1. 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
H. Rott –CoReH2O IGARSS 2011

2. 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
H. Rott –CoReH2O IGARSS 2011

3. 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.
H. Rott –CoReH2O IGARSS 2011

4. Observation Requirements
Spatial scale Sampling Accuracy
Primary parameters
Regional/Global (days) (rms)
3 cm for SWE  30 cm,
Snow water equivalent 200 m / 500 m 3-15
10% for SWE > 30 cm
Snow extent 100 m / 500 m 3-15 5% of area
Glacier snow
200 m / 500 m 15 10% of winter maximum
accumulation
Secondary parameters
Snow Glaciers Lake and river ice Sea ice
Melting snow Diagenetic Ice area; freeze Snow on ice (SWE,
area, snow facies types, up and melt melt onset and area);
depth glacial lakes onset type and thickness of
thin ice
H. Rott –CoReH2O IGARSS 2011

5. CoReH2O – Instrument Design Parameters
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
H. Rott –CoReH2O IGARSS 2011

6. Flowline for SWE Retrieval Algorithm
H. Rott –CoReH2O IGARSS 2011

7. 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
H. Rott –CoReH2O IGARSS 2011

8. Semi-empirical RT-Formulation for Snow over Soil
Semi-empirical RT Model (sRT) – Single Layer P
r
P
t
Basic Equation:
Air
qi   s qi   s qt    qt s qt t qt 
q
s t
pq
as
pq
v
pq
2
pq
g
pq
sas
q'
Snow sv
One-Way Loss Factor: d s, t s
sg
Lqt   exp ke d s secqt   exp k 'e SWE secqt 
Ground
ke
ke '  ka ' k s '´ Scattering
s Extinction coefficient for unit mass
Formulation for forward computation:
    2k 'e SWE    2k 'e SWE 
s t
qi   s as
qi    qt 0.75 pq cosqt 1  exp 
2
   s pq qt exp 

g
 cos q 

 cos qt
pq pq pq

    t 
T(q).. Power transmission coefficient;  … Scattering albedo
H. Rott –CoReH2O IGARSS 2011

9. 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.
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.
2
 
J x    2 i x1 ,...., xq ; c1i , c2i ....., cri   Zi   2 x j  xj 
n q
1 2 1
Cost function
i 1 2s i j 1 2 j
For iteration
Forward model a-priori SWE, re
H. Rott –CoReH2O IGARSS 2011

10. 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
Configuration Parameter: from auxiliary data / for
Ts Mean snow pack temperature
computing ka (”)
Configuration Parameter: auxiliary data or default value/
s Mean snow pack density
for computing T(pq) and q(t)
Std. deviation of surface height at Configuration Parameter: Pre-scribed / for computing
rmsas
air/snow interface sas (small contribution to total backscatter)
Backscatter coefficient at ground From pre-snowfall backscatter measurements in 4
sg (f, pq)
surface channels
RT model parameters (empirical)
Coefficient for frequency Relation based on experimental data for linking ks(f2) to
As
dependence of ks ks(f1). Presently used default value As=3.2
Cross- to co-polarized ratio for ks Relation based on experimental data for deriving ks (pq)
Ap
(depolarization factor) from ks(pp); presently linked to grain size
H. Rott –CoReH2O IGARSS 2011

11. Performance Analysis for SWE Retrieval - Simulations
Example for test case using SIMULATED RADAR BACKSCATTER - X_vv
-2
Synthetic Scene Generator xvv_snow_mean
-3
xvv_ref_mean
-4
SIGMA_0 [dB]
Input for simulation -5
-6
-7
FP-ID SWE [m] re [mm]
-8
-9
FP01 0.1 0.3
-10
FP02 0.1 0.5 FP01 FP02 FP03 FP04 FP05 FP06 FP07 FP08 FP09
Basic Test ID
FP03 0.1 0.7
SIMULATED RADAR BACKSCATTER - Ku_vv kuvv_snow_mean
FP04 0.3 0.3
-2 kuvv_ref_mean
FP05 0.3 0.5 -3
SIGMA_0 [dB]
-4
FP06 0.3 0.7 -5
-6
FP07 0.5 0.3 -7
-8
FP08 0.5 0.5 -9
-10
FP09 0.5 0.7
FP01 FP02 FP03 FP04 FP05 FP06 FP07 FP08 FP09
H. Rott –CoReH2O IGARSS 2011 Basic Test ID

12. Performance Analysis – Effect of Snow Density
H. Rott –CoReH2O IGARSS 2011

13. Performance Analysis – Effect of Snow Density
Retrieval statistics for different snow cover states
using Synthetic Scene Generator
H. Rott –CoReH2O IGARSS 2011

14. Performance Analysis with NOSREX Data
Field campaign
Sodankylä 2010-11
SnowScat s°
17 GHz, 10 GHz
SWE time series
GWI
H. Rott –CoReH2O IGARSS 2011

15. Retrieval Tests – Effect of Background s°
Retrieval input data
Snow Density Snow RV – Grain radius Cost-function Reference
Temperature (mean, stdev) (0 without RV-SWE) Backscatter
200 kg/m³ -5 0.5, 0.4 mm 0 December
200 kg/m³ -5 0.5, 0.4 mm 0 October
H. Rott –CoReH2O IGARSS 2011

16. 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).
H. Rott –CoReH2O IGARSS 2011