Group on Earth Observation Webinar. 28 June 2013 Steef Peters + GLaSS Partners & advisory board http://www.glass-project.eu/. GEO webinar, 2013-06-28. Outline. Consortium Rationale for GLaSS Scope and overall aim Specific objectives Project structure and phases
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Group on Earth Observation Webinar
28 June 2013
Steef Peters + GLaSS Partners & advisory board
GEO webinar, 2013-06-28
Prof. Yunlin Zhang (China: Taihu Lake Laboratory Ecosystem Research Station, Nanjing Institute of Geography and Limnology)
Prof. Arnold Dekker (Australia: CSIRO, aquatic earth observation research team within the Environmental Earth Observation research group)
Dr. Steven Greb (USA: senior scientist at the Wisconsin Department of Natural Resources)
consortium + advisory board
Rationale for GLaSS
Lakes in the North are more rapidly
Most lakes are becoming warmer
In deep lakes global warming
could cause semi-permanent
Pressures on lakes
Precipitation relocation (droughts or excess wet periods)
caused by global climate change can have
severe consequences for lakes
Scope and overall aim
2013 or 2014 or...2015
Prepare for use of Sentinel data in the context of lakes and reservoirs
Ingest large quantities of Sentinels data
Automatic processing to higher level products
Data-mining and search techniques for large quantities of data
Access tools for the wider group of space data users
Demonstrate applications including data validation activities
Attract active participation of researchers and students
Activities in the global domain
Who are the users?
Preparation, inventory of user requirements & system specification
of additional tools (data mining
and improved algorithms)
Trainings and course ware development
Detailed project structure
Note the low SNR levels... MERIS SNR > 1000
Chl-a band ratio
TSM from any band 2..6 depending on concentration range
Chl-a band ratio
Short Revisit times for optical payload, even with 1 single satellite
OLCI: Ocean and Land Colour Instrument comparison to MERIS
Pushbroom Imaging Spectrometer (VIS-NIR) – similar to MERIS
The GLaSS system
Source: GLaSS D2.1 User requirements report: CNR, SYKE, VU/VUmc, BC, TO, BG, 2013-05
Source: GLaSS D2.1 User requirements report:
CNR, SYKE, VU/VUmc, BC, TO, BG, 2013-05
TO: Study sites:
Water remote sensing group in Tartu Observatory Dr. Anu Reinart & colleagues
Underwater light field, optical properties of
lakes, MERIS validation
Starting data (2/2)
Aeronet-OC station at Pålgrunden
Inlet – Bay of Mariestad
Foto & Data: A. Hommersson & S.Kratzer (SU)
Above water reflectance measured using Wisp-3 Spectroradiometer (in cooperation with University Twente)
Continuous measurements of Chl-a fluoresence
Shallow lakes with high eutrophication and potentially toxic algae
(Lake Peipsi, Lake Ijssel)
Small lakes with high CDOM concentration (boreal lakes)
Mine tailing ponds
Deep clear lakes with increasing eutrophication (alpine lakes, East African lakes, Great Lakes)
Shallow lakes with low transparency due to sediment resuspension
(Lake Marken, Tropical lakes)
WFD reporting based on GLaSS products
Case studies: example Lake Malawi
Sol V.M., Peters S.W.M., Aiking H. -
Toxic waste storage sites in EU
countries - A preliminary risk
inventory (download http://www.
wwffreshwater.org). ISBN 90-5383-
656-X, Institute for Environmental
Studies, Vrije Universiteit, Amsterdam,
The Netherlands, 1999, 82 pp
Atmospheric correction of S2 and OLCI
-Can we treat OLCI as MERIS and use the tools that are available?
-How quickly will we be able to work with the atmo-corr 400 nm band as well?
-Will S2 data come with the same ancillary data as MERIS and OLCI
-Will Atmo-corr take the height dependent Rayleigh correction into account?
-Is there a possible synergy between OLCI and S2 wrt atmo-corr?
-Will the standard landmask be sufficient?
-Is the adjacency effect relevant, should we correct for that? Are the tools sufficient?
-How quickly will new tools be accessible to the users and through which route: BEAM, ODESA, dedicated services (MIP?)
-How to organize the match-up validation given the revisiting frequencies
Lake water quality algorithms for S2 and OLCI
-Can we build a sufficiently large dataset (through LIMNADES?!) to develop and test generic algorithms
-Should that dataset contain just spectra and concentrations, or also (S)IOPs?
-Can we collect sufficient data to do a sound validation of algorithms
-If not, can we develop data-poor methods that at least confirm the applicability of algorithms
-How to proceed towards new generic products (phytoplankton functional types, particle size distribution, fraction inorganic to organic matter, WFD relevant indicators) if large scale validation is inherently difficult
Ongoing and future cooperations
Thanks for your attention!