeeeee

U

3

2

1

000000

][

332313

322212

312111

31

3 ][][eeeeeeeee

UU

][][][)()()()(][ 33

23

13333222111

3

1iiii3 TTTeeeeeeeeT

0321

33

23

13

3

32

22

12

2

31

21

11

1

eee

eeee

eeee

e

where:

3 real positive eigenvalues 3 orthonormal eigenvectors

][][][ 321 SSS

Slide 40

Scattering Entropy / Anisotropy

Slide 40

Coherency Matrix Diagonalisation:

][][][)()()()(][ 33

23

13333222111

3

1iiii3 TTTeeeeeeeeT

32

32

32

32

PPPPA

:

321

iiP

:i3

3

1ii PPH log:

with:Scattering Entropy:

Scattering Anisotropy:

Totally Polarised Scatterer

Totally Unpolarised Scatterer

0H

1H1H0 where:

0A

1A1A0

2 Equal Secondary Scattering Processes

Only 1 Secondary Scattering Processwhere:


11

Slide 41Slide 41

Scattering Entropy Images

C-band L-band

H=0

H=1


Slide 42Slide 42

Scattering Anisotropy Images SIR-C / Test Site: Kudara,Russia

C-band L-band

A=0

A=1

Slide 43

Mean Scattering Parameters: Eigenvector

Slide 43

Coherency Matrix Diagonalisation:

321

iiP

:

Mean α-Angle:

Mean β-Angle:

Mean γ-Angle:

Mean δ-Angle:

332211 PPP

332211 PPP

332211 PPP

332211 PPP

)()()(][ 3332221113 eeeeeeT

)exp()sin()sin()exp()cos()sin(

)exp()cos(

ii

ie

)exp()sin()sin()exp()cos()sin(

)exp()cos(

iii

iii

ii

i

ii

ie

Eigenvectors: Appearance Probabilities:

Mean Scattering Mechanism

Mean ε-Angle: 332211 PPP

Slide 44Slide 44

-Angle Images

C-band L-band

a=0°

a=90°

a=45°



12

Slide 45

H- Angle Plane

0 0.2 0.4 0.6 0.8 10

10

20

30

40

50

60

70

80

90

Low Entropy Multiple Scattering

Medium Entropy Multiple

Scattering

Low Entropy Surface Scattering

Medium Entropy Dominant Surface Scattering

Medium Entropy DipolScattering

Low Entropy Dipol Scattering

0 0.2 0.4 0.6 0.8 10

10

20

30

40

50

60

70

80

90

High Entropy MultipleScattering

Entropy (H)Al

pha

( )

Slide 46Slide 46


• Pauli Matrice Decomp.



Slide 47

Freeman 3 Component Decomposition

Slide 47

][][][][ VDS TTTT

000010

fT

2

SS

][

000010

fT

2

DD

][

100010002

fT VV ][

Vegetation Scattering : Bragg-Scattering + Dihedral Scattering + Random Volume of Dipols

V

VDSDS

DSVDS

fffffßf

fßfffßfT

00002

][

22

4 Equations for

5 Unknowns

where and

Re(HHVV)-fv/3 > 0 Single bounce is dominant alpha =1

Re(HHVV)-fv/3 < 0 Double bounce is dominant beta =1

Slide 48

3 Component Freeman Decomposition (San Francisco)

Slide 48

Pauli |HH-VV|, |HV|, |HH+VV| Freeman/Durden, PD, PV, PS

AirSAR JPL


13

Slide 49

][][][ / VDS TTT

Slide 49

Volume shape parameter of

a randomly oriented volumedipole sphere

=1/3 =1

)3()||1( 2 VVGG fPfPScattering Power Components:

Distinction between dihedral and

surface of the ground component:

1 dihedral

≥1 surface

100010001

000010||

0000 2

33

22

1211

12 VG ffT

TTTT

Freeman 2 Component Decomposition

Slide 50

Summary: Polarimetric Parameters / Scattering Matrix

Slide 50

• Scattring Amplitudes

• Total Power

• Amplitude Ratios

• Pol Phase Differences

• Helicity

| | exp ( ) | | exp ( )exp( )exp( )[ ]| | exp ( ) | |

HH HH VV XX XX VVVV

XX XX VV VV

S i S ii r iSS i Sr

Five Parameters: 3 Amplitudes & 2 PhasesAbsolute PhaseFactor

[ ] HH XX

XX VV

S SS

S S

0 * 0 * 0 *: | | : | | : | |HH HH HH N XX XX XX N VV VV VV NS S S S S S

0 0 0 0 0 0 0/ / / ( )HH VV XX VV XX HH VV

:HHVV HH VV

: | | | |LL RRHel S S

2 2 2: | | 4 | | | |VV XX VVTP S S S

Slide 51

Summary: Second Order Polarimetric Parameters

Slide 51

• Correlation Coefficients

• Scattering Entropy

• Scattering Anisotropy

• Polarimetric Alpha Angle

• Polarimetric Beta Angle

3

Ak B

C

3 3 3[ ] :

