- microwave remote sensing group igarss 2011, july 23-29, vancouver, canada 1 m. brogioni 1, s....

- Microwave Remote Sensing Group

IGARSS 2011, July 23-29, Vancouver, Canada 1

M. Brogioni1, S. Pettinato1, E. Santi1, S. Paloscia1, P. Pampaloni1, E. Palchetti1, J. Shi2,3, C. Xiong1,2,

1Institute of Applied Physics - IFAC-CNR, Firenze, Italy

2Institute for Remote Sensing Applications, Beijing, China 3University of California, Santa Barbara (CA), USA

The Potential of Cosmo-Skymed SAR Images in Mapping Snow Cover and

Snow Water Equivalent


IGARSS 2011, July 23-29, Vancouver, Canada

Outline

Motivations

The ASI Cosmo-Skymed mission and data

Model investigations

Experimental Results

Retrieval of Snow cover and Snow Water Equivalent

2



Introduction

3

Several experiments have documented the ability of C- band SAR in mapping the extent of wet snow. But the high transmissivity of dry snow cover at this frequency makes difficult to detect it.

The study aims at evaluating the potential of X-band COSMO-Skymed SAR in generating snow cover maps and estimating snow water equivalent



The ASI/Cosmo-Skymed mission

4

4 medium-size satellites, equipped with an X-band SAR HH, VV, HV, VH pol

sun-synchronous orbit at ~620km height

Full constellation revisit time : 12 h

- 1 Spotlight mode, for metric resolutions over small images- 2 Stripmap modes, for metric resolutions over tenth of km images; one mode is polarimetric with images acquired in two polarizations- 2 ScanSAR for medium to coarse (100 m) resolution over large swath



Example of COSMO-Skymed data

CSK 2, Himage, HH, = 26.5°

Temporal variation of backscattering on alpine regions

CSK® © ASI



Model Investigation: Snow backscattering model

Snow as a single layer of identical scatterers

Flat air-snow interface

Rough snow –soil interfacee2

e0

eS

e0

z=0

z=-d

AIR

SNOW

GROUND

DMRT-QCA(Tsang et al., 2007)

Multiple scattering effects

Mie Scattering

Stickyness

Snow volume scattering

6

Surface scattering AIEM

(Chen et al., 2004)



The surface scattering: The AIEM model

)()()()( SSSS qpc

qpkc

qpk

qpo

The normalized scattering coefficient is composed of three terms: Kirchhoff, cross and the complementary one.



Volume scattering: The DMRT/QCA Model(Tsang et al. 2007)

8

zIPdd

zIPddzIkdz

zIde

,,,;,sin

,,,;,sin,,,,

cos

''''2

0

''2

0

'

''''2

0

''2

0

'

i

i

i

i

s

s

s

s

V

U

I

I

PP

PP

P

P

V

U

I

I

12

12

2

1

4443

3433

22

11

12

12

2

1

00

00

000

000 qfP2

1111

qfP2

2222

qffP *221133 Re

4433 PP

qffP *221134 Im

3443 PP

coscos1

121

1)()()()(

111

max

nNn

Nnn

Mn

Mn

N

nr

XTXTnn

n

KkR

if

coscos1

121

1)()()()(

122

max

nNn

Nnn

Mn

Mn

N

nr

XTXTnn

n

KkR

if



Model Simulations

(DMRT – QCA model)

9

Data chosen to account for the different type of snow cover on the Alps

Frequency (GHz) 5.3, 9.6, 17.2

Polarization VV, HH, HV

Incidence angle (deg) 20 - 50

Density (Kg/m3) 200 - 500

Grain radius (mm) 0.1 - 1.5

Snow depth (cm) 20 - 300

Soil smooth



Model SimulationsExtinction and Penetration depth

10

Radius

Crystal radius (mm)

Frequency Band

Penetration depth (1/ke) (m)

250 Kg/m3 350 Kg/m3

0.5 C 66.8 81.9 X 9.5 17.5

Ku 1.2 2.5

0.9 C 18.8 39.3 X 2.3 5.31

Ku 0.33 0.67

1.3 C 7.4 17.8 X 0.99 2.16

Ku 0.15 0.29

Density



Sensitivity of backscattering to grain radius

11



Model Simulations: Sensitivity to SWECrystal radius: 0.1 mm – Incidence angle: 35°

12

Bac

ksca

tter

ing

(dB

)

5.3 GHz

SWE

Total scattering

Snow contribution

Soil contribution

9.6 GHz

SWE

Density 150-400

SWE

17.2 GHzBac

ksca

tter

ing

(dB

)


IGARSS 2011, July 23-29, Vancouver, Canada 17.2 GHz

SWE (mm)

Bac

ksca

tter

ing

(dB

)

13

9.6 GHzBac

ksca

tter

ing

(dB

)

SWE (mm)

Total scattering

Snow contribution

Soil contribution

5.3 GHz

SWE (mm)

