studies of ku- and x-band radar interactions with the snow...

POLinSAR 2007, Frascati

Studies of Ku- and X-Band Radar Interactions with the Snow Cover in Preparation of the CoreH2O Mission

CoreH2O - COld REgion Hydrology High resolution Observatory

Helmut Rott1, Don Cline2, Keith Morrison3, Thomas Nagler4,Jouni Pulliainen5, Helge Rebhan6, Jiancheng Shi7, Simon Yueh8

1 University of Innsbruck, Austria2 NOAA-NOHRSC, Chanhassan, USA3 Cranfield University, UK4 Environmental Earth Observation IT, Innsbruck, Austria5 Finnish Meteorological Institute, Helsinki, Finland6 ESA-ESTEC, Noordwijk, NL7 University of Calfornia, Santa Barbara, USA8 JPL-Caltech, Pasadena, USA


The Science Team of the CoreH2O Proposal

Monique Bernier - CANRobert G. Brakenridge - USADonald Cline - USARobert E. Davis -USAJean-Pierre Dedieu - FWolfgang Dierking - DEMatthias Drusch - ECMWFRichard Essery - UKPierre Etchevers - FMartti Hallikainen - SFSvein-Erik Hamran - NStefan Kern - DERon Kwok - USAPeter Lemke - DEEirik Malnes - NKyle McDonald - USAHeinz Miller - DE

Keith Morrison - UKThomas Nagler - ATSon Nghiem – USAJohannes Oerlemans – NLPaolo Pampaloni - ITKim Partington - UKShaun Quegan - UKWalter L. Randeu - ATHelmut Rott – AT (Chair)Mathias Schardt - ATJiancheng Shi - USADetlef Stammer - DERasmus T. Tonboe - DKWolfgang Wagner - ATAnne Walker - CANUrs Wegmüller - CHSimon Yueh - USA

Industrial Partner: EADS Astrium GmbH Friedrichshafen


Contents of Presentation

• Mission objectives

• Observation requirements

• Preliminary technical concept

• Developments for snow scatter modelling by the DDA approximation

• Concept for SWE retrieval algorithm based on DMRT and target decomposition

• Field experiments on Ku- and X-band snow signatures


Mission Contributions to the Study of Cryospheric Processes

CoRe-H2O addresses all main elements of the cryosphere

ATMOSPHERE

Temperature, Pressure, Motion,Composition (clouds, precipitation,trace gases, etc.)

LAND SURFACES

Land cover, Water cycle, Albedo,Surface temperature, Wet lands,Eco-systems, Carbon cycle

OCEAN

Temperature, Salinity, Circulation,Sea level

Frozen GroundPermafrost

Heat exchangeGas exchangeCarbon cycle

Snow Cover

AccumulationMeltRunoffWater supplyExchange of heat, moisture momentumAvalanches

Ice SheetsIce Shelves

Mass balanceAccumulationExchange of heat, momentum and water

River andLake Ice

ExtentThicknessEnergy exchangeRunoff routing

Sea Ice

ThicknessGrowthSnow accumulat.MeltExchange of heat, momentum

GlaciersIce Caps

Mass balanceAtmospheric forcingRunoffWater supply


Key Scientific Questions: The Role of Snow and Glaciers for Climate Research and Hydrology

The role of snow in the global climate system Improved understanding and modelling of snow cover - climate feedbacks are needed for adequate representation of snow in climate models.

The role of snow in hydrological processesIn high and mid latitudes snow cover is a key parameter of the water and energy cycle of land surfaces. The interactions between atmospheric circulation, land surfaces and hydrological processes are an open problem for climate modelling.

The role of snow for water management and flood control:

Improved models with distributed input data are needed to improve flood forecasting and water resources management.

Glacier mass balance and runoff modelling

Snow extent and mass (Snow Water Equivalent, SWE) are key input parameters for those models.Presently spatially distributed data of SWE are not measured directly, but inferred from few point measurements, resulting in large errors


Variable Spatial Scale Repeat Interval Seasonal Snow Cover Snow extent 100 – 500 m 3-15 d Extent of melting snow 100 – 500 m 3 dSnow depth 200 – 500 m 3-15 dWater equivalent 200 – 500 m 15 dGlaciersFacies type 100 m 15 dWinter snow accumulation 200 – 500 m 15 dFrontal position, lakes 50 m 15 dFreshwater Ice Area 25 - 50 m 3 dSea IceSnow Depth 200 - 500 m 3 -15dIce Type 50 - 100 m 3 dIce Motion 0.5 to 1 km 3 dIce Melt 50 – 100 m 3 dSurface WaterExtent of open water areas 50 – 100 m 3-15 d

CoReH2O – Key Observation Requirements


25 m x 8 m25 m x 10 mScanSAR resolution

<-25dB<-25dBNESZ

10 MHz10 MHzTransmit chirp BW

3 kW4 kWPeak transmitter power

100 km100 kmSwath width

46Nr. of ScanSAR beams

24 km16 kmAnt. beam footprint elev.

2.2 m x 4.4 m (parabolic)1.2 m x 3.3 m (3 reflectors)Antenna size

VV, VHVV, VHPolarization

9.6 GHz17.2 GHzFrequency

X-band SARKu-band SARParameter

CoReH2O – Preliminary Instrument Design Parameters


CoReH2O – Ku-band SAR Satellite Configuration - V01

Antenna: 3 parabolic reflectors. Overall length: 3.3 m (az.) x 1.2 m (el.)

Total satellite mass ≤ 850 kg

activefeeds

beam

dire

ctio

n

ϑ ϑ

x

f

Beam scanning by feed defocusing

ObservationDirection

Swath

Width

Satellite Ground Track

Multiple subswa

to achieve wide sw


Antenna BeamBoresight

S/C Flight Direction

Radiator Panel

Solar Array

CoReH2O – X-band SAR Configuration - V01

X-band SAR Satellite Configuration Switch matrix for X-band SAR feed array

Technical Challenge: dual frequency SAR with single reflector antenna


Combination of zeroth-order approximation of RT theory and field approach, with wave approach to define scattering in a small volume unit (DDA approximation). Theory by J. Pulliainen and M. Hallikainen, HUT. Software implemented by VEXCEL UK

Dense Medium Radiative Transfer, DMRT. Applied for numerical simulation of σ° for a wide range of snow conditions. Algorithm developed for SWE estimation with dual-frequency (9.6 & 17 GHz) & polarization (VV and HV) SAR by scattering decomposition (J.C. Shi, UCSB)

• Depolarization factor for dry snow proportional to the scattering contribution in co-polarization signals; used to decompose surface and volume scattering

• Frequency ratio of volume scattering components at X- and Ku-bands used for estimating snow optical thickness τ. Single scattering albedo estimated from τand σv.

• With the estimations of scattering albedo and optical thickness at two frequencies, SWE can be retrieved.

Models for Snow Scattering and SWE Retrieval


Backscattering from a Multilayer Snowpack

Total backscattering0000

)cos()cos(

groundsnowsnowpackairsnowsnowairt

isnowpacksnowairtotal ATT −−−− ++= σ

θθ

σσσ

∑ ∏=

−

=⎟⎟⎠

⎞⎜⎜⎝

⎛=

n

i

i

jjitotsnowpack t

1

1

0

20,

0 σσ

∏=

=n

jjsnowpack tA

0

2

Multilayer volume Two-way

attenuation

h

d

θi

A single layer is split up into K volume units

DDA solution of scattered electric field by solving the field inside each cubical volume element (approximated as a dipole with volume of the cube) and by summing the fields originating from each scatterer

( )[ ]ttet

e

tVvolsnow h θκ

κ

θσσ sec2exp1

2

cos11,

1,

1,1, −−=° −


Volume Scatter Modelling

Model Approach:• Applies a general modeling tool to describe volume backscatter and extinction• Combines the deterministic Discrete Dipole Approximation (DDA) with a common

vector Radiative Transfer (RT) approach

• An ensemble of infinitesimal snow samples representing certain ice volume filling factors and grain sizes are generated, i.e. random realizations of snow structure

• DDA solution on scattering amplitude is calculated for each 3-D grid of ice cubes (including multiple scattering, all polarisations)

• Volume scattering and extinction coefficients for different snow layers (with specific physical characteristics) are calculated from scattering amplitudes

• RT solution on backscattering coefficient at different polarizations is calculated applying the inherent properties determined by DDA calculation


Monte Carlo Simulation of Snow Packs

• Collection of snow volume units with random localization and orientation of snow grains (ice crystals)

• Constant average properties in a single collection: snow density, mean snow grain size and snow grain size standard deviation

Snow grain r=0.4 mm


Example of σ° at 17 GHz with DDA Approximation

30 40 50 60 70 80-50

-40

-30

-20

-10

0

incident angle (deg)

back

scat

terin

g (d

B)

VV-backscattering component for air-snow-ground-model with DDA in 17 GHz

totalvolumeair-snowsnow-ground

30 40 50 60 70 80-50

-40

-30

-20

-10

0


back

scat

terin

g (d

B)

HH-backscattering component for air-snow-ground-model with DDA in 17 GHz

totalvolumeair-snowsnow-ground

30 40 50 60 70 80-25.6

-25.4

-25.2

-25

-24.8

-24.6

-24.4

-24.2


back

scat

terin

g (d

B)

HV-backscattering component for air-snow-ground-model with DDA in 17 GHz

totalvolume

30 40 50 60 70 80-25.6

-25.4

-25.2

-25

-24.8

-24.6

-24.4

-24.2


back

scat

terin

g (d

B)

VH-backscattering component for air-snow-ground-model with DDA in 17 GHz

totalvolume

Snow depth 2 m Grain r = 0.4 mm Cubic grain shape

0 0.2 0.4 0.6 0.8 1

-50

-40

-30

-20

-10

0

snow depth [m]

volu

me

back

scat

terin

g [d

B]

VVVH

Needles randomly alignedl = 0.8 mm r = 0.2 mmθ = 30°


Minimum Maximum Interval

Exponential function

0.25 cm 3.0 cm 0.25 cm2.5 cm 25 cm 2.5 cm

Parameters

rms. height

Co-lengthCo-function

Ice FractionParticle Radius 0.2 mm 2.0 mm 0.2 mm

15 % 50 % 5 %

Frequency 9.5 GHz 17 GHz

Snow Temp. -15°C -3°C 3°C

Input parameters for Database Simulation

Simulation model Characteristics – second-order RT model1. Snow – Dense Medium Model (DMRT)2. Surface – Advanced Integral Equation Model (AIEM)3. Snow-surface interaction – Bistatic AIEM & DMRT

DMRT Numerical Simulation of Dry Snow by J.C. Shi


Main Scattering Components for SWE Estimation

spq

vpq

svpq

apq

tpq σ+σ+σ+σ=σ

svpq,a

pq σσ

Not Important for dry snow Required

Has impact but complex

pqspqpqspq L)f(T)f( ⋅σ⋅=σ 02

[ ]rcos/)f(exppqL θτ⋅−= 2

)]f(ppL[)f()rcos()i(ppT.)f,i(vpp −⋅ω⋅θ⋅θ⋅=θσ 12750

Surface scattering from air-snow interface and scattering term from the snow-ground interaction

Snow volume scattering term

Surface scattering term)f(spq0σ

Surface scattering from snow-ground interface


Scattering Contribution Characteristics at 9.6 and 17 GHz 35° Incidence

Each Scattering Contribution in % for 3 Components

17-VV9.6-VV17-VH

Volume

Interaction

Surface9.6-HV


Decomposing Scattering Components

)/BA(/)f('F tvvtvh

tvvsvvs σσ⋅+−≈σσ= 1

tvv

tvh

tvv

vvvs bafF σσσσ //)( +≈=

tvv

tvh

tvv

svvv

vvv BA σσσσσ /*/)( +⇔+

Depolarization factor is proportional to scattering contributions of either the direct volume scattering or the sum of volume and surface-volume interaction components

9.6 GHz 17 GHz

tvv/v

vv σσ

tvv/t

vh σσ tvv/t

vh σσ

tvv/)sv

vvvvv( σσ+σ

tvv/s

vv σσ


Estimation of Albedo for Correction of Grain Size Effect

)(),.( 1769 ωω

)().(

1769

ττUsing the estimated

Optical thickness ratio is highly correlated to albedo

)().( 1769 ωω +

)().(

1769

ττ

)().(

1769

ωω

Grain Size Effect Correction

)f())f((sd)f(a τ⋅ω−=⋅κ 1

)(),.(),(),.( 17691769 ττωωUsing estimated

To derive the absorption part of the optical thickness


Correction of Snow Density and Temperature Effects for SWE Retrieval

Correction on effects of snow density and temperature

1. Absorption coefficient is linearly related to snow density at a given temperature

2. Ratio of absorption coefficients from two frequency depends only on temperature

density

temperature

SWE estimation

)(/).(e))(/).(log(dd).(c)d).,((Logba)SWElog(

aaaa

sasa17691769

696917κκκκ

κκ⋅+⋅+

⋅⋅+⋅⋅+=

SWE estimation using τa

with simulated data base at θ = 35°

κa


Signature and SWE Retrieval Studies with low Resolution Scatterometer Data

Snow accumulation at NASA-SW station in Greenland and QSCAT retrieval (S. Nghiem)

SWE retrieved from QuikSCAT Ku-band data using a radiative transfer model function comparedvto observations (Cline, 2004).


In Situ (Snow Depth, Snow Pits)

CIR Aerial Photography/Orthoimagery

LiDAR DEM (Airborne Snow Depth Mapping)

AMSR-E (Spaceborne PM Radiometer)

QuikSCAT (Spaceborne Ku Scatterometer)

TerraSAR-X (Spaceborne X SAR)

POLSCAT (Airborne Ku Polarimetric Radar)

Alaska 2007-2008

Colorado 2006-2007Data Collection

Field Experiments:CLPX-II Cold Land Processes Experiment

Don Cline

Simon Yueh


GB-SAR Snow Campaign10-18 GHz• Univ. Cranfield• ENVEO IT Innsbruck• ESA-ESTEC

studies of ku- and x-band radar interactions with the snow...

Documents