AA AB AC

X k k BA BB BC

CA CB CC

Nine Parameters: 3 Real & 3 Complex Elements

*

* *

| |:| | | |

HH VVHHVV

HH HH VV VV

S SS S S S

*

* *

| |:| | | |

LL RRLLRR

LL LL RR RR

S SS S S S

2 3 2 3

2 3 2 3

: P PAP P

1 2 3

: iiP

3

31

: logi ii

H P P

1 1 2 2 3 3: P P P

1 1 2 2 3 3: P P P

1: ( )21: ( )2

: 2

HH VV

HH VV

XX

A S S

B S S

C S

:

: 2

:

HH

XX

VV

A S

B S

C S

Pauli Scat. Vector Lexico. Scat. Vector

Slide 52Slide 52

·Agriculture/Land-UseCrop Classification/Moisture Content EstimationUrban Area mappingUrban Topography for Mobile comms

·ForestryBiomass Estimation: (Saturates For High Biomass)

C-band saturation at 50 tons/hectareL-band saturation at 100 tons/hectareP band saturation at 200 tons/hectare

DeforestationForest Canopy Height EstimationTree Species DiscriminationForest Re-growth Monitoring

·GeologyPlayas :Smooth Natural Surfaces(rms = 1cm)Alluvial fans, Sand Dunes, MorainesSedimentary Rock formationsLava Flows (extreme in surface roughness)Weathering Erosion StudiesSurface Roughness Estimates

·HydrologyFlood mapping/Forest InundationSnow HydrologySoil Moisture

·Sea Ice/OceanographyIce Roughness/Thickness StudiesPolar Ice Cap StudiesExtra-Terrestrial Ice/Water Studies

·MeteorologyRain rate estimationWater/Ice particle studies

Severe Storm/Flood warning

·Topography/CartographyDirect Surface Slope EstimationAccurate DEM GenerationDifference of DEMS for Vegetation mapping

·Humanitarian DeminingSurface Penetrating Radar (SPR)SAR for Mine Field Detection

Applications of Radar Polarimetry


14

Slide 53Slide 53

Soil Moisture Estimation

• Surface characterisation

• mv estimation over bare surface

• mv estimation under the vegetation

Slide 54

Scattering @ Natural (Bare) Rough Surfaces

Slide 54

SURFACE ROUGHNESS

rms - height σ [cm]

autocorrelation length l [cm]

VOLUMETRIC MOISTURE CONTENT

mv [vol. %]The Backscattered signal depends on themoisture, and roughness of the surface.

Single channel SAR cannot resolveunambiguously the bare surface scatteringproblem.

Surface Characterisation

Slide 56Slide 56

Models for the Estimation of Bare Surface

Modeling and Inversion

EMPIRICALEXTENSIONS

THEORETICALMODELS

Geometrical-Optic 1963

Physical-Optic 1963

Oh Model 1992

Dubois Model 1995Integral Equation 1992

Shi Model 1997

X-Bragg 1999

MODEL BASEDEXTENSIONSSmall Perturbation 1988

Time

• very complex• inversion is

restrictedpossible

• inversion with dual pol observables

• based on regressionscoefficients of a specific test site

• simple modelextension

• based on SPM (1st order) with an roughness extension

Slide 57Slide 57

Models for the Estimation of Vegetated Surface

Modeling and Inversion

THEORETICALMODELS

Vegetation Models(Stiles 2000) SEMI-MODEL BASED

EXTENSIONS

SCATTERING DECOMPOSITION

APPROACHES

Eigen-DecompositionModel based-Decomposition

Hajnsek 2009Jagdhuber 2012

Time

• very complexscat. models

• inversion is not possible

Optimisation (physical and numerical)

Ari 2010Neumann 2010

• simple canonical scat. models

• inversion possible

• extended scat. models• inversion with higher

computation time


15

Slide 58

Small Perturbation Model

Slide 58

Roughness:Depolarises the incident wave introducing(depolarised) HV and decreasing polarimetriccoherence.Ambiguity: Terrain Slopes introduce also a HVcomponent but this is co-related to HH and VV.

