and L- band Interferometric SAR (InSAR)

Comparative Analysis of Soil Moisture Retrieval using 

C- and L- band Interferometric SAR (InSAR) 

Brian W. Barrett 1 

Ned Dwyer 2 , Padráig Whelan 1 

1 Dept. of Zoology, Ecology and Plant Science (ZEPS), University College Cork (UCC) 

2 Coastal & Marine Resources Centre (CMRC), University College Cork, (UCC) 

UCC 

University College Cork

Presentation Overview 

• Why measure soil moisture 

• Objective 

Acquisition 

• Why use Microwave RS 

• Data Selection 

Analysis 

• InSAR & DInSAR 

Application 

• Soil moisture estimation 

3 rd Annual Irish Earth Observation Symposium 

12 &13 th November 2009

Why Soil Moisture 

The Water Cycle 

0.05% 

Key Impact 

• Climatology 

• Meteorology 

• Agriculture 

• Ecology 

• Socio-economic 

Objective 

Soil moisture retrieval 

ESA® 

using interferometric 

SAR techniques 



Microwave Remote Sensing 

– Uses frequencies between 0.3 and 300 GHz 

– Active and Passive 

– SAR 



Microwave Remote Sensing & Soil Moisture 

• Theory is based on the large contrast between the 

dielectric properties of liquid water (ε~ 80) and dry soil (ε~6) 

• Many radar models & methods have been developed for 

the estimation of soil moisture, e.g. IEM, Oh, Dubois, WCM. 

•These models only use the amplitude part of the 

backscattered signal. 

•Very little has been reported concerning the sensitivity of the 

signal phase to soil moisture content. 

= + 

•InSAR and DInSAR: established techniques for the creation 

of DEM`s and detecting surface displacements. 

SLC Amplitude Phase

Interferometric Synthetic Aperture Radar (InSAR) 

Repeat-pass Interferometry 

•2 independent passes of the sensor over the 

target 

S M 

i 

B 

B 

B 

S S 

B || 

•The SAR system records both the amplitude 

i 

(strength) and the phase (time) of the 

backscattered microwave signals 

θ 

R M 

R S 

H 

•Interferometry calculates on a pixel by pixel 

P 

basis, the phase difference (interferogram) 

between two successive SAR images, 

acquired from slightly different geometries, 

cancelling out the reflectivity phase term. 

Ellipsoid 

P’ 

H P 

InSAR Geometry 



SAR Interferometry (InSAR) 

L- band 

15 th July 2007 

FBD HH 

Complex Interferogram 

30 th August 2007 

FBD HH 

Azimuth 

Coherence 

Range 

Co- registration of SLC`s to 

common geometry 



InSAR Data Selection 

•The SAR data selection has a significant influence on the resulting interferogram. 

•Selection criteria vary according to the specific needs of an investigation however the 

primary and most straightforward concern is the availability of the data. 

•The ┴ baseline and the time interval between acquisitions are the main factors affecting the 

coherence . 

•Total decorrelation occurs when the critical baseline is reached. 

B 

CRIT 

R 

2L cos 

r 

Comparison of Critical Baselines 

Sensor Bandwidth Incidence Angle Critical Baseline (m) 

ENVISAT ASAR I1 16 MHz ~18.7 o ~745 

ENVISAT ASAR 12 16 MHz ~22.7 o ~936 

ALOS PALSAR FBD 14 Mhz ~38.7 o ~6550 

ALOS PALSAR FBS 28 MHz ~38.7 o ~13100 

ALOS PALSAR PLR 14 MHz ~23.7 ~3287 



SAR Data Acquisition 

**Systematic Observation Strategy** 

ALOS PALSAR 

ENVISAT ASAR 

Launch: 24 th January 2006 

Altitude: 692km 

λ: 23.6cm 

Cycle: 

46 days 

Launch: 1 st March 2002 

Altitude: 800km 

λ: 5.66cm 

Cycle: 35 days 

SLC FBS – Fine Beam Single 

SLC FBD – Fine Beam Dual 

SLC PLR – Polarimetric 

16 Acquisitions (4-8-4) 

SLC APS – Alternating polarisation 

37 new Acquisitions (HH/HV & HH/VV) 



Interferometric Pairs 

Pair Master Slave Day diff Baseline Diff 

(m) 


12 &13 th November 2009 

Track Swath 2π 

Ambiguity 

Height 

Doppler 

Centroid 

Diff (Hz) 

ASAR APS 

1. 06/07/2007 10/08/2007 35 75 352 I1 89.7 4.8 

2. 01/06/2008 06/07/2008 35 187 80 I1 35.7 1.3 

3. 06/07/2008 19/10/2008 105 52 80 I1 129.5 9.3 

4. 26/08/2008 04/11/2008 70 43 309 I2 188.2 26.4 

5. 05/06/2009 10/07/2009 35 65 352 I1 101.9 -34.3 

PALSAR FBD 

Frame 

6. 15/7/2007 15/10/2007 92 974 3 1030 59.5 30.7 

7. 15/7/2007 30/8/2007 46 838 3 1030 69.2 45.8 

8. 30/8/2007 15/10/2007 46 537 3 1030 108.1 -15.1 

9. 20/07/2009 04/09/2009 46 604 3 1030 106.6 5.5 

PALSAR FBS 

10. 1/3/2008 16/4/2008 46 597 3 1030 97.2 -28.7 

11. 15/1/2008 16/4/2008 92 1156 3 1030 55.7 -11.2 

12. 15/1/2008 1/3/2008 46 802 3 1030 68.4 17.4 

FBD2FBS 

13. 15/1/2008 15/10/2007 92 706 3 1030 91.2 1.7 

14. 1/3/2008 1/6/2008 92 886 3 1030 65.5 -55 

15. 16/4/2008 1/6/2008 46 419 3 1030 138.7 -26 

PALSAR PLR 

16. 7/10/2008 22/11/2008 46 484 668 1040 67.7 23.2

Interferometric Processing 

Two SLC Images 

Baseline Estimation 

Co-registering the two SLC`s 

Complex Multiplication of the two SLC`s (Interferogram generation) 