Bac

ksca

tter

ing

(dB

) Model Simulations: Sensitivity to SWE

Crystal radius: 0.3 mm – Incidence angle: 35°



Total scattering

Snow contribution

Soil contribution

17.2 GHz

SWE (mm)

Bac

ksca

tter

ing

(dB

)

5.3 GHz

SWE (mm)

Bac

ksca

tter

ing

(dB

)

Model Simulations: Sensitivity to SWECrystal radius: 0.5 mm – Incidence angle: 35°

9.6 GHz

SWE (mm)

Bac

ksca

tter

ing

(dB

)



Model Simulations

Sensitivity to SWE

15

5.3 GHz 9.6 GHz 17.2 GHz

Bac

ksca

tter

ing



Experimental sensitivity to Snow Depth:Temporal trends

16

Wet snow

D SWE

Depth Hoar



Generation of snow cover maps and Retrieval of SWE



Principle of the algorithm

18

DEM + air temperature

Optic

SAR

clear sky snow cover

wet snow

clouds ?

dry snow SWE

snow cover + SWE wet snow

dry/wet snow

clear cloudy

Ref. Image

Threshold ANN

for high SWE values



Validation of SWE Algorithm with experimental X-band data

19

Date Sensor Sensor mode Polarization08/03/2009 CSK2 STR_HIMAGE HH27/05/2009 CSK2 STR_HIMAGE HH14/07/2009 CSK2 STR_HIMAGE HH22/01/2010 CSK2 STR_HIMAGE HH26/03/2010 CSK2 STR_PINGPONG VV/VH29/03/2010 CSK1 STR_PINGPONG VV/VH02/09/2010 CSK1 STR_PINGPONG VV/VH



First verification of SWE Algorithm with exper. data

20

22/01/2010 08/03/2009 27/05/2009

SWE (200

Kg/m3)

SWE (300

Kg/m3)SWE NN

SWE (200

Kg/m3)

SWE (300

Kg/m3)SWE NN

SWE (200

Kg/m3)

SWE (300

Kg/m3)SWE NN

Monti Ornella 272 408 270 500 750 masked 194 291 wet snow

Col dei Baldi 268 402 350 574 861 544 90 135 wet snow

Pradazzo 192 288 280 306 459 400 no data no data -

Ravales 280 420 masked 488 732 masked 260 390 masked

Cherz 200 300 290 240 360 270 no data no data -

26/03/2010 29/03/2010

SWE

(200 Kg/m3)SWE

(300 Kg/m3) SWE NNSWE

(200 Kg/m3)SWE

(300 Kg/m3) SWE NN

Monti Ornella 304 456 380 332 498 438

Col dei Baldi 296 444 390 294 441 masked

Cima Pradazzo 204 306 masked 198 297 masked

Ravales 304 456 378 332 498 480

Cherz 270 405 masked 230 345 masked

Single polarization

Dual polarization(co & cross )



Example of Snow Cover Area

21

January 22, 2010 March 29, 2011

SWE

40 Km



Summary and conclusions

22

The sensitivity of ASI/Cosmo-Skymed X-band SAR to snow cover and SWE has been investigated by using experimental results and model simulations.

An algorithm to generate snow cover maps by combining optical and SAR data has been developed and validated

It has been found that X-band data can contribute to the retrieval of SWE for snow depth higher than about 40-50 cm and relative high crystal size .

More investigations and data validations are needed to demonstrate the full potential of Cosmo-Skymed SAR in snow detection

AknowledgmentThis work has been funded by the Italian Space Agency (ASI) under

the COSMO-Skymed project 1720



Model simulations

Sensitivity of X band backscattering to snow density

24

Snow depth : 1 m - Grain radius : 0.5 mm



Model investigations : Snow-pack scattering

26

Z^

z=0

z=-dN

AIR

SNOW

GROUND

z=-d1

z=-d2

z=-dN-2

z=-dN-1

•Density

•Depth

•Size/shape of crystals

• Liquid water contet

•Height St Dev

• Correlation length

• Autocorrelation function



Test of SWE Algorithm with simulated data

27

10000 input values randomly varied: 5000 for training - 5000 for test

Snow depth =10 - 150 cmDensity = 200-300 kg/m3

Grain radius = 0.1 – 1.0 mmIncidence angle = 20°-70°

Single polarization (RMSE=~ 32 mm)

y = 0.9495x + 11.107R2 = 0.9342

0

100

200

300

400

500

600

0 100 200 300 400 500 600

SWE misurato (mm)

SW

E c

alc

ola

to (

mm

)

Dual polarization (RMSE=~ 25 mm)



MODISsnow cover

Generation of dry/wet snow cover maps04/05/2009

SARwet snow

SAR + MODIS04/05/2009

100 km

28

- microwave remote sensing group igarss 2011, july 23-29, vancouver, canada 1 m. brogioni 1, s....

Documents

remote sensing applications

snow characteristics

mapping snow cover

snow cover maps

snow backscattering

canada model investigation

snow depth cm20

extent of wet snow