Moisture content:Effects the scattering amplitude of allpolarisations. For “smooth” (with respect towavelength) surfaces the co-polarimetric ratio(HH/VV) becomes independent of roughness

Polarimetry provides an observation space that allows to separate

roughness from moisture effects.

ε,θR00ε,θR

Z)θcos(k2jSSSS

SrP

rS

VVVH

HVHH

22rr

2r

2r

P)θsinεθcosε(

))θsin1(εθ)(sin1ε(R

θsinεθcosθsinεθcos

R2

r

2r

S

Exact solution of Maxwell Equation for s 0

Z = F.T.(E(x, y))

θ = Incidence angle and k = 2π/λ

p

s

VV

HH

RR

SS

is independent of roughness

Slide 59Slide 59

Small Perturbation Model (Bragg-Model)

Measured versus estimated volumetric moisture content mv [vol. %] using SPM

0

0.2

0.4

0.6

0.8

1

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0m v [vol %]

ks

elbe0-4elbe4-8weih0-4weih4-8

Validity range of the SPM for the ground measurements - surface roughness against

volumetric soil moisture

SPM

0

10

20

30

40

0 10 20 30 40measured m v [vol. %]

estim

ated

mv

[vol

. %]

Weiherbach 0-4 cmWeiherbach 4-8 cmElbe 0-4 cmElbe 4-8 cm

Slide 60Slide 60

Semi-Empirical Models

Oh-Model Dubois-Model

Y. Oh, K. Sarabandi and F.T. Ulaby, "An Empirical Model and an Inversion Technique for Radar Scattering from Bare Soil Surfaces", IEEE

Transactions on Geoscience and Remote Sensing, vol. 30, no. 2, pp. 370-381, 1992.

2

31

0

00201

ks

vv

hh ep

ks

vv

hv eq 123.0 00

0

s0xx : backscattering coefficient for different polarisations

q : incidence angle in radianser : real part of the dielectric constant

s : RMS heightk : wavenumber (2p/l)

G 0 : Fresnel reflectivity of the surface at nadir

P. C. Dubois, J. J. van Zyl and J. Engman, "Measuring Soil Moisture with Imaging Radar", IEEE Transactions on Geoscience and Remote Sensing,

vol. 33, no. 4, pp. 915-926, 1995.

7.04.1tan028.05

5.175.20 sin10

sincos10

kshh

7.01.1tan046.03

335.20 sin10

sincos10

ksvv

q : incidence angle in radianser : real part of the dielectric constant

s: RMS heightk : wave number (2p/l)

l : wavelength [cm]

Cross-Pol Amplitude Relation Co-Pol Amplitude Relation

Slide 61Slide 61

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40

measured mv [vol %]

estim

ated

mv

[vol

%]

0-4 elbe4-8 elbe0-4 weiherbach4-8 weiherbach

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40measured mv [vol %]

estim

ated

mv

[vol

%]

0-4 elbe4-8 elbe0-4 weiherbach4-8 weiherbach

Dubois Oh

Quantitative Estimation of Volumetric Moisture mv [vol %]

0-4 cm 4-8 cm corr.

Elbe RMSerror 14 10 -/-

Weih RMSerror 11 17 0.2/0.4

0-4 cm 4-8 cm corr.

Elbe RMSerror 7 12 -/-

Weih RMSerror 18 25 0.5/0.6


16

Slide 62Slide 62

Dubois Oh

Quantitative Estimation of Surface Roughness ks

ks corr.

Elbe RMSerror 0.21 0.6

Weih RMSerror 0.19 -0.6

0

0.2

0.4

0.6

0.8

1

0.0 0.2 0.4 0.6 0.8 1.0measured ks

estim

ated

ks

e lbeweiherbach

0

0.2

0.4

0.6

0.8

1

0.0 0.2 0.4 0.6 0.8 1.0

measured ks

estim

ated

ks

e lbeweiherbach

ks corr.

