Use of SAR in applications

ESA-MOST Dragon Programme Advanced training course in Land Remote Sensing, Beijing, October 2005SAR Day 1- Lecture 3 Thuy LE TOAN, CESBIO, France

Use of SAR in applicationsMethodology development

Thuy Le Toan

Centre d'Etudes Spatiales de la Biosphère (CESBIO), Toulouse, France


Use of SAR data in applications (1)

SAR data are used in ocean applications for which the SAR is unrivalled, such as

• wind and wave• ocean oil slick• sea ice monitoring for navigation


Arabic Sea, West of Bombay

Oil slick detection, SIR-C image



Radarsat image, Scansar Narrow A

Saint-Laurent golf, Quebec (Canada)Surface : 140 km x 159 km, 21/01/96Resolution : 50 mIncidence : 20°-40°Horizontal polarization

Use of SAR for ship routing in the Arctic zone.Radarsat has been optimized to facilitate the water/ice discrimination



Use of SAR in land applications

For land applications, the SAR data are unrivalled for:Displacement measurement (by interferometry)All weather change detection (flood, storm damage..)Mapping of geological features

Other potential applications are based on the use of multitemporal intensity SAR data. For C-band SAR:

Crop monitoringWetland and water bodies mappingForest mapping at regional scaleLand use mapping


Flood monitoring using ASAR (1)

• Water response varies with wind conditions• Temporal change detection is more robust



• Change in backscatter ?• At HH, VV, HV?

• In no wind condition?• When water surfaceis affected by wind?

soil soilwater

water

water


Land cover using ASAR

• Detection of structure, features and surface type with low temporal change• For surfaces with strong temporal change, a-priori knowledge and good choice of data (dates, polarisation, incidences) are required

Tianjing


400km

CHINA

MONGOLIA

RUSSIAHailar

Hulun Nur

Buir Nur

400km

Wide Swath imageInterest: large area (400 km width) with a resolution of 75 m(MERIS resolution is 250 m, MODIS , 300-500 m and SPOT VGT 1km)

Land cover at large scale (1)

Russia, Mongolia, China borderAugust (R), October (G), November (B) 2004


RUSSIA

Land cover at large scale (2)

Detail of WS colour compositeimage


65km

Land cover at large scale (3)ASAR WSM forest map GLC 2000 forest map

Forest map using temporal change Land cover map using SPOT VGT


Crop monitoring

Crop responses varies with crop type, growth stage, soil moisture and roughness.

The same crop type may have various backscatter responsesat a given time. For crop mapping, unsupervised classification with training samples, as applied to optical data, may not work.

A-priori knowledge (types of crop in the region, approximatecrop calendar, cultivation practices) required for- choice of appropriate acquisition dates- choice of polarisation, incidence angle


• Physical background• Experiment• Theoretical modelling and simulation

• Development of algorithms

• Testing in other conditions

• Improvement of the methods

• Integration in research or application programme

Steps from research to application

Rice monitoringTo illustrate


Water

Scattering from a rice canopyAt C band, HH and VV: the dominant scattering mechanism is thedouble bounce vegetation-waterHH>VV because of the stronger attenuation of VV by verticalstems (and Fresnel reflection RH > RV )

HH and VV increases with the plantbiomass.The increase is veryimportant (up to 10 dB during the growth season) (Le Toan et al., 1997).

HH/VV is related to biomass


Sapporo

Fukuoka Osaka

YokohamaKyoto

NiigataSendai

140°

40°

38°

N

S

W E

Test siteLEGEND :

Japan Sea

Pacific O

cean

AKITA

JAPON

500 km

Java Sea

N

S

W E

Indian Ocean

Test siteLEGEND :

8°

6°

110°

SEMARANG

Yogyakarta

Surakarta

Pekalongan

JAVA (INDONESIA)

Jakarta

500 km

JATISARI

Tropical riceShort Cycle :120-130 days

Temperate riceLong Cycle:150 days

Experimental studies


150-20-18-16-14-12-10-8-6-4-20

0 10 20 30 40 50 60 70 80 90 100110120130140Age (# days after sowing)

Bac

ksca

tter

ing

Coe

ffic

ient

(dB

)

0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000

Bac

ksca

tter

ing

Coe

ffic

ient

(dB

)

-200 10 20 30 40 50 60 70 80 90 100

Plant height (cm)-18-16-14-12-10-8-6-4-20

Semarang test siteAkita test site

-20-18-16-14-12-10-8-6-4-20

Moist Biomass (g/m²)

Bac

ksca

tter

ing

Coe

ffic

ient

(dB

)

Sowing Stem elongation

ERS data analysis


c

b

The rice plants are planted by bunches, regularly placed on the fieldWithin a rice bunch, the cylindric stems arerandomly placed with uniform distribution inside acircleThe stems have leaves of elliptical disc shape

Electromagnetic modelling


Water

Water

Water

Water

1 2

3 4

With t = index for stem or leafv = volumes = surfacet i=1

1 2 3 4

( )E r eikr

r vit

svit

vsit

svsit

Nt= ∫ + ∫ +∫ + ∫⎛

⎝⎞⎠∑∑

Electromagnetic modelling


Bac

ksca

tter

ing

Coe

ffic

ient

(dB

)Model data comparison

-20-18-16-14-12-10-8-6-4-20

0 500 1000 1500 2000 2500 3000 3500 4000Moist Biomass (g/m²)

