Major Gaps in Current EO Measurement...

Snow Cover Applications:

Major Gaps in Current EO Measurement

Capabilities

Thomas NAGLER

ENVEO Environmental Earth Observation IT GmbH

INNSBRUCK, AUSTRIA

Polar and Snow Cover Applications

User Requirements Workshop, 23 June 2016, Brussels

Outline

Thomas Nagler23 June 2016

• Motivation for EO monitoring snow

• Users of snow products

• Observation Requirements

• Status and gaps of main snow cover parameters

• Capabilities of Copernicus Sentinel and MetOP-SG

• Summary

Motivation for EO snow monitoring

3

CANDIDATE EARTH EXPLORER TO OBSERVE

SNOW AND ICE FOR A BETTER

UNDERSTANDING OF THE WATER CYCLE

Annual snow accumulation divided by runoff

Ratio snowfall/runoff 50% in basin

Basins with 50% runoff coming from snowmelt

regions

Barnett et al., 2005

Main Questions:

role of seasonal snow cover on the

water cycle including impact on water

resources and effects on energy and

radiation budget

parameterization and downscaling of

snow and ice processes for land

surface and climate models

Impact of snow cover on permafrost

evolution and carbon exchange in high

latitudes

The need for improved snow observations is addressed in several international

programs and their strategy documents (IPCC; UNEP; ACIA; GCOS; WMO GCW)


Needs for Snow Services in Europe

• Climate monitoring institutions

• Hydropower companies

• Energy traders

• Hydrological service

• Meteorological services

• Avalanche warning centres

• Road, Railway and River Authorities

• Geotechnical & Construction companies

• Ecologists

• Reindeer herders

• Environmental agencies

CRYOLAND USER GROUP:

60 Organisations from 15 European countries +and

3 EU organisations:


Observation Requirements for

main land snow products Parameter Spatial

Resolution

Time Interval Accuracy

Total Snow Area 1 km (T)

100 m (G)

1 day

12 hr

10 % (T)

5 % (G)

Snow Mass (SWE)

on land

1 km (T)*

100 m (G)

6 day (T)

1 day (G)

10 mm *

Melting Snow Extent** 500 m (T)

100 m (G)

3 d (T)

1 d (G)

10 % (T)

5 % (G)

Snow / Ice Albedo 8 km (T)

5 km (G)

6 day (T)

1 hr (G)

1% (T) of irradiance

0.5 % (G)

Snow / Ice Surface

Temperature

1 km (T)

100 m (G)

1 d (T)

12 h (G)

-

Snow grain size -- -- Optical grain size: 0.25 mm

(IGOS Cryosphere, 2007, *GCOS 2016 Draft, **Proposed for parameter)Threshold, Goal Requirement

Total Snow Area


Current daily Snow Extent product are primarily based on medium resolution optical

satellite data using VIS and SWIR bands for detecting snow. Mature algorithms

available:

• Binary Snow Product map of snow / no snow / clouds

• Fractional Snow Cover Product provides estimate of fractional snow extent

within pixel

• Two types of products: canopy adjusted (snow on ground); viewable snow

Issues of optical snow products

• Disagreement of individual snow extent products (assessed within ESA SnowPEx)

• Higher uncertainty of snow cover fraction in mountains and forests

• Problems in cloud / snow screening leads to errors in snow maps

• Improvement of resolution in complex terrain (mountains) needed

MEaSUREsJASMES

MDS10C

JASMES

GHRM5C

AVHRR

Pathfinder

AutoSnow MOD10_C5GlobSnow SCAGNOAA IMS

CryoClim

Snow Extent Products in EASE-GRID 2.0


Jan-Feb-Mar 2008,

all pixels

Snow Extent - accuracy and

agreement of NH snow products

unforested

Intercomparison of snow products show

differences in the fraction of snow cover

Unbia

sed

RM

SE

[%

]

Landsat Snow Algorithm:

Global / NH Snow Products

versus a high resolution snow

reference data set


AATSR 1000 m MERIS + AATSR 300 m FSC

Snow Extent - S3 SLSTR & OLCI


Product derived from current satellite-

based C-band SAR Systems:

• Extent of snow melt area based

on backscatter sensitivity to wet snow

Issues of Snow Melt Mapping using C-Bd SAR:

• Limited applicability in forests

• current SAR sensors provide snapshot of melt extent at

the acquisition time (ascending: morning; descending

evening), but high variability of snow melt area due to

weather and illumination

Snow Melt Area


Sentinel-1 Snow Melt Extent Product


S1A Snow Melt Product: 100 m pixel spacing

S1A observation interval (mid lat): ~6 d

S1A+B observation interval (mid lat): ~3 d

SAR - Sentinel-1:

• Algorithm based on change

detection is mature. (Nagler et al.