Elbe RMSerror 0.24 -

Weih RMSerror 0.19 -0.7

Slide 63

Surface Parameter @ X-Bragg

Slide 63

Surface scattering model for the estimation ofsoil moisture content & surface roughness Back Scattering

I. Hajnsek, E. Pottier, S. Cloude, Surface Parameter Estimation using Polarimetric SAR, IEEE TGARS, 2003, vol. 41, no. 4, pp. 727-745

))4(sinc1(00

0))4(sinc1( )2(sinc*0)2(sinc

13

1312

121

CCC

CCT

23 2

1=C PS RR **2 PSPS RRRRC 2

1 PS RRC

0000

02*

*2

PSPSPS

PSpSPS

RRRRRR

RRRRRR

T

22

22

)sincos())sin1()(sin1(

rr

rrPR

2

2

sincos

sincos

r

rSR

surface

sensor

Bragg Surface X-Bragg Surfaceextension

Advantage:Simple model with direct relation to soil moisture content

Disadvantage:not realistic enough for natural surfaces

20 1

Advantage:Integration of a roughness & decorrelation term provides natural surface statistics

Disadvantage:very sensitive to vegetation cover

Slide 64Slide 64

Prediction of the X-Bragg Model I

(HH+VV)(HH-VV) coherence versus b1 for different local incidence angles

Cross polarised power versus b1 for different local incidence angles

Slide 65Slide 65

Variation of Anisotropy with Roughness (Model Parameter ß1 )

Variation of Entropy/Alpha values with Dielectric Constant (45 degrees AOI)

Prediction of the X-Bragg Model II


17

Slide 66Slide 66

Frequency:1.5to18.5 GHz10°20°

30°

40°

50°

Anisotropy versus frequency plot forthe EMSL data

Entropy/alpha angle plot for the EMSL data

Experimental Data from Anechoic Chamber (JRC - Ispra)Collected in European Mircowave Signature Laboratory (EMSL)

quad-pol scattering matrix(HH,VV,HV,VH); s = 0.4 cm; surface correlation lenght l = 6; dielectric constant ‘ = 8; frequency range = 1.5-18.5

Slide 67

Inversion Results @ X-Bragg – Early Example on ELBE 2000

Slide 67

Volumetric Moisture

N

0 500 m

Field 10

Field 13

Field 14

Field 16

0

10

20

30

40

0 10 20 30 40measured m v [vol %]

estim

ated

mv [v

ol. %

]

Elbe 0-4 cmElbe 4-8 cmWeiherbach 0-4 cmWeiherbach 4-8 cm

Volumetric Moisture mv [%]

0

30

Vol. [%]

Slide 68

Bare Surfaces: Soil Moisture Estimation @ AQUIFEREX 05

Slide 68

E-SAR / Test Site: Aquiferex, Tunisia

L-band / Pauli RGB Vol. Soil MoistureDielectric ConstantSlide 69Slide 69


• Surface Characterisation



• 3 component model decomp.

• improvement of the 3 component model decomp.

• multi-angle approach combined with 3 comp. model decomp.

• hybrid decomposition


18

Slide 70

3 Component Freeman Decomposition

Slide 70

Surface Dihedral Volume

33

22

1211V

2

D2

S

T000TT0TT

100010002

4f

00001α0α|α|

f0000|β|β0β1

f12

Bragg Fresnel Random Volumeof Dipoles

0000|β|β0β1

fT 2

*

SBraggVH

VH

RRRRβ

22rr

2r

2r

V)θsinεθcosε(

))θsin1(εθ)(sin1ε(R


R2

r

2r

H

Slide 71

3 Component Freeman Decomposition

Slide 71

33

22

1211V

2

D2

S

T000TT0TT

100010002

4f

00001α0α|α|

f0000|β|β0β1

f12

Bragg Fresnel Random Volumeof Dipoles

iPTPS

STSS

PT

ST

PS

SS

eRR00RR

R00R

R00R

1001

]S[


R2

T/S

2T/S

)T/S(S

θsinεθcosεθsinεθcosε

R2

T/ST/S

2T/ST/S

)T/S(P


Slide 72

AGRISAR Campaign in Northern Germany 2006

Slide 72

1. Agricultural data base over a whole vegetation growth period - April - August 2006

2. 16 data acquistions (DLR’s E-SAR) & ground measurements

3. Support by ESA for the space segment - Sentinel Program

Soil Moisture Station

Bowen-Ratio Station

Laser-Profiler

ASD

Thermal Infrared

LAS

DEMMIN

Slide 73Slide 73

dihedralvolumesurface

03/5/2006 14/6/2006 02/8/2006 03/5/2006 14/6/2006 02/8/2006

Freeman-Durden 3-Component Decomposition (AGRISAR 2006)

C-band L-band


19

Slide 74

3 Component Decomposition and Modifications

Slide 74

Total backscatter = + +


000010

LfT

2

2SDmoD

||

Dihedral modification (modified Fresnel coefficients) (εs, εt, θ, φ, LS):

Incorporation of a surface scattering loss LS

1L0 S

A1ks ))cos()(exp( 22S ks2L

[Lee et al., 2005]