Effect of Polarisation

ERS (VV)

RADARSAT (HH)

Solid line: modelling result

• Strong increase from transplanting to grain maturity• HH>VV• Increase smaller at high incidence

Effet of Incidence angle

-16-15-14-13-12-11-10-9-8-7-6-5-4

0 20 40 60 80 100 120Age (# days after sowing)B

acks

catt

erin

gC

oeff

icie

nt (d

B)

C-HH-23°

C-HH-43°

Solid line: modelling result


Date

-20

-18

-16

-14

-12

-10

-8

-6

-4

-2

0

02-n

ov

12-n

ov

22-n

ov

12-d

ec

22-d

ec

01-ja

n

11-ja

n

21-ja

n

31-ja

n

20-f

eb

01-m

ar

11-m

ar

21-m

ar

31-m

ar

Bac

ksca

tter

ing

Coe

ffic

ient

(dB

)

Late23/01/1994

10/02/1994 16/02/1994

06/03/1994Acquisition ERS-1

02-e

éc

10-f

eb

10-a

pr

20-a

pr

30-a

pr

10-m

ay

20-m

ay

30-m

ay

Early

Large variability of backscatter of rice field at a single dateMethod based of a « rice signature » not applicable

Methodology developmentSimulation of non uniform crop calendar (Indonesia)


Methodology development

50

Bruniquel. 1996

05

1015202530354045

0 1 2 3 4 5 6

Err

or(%

)

L=25L=9

L=1

L=256L=169 L=81

∆σ°(dB)

To detect rice/non rice based on temporal changeChange >3 dB: rice

Change 3 dB -->Number of looks required: 50 to 75Field size -->Window size for spatial filtering (1, 3x3,5x5 ..)

-->Number of images needed for filtering


Example of Chaindeveloped using:

•Gamma ASAR (Gamma RS)

•Multi-image filtering(Quegan et al., 2000)

• Temporal change (Le Toan et al., 1997)


1 2 M…………….

.

.

1 2 M…………….date 1 date Mdate 2

1 2 M…………….

1 2 M…………….

Analysis , RetrievalClassification

Spatial filteringGeocoding

Multi image filtering

CalibrationRegistration

Initial images

Calibrated coregistered

Filtered

Filteredgeocoded


Rice in Vietnam


Method validation

Good classification : 93,35%


Rice mappingERS map 1996-1997Blue : single crop Green : double crop Red : triple crop

GIS 1999


Bac

ksca

tter

ing

coef

ficie

nt (d

B)

Dry biomass (g/m²)

ERS-20-18-16-14-12-10-8-6-4

0 200 400 600 800 1000 1200 1400 1600

Retrieval of rice biomass


Sowing date

Climatic dataT°. Radiation. Humidity. Rainfall

Development rate

Development stageBiomass

Parti--tioning

Leaves

Stems

Panicles

Roots

CO2 assimilation

YIELD

Biomass

Sowing date Radar Data (s° vs. Age)Biomass Radar Data (s° vs. Biomass)

Rice production model : ORYZA 1 (Kropff et al.. 1994)Use of radar in crop models


Jiangsu Province

Rice monitoring using ENVISAT


Rice monitoring using ENVISAT

ENVISAT• Polarisation (2 instead of 1)• Choice of incidence angle• Use of Wide Swath mode for large regions or provinces

Improvement of the method

Rice in China• Narrow Field • Change in practices (one or two rice crop per year)• Mid season drainage• Different varieties


Small field: powerful filtering

Filtering using 20 images (2 polarisations, 10 dates)


Use of polarisation

HH VVHongze (Jiangsu) 2004 09 06


Magenta=HH, Green=VV 34km*38kmSeptember 6th, 2004 , Hongze area

yellow=rice, red=urban, black=other

Rice mapping at a single dateUsing HH/VV


Hybrid rice June 15 2005


Japonica rice 15 June 2005


Japonica rice

Hybrid rice

Mapping of varieties


Hybrid rice

Mapping of rice varieties


Japonica Rice with differentcalendar

Hybrid rice

Mapping of rice varieties


Seasonal variations of HH/VV ratio and rice wet biomassmeasured in 2004 at a test field (Gaoyou, province of Jiangsu)

Retrieval of biomass


magenta : Aug18th

Green : October 27th

210 km * 127.5 km

75m pixel size

Funing

HuaiAn

Chuzhou

Suqian

Shuyang

Yancheng

ASAR WSM region North of Qingjiang, Jiangsu province

Regional rice mapping


XinqiRiver

Guboshanhou River

JieyuRiver

Guangriver

QiangweiRiver

Huaimuxin

River

Lianyungang

Guannian

Guanyun



HuaimuxinRiver

LulanRiver

Donghai

FengshanReservoir

HengGeReservoir



Summary

• A number of applications using SAR data are outlined

• Rice monitoring is presented as illustration for the differentsteps towards application

• Knowledge of the SAR scattering physics and SAR data statistical properties help to develop methodologyfor using SAR data in applications

Remark: Availability of SAR data for the appropriate dates iscrucial

Use of SAR in applications

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