pptx - EARSeL, European Association of Remote Sensing

Report
CryoLand – GMES Service Snow and Land Ice
2011 – 2015
EU FP7 Project No. 262925
ENVISAT MERIS 22 März 2011
Preparation of European Snow,
Glacier and Lake/River Ice
Services within the Copernicus
CryoLand Project
Thomas Nagler1; Gabriele Bippus 1, Helmut Rott1;
Christian Schiller2; Sari Metsämäki3, Olli-Pekka Mattila3,
Riitta Teiniranta3; Kari Luojus4; Hans Larsen5;
Rune Solberg 6; Oivind Due Trier6; Eirik Malnes7;
Andrei Diamandi 8; Andreas Wiesmann 9;
David Gustafsson10
CryoLand – GMES Services Snow and Land Ice
(FP7 Project 2011-2015)
PRIMARY OBJECTIVE
Develop, implement and validate an operational, sustainable service for
monitoring snow and land ice as a Downstream Service within
Copernicus Initiative of EC and ESA.
The project prepares the basis for a future cryospheric component of the
Copernicus Land Monitoring Service.
Contents:
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•
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•
•
•
•
Products Specifications
Example Products for Snow, Glaciers, Lake / River Ice
Product Validation
Preparation for Sentinels
Access to Products
Summary
Announcement on „Satellite Snow Products Intercomparison & Evaluation
Exercise - SnowPEX“
CryoLand User Group
CRYOLAND USER GROUP:
60 Organisations from 15 European countries + 3
EU organisations:
• Product & Service Requirements and Specs
• Product & Service Testing and Evaluation
•
•
•
•
•
•
•
•
Hydropower companies
Energy traders
Hydrological services
Meteorological services
Climate monitoring institutions
Avalanche warning centres
Road, Railway and River Authorities
Geotechnical & Construction
companies
• Ecologists
• Reindeer herders
• Environmental agencies
lissig | earsel | 3-6 feb 2016 | berne
Thomas Nagler
Consolidated Snow Products and Services
Spatial
Resolution
Temporal
Coverage
Coverage
Latency Time
EO Sensors
500
(1000) m
Daily, full year
35N – 72 N
11W – 45E
<1 day
MODIS, (VIIRS)
Sentinel S3
10 – 25 km
Daily, dry snow
season
35N – 72 N
11W – 45E
<2 day
SSMI/S, AMSR2
100 m
Daily,
Melting Period
Mountain
Regions
<1 day
Radarsat,
ASAR (archived),
Sentinel S1
Daily, full year
Alps, Nordic,
Baltic Sea
area
<1 day
MODIS
Sentinel S1, S3
250 m – 500 m
Daily, full year
Alps, Nordic,
Baltic Sea
area
<1 day
MODIS
Sentinel S1, S3
1000 m
Daily
Scandinavia
<1 day
1000 m
Daily
Scandinavia
<1 day
Product Type
Snow extent,
Pan-European
Snow Water
Equivalent PanEuropean
Melting snow area
Snow extent,
regional
Snow extent,
regional
250 m – 500 m
Snow Surface
Wetness
Snow Surface
Temperature
lissig | earsel | 3-6 feb 2016 | berne
Thomas Nagler
MODIS,
Sentinel S3
MODIS,
Sentinel S3
Glacier & Lake/River Ice Products
Product Type
Spatial
Resolution
Coverage
Grid /
Projection
0.5 m – 10 m
Local, regional
(on user
request)
Lat/Lon /
WGS84,
UTM / WGS84
0.5 m – 10 m
Local, regional
(on user
request)
Latency Time
EO Sensors
3 months
High
resolution
Optical, SAR
Lat/Lon /
WGS84,
UTM / WGS84
3 months
High
resolution
Optical, SAR
1 m – 30 m
Local
(on user
request)
Lat/Lon /
WGS84,
UTM / WGS84
3 months,
10 days (quick
analysis),
hours (emergency)
High
resolution
Optical, SAR
Glacier Ice
velocity
1 m – 50 m
Local
(on user
request)
Lat/Lon /
WGS84,
UTM / WGS84
3 months
SAR
Lake ice
classification
(4 classes)
250 m
Regional
(Baltic)
Lat/Lon /
WGS84
3 days
River Ice
Extent
1 m – 50 m
Local
Lat/Lon /
WGS84
3 days
(ice jams: < 1 day)
Glacier
outlines
Snow/ice area
on glaciers
Glacier lakes
lissig | earsel | 3-6 feb 2016 | berne
Thomas Nagler
MODIS
Sentinel 3
High / Medium
res. SAR
Approach for product and service improvement
towards user needs
1.
User requirements / dialogue
for improved product
requirements and specification
2.