))4(sin1(||2100

0))4(sin1(||21)2(sin

0)2(sin1

2

2

c

cc

c

fT SXB

Surface modification (X-Bragg) (εs, θ, ):

[Hajnsek et al., 2001]

sensor

Incorporation of a roughness and HV-decorrelation term (δ)

100010002

4000010||

0000||01

0000 2

2

33

2212

1211V

DSfff

TTTTT

000010||

0000||01

00000 2

2'22

'12

'12

'11

DS ffTTTT

Decision rule = or0

0SS VVHH

*Re

0

0SS VVHH

*Re

[Freeman & Durden, 1998, Yamaguchi et al., 2006]Slide 75Slide 75


X-Bragg Volume 1 Volume 2

Modified Decompositions @ L-band for 03/5/2006

Volume 3Modified Dihedral

Slide 76Slide 76

Time Series of Decompositions on Field Basis @ L-band

Wheat (No. 250) – far range

Wheat (No. 230) – near range

Slide 77Slide 77

Time Series of Decompositions on Field Basis @ L-bandRape (No. 140) – near range

Rape (No. 101) – far range


20

Slide 78Slide 78

03/5/2006 14/6/2006 02/8/2006

Inverted Soil Moisture @ L-band Volume 2 (surface) and modified Dihedral (dihedral)

Land use

0

10

20

30

40

50vol. %

Slide 79Slide 79

Time Series of Inverted Soil Moisture @ L-band

Wheat field (No. 250) Wheat field (No. 230)

+ Bragg X-Bragg Volume 1 Volume 2 Volume 3*Surface Dihedral ±30% Intervall

Slide 80Slide 80

Time Series of Inverted Soil Moisture @ L-band

Rape field (No. 140) Rape field (No. 101)

+ Bragg X-Bragg Volume 1 Volume 2 Volume 3*Surface Dihedral ±30% Intervall

Slide 81Slide 81










21

Slide 82

Polarimetric Decomposition Techniques & Inversion Scheme

Slide 82

No

Vegetated soil

Volume power

extraction

Model-based decomposition

H/criterion

Volume power

correction

Scatteringdominance

Eigenvaluedecomposition

Calculation of entropy and alpha

Soil moisture inversion from surface and dihedral components

Calculation ofground

components

Soil moistureinversion fromX-Bragg model

YesBare soil

Volume orientation

Total soil moisture

0 10 20 30 40 50vol.-%

Soil Moisture MapWeisseritz Region

Slide 82Slide 83

Modeling of Volume Orientation

2

2

log10HH

VV

rS

SP

)sin(21 pdf )cos(

21 pdf

Slide 83

Volume modelling of a cloud of uniformly

shaped (ρ) particles with an orientation (τ)

in azimuthal direction

Selection of volume orientation by polarisation power ratio Pr:

General Case Vertical dipoles[Pr < -2dB]

Random dipoles[-2dB < Pr < 2dB]

Horizontal dipoles[Pr > 2dB]

21

pdf

8000750515

30V

VfT

100010002

4V

VfT

8000750515

30V

VfT

[Yamaguchi et al., 2005]

0 20 2/2/

300024041

CCCCC

fT VV

Slide 85

Vegetation GroundSAR data

- = +

Negative Powers =

Too Strong Volume Subtraction!

Eigenanalysis of GroundComponents

Set Eigenvalues to zero (lower calculus limit)

Solutions for fv :

Slide 85

Correction of Volume Intensity fv with Eigen-based analysis

[van Zyl et al., 2008]

1 334vf T

2,3 11 22

2 * 211 12 12 11 22 22

2

8 4 4

vf T T

T T T T T T

Solution

Find Minimum Decomposiition with

Corrected Volume Intensity

fvcorr

11 12 1 4*

12 22 4 2

33 3

0 00 0

0 0 0 0v

T T C CT T f C C

T C

Surface DihedralT T

Slide 86

3 Component Decomposition and Modifications

Slide 86

))(sin(||

))(sin(||)(sin

)(sin

4c12100

04c1212c

02c1

fT

2

2SXB

Surface model (X-Bragg) (εs, θ, ):

Incorporation of a roughness and HV-decorrelation term

[Hajnsek et al., 2001]

sensor

Total backscatter = + +


300024041

000010||

))4(sin1(||5.0000))4(sin1(||5.0)2(sin0)2(sin1

0000

,

2

2

2

33

2212

1211

CCCCC

fLfc

ccc

fT

TTTT

corrvvDS

000010||

))4(sin1(||5.0000))4(sin1(||5.0)2(sin0)2(sin1

0000 2

2

2

'33

'22

'12

'12

'11

vDS Lfc

ccc

fT

TTTT

Scattering dominance = or0

0SS VVHH

*Re

0

0SS VVHH

*Re

[Freeman & Durden, 1998, Yamaguchi et al., 2006]

