coreh2o, a dual frequency radar satellite for cold regions hydrology.pdf

H. Rott –CoReH2O IGARSS 2011

CoReH2O – A Dual Frequency Radar Satellite for Cold Regions Hydrology

H. Rott1, D. Cline2, C. Duguay3, R. Essery4, P. Etchevers5, I. Hajnsek6, M. Kern7, G. Macelloni8, E. Malnes9, J. Pulliainen10, S. Yueh11

1 University of Innsbruck & ENVEO IT, Austria

2 NOAA, NWS, Hydrology Laboratory, USA

3 University of Waterloo, Canada

4 University of Edinburgh, UK

5 Meteo-France, Saint Martin d’Héres, France

6 DLR-HR, Germany & ETH Zürich, Switzerland

7 ESA-ESTEC, Noordwijk, NL

8 IFAC-CNR, Firenze, Italy

9 NORUT IT, Tromsǿ, Norway

10 Finish Meteorological Institute, Helsinki, Finland

11 JPL-Caltech, Pasadena, USA


Outline of the Presentation

• Summary of mission objectives

• Observation requirements

• Retrieval concept for snow mass

• Inversion of RT model

• Examples for performance analysis

- with simulated data

- with experimental data

• Conclusions


Objectives: Improved Snow and Ice Observations

For climate research

• Snow and ice – two essential climate elements not well represented in climate models

• In particular, snow mass is poorly known

Hydrology and surface/atmosphere exchange processes

• High-resolution data are needed to account for spatial variability of snow

Glacier mass balance – climate interactions

• An essential climate variable measured only for few glaciers

• Global data are needed to quantify response to climate forcing

Snowmelt and glacier runoff - a crucial water resource

• Snow cover and glacier retreat caused by climate change may affect the

water supply to hundreds of millions of people.

• New models using spatially detailed snow observations are needed to

improve water management and support adaptation to changes.


Observation Requirements

Primary parameters Spatial scale

Regional/Global

Sampling

(days)

Accuracy

(rms)

Snow water equivalent 200 m / 500 m 3-15 3 cm for SWE 30 cm,

10% for SWE > 30 cm

Snow extent 100 m / 500 m 3-15 5% of area

Glacier snow

accumulation 200 m / 500 m 15 10% of winter maximum

Secondary parameters

Diagenetic

facies types,

glacial lakes

Glaciers

Ice area; freeze

up and melt

onset

Lake and river ice

Melting snow

area, snow

depth

Snow

Snow on ice (SWE,

melt onset and area);

type and thickness of

thin ice

Sea ice


Parameter Ku-band SAR X-band SAR

Frequency 17.2 GHz 9.6 GHz

Polarization VV, VH

Swath width, Inc angle ≥ 100 km; 30° to 45° range

Spatial resolution ≤ 50 m x 50 m (≥ 4 ENL)

NESZ ≤ -25dB VH ≤ -27dB VH

Radiom. Stability / Bias ≤ 0.5 dB / ≤ 1.0 dB

Antenna concept Single reflector with multiple beam feed array

Peak RF power 1.2 kW; 1.8 kW (2 concepts) 1.8 kW; 3.5 kW

Nr. of ScanSAR beams 6 6

CoReH2O – Instrument Design Parameters


Flowline for SWE Retrieval Algorithm


SWE Retrieval Algorithm - Iteration

A semi-empirical radiative transfer model

is used for forward computations to

enable efficient iteration for 2 free

parameters: SWE, re


Semi-empirical RT-Formulation for Snow over Soil

Semi-empirical RT Model (sRT) – Single Layer

Basic Equation:

Ground

Snow

Air

d ts s,

q

q'

trP

P

s

s

v

sas

Scattering

g

tt

g

pqtpqt

v

pqi

as

pqi

t

pq t qqsqqsqsqs 2

t

et

g

pq

t

etpqtpqi

as

pqi

t

pq

SWEkSWEk

qqs

qqqqsqs

cos

'2exp

cos

'2exp1cos75.0 2

tetset SWEkdkL qqq sec'expsecexp

One-Way Loss Factor:

s

esae

kkkk

'´'' Extinction coefficient for unit mass

Formulation for forward computation:

T(q).. Power transmission coefficient; … Scattering albedo


Frequency dependence of scattering is parameterized based on experimental data and numerical simulations for closely packed snow grains:

Wavelength exponent A = 3.2 is used as default value for seasonal snow, based on experimental data and numerical simulations (e.g. Tse et al., 2007). Further work needed to establish relations to snow morphology/snow type.

sRT – Parameterization of Snow Volume Backscatter

Initial value of Scattering coefficient:

The sRT scattering coefficient, ks , at f1 (17.2 GHz VV) is related to “effective grain size” re

which is used as input parameter for specifying the scattering efficiency in this channel.