Algorithm development /
adjustment to match user
needs
Generation of test products
and validation for different
regions and periods
User consultation and
feedback on product and
services
3.
4.
5.
•
Final algorithms,
fully validated products and
services
lissig | earsel | 3-6 feb 2016 | berne
Thomas Nagler
User Requirements for
Products and Services
Algorithm
development /
adjustment
Test
products
Development
cycle
User
consultation /
feedback
Validation
Approved Algorithms,
Validated Products and
Services
Pan-European Fractional Snow Cover
Product
Product Specifications:
4/3/2013
– Domain:
72°N 11°W – 35°N 50°E
– Projection: LatLon/WGS84
– Pixel size: 0.005° (ca 500 m)
– Latency: < 1 day
Status:
– Algorithm: SCAMOD (SYKE)
(550 nm Band)
– Sensor: MODIS
(Backup VIIRS, )
– Regional service integration,
processing chain and portal
implemented
– Attached uncertainty map
– Operational NRT Service for
2013/14
– Time series since 2000 in
generation
lissig | earsel | 3-6 feb 2016 | berne
Thomas Nagler
Grey: Clouds
Pan-European SWE Product
Product Specifications:
4/3/2013
– Domain:
72°N 11°W – 35°N 50°E
– Projection: LatLon / WGS84
– Pixel size: 0.1deg; ca 10 km
– Temporal resolution: Daily
– Latency: < 2 day
Status:
– Algorithm based on H-SAF
and GlobSnow
– Assimilates passive
microwave observations
and ECMWF weather
station data
Grey: Mountains
lissig | earsel | 3-6 feb 2016 | berne
Thomas Nagler
Melting Snow Area – Alps, Scandinavia
Binary map of wet snow
from Multi-temporal SAR
data
Innsbruck
• 100 m pixel size
• Projection: Geographic,
UTM, Lambert EA Austria
• Demonstration Products:
Time series of Products uses
archived ENVISAT ASAR
data
• NRT service with Radarsat
for Scandinavia spring
2013/14
lissig | earsel | 3-6 feb 2016 | berne
Thomas Nagler
9 June 2006, ENVISAT ASAR WSM.
Red – wet snow extent, Yellow – layover / foreshortening
Snow Extent Product Quality Assessment
Quality Assessment of Snow
Extent Products is performed
in different environments:
• Fractional SE products from
high resolution optical
images:
• Very High resolution
images ( IKONOS,
SPOT5, Quickbird)
• Landsat TM/ETM+
• In-situ snow transects
measured operationally by
SYKE in Finland
VHR Optical Images - Landsat TM/ETM+ In-situ snow transects
lissig | earsel | 3-6 feb 2016 | berne
Thomas Nagler
Accuracy assessment of SE
products and services is still
ongoing according to the
planning of the project.
Accuracy Assessment of SE Products
MODIS SCAMOD –
Pan-European FSC
versus In-situ Snow
transects
High and Very High resolution Images
provide detailed snow information in
mountains and forests (sparse->dense)
and enable the quality assessment of
CryoLand SE products in these areas.
Landsat TM 25.10.2003
Pixel
Syke
106714
lissig | earsel | 3-6 feb 2016 | berne
RMSD
BIAS
R
17.3
-3.93
0.89
(unforested)
Thomas Nagler
Concept for SENTINEL-3 Snow Mapping
using SLSTR (AATSR) and OLCI (MERIS)
Sentinel-3:
SLSTR (AATSR):
0.5 – 1.6, -3.7 mm+TIR;
1 km
OLCI (MERIS):
0.4.-1.2 mm; 300 m
Preparation for Snow Mapping using
SENTINEL-3 SLSTR & OLCI
AATSR 1 km 300 m
MERIS/AATSR
Input Data for FSC:
SLSTR (AATSR): 0.5 - 3.7 mm+TIR; 1 km
OLCI (MERIS): 0.4.-1.2 mm; 300 m
Algorithm:
• MS Unmixing
• Local Adaptive Endmembers
Fractional Snow Extent Map
Preparation for Snow Mapping using Sentinel-1
IWS Dual Pol Data
Sentinel-1 IWS
TOPS Mode
Swath ~250 km
Dual Pol: Co, Cross
(VV,VH)
Resolution:
5 m Slant Range
20 m Azimuth
ENVISAT ASAR Alternating Pol.
IS 6 Polarisation VV and VH :
- 7 Feb 2008 13 May 2008
Dynamic Range of Backscatter Ratio
Example of Wet Snow Map from
ASAR AP VV & VH 13 May 2008
Dual Pol: VV & VH
16/ October 2012 Sentinel-3 OLCI SLSTR Preparatory WS
Thomas Nagler
Processing Line of Glacier Area Product
Product information:
• A standardized, semiautomated processing line using
MS (V)HR satellite data and
DEM ad input: ice snow
detection automatic, manual
post-processing required over
debris covered areas.