000010

LfT

2

VDmoD ||

Dihedral model (Fresnel with vegetation attenuation) (εs, εt, θ, φ, LV):

Incorporation of a vegetation attenuation LV

V

2

V

1

P

P

min

max

))/(exp( minmax 1LV


22

Slide 87Slide 87

1. Soil moisture estimation under different land cover with measurement approaches at different scales

2. Goal: Support flood forecasting by identification of critical catchment states

2 Radar data acquisition flights (single pol X-, dual-pol C- and quad-pol L- and P-band)Ground measurements (soil moisture,soil roughness, biomass,…)

OPAQUE – Campaign May 2007

TDR and GPR

t

NCorner–reflectorWeißeritzFlight stripTest site

Winter triticaleWinter wheatSummer barleyWinter barley

Grass landWinter rapeSummer cornBare soil

Slide 88

Volume Orientation and Scattering Dominance @ L-band

Slide 88

2% 11%

12%

31%18%

5%

21% surface dominant

vertical

horizontal

random

volume orientation

verticalhorizontalrandom

volume orientation

X-Bragg only

dihedral dominant

rang

e

azimuth

Slide 89

Slide 89

L-Band Local incidence

Model-based Decomposition @ L-band

Land use

0°

18°

36°

54°

72°

90°degrees

Grass landWinter rapeSummer cornBare soil

Winter triticaleWinter wheatSummer barleyWinter barley

range

azim

uth

Slide 90

Inverted Soil Moisture @ L-band

Slide 90

+ =+

Dihedral TotalSurfaceX-Bragg

0 10 20 30 40 50vol.-%

range

azim

uth


23

Slide 91Slide 91

Validation with TDR Measurements on Winter Wheat

Vegetationheight:

55cm

Row distance:

10cm

Wet biomass:

2,85kg/m²

Slide 92Slide 92

Validation with TDR Measurements on Summer Barley

Vegetationheight:

45cm

Row distance:

23cm

Wet biomass:

0,93kg/m²

Slide 93Slide 93

Validation with TDR Measurements on Winter Triticale

Vegetationheight:

85cm

Row distance:

10cm

Wet biomass:

3,34kg/m²

Slide 94Slide 94

Validation with TDR Measurements on Winter Barley

Vegetationheight:

70cm

Row distance:

10cm

Wet biomass:

3,31kg/m²


24

Slide 95Slide 95









Slide 96

Slide 96

Multi-Angle Approach for Soil Moisture Inversion Rate Increase

vol.%0

1020

3040

50

Single angular1 acquisition

master

Bi-angular2 acquisitions

master -perpendicular

Tri-angular3 acquisitions

master -opposite -

perpendicular

Flight Lines Master Opposite Perpendicular

0°

18°

36°

54°

72°

90°

Testsite: Weisseritz Watershed; E-SAR L-band

Slide 97Slide 97

Inversion Rate of Soil Moisture Estimation @ different AOI

One acquisition

Single angular

master opposite perpendicular

40.32% 29.87% 48.53%

Two acquisitions

Bi-angular

master-opposite

master-perpendicular

opposite-perpendicular

55.87% 63.39% 60.71%

Three acquisitions

Tri-angular

master-opposite-perpendicular

70.89%

Slide 98Slide 98

Field meanvalues

Validation of Soil Moistures @ different Incidence Angles

Sampling box: 13x13 pixelsBox coverage: 70%

Single angularinversion (p)

Bi-angularinversion (o-p)

Tri-angularinversion (m-o-p)


25

Slide 99Slide 99

Single angular

RMSE [vol.%] between Estimated and Measured Soil Moistures for Different Incidence Angle Constellations

Bi-angular Tri-angular- = out of scene < = too less values for a valid analysis

Fields m o p m-o m-p o-p m-o-p

Winter Triticale 6.34 6.34 10.33 5.42 6.27 9.46 5.72

Winter Barley < < 9.75 7.76 6.85 8.78 6.94

Winter Rye 5.25 6.11 5.86 5.81 4.89 6.42 5.27

Winter Wheat 4.52 < 9.79 4.41 5.04 9.54 4.98

Summer Oat 8.55 6.43 - 6.35 8.55 6.43 6.35

Mean 6.17 6.29 8.93 5.95 6.32 8.13 5.85

Slide 100Slide 100

Slide 100

0

1

2

3

4

5

6

7

8

9

10

11

master opposite perpendicular m-o m-p o-p m-o-p

vol.%

RMSE @ Different Incidence Angle Constellations

Summer oatWinter triticaleWinter barleyWinter ryeWinter wheatNot enough valid pixelsOut of scene

RM

SE

[

]

RMSE=root mean squareerrorm=mastero=opposite p=perpendicular

Single angular Bi-angular Tri-angular

Slide 101Slide 101









Slide 102

Removal of Vegetation Component

Basic Principle of Hybrid Polarimetric Decomposition

102

Not physically constraint volume intensity!

