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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

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

  • Sentinel 2 and 3 characteristics

  • Validation

  • Case studies

  • Atmospheric correction questions

  • Algorithm development questions

  • Expected achievements

  • Collaborations

2


The consortium

  • Steef Peters, Annelies Hommersom, Kathrin Poser, Nils de Reus, Marnix Laanen, Semhar Ghezehegn: Water Insight, Wageningen, Netherlands

  • Sampsa Koponen, Kari Kallio, Jenni Attila, Timo Pyhälahti, Mikko Kervinen, Saku Anttila: SYKE, Helsinki, Finland

  • Karin Schenk, Thomas Heege, EOMAP team: EOMAP, Oberpfaffenhofen, Germany

  • Marieke Eleveld, Steef Peters, VU/IVM, Amsterdam, The Netherlands

  • Ana Ruescas, Norman Fomferra, Carsten Brockmann, Kerstin Stelzer: Brockmann Consult, Geesthach, Germany

  • Claudia Giardino, Mariano Bresciani, CNR, Milano, Italy

  • Krista Alikas, Anu Reinart, Kristi Uudeberg, Ilmar Ansko, Martin Ligi, Tartu Observatory, Estonia

  • Petra Philipson, Niklas Hahn: Brockmann Geomatics Sweden, Stockholm, Sweden

3


Advisory board

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)

4



Rationale for GLaSS

  • Man-induced processes (eutrophication, influx of suspended solids, acidification) influence surface water quality

  • Largely unknown effect of climate change

  • and global warming

  • Water quality focus of monitoring agencies and the public, subject

  • of several European Directives and regional conventions

  • Integrated approach towards watershed and surface water quality management required,

  • based on a scientific understanding of the spatio-temporal aspects

  • of the processes and their interactions

6


Lakes in the North are more rapidly

becoming warmer

Most lakes are becoming warmer

In deep lakes global warming

could cause semi-permanent

stratification


Pressures on lakes

(withdrawal, waste)

are increasing

Precipitation relocation (droughts or excess wet periods)

caused by global climate change can have

severe consequences for lakes


Scope and overall aim

  • Earlier EC studies (e.g. Geoland and Geoland-2) approached inland water quality from a modeling point of view, not using satellite observations for model calibration and validation

  • Advent of Sentinel 2 and 3 with their high spatial and temporal

  • resolution will make such monitoring, model calibration and validation

  • feasible

  • GLaSS intends to contribute to integrated studies with innovative, detailed and reliable spatio-temporal monitoring information on key water quality aspects to underpin new policy making and management options

2013 or 2014 or...2015

9


1001011010101010000111010100010011101001010100010100110010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

Specific objectives

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

10


Project structure10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

11


Who are the users?10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100


Project phases10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

Preparation, inventory of user requirements & system specification

Implementation

of additional tools (data mining

and improved algorithms)

System implementation

Trainings and course ware development

Validation

of results

Global

use cases

13


Detailed project structure10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100


Sentinel 2 properties
Sentinel 2: properties10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100


Note the low SNR levels... MERIS SNR > 100010010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

CDOM

@higher concentrations

Case1

Chl-a band ratio

TSM from any band 2..6 depending on concentration range

Case2

Chl-a band ratio


Sentinel 3 optical revisit time and coverage
Sentinel-3 Optical Revisit time and coverage10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

Optical missions:

Short Revisit times for optical payload, even with 1 single satellite

  • Data delivery timeliness:

  • Near-Real Time (< 3 hr) availability of the L2 products

  • Slow Time Critical (STC) (1 to 2 days) delivery of higher quality products for assimilation in models (e.g. SSH, SST)


OLCI: Ocean and Land Colour Instrument comparison to MERIS10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

Pushbroom Imaging Spectrometer (VIS-NIR) – similar to MERIS

  • Key Improvements:

    • More spectral bands (from 15 to 21): 400-1020 nm

    • Broader swath: 1270 km

    • Reduced sun glint by camera tilt in west direction (12.20°)

    • Absolute (relative) accuracy of 2% ( relative 0.5%)

    • Polarisation sensitivity < 1%

    • Full res. 300m acquired systematically for land & ocean

    • Reduced res. 1200m binned on ground (L1b)

    • Improved characterization, e.g. straylight, camera boundary characterization

    • Ocean coverage < 4 days, (< 2 days, 2 satellites)

    • Timeliness: 3 hours NRT Level 2 product

    • 100% overlap with SLSTR

    • => Improved L2 products e.g., Cla, Transparency, TSM, Turbidity, PFTs, HAB, NDVI, MGVI, MTCI, faPAR, LAI


