INFORM project overview and statusinform.vgt.vito.be/...GEOWEBINAR25092014_final.pdf · – lack of adequate analysis methods – lack of adequate low-cost EO data – lack of uncertainty

GEO Inland and Coastal Water Quality - Webinar

25.09.2014

http://www.earthobservations.org/webinar_wq.php

INFORM project overview and status

Ils Reusen, VITO | [email protected]

Els Knaeps, VITO | [email protected]

and the INFORM consortium

http://www.earthobservations.org/webinar_wq.php

mailto:[email protected]



25.09.2014

Improved monitoring and forecasting of ecological status of European

INland waters by combining Future earth

ObseRvation data and Models


25.09.2014

Why focus on inland waters?

• Fishing, recreation, water supply, transport, waste disposal, irrigation, …

increased pressures on EU inland waters asks for sustainable water management

• Monitoring of inland water quality required by

– EU Water Framework Directive (2000/60/EC)

– EU Habitats Directive (92/43/EEC)

– EU Shellfish Waters Directive (2006/113/EC)

– EU Drinking Water Directive (98/83/EC)

– EU Bathing Water Directive (2006/7/EC)

– EU Nitrates Directive (91/676/EEC)

– EU Urban Waste Water

– ...

• Environmental Impact Assessments (e.g. by dredging companies)


25.09.2014

Blueprint to Safeguard Europe's Water Resources (COM/2012/673)

mentions Copernicus

“THE STATUS OF EU WATERS IS NOT DOING WELL ENOUGH! “

“The Water Information System (WISE) … will benefit from the development of

INSPIRE, SEIS and Copernicus and from current water research works under

FP7 and those to be conducted under H2020”


25.09.2014

Copernicus

• Copernicus = The European Earth Observation Programme

• Copernicus services address six main thematic areas: » Land Monitoring (operational)

» Marine Monitoring (pre-operational phase)

» Atmosphere Monitoring (pre-operational phase)

» Emergency Management (operational)

» Security (development phase)

» Climate Change (development phase)

• All are provided free of charge to users

• http://www.copernicus.eu

http://www.copernicus.eu/pages-principales/services/pages-principales/services/land-monitoring/

http://www.copernicus.eu/pages-principales/services/pages-principales/services/marine-monitoring/

http://www.copernicus.eu/pages-principales/services/pages-principales/services/atmosphere-monitoring/

http://www.copernicus.eu/pages-principales/services/pages-principales/services/emergency-management/

http://www.copernicus.eu/pages-principales/services/pages-principales/services/security/

http://www.copernicus.eu/pages-principales/services/pages-principales/services/climate-change/

http://www.copernicus.eu/


25.09.2014

White Paper Copernicus Inland Water Services

• Discussion document to extend Copernicus Land Monitoring Service portfolio with inland water services demonstrated in FP7 Space projects

• Proposed Copernicus inland water services » Irrigation Water Abstraction Monitoring and Control Service

» Pan-European Inland Water Quality Monitoring Service

» Water scarcity and drought monitoring and forecasting Services

» Inland water quantity monitoring service


25.09.2014

Earth Observation for monitoring inland waters?

• Today: underutilized

– complexity and variability of these inland waters

– lack of adequate analysis methods

– lack of adequate low-cost EO data

– lack of uncertainty estimates

Report GEO inland and coastal Water Quality

Algorithm Workshop, Washington DC, May

2009:

“There is a lack of appropriate/dedicated satellite

sensors for nearshore coastal and inland water

quality applications”.


25.09.2014

Earth Observation & biogeochemical models?

Assimilation of EO products into biogeochemical models allows for analysis of the cause-effect relationships governing a status change, forecast the response to pressures and evaluate different management actions.

“the future lies in the combined utilization of in situ data, remote

sensing, and modeling.”

Tiffany A.H. Moisan, Shubha Sathyendranath and Heather A.

