Mapping Snow with Cosmo-Skymed SAR

- Microwave Remote Sensing Group

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

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

1
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

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

3


The ASI/Cosmo-Skymed mission
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

4


Example of COSMO-Skymed data
Temporal variation of backscattering on alpine regions

CSK® © ASI

CSK 2, Himage, HH, = 26.5°


Model Investigation: Snow backscattering model
AIR
 Snow as a single layer of identical
z=0
S
scatterers
SNOW
z=-d
 Flat air-snow interface
GROUND  Rough snow –soil interface

Multiple scattering effects
Snow volume
scattering DMRT-QCA Mie Scattering
(Tsang et al., 2007)
Stickyness

Surface scattering AIEM
(Chen et al., 2004)
6


The surface scattering: The AIEM model

o k kc c
qp (S ) qp (S ) qp (S ) qp (S )

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

7


Volume scattering: The DMRT/QCA Model
(Tsang et al. 2007)
2 2
d I , ,z ' ' '
cos ke I , , z d sin d P , ; ', '
I '
, ', z
dz 0 0

2 2
' ' ' ' ' '
d sin d P , ; , I , ', z
0 0

2
I1 s P11 0 0 0 I1i P11 f11 q P33 P44
2
I 2s 0 P22 0 0 I 2i P22 f 22 q P43 P34
*
U12 s 0 0 P33 P34 U12i P33 Re f11 f 22 q
*
V12 s 0 0 P43 P44 V12 i P34 Im f11 f 22 q

N max
i 1 2n 1 ( M ) ( M )
f11 Tn X n n cos Tn( N ) X n N )
(
n cos
1 R k Kr n 1 n n 1

N max
i 1 2n 1 ( M ) ( M )
f 22 Tn X n n cos Tn( N ) X n N )
(
n cos
1 R k Kr n 1 n n 1

8


Model Simulations
(DMRT – QCA model)

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

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

9

Model Simulations
Extinction and Penetration depth

Density

Crystal Frequency Penetration depth (1/ke)
radius Band (m)
(mm)
250 Kg/m3 350 Kg/m3
Radius C 66.8 81.9
0.5 X 9.5 17.5
Ku 1.2 2.5
C 18.8 39.3
0.9 X 2.3 5.31
Ku 0.33 0.67
C 7.4 17.8
1.3 X 0.99 2.16
Ku 0.15 0.29
10


Sensitivity of backscattering to grain radius

11


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

SWE SWE
Backscattering (dB)

Density 150-400
5.3 GHz 9.6 GHz

Total scattering
SWE
Snow contribution
Backscattering (dB)

Soil contribution

12
17.2 GHz



SWE (mm) SWE (mm)
Backscattering (dB)

Backscattering (dB)
5.3 GHz 9.6 GHz

Total scattering
SWE (mm)
Snow contribution
Backscattering (dB)

Soil contribution

13
17.2 GHz



SWE (mm) SWE (mm)
Backscattering (dB)

Backscattering (dB)
5.3 GHz 9.6 GHz

Total scattering
SWE (mm)
Snow contribution
Backscattering (dB)

Soil contribution

17.2 GHz 14

- Microwave Remote Sensing Group Model Simulations
5.3 GHz 9.6 GHz 17.2 GHz
Sensitivity to SWE
Backscattering

15


Experimental sensitivity to Snow Depth:Temporal trends

Wet
snow

SWE

16
Depth Hoar


Generation of snow cover maps
and Retrieval of SWE

17


Principle of the algorithm
clear sky dry/wet snow snow cover
Optic
clouds ?

snow cover + SWE
wet snow
SAR clear
cloudy
wet snow
Threshold
dry snow ANN SWE
Ref. Image
DEM + air
temperature
for high SWE
values

18


Validation of SWE Algorithm with experimental
X-band data

Date Sensor Sensor mode Polarization
08/03/2009 CSK2 STR_HIMAGE HH
26/03/2010 CSK2 STR_PINGPONG VV/VH
19


First verification of SWE Algorithm with exper. data
22/01/2010 08/03/2009 27/05/2009
SWE SWE SWE SWE SWE SWE
(200 (300 SWE NN (200 (300 SWE NN (200 (300 SWE NN
Kg/m3) Kg/m3) Kg/m3) Kg/m3) Kg/m3) Kg/m3)
Single Monti Ornella 272 408 270 500 750 masked 194 291 wet snow
polarization
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 SWE SWE SWE
(200 Kg/m3) (300 Kg/m3) SWE NN (200 Kg/m3) (300 Kg/m3) SWE NN
Dual polarization
Monti Ornella 304 456 380 332 498 438
(co & cross )
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
20


Example of Snow Cover Area

40
Km

SWE

January 22, 2010 March 29, 2011

21


Summary and conclusions
 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
Aknowledgment
This work has been funded by the Italian Space Agency (ASI) under
the COSMO-Skymed project 1720
22


23


Model simulations
Sensitivity of X band backscattering to snow density

Snow depth : 1 m - Grain radius : 0.5 mm

24


25


Model investigations : Snow-pack scattering
^
Z •Density


AIR
•Depth
z=0
•Size/shape of
z=-d 1 crystals
z=-d 2

• Liquid water
SNOW contet

z=-d N-2

z=-d N-1
•Height St Dev
• Correlation
length
z=-d N

• Autocorrelation
GROUND
function

26


Test of SWE Algorithm with simulated data
10000 input values randomly varied:
5000 for training - 5000 for test

Snow depth =10 - 150 cm
Density = 200-300 kg/m3
Grain radius = 0.1 – 1.0 mm
Incidence angle = 20 -70

Single polarization (RMSE=~ 32 mm)

600

y = 0.9495x + 11.107
R2 = 0.9342
500

400

SWE calcolato (mm)
300

200

100

0
0 100 200 300 400 500 600
SWE m isurato (m m )

Dual polarization (RMSE=~ 25 mm)
27


Generation of dry/wet snow cover maps
04/05/2009
100 km

SAR SAR + MODIS MODIS
wet snow 04/05/2009 snow cover

28

Mapping Snow with Cosmo-Skymed SAR

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