major gaps in current eo measurement...
TRANSCRIPT
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)
Thomas Nagler23 June 2016
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:
Thomas Nagler23 June 2016
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
Thomas Nagler23 June 2016
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
Thomas Nagler23 June 2016
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
Thomas Nagler23 June 2016
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
Thomas Nagler23 June 2016
Sentinel-1 Snow Melt Extent Product
Thomas Nagler23 June 2016
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
Thomas Nagler23 June 2016
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°
Thomas Nagler23 June 2016
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°:
Thomas Nagler23 June 2016
Snow Albedo
Thomas Nagler23 June 2016
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
Thomas Nagler23 June 2016
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
Thomas Nagler23 June 2016
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