• Product generation within
CryoLand is done on User
Request: selected glaciers in
Austria, Greenland, Kyrgyzstan,
Bhutan, and Norway
• Products generated according to
GLIMS and INSPIRE standards
lissig | earsel | 3-6 feb 2016 | berne
Thomas Nagler
Glacier Area Maps for Hohe Tauern Alps
1.9.2009, SPOT–5 (2.5 m, PAN + MS)
Glacier
1998
2009 Area
Ratio
ofArea
SnowChange
and Total
Glacier
0.46
0.14
0.29
Extent of Glacier Lakes
• Glacier Lake Extent derived from optical satellite
data and SAR data
• Method applies classification and manual postprocessing and uses existing lake boundaries
• Glacier Lake Extent of multiple years for Lake
Tininnilik, Greenland, Kyrgyzstan and Bhutan
• Validation is carried out with in-situ observations
in collaboration with users
ENVEO, NORUT
Lake outburst
observed
between
2010/06/28 (red)
and 2010/07/05
(blue) using
SAR.
Comparison with
GLL (white) from
Landsat 7 ETM+
of 2010/08/17.
SAR Analyses: NORUT
LS7 Analysis: ENVEO
lissig | earsel | 3-6 feb 2016 | berne
Thomas Nagler
Lake Ice Extent - Product
full snow cover
partial snow/
white ice cover
clear ice/
columnar ice
open water
SYKE Lake Ice Extent
In-situ Snow depth
Reflectance (550 nm)
Lake Ice Product:
• Daily, generated NRT
• 250 m pixel
• Covering Baltic drainage basin
• Sensor: MODIS
Time
The threshold reflectances were determined from
time series of MODIS reflectance observations
from the surroundings of in-situ measurements
for snow cover thickness on ice.
lissig | earsel | 3-6 feb 2016 | berne
Thomas Nagler
Lake Ladoga
Lake Ice product 17 April 2013
Product and Information Exchange
Tools
User
Infrastructure
CryoLand
GeoPortal
GEOSS Common
Infrastructure
https://cryoland.eu
Protocols: CSW, WMS, WFS, WPS, PDP
Provided
data
GMES Space
Space
(general)
In-situ Data
Auxiliary
Data
INSPIRE
Portal
……
Portal
Products accessible by
• Geoportals,
• GIS Systems,
• Automatic Script
Downloading
GMES Core
Services
Summary
• Product and Service Specifications for potential Copernicus
Snow and Land Ice Services were compiled together with a wide
user group.
• Near Real Time Services for Pan-European FSC and SWE and
lake / river ice products winter 2013/2014: Products are made
available to the public through CryoLand System
(https://www.cryoland.eu).
• Generation of glacier products in Alps, Scandinavia, Greenland
and Himalayan Mountains as requested by CryoLand users.
• Validation of products and quality assessment using high
resolution optical data and in-situ data is ongoing and
specification of.
• Algorithms and processing lines are adapted to make use of
advanced imaging capabilities of Sentinel satellites, testing with
more data sets was limited due to the lack of suitable data sets.
SNOWPEX
Satellite Snow Products Intercomparison & Evaluation Excercise
An ESA initiative contributing to WMO Global Cryosphere Watch and WCRP CLiC
carried out under the lead of ENVEO
in cooperation with FMI, SYKE, EC and CCRS.
and associated international contributors
US National Ice Center / NOAA/ NESDIS,
Cryospheric Processes and RS Laboratory, City University of NY
Global Snow Lab, Rudgers University
Cryospheric Sciences Laboratory, NASA
SnowPEX Objectives
SnowPEX aims to bring together scientists and institutions of seasonal snow pack monitoring
for assessing the quality of current satellite-based snow products derived from EO data, and
working out guidelines for improvement.
The primary objectives are
•
Intercompare and evaluate global / hemispheric (pre) operational snow products
derived from different EO sensors and generated by means of different
algorithms, assessing the product quality by objective means.
•
Evaluate and intercompare temporal trends of seasonal snow parameters from
various EO based products in order to achieve well-founded uncertainty
estimates for climate change monitoring.
•
Elaborate recommendations and needs for further improvements in monitoring
seasonal snow parameters from EO data.
SNOWPEX organizes 2 International Workshops on Snow Product Intercomparison
(IWSPI) . The 1st IWSPI is planned to be held in July 2014 in Washington DC
(TBC).
Point of Contact: [email protected]

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