Interferogram flattening Interferogram filtering (Adapted Goldstein filter) 

DEM (90m SRTM) 

Coherence Generation 

Phase unwrapping 

Geo-referencing 

Phase editing Orbital refinement DEM Interferogram re-flattening 



Analysis - Coherence 

Study Area 

Great Island 

Cork, Eire 

OSi© 



Interferometric Correlation (Coherence) 

The coherence is a measure of the phase correlation between two co-registered complex SAR 

images, I 1 and I 2 and is defined as the correlation coefficient 

I 

1 

I 

I 

1 

2 

I 

2 

I 

2 

I 

1 

γ = γ System Noise 

+ γ Geomtrical 

+ γ Temporal 

1 st March 08 L-band 

HH Amplitude Image 

Coherence 

Image 

16 th April 08 L-band HH 

Amplitude Image 



Temporal Coherence Loss – C-band 

T┴ 35 T┴ 70 T┴ 105

L-band FBD HH Coherence 

20 th July 2009 

4 th Sept 2009 

Temporal change that causes loss 

of coherence can be caused by two 

types of decorrelation factors 

-a permittivity change 

- or a scatterer geometry change 

between the two image acquisitions 

Red – high coherence 

Green – backscatter decrease 

Blue – backscatter increase 

20th July 2009 H 84.67cm 4th Sept 2009 H 65.14cm 

M v 

σ 0 (dB) M v 

σ 0 (dB) 

PS01 8.80% -7.47647 18.23% -5.23561 

PS02 8.53% -9.67607 11.23% -5.97024 

PS03 8.90% -8.52073 13.03% -6.17416 

PS04 10.52% -11.3852 16.03% -9.22385 

PS05 9.62% -8.79838 9.03% -5.86285 

PS06 10.09% -8.69904 9.00% -6.03956 

PS07 6.38% -13.3604 9.20% -11.3553 

PS08 3.44% -8.14554 13.20% -5.93864

L-band Coherence 

20th July 2009 4th Sept 2009 

M v 

σ 0 (dB) M v 

σ 0 (dB) 

SM01 5.94% -15.7221 15.13% -11.9309 

SM02 4.80% -5.27002 13.50% -6.2148 

SM03 5.00% -11.2345 18.63% -17.1424 

SM07 5.78% -11.9314 25.83% -15.9249 

SM08 4.72% -14.7369 25.73% -16.4532 

SM04 22.70% -20.3621 24.47% -20.8223 

SM05 18.03% -18.4512 22.77% -17.6879 

SM06 17.63% -15.175 26.73% -14.7335

DInSAR for Soil Moisture Estimation 

D R 

m sin( 

) 

S 1 & S 2 

D 

2 

D 

R 

θ 

m 

D R 

P 

dz 

2 

m sin 

θ 

P′ dx 

H 

m 

2 

. 

.sin 

D 

After 

P’ 

P 

Ellipsoid 

Before 

H P 



DInSAR for Soil Moisture Estimation 

Flat 

Elevation 

Disp 

Atmos 

Noise 

D R is actually an extra phase shift in addition to the 

phase differences resulting from the surface 

topography. 

DEM of the area is used to model the 

topographically-induced phase difference and 

subtract it from the interferogram (2-pass DInSAR) 

Raw Interferogram 

To examine the relationship between SAR phase 

and soil moisture, a time-series of 16 differential 

interferograms spanning 26 months from July 2007 

to September 2009 using ALOS PALSAR and 

ENVISAT ASAR data were processed. 

Differential Interferogram 



T┴35 

T┴105 

T┴35 

T┴70 

T┴35 

Time series of ASAR C- 

band Interferograms 

showing phase changes 

during 35 -105 days 



PALSAR FBS Differential Interferograms 

•One full phase cycle in a differential interferogram is equivalent to a deformation of λ/2 along 

the slant range direction or LOS. 

•For L-band, one color cycle in the interferogram represents 11.5cm of range change between 

the ground surface and the radar antenna. 



T┴46 

T┴92 

T┴46 

T┴46 

T┴46 

Figure: Time series of PALSAR L- 

band Interferograms showing 

phase changes during 46 -92 days 



Phase Changes 

L- band 

20/07/2009 – 04/09/2009 

C- band 

05/06/2009 – 10/07/2009 



Phase Changes due to Soil Moisture 

Soil moisture is expected to vary 

between fields because of differences in 

evaporation and drainage due to cover 

crops, soil type, and farming practices. 

L-band July-August 2007 

No causal mechanisms other than soil 

moisture has been identified that can explain 

changes across such linear boundaries. 

July 2007 August 2007 



Conclusions 

• The higher sensitivity of L-band SAR signal 

into the canopy reduces the sensitivity to 

vegetation constituents and improves the 

SAR capability to estimate and monitor soil 

moisture content. 

• In vegetated areas, the longer wavelength 

L-band SAR provides more useable 

interferometric pairs over longer timeframes 

than provided by C-band SAR. 

• Differential interferograms have been 

presented that contain abrupt changes in 

phase patterns at field boundaries where 

soil moisture variances would be expected 

dues to differences in tilling, evaporation 

etc. 

Further efforts for statistical analysis of 

Interferometric phase differences using in 

situ soil moisture measurements are 

required to verify whether they can be 

useful for quantitative soil moisture retrieval.

and L- band Interferometric SAR (InSAR)

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