Principles of Remote Sensing 10: RADAR 3Applications of imaging RADAR

Dr. Mathias (Mat) Disney

UCL Geography

Office: 113, Pearson Building

Tel: 7670 0592

Email: mdisney@ucl.geog.ac.uk

www.geog.ucl.ac.uk/~mdisney

AGENDA

• Single channel data• Radar penetration

• Multi-temporal data• Vegetation, and modelling

• Agriculture & water cloud model• Forest structure and coherent models

• Multi-parameter

Observations of forests...

• C-band (cm-tens of cm)– low penetration depth, leaves / needles / twigs

• L-band– leaves / branches

• P-band– can propagate through canopy to branches, trunk and ground

• C-band quickly saturates (even at relatively low biomass, it only sees canopy); P-band maintains sensitivity to higher biomass as it “sees” trunks, branches, etc

• Low biomass behaviour dictated by ground properties

• Surfaces - scattering depends on moisture and roughness• Note - we could get penetration into soils at longer wavelengths

or with dry soils (sand)

• Surfaces are typically– bright if wet and rough

– dark if dry and smooth

• What happens if a dry rough surface becomes wet ?

• Note similar arguments apply to snow or ice surfaces.

• Note also, always need to remember that when vegetation is present, it can act as the dominant scatterer OR as an attenuator (of the ground scattering)

EasternSahara desert

SIR-APenetration 1 – 4 m

Landsat

Safsaf oasis, Egypt

SIR-C L-band 16 April 1994Landsat

Penetration up to 2 m

Single channel data

• Many applications are based on the operationally-available spaceborne SARs, all of which are single channel (ERS, Radarsat, JERS)

• As these are spaceborne datasets, we often encounter multi-temporal applications (which is fortunate as these are only single-channel instruments !)

• When thinking about applications, think carefully about “where” the information is:-

– scattering physics– spatial information (texture, …)– temporal changes

Multi-temporal data

• Temporal changes in the physical properties of regions in the image offer another degree of freedom for distinguishing them but only if these changes can actually be seen by the radar

• for example - ERS-1 and ERS-2:-– wetlands, floods, snow cover, crops– implications for mission design ?

Wetlands in Vietnam - ERS

Oct 97 Jan 99 18 Mar 99 27 May 99

Sept 99 Dec 99 Jan 00 Feb 00

Wetlands...

SIR-C (mission 1 left, mission 2 centre, difference in blue on right)

Floods...

Maastricht

A two date composite of ERS SAR images

30/1/95 (red/green)

21/9/95 (blue)

Snow cover...Glen Tilt - Blair Atholl

ERS-2 composite

red = 25/11/96

cyan=19/5/97

Scott Polar Research Institute

Agriculture

Gt. Driffield

Composite of 3 ERS SAR images from different dates

OSR - Oil seed rapeWW - Winter wheat

ERS SAR

East Anglia

Radar modelling

• Surface roughness• Volume roughness• Dielectric constant ~ moisture• Models of the vegetation volume, e.g. water cloud model

of Attema and Ulaby, RT2 model of Saich

Multitemporal SHAC radar image

Barton Bendish

Water cloud model

cos

2

cos

20 exp.exp1cos

BLBL

sDmCA

A – vegetation canopy backscatter at full cover

B – canopy attenuation coefficient

C – dry soil backscatter

D – sensitivity to soil moisture

σ0 = scattering coefficient

ms = soil moisture

θ = incidence angle

L = leaf area index

Vegetation

Values of A, B, C, D

Parameter Value Units / description

A -10.351 dB

B 1.945 Fractional canopy moisture

C -23.640 dB

D 0.262 Fractional soil moisture

Response to moisture

So

urc

e:

Gra

ha

m 2

00

1

Detection?

SAR image

In situ irrigation

Source: Graham 2001

Simulated backscatter

r2 = 0.81

-11

-10

-9

-8

-7

-6-11 -10 -9 -8 -7 -6

Actual backscatter (dB)

CH

IPS

simulated backscatter (dB

)

cos

2

cos

2

0 exp.exp1cosBLBL

sDmCA

r2 = 0.81

Canopy moisture

r2 = 0.96

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Measured fractional canopy moisture

Sim

ulat

ed fr

actio

nal c

anop

y m

oist

ure

r2 = 0.96

Applications

• Irrigation fraud detection• Irrigation scheduling• Crop status mapping, e.g.

disease, water stress

Multi-parameter radar

• More sophisticated instruments have multi-frequency, multi-polarisation radars, with steerable beams (different incidence angle)

• Also, different modes– combinations of resolutions and swath widths

• SIR-C / X-SAR• ENVISAT ASAR, ALOS PALSAR,...

Flevoland April 1994

(SIR-C/X-SAR)

(L/C/X composite)

L-total power (red)

C-total power (green)

X-VV (blue)

Thetford, UK

AIRSAR (1991)

C-HH

Thetford, UK

AIRSAR (1991)

multi-freq composite

Thetford, UK

SHAC (SAR and Hyperspectral Airborne Campaign)

http://www.neodc.rl.ac.uk/?option=displaypage&Itemid=66&op=page&SubMenu=66Disney et al. (2006) – combine detailed structural models with optical AND RADAR models to simulate signal in both domains

Drat optical model + CASM (Coherent Additive Scattering Model) of Saich et al. (2001)

Coherent RADAR modelling

Thetford, UK

SHAC (SAR and Hyperspectral Airborne Campaign)

http://www.neodc.rl.ac.uk/?option=displaypage&Itemid=66&op=page&SubMenu=66Disney et al. (2006) – combine detailed structural models with optical AND RADAR models to simulate signal in both domains

Drat optical model + CASM (Coherent Additive Scattering Model) of Saich et al. (2001)

Coherent RADAR modelling

Optical signal with age for different tree density (HyMAP optical data)

Coherent (polarised) modelled RADAR signal (CASM)

OPTICAL

RADAR

An ambitious list of Applications...

• Flood mapping, Snow mapping, Oil Slicks• Sea ice type, Crop classification,• Forest biomass / timber estimation, tree height• Soil moisture mapping, soil roughness mapping / monitoring• Pipeline integrity• Wave strength for oil platforms• Crop yield, crop stress• Flood prediction• Landslide prediction

CONCLUSIONS

• Radar is very reliable because of cloud penetration and day/night availability

• Major advances in interferometric SAR

• Should radar be used separately or as an adjunct to optical Earth observation data?

ALOS

Speckle filtering

– Mean– Median– Lee– Lee-Sigma– Local Region– Frost– Gamma Maximum a Posteriori (MAP)

– Simulated annealing: modelling what the radar backscatter would have been like without the speckle

Original SAR data

Frost filter

Gamma MAP filter

Simulated annealing

Ret

ford

, UK

ER

S-2

SA

R d

ata

Apr

il –

Sep

tem

ber

1998

Original SAR data

Frost filter

Gamma MAP filter

Simulated annealing

Recommendation : use these two

Discussion question

• What sort of radars are preferred for the following applications to be successfully realised and what is the physical basis?

– Forest mapping– Flood extent– Soil moisture in vegetated areas– Snow mapping