In order to provide a link to common formulations, the initial value of ks is computed with the Rayleigh approach for frequency f1 =17.2 GHz as f(re).

In the iteration ks is a free parameter to match forward computations and measurements.

2

12

2

211

12 2

1.....,,;,....,

2

1

q

j

jj

j

iriiiqi

n

i i

xxZcccxxxJs

Cost function

For iteration

Forward model a-priori SWE, re


Input Parameters for sRT Forward Model

Symbol Name Source / Role in retrieval and forward model

Snow pack (single layer)

SWE Snow water equivalent Free variable

re Effective grain radius Free variable , related to ks at f1 = 17 GHz

Ts Mean snow pack temperature Configuration Parameter: from auxiliary data / for

computing ka (”)

s Mean snow pack density Configuration Parameter: auxiliary data or default value/

for computing T(pq) and q(t)

rmsas Std. deviation of surface height at

air/snow interface

Configuration Parameter: Pre-scribed / for computing

sas (small contribution to total backscatter)

sg (f, pq) Backscatter coefficient at ground

surface

From pre-snowfall backscatter measurements in 4

channels

RT model parameters (empirical)

As Coefficient for frequency

dependence of ks

Relation based on experimental data for linking ks(f2) to

ks(f1). Presently used default value As=3.2

Ap Cross- to co-polarized ratio for ks

(depolarization factor)

Relation based on experimental data for deriving ks (pq)

from ks(pp); presently linked to grain size


Performance Analysis for SWE Retrieval - Simulations

-10

-9

-8

-7

-6

-5

-4

-3

-2

FP01 FP02 FP03 FP04 FP05 FP06 FP07 FP08 FP09

SIG

MA

_0

[d

B]

Basic Test ID

SIMULATED RADAR BACKSCATTER - X_vv

xvv_snow_mean

xvv_ref_mean

-10

-9

-8

-7

-6

-5

-4

-3

-2

FP01 FP02 FP03 FP04 FP05 FP06 FP07 FP08 FP09

SIG

MA

_0 [d

B]

Basic Test ID

SIMULATED RADAR BACKSCATTER - Ku_vv kuvv_snow_mean

kuvv_ref_mean

Input for simulation

FP-ID SWE [m] re [mm]

FP01 0.1 0.3

FP02 0.1 0.5

FP03 0.1 0.7

FP04 0.3 0.3

FP05 0.3 0.5

FP06 0.3 0.7

FP07 0.5 0.3

FP08 0.5 0.5

FP09 0.5 0.7

Example for test case using

Synthetic Scene Generator


Performance Analysis – Effect of Snow Density


Performance Analysis – Effect of Snow Density

Retrieval statistics for different snow cover states using Synthetic Scene Generator


Performance Analysis with NOSREX Data

SnowScat s°

17 GHz, 10 GHz

SWE time series

GWI

Field campaign Sodankylä 2010-11


Retrieval Tests – Effect of Background s°

Snow Density Snow Temperature

RV – Grain radius (mean, stdev)

Cost-function (0 without RV-SWE)

Reference Backscatter

200 kg/m³ -5 0.5, 0.4 mm 0 December

200 kg/m³ -5 0.5, 0.4 mm 0 October

Retrieval input data


Conclusion

• The CoRe-H2O mission addresses a particular gap in present cryosphere

monitoring: spatially detailed observations of snow mass (SWE).

• A dual frequency, dual polarized Ku- and X-band SAR sensor is proposed as

tool for SWE measurements.

• The baseline retrieval method for SWE is based on iterative inversion of a

semi-empirical RT model, applying a statistical concept.

• Experimental data are essential for calibrating and testing the forward model

and inversion algorithm.

• Important contributions to the experimental data base are supplied by the

NOSREX Campaign (17& 10 GHz in situ), CAN-SCI (17 & 10 GHz in situ),

CLPX PolScat (14 GHz), TerraSAR-X (9.6 GHz).

• Activities for scientific mission preparation are dealing with assimilation of

CoRe-H2O products in snow process models, including the extraction of

auxiliary data for input to the SWE retrieval, and further field campaigns (with

the new 17 & 10 GHz airborne SnowSAR of ESA and in situ sensors).

coreh2o, a dual frequency radar satellite for cold regions hydrology.pdf

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