Volume intensity ( fv)2

33 sin/2 vv tf

0000sincossincos0sincossincos

sin000sin000cos2

200

00

22,,

,,22

2

2

2

33

22*12

1211

ssddsdsdsd

sdsdsdddss

v

v

vv ffff

fffff

ttttt

signature groundSurface Dihedral VolumePolarimetric Signature

= + +

vegetation groundsignature

- fv = +

Model-based decomposition for vegetation volume removal

Eigen-based Decompositionof ground for soil moisure

vegetation

+Hybrid Polarimetric Decomposition

Physically constraint volume intensity ( fvc) with surface scattering (αb)

2 * * 2 2vc 11 22 12 12 12 124 csc(2 ) ( ( )cos(2 )) sin(2 ) /1 3cos(2 )b b b vf t t t t t t

Graphics from www.vectorarts.net

Slide 102


26

Slide 103

Retrieval of the Ground Scattering Components

103

Scattering mechanisms of ground (d, s)Physically Meaningful Separation of Scattering Mechanisms (d,s)

Orthogonality conditiond + s= /2

[0, /4] Surface scattering

[/4, /2] Dihedral scattering

s

d

Intensity of ground (fd, fs)

From eigenvalues:

From eigenvectors:

groundvegetation

- fvc = +

0000sincossincos0sincossincos

22,,

,,22

ssddsdsdsd

sdsdsdddss

ffffffff

Model-based decomposition for vegetation volume removal

Eigen-based decompositionof ground for soil moisure Hybrid Polarimetric Decomposition

103

Eigen-based Decomposition of Ground Components

Graphics from www.vectorarts.net

Slide 103Slide 104

Soil Moisture Inversion from Surface Scattering Component

mins

m

Minimization

s

Pedo-Transfer Function of

Topp et al. (S<40)Roth et al. (S>40)

Soil moisture [vol.%]

Surface scattering componentfrom hybrid polarimetric

decomposition

Polarimetric SAR data

Bragg scatter modeling with locand a variety of soil dielectric constants S

Surface scattering model

tan( )s HH

HH

RR

VVm

VV

RR

RHH , RVV = f(S ,loc)

Slide 104

Slide 105

Soil Moisture Inversion along Vegetation Growth Cycle range

azim

uth

Land use April, 19th

εs-level=20

25cm

5cm

Soil moistureMax. vegetation height

Soil moisture station

Height:18cmWet biomass:1.88kg/m²

5

12

19

26

33

40

[vol.%]

June, 7th


5

9

13

17

21

25

[vol.%]

εs-level=10

0

4

8

12

16

20

[vol.%]

July, 5th


εs-level=8

Winter rape

AgriSAR 2006L-band

Quad-pol

Slide 106Slide 106

APRIL JUNE

JULY

RMSE=6.75 RMSE=6.98

RMSE=4.43 validation box: 13x13 pixels

Validation of Moisture Inversion with Field Measurements (TDR)

RMSE [vol.%] APRIL JUNE JULYsugar beet 7.55 11.90 4.68

winter wheat 8.06 6.15 3.40winter rape 5.70 2.62 2.06

summer corn 5.34 3.20 5.25


27

Slide 107

Soil moisture inversion is still object of scientific research:

Main research focuses on the estimation of mv under the vegetationOne attempt is to use SAR polarimetryfor scattering mechanism decomposition in order to characterise and subtract volume from a ground component (surface/dihedral)PolSARPro provides the possibility to invert soil moisture over bare fields and allows a decomposition of scattering mechanisms

Outlook: Inversion procedures for vegetated areas are still missing

Slide 107

Summary Soil Moisture Estimation

Slide 108

Basic Text Books for SAR-Polarimetry and Pol-InSAR:Cloude, S., Polarisation: application in remote sensing, Oxford University Press, p. 352, 2009.Boerner, W. M. et al., ‘Polarimetry in Radar Remote Sensing: Basic and Applied Concepts’, Chapter 5 in F. M. Henderson, and A.J. Lewis, (ed.), “Principles and Applications of Imaging Radar”, vol. 2 of Manual of Remote Sensing, (ed. R. A. Reyerson), Third Edition, John Willey & Sons, New York, 1998. Elachi, C. & Van Zyl, J. J., ‘Introduction to the physics and techniques of Remote Sensing’, John Wiley and Sons, p. 552, 2006.Lee, J.S. & Pottier, E. ‘Polarimetric Radar Imaging: From Basics to Aapplications’, CRC Press, 2009.Woodhouse, I., ’Introduction to Microwaves Remote Sensing’, CRC Taylor & Francis, p. 370, 2005.