2016; Nagler & Rott 2000)

• Current acquisition planning

provides snapshot of melting snow

extent (ascending: morning;

descending: evening)

• To observe diurnal melt / refreeze

cycle additional images acquired

during the day are needed.

• Retrieval not possible in dense

forested areas (forests are masked

out)

• Transition to Continental / NH

monitoring requires proper

acquisition planning.Thomas Nagler23 June 2016

Issues of current SWE products

• Coarse resolution products (~25 km), problems in mountain terrain and

forests.

• Significant disagreement of available products (high uncertainty in SWE

retrieval, and saturation at )

• Need for high resolution SWE product (esp. for complex terrain) has

been identified by GCOS 2016 (draft)

Mass of Terrestrial Snow

SWE Products from Passive Microwave data

Uses multi frequency brightness temperatures

18.7 & 37 GHz, (10.6 & 32 GHz)

Advanced products: assimilation of in-situ

snow data in retrieval

Daily global coverage, independent day / night

/ clouds

Long data records

GlobSnow 2.0 SWE Product


EO Concepts for SWE Monitoring

23 June 2016 Thomas Nagler

Approach Strengths Weaknesses

Radar (Scat or SAR):

Dual: Ku & Ka

Single: Ku, Ka

sensitive to SWE & melt; high resolution;

independent of clouds/illumination

algorithm maturity, coverage, SWE

saturation, forests

InSAR

L- , C-Band

direct SWE sensitivity; high resolution

avoids volume scattering issues

forests, complexity; requires

advanced acquisition plan

LIDAR direct observation of snow depth; very

high resolution, minor forests and

topographic issues

SWE retrieval requires snow densit

InSAR SWE RetrievalRadar (Scat or SAR)

Sensitivity of backscatter

to SWE depends on

scattering albedo:

Dual F: Ku + Ka

Single F: Ku, Ka

See: CoReH2O -

EE7 Mission proposal

SWEk

i

snow

cos

6.1

for i<50°


InSAR SWE Retrieval

SWE Snow Depth (0.3 g/cm3)

L-Band: 120 mm 0.40 m

C-Band: 28 mm 0.09 m

Continues Groundbased SAR measurementsWattener Lizum

Austria

Phase Sensitivity on SWE

Time series of interferometric phase

(C-Bd) at one point Time series of retrieved SWE

=2p, =40°:


Snow Albedo


Snow Albedo depends on

- metamorphic snow properties (grain size, dust contamination, …)

- strong angular reflectance distribution varies significantly with wavelength and

snow microphysical properties

Snow albedo is needed for accurately determination of the radiation and energy

budget and is observed using medium resolution optical sensors.

0.6 mm 1.5 mm

Hemispheric Albedo = 0.935 0.0504

Conversion of from directional

albedo measurements by satellite

sensors to spectral hemispheric

albedo requires accurate

characterization of the angular

reflectance distribution

SOLUTION:

Multi-directional measurements

are needed to determine angular

reflectance characteristics

v=30°

60°

70°

Zenith Angle = 60°

Capabilities of European S1, S2 and S3

and MetOP-SG


SAR MSI OLCI SLSTR SRAL VII IRS SCA MWI 3MI

Snow Extent

Snow Melt

Area

coarse

coarse

Snow Water

Equiv.

coarse

coarse

Snow

Albedo

Snow Surf.

Temp.

S1 S2 S3 MetOP-SG

Summary on EO Snow Observations


Parameter Status Gaps

Total Snow Area VIS, NIR & TIR imager, some problems

in cloud / snow discrimination; available

products show significant differences

higher resolution required for

complex terrain (mountains)

cloudiness / polar night; in some

products filled with coarse IMWR;

Snow Mass

(SWE) on land

Low spatial resolution SWE maps

available from IMWR, but at

comparatively large uncertainty.

IMWR SWE: accuracy needs to

be improved; problems with

spatial resolution in complex

terrain, forests, saturation over

deep snow.

High resolution product needed.

Snow Melt Extent C Band SAR provide snapshot,

algorithms mature for mountain regions.

Problems in forests. Melt extent

depends on acquisition time;

Snow / Ice Albedo Hemispheric snow albedo derived from

medium resolution spectral imagers

(Sentinel-3, MODIS, VIIRS)

Accuracy impaired by angular

effects of surface reflection

(BRDF) and of atmosphere

(aerosol scattering), requiring

multi-angular measurements

Major Gaps in Current EO Measurement...

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