The GLaSS 10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

system


The GLaSS system10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

Source: GLaSS D2.1 User requirements report: CNR, SYKE, VU/VUmc, BC, TO, BG, 2013-05


Source: 10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100GLaSS D2.1 User requirements report:

CNR, SYKE, VU/VUmc, BC, TO, BG, 2013-05


Bc expansion of beam with rapid miner
BC: Expansion of BEAM with10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100Rapid Miner

  • Freely available open-source data mining and analysis system

  • GUI mode, server mode (command line), or access via Java API

  • Simple extension mechanism

  • More than 500 operators for data integration and transformation, data mining, evaluation, and visualization

  • Standardized XML interchange format for processes

  • Graphical process design for standard tasks, scripting language for arbitrary operations


Data visualisation
Data Visualisation10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100


Graphical application builder
Graphical Application Builder10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100


Trained model decision tree
Trained Model (Decision Tree)10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100


Validation10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

  • validation of L2 reflectance and L2 water quality products in nearby lakes

27


Lake peipsi and v rtsj rv

TO: 10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100Study sites:

lakes

Lake Peipsi and Võrtsjärv


Water remote sensing group in 10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100Tartu Observatory Dr. Anu Reinart & colleagues

Underwater light field, optical properties of

lakes, MERIS validation

)


Transect measuremets in lake peipsi
Transect measuremets in 10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100Lake Peipsi


AERONET10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

?

Garda

WISP-3

Flow-through systems

ASD-FR

0+

0-

Water-lab

(Spectrophotometer,

HPLC)

Fluorimeters

Ramses

ac9

H6

Commercial software:

ENVI, ArcGIS

ATCOR, MODTRAN

Hydrolight-Ecolight (5.0)

CNR Facilities

Sun-photometer

31


CNR-Data10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

Starting data (2/2)

Remotely sensed

32


SYKE10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

  • Validation

    • ASD spectrometers, WISP3?

    • SYKE laboratory

    • CDOM dominated

    • lakes


Bg lake v nern
BG: Lake Vänern10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

Aeronet-OC station at Pålgrunden

Inlet – Bay of Mariestad

  • Chl: 8.04 ug/l

  • SPM: 2.49 mg/l

  • CDOM: 1.57 m-1

  • Secchi: 2.9 m

  • Chl: 1.96 ug/l

  • SPM: 0.63 mg/l

  • CDOM: 1.06 m-1

  • Secchi: 6.2 m

Clear water

  • Chl: 35.79 ug/l

  • SPM: 30.49 mg/l

  • CDOM: 3.20 m-1

  • Secchi: 0.4 m

River mouth

  • Chl: 11.5 ug/l

  • SPM: 4.53 mg/l

  • CDOM: 4.68 m-1

  • Secchi: 1.8 m

Dättern

Foto & Data: A. Hommersson & S.Kratzer (SU)


Monitoring service
Monitoring service10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100


Ivm wi lake marken lake ijssel
IVM + WI : Lake Marken 10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100& Lake IJssel


Lake ijssel match up collection
Lake IJssel match-up collection10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

Above water reflectance measured using Wisp-3 Spectroradiometer (in cooperation with University Twente)

Continuous measurements of Chl-a fluoresence


Case studies10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

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

38


Case studies: example Lake Malawi10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100


Mean monthly meris fr
Mean monthly MERIS FR10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100


Case studies mine tailing ponds
Case studies: Mine tailing ponds10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

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 OLCI10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

-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 OLCI10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

-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


Expected achievements10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

  • Sentinel 2 and 3 data formats integrated into user-accessible tools

  • Generic tools for S2 and S3 processing available to user community (Atm. Correction, feature extraction, time series analysis, etc.)

  • Updated algorithms for inland water analysis (Chl-a, TSM, CDOM, PC etc) for S2 and S3 tested and validated

  • Test datasets of S2 and S3 (simulated, and hopefully real) available

  • Material from use cases available for education and training

  • Global user community informed about GLaSS products and actively using project tools, data and results

45


Ongoing and future cooperations10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100

  • There is information and knowledge exchange between GLaSS and ESA-Biodiversity II (BC) and Globolakes (University of Stirling)

  • Possibilities for Future Cooperation in USA, Australia and China via the Advisory Board

  • GLaSS team is accepted as S3VT

  • Cooperation to be set up with new FP7 projects INFORM (VITO) and Sensyf (Deimos)

  • GLaSS in-situ data to go into the LIMNADES database

  • Close contact with GEO

46


Thanks for your attention! 10010110101010100001110101000100111010010101000101001100101101010101000011101010001001110100101010001010010010110101010100001110101000100111010010101000101001001011010101010000111010100010011101001010100010100


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