Bouman (2012). Ocean Color Remote Sensing of Phytoplankton

Functional Types, Remote Sensing of Biomass - Principles and

Applications, Temilola Fatoyinbo (Ed.), ISBN: 978-953-51-0313-4,

InTech, Available from: http://www.intechopen.com/books/remote-

sensing-of-biomass-principles-and-applications/remote-sensing-of-

marine-phytoplankton-biomass

http://www.intechopen.com/books/remote-sensing-of-biomass-principles-and-applications/remote-sensing-of-marine-phytoplankton-biomass
























25.09.2014

EU FP7-SPACE project INFORM

• Collaborative project - THEME [SPA.2013.1.1-07] [Remote sensing methods]

• Start date: 1/1/2014

• Duration: 48 months

• 9 beneficiaries from 7 EU Member States

• Requested EU contribution: € 1 991 902. 97

• Grant agreement n° 606865


25.09.2014

To develop novel user-driven products for inland water quality monitoring by using new innovative methods integrated into models which fully exploit the capabilities of upcoming Earth Observation missions (Sentinel-2, Sentinel-3, EnMAP and PRISMA)

Main concept


25.09.2014

Sentinel-2

• Sentinel-2A launch readiness: 30 April 2015

• Global revisit time: 5 days with 2 satellites

• MSI (Multi Spectral Instrument) » 13 spectral bands: 443 nm– 2190 nm (including 3 bands

for atmospheric corrections)

» Spectral resolution: 15 nm– 180 nm

» Spatial resolution: 10 m, 20 m and 60 m

» Swath: 290 km

© ESA P. Carrill


25.09.2014


25.09.2014

Sentinel-3

• Sentinel-3A launch readiness: end of 2015

• 2 day global coverage

• OLCI (Ocean and Land Colour Instrument) » Swath width: 1270 km, with 5 tilted cameras

» Spatial sampling: 300 m (full resolution mode)

» Spectral range: 21 bands [0.4-1.02] μm

© ESA PJ. Huart


25.09.2014

Normalized OLCI SRFs, bands 1 to 21, plotted versus wavelength [nm] from C. Pelloquin, J. Nieke, SENTINEL-3 OLCI AND

SLSTR SIMULATED SPECTRAL RESPONSE FUNCTIONS (S3-TN-ESA-PL-316) OLCI spectral bands = MERIS heritage+additional bands: Oa1 (400 nm): aerosol correction, improved water constituents retrieval Oa9 (673,75 nm): improved fluorescence retrieval and smile correction Oa14 (764,375 nm): atmospheric correction Oa15 (767,5 nm): cloud top pressure, fluorescence over land Oa20 (940 nm): water vapour absorption, atmospheric/aerosol correction Oa21 (1020 nm): atmospheric/aerosol correction


25.09.2014

EnMAP

» Expected launch date: 2017

» Hyperspectral

» Spectral range from 420 nm to 1000 nm (89 VNIR bands-8.1 nm FWHM) and from 900 nm to 2450 nm (155 SWIR bands-12.5 nm FWHM)

» Swath width 30 km

» Spatial resolution of 30 m x 30 m

» Off-nadir (30°) pointing feature for fast target revisit (4 days)


25.09.2014

PRISMA

» Expected launch date: 2017

» Hyperspectral

» Spatial resolution: 20-30 m (Hyp) / 2.5-5 m (PAN)

» Swath width: 30-60 km

» Spectral range: 0.4 - 2.5 µm (Hyp) / 0.4 - 0.7 µm (PAN)

» Continuous coverage of spectral ranges with 10 nm bands


25.09.2014

http://www.apex-esa.org

APEX airborne hyperspectral imaging sensor for Simulation Calibration Validation of satellite sensors/products


25.09.2014

INFORM consortium

Participant organisation name

Participant short name Country

VLAAMSE INSTELLING VOOR TECHNOLOGISCH ONDERZOEK N.V.