Slide 108

References (Part I)

Slide 109

Surface Parameter Inversion:Hajnsek, I., Pottier, E. & Cloude, S.R., ‘Inversion of Surface Parameters from Polarimetric SAR’, IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 4, pp. 727-745, 2003.Lee, J.-S., Boerner, W.-M., Ainsworth, T. L., Hajnsek, I., Papathanassiou, K.P., Lüneburg, E., ‘A Review of Polarimetric SAR Algorithms and their applications’, Journal of Photogrammetry and Remote Sensing, vol. 9, No. 3, pp. 31-80, 2004.Hajnsek, I. & Cloude, S., ‘The Potential of InSAR for Quantitative Surface Parameter Estimation’ Can. J. Remote Sensing, vol. 31, no.1, pp. 85-102, 2005Hajnsek, Irena; Jagdhuber, Thomas; Schön, Helmut; Papathanassiou, Kostas (2009): Potential of Estimating Soil Moisture under Vegetation Cover by means of PolSAR. IEEE [Hrsg.]: IEEE Transactions on Geoscience and Remote Sensing, IEEE, vol 47, no 2, pp. 442 – 454.M. Arii, J.J. van Zyl and Y. Kim: A general characterization for polarimetric scattering from vegetation canopies, IEEE Transactions on Geoscience and Remote Sensing, vol. 48, pp. 3349-3357, 2010. M. Neumann and L. Ferro-Famil: Extraction of particle and orientation distribution characteristics from polarimetric SAR data, in Proc. of 8th European Conference on Synthetic Aperture Radar, June 7-10, 2010, Aachen, Germany, VDE, pp. 422-425.Jagdhuber, T., I. Hajnsek, A. Bronstert & K.P. Papathanassiou, ‘Soil Moisture Estimation under Low Vegetation Cover Using a Multi-Angular Polarimetric Decomposition‘, IEEE Transactions on Geoscience and Remote Sensing,10.1108/TGRS.2012.2209433, p. 1-15, 2012.Jagdhuber, T. , I. Hajnsek, K.P. Papathanassiou, & A. Bronstert, ‘Soil Moisture Retrieval Under Agricultural Vegetation Using Fully Polarimetric SAR‘, Proc. of IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, July 22-27, pp. 1481-1484 , 2012.Jagdhuber, T., ‘Soil Parameter Retrieval under Vegetation Cover Using SAR Polarimetry, PhD Thesis, University Potsdam, Potsdam, supervised by Prof. Hajnsek and Prof. Bronstert, Date of Defense: 05.07.2012, http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-60519, 2012.Bronstert, A., C. Creutzfeldt, T. Gräff, I. Hajnsek, M. Heistermann, S. Itzerott, T. Jagdhuber, D. Kneis, E. Lück & D. Reusser, ‘Potentials and constraints of different type of soil moisture observations for flood simulations in headwater catchments‘, Natural Hazards, Vol. 60, pp.879–914 , 2012.

Slide 109

References (Part II)

Slide 110Slide 110

Acknowledgement to the AgriSAR Team

06/06/06

INTA


28

Slide 111

Acknowledgement to the OPAQUE Team

Slide 111

30/05/2007

Slide 112

Part II: Exercises with L-band Airborne Data

Read the airborne SAR dataSpeckle Filtering (refined Lee)Oh, Dubois and X-Bragg Inversion

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HH HV

VH VV

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Test Date Used for the Exercise

Testsite: DemminLocation: Northern GermanyAcquisition Date: May 2012Frequency: L-bandData size: az: 2.75km rg: 2.2 kmPolarisation: 4 SLCResolution: az: 60cm x rg: 3.8mRows and colums: 7981 x 1837


Pauli RGB

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First Steps in PolSARPro

Please open PolSARProDefine your environmentOpen the DLR‘s acquired test Radar data

2nd advanced course on radar polarimetry 2013 01/24/2013 · 2nd advanced course on radar...

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