VITO - Coordinator BELGIUM Ils Reusen, Els Knaeps, Sindy Sterckx, Liesbeth De Keukelaere, Dries Raymaekers, …

CONSIGLIO NAZIONALE DELLE RICERCHE

CNR ITALY Mariano Bresciani, Claudia Giardino, …

EOMAP GmbH & Co.KG EOMAP GERMANY Karin Schenk, Philip Klinger, Thomas Heege, …

THE UNIVERSITY OF STIRLING

U STIRLING UK Peter Hunter, Andrew Tyler, Evangelos Spyrakos …

INSTITUT ROYAL DES SCIENCES NATURELLES DE BELGIQUE

RBINS BELGIUM Dimitry Van der Zande, Kevin Ruddick, …

STICHTING DELTARES Deltares THE NETHERLANDS Miguel Dionisio Pires, …

PLYMOUTH MARINE LABORATORY

PML UK Giorgio Dall’Olmo, Steve Groom + Stefan Simis, …

MAGYAR TUDOMANYOS AKADEMIA OKOLOGIAI KUTATOKOZPONT

MTA OK HUNGARY Matyas Presing, …

KLAIPEDOS UNIVERSITETAS

KLAIPEDOS UNIVERSITETAS

LITHUANIA Arturas Razinkovas-Baziukas, …


25.09.2014

INFORM Steering Advisory Board (SAB)

• Members

– Dr. Tiit Kutser, Remote Sensing and Marine Optics Department, Estonian Marine Institute, University of Tartu, Estonia

– Dr. Stewart Bernard, CSIR-NRE (Centre of High Performance Computing), South-Africa

– Dr. Vittorio Brando, CNR-IREA

• Tasks

– To provide recommendations at the SAB01 meeting (January 2014)

– To formulate scientific comments on the INFORM progress and to provide recommendations at SAB02 meeting (Mid-term, January 2016)


25.09.2014

INFORM End-User Advisory Board (EUAB)

• Members

– Marc Sas/Boudewijn Decrop, International Marine and Dredging Consultants (IMDC), Belgium

– Marco Bartoli, Expert ecologist, University of Parma, Life Sciences Department

– Ute Menke, advisor Network Water, Rijkswaterstaat, the Netherlands

– István Kóbor head of laboratory, Central-Transdanubian Water Directorate, Hungary

– Geoff Phillips/Bill Brierley, Research, Monitoring and Innovation. Environmental Agency (EA) for England & Wales

– Alfred Johny Wüest, EAWAG, aquatic research institute, Switzerland

– Algirdas Stankevičius, Head of the Marine Research Department of the Ministry of Environment, Lithuania = COPERNICUS USER FORUM member

– Thomas Wolf, Environmental Agency of Baden-Wuerttemberg (LUBW), Germany

• Tasks

– To provide user requirements for INFORM developments at the EUAB01 (March 2014) and EUAB02 (Mid-term, January 2016)

– To attend the INFORM EUAB03 results uptake workshop (December 2017)


25.09.2014

European approach Site Country Characteristic Lake Balaton

Kis Balaton

Hungary Largest shallow lake in Central

Europe, meso-oligotrophic

Water Protection System,

hypereutrophic

Curonian lagoon Lithuania Hypereutrophic coastal lagoon

Lakes Mantua Italy Small and shallow artificial eutrophic

basins

Lagoon of Venice Italy Turbid coastal lagoon

Lake Constance Germany,

Switzerland,

Austria

Meso-oligotrophic lake

Gironde river France Highly turbid river

Scheldt river Belgium Highly turbid river

Lake Windermere UK Mesotrophic lake

Loch Lomond UK Warm, monomictic basin.

Oligotrophic northern basin,

mesotrophic southern basin

Loch Leven UK polymictic, nonstratifying and

eutrophic shallow lake

Ijsselmeer The Netherlands Eutrophic lake, largest freshwater

lake area in Northwestern Europe

Markermeer is a turbid lake.

+Lake Geneva, Switzerland


25.09.2014

INFORM concept (detail)

Properties of upcoming EO sensors (Sentinel-2, Sentinel-3, EnMAP, PRISMA) Improved spatial resolution Increased spectral coverage to

shorter and longer wavelengths Improved spectral resolution

New/improved products Atttenuation and euphotic depth TSM and turbidity Yellow matter Phytoplanktion functional types Stratification Macrophytes Phytoplankton primary production Sun-induced chlorofyll fluorescence

Innovative analysis methods and improved atmospheric correction

Improved modelling

End-users

End-users


25.09.2014

WP objectives • WP1 Management (VITO)

– Legal management

– Financial management

– Administrative management

• WP2 Scientific coordination (VITO)

– Scientific coordination of the project

• WP3 End-user interaction (CNR)

– To explore the end-user requirements in terms of water quality products

– To stimulate project results’ uptake by the end-users and industry

• WP4 Data gathering (VITO)

– To inventory existing data, identify data gaps

– To acquire new (in-situ, APEX hyperspectral and satellite) data » Development Campaign – 2014

» Testing Campaign – 2016


25.09.2014

WP objectives

• WP5 Algorithm development and validation (U STIRLING)

– Development and validation of EO products, and estimation of their uncertainty for WP6

» Atmospheric correction (RBINS)

» Attenuation and euphotic depth (RBINS)

» TSM and turbidity (VITO)

» Yellow matter (PML)

» Phytoplankton functional types (CNR)

» Stratification (EOMAP)

» Macrophytes (CNR)

» Phytoplankton primary production (U STIRLING)

» Sun-induced chlorophyll fluorescence (U STIRLING)


25.09.2014

WP objectives

• WP6 EO-model integration (Deltares)

– Integration of Earth Observation (EO) & In-Situ (IS) data and Water Quality (WQ) modelling

• WP7 Demonstration (EOMAP)

– To demonstrate to end-users

» the INFORM prototype algorithms applied to new satellite sensors and

» the added value of INFORM EO products for WQ model validation and forecasting

– To test the compliance of INFORM EO products with end-user requirements


25.09.2014

WP objectives

• WP8 Dissemination (VITO)

– To disseminate the project objectives, progress and results

– To raise the awareness of the INFORM project

– To give recommendations for future satellite missions

– To organise a results uptake workshop


25.09.2014

Interdependency of Work Packages


25.09.2014

WP3: End-user interaction (Leader: CNR)


25.09.2014

Kick-off end-user requirements

• EUAB end-user requirements formulated at the EUAB01 meeting, 20-21 March 2014, Venice:

– General conclusion: the benefits that harmonized MULTI-TEMPORAL AND SPATIAL information derived from satellite images can give with respect to the traditional in-situ monitoring techniques based on point measurements was pointed out as the most important improvement compared to their current practices.

– In addition following requirements were formulated:

» TEMPORAL AND SPATIAL RESOLUTION: Monthly temporal frequency of EO data, with a spatial resolution of 100 m. Exceptions are TSM, Turbidity and Chl-a maps which are required daily.


25.09.2014

» ACCURACY: Associated information about the quality of pixel values; robust algorithms with reference to literature or algorithm theoretical basis document (ATBD).

» CONSISTENCY: Consistency between products derived from different sensors; a robust atmospheric correction with reference to literature or ATBD.

» TAXONOMY: A standardized taxonomy (e.g. parameters names, measurement units, legend, color code) is received as a prerequisite for a harmonized EU-wide inland water quality monitoring.

» ACCESSIBILITY: Easily accessible data and downloadable preferably by Web Map Service (WMS); training is requested.

Kick-off end-user requirements


25.09.2014

WP4: Data gathering (Leader: VITO)


25.09.2014

INFORM Development campaign -Balaton 2014

• Lake Balaton and Kis Balaton wetland (Hungary)

• Data acquisition window: 7-28 July 2014

• In-situ measurements (optical properties and water constituents) concurrent with satellite (Landsat8-OLI and HICO) and airborne hyperspectral (APEX) acquisitions


25.09.2014

Balaton

Kis Balaton

Lake Balaton

Marcali Reservoir

Balaton Limnological

Institute

Balaton


25.09.2014

Surface area 592 km2

Catchment area 5772 km2

Length 78 km

Mean (max) width 9.1 (15) km

Mean (max) depth

3.2 (11) m

Water volume 1861 million m3

Retention time 3-8 years

Shoreline length 235 km

• Largest lake in central Europe

• Very shallow and well-mixed

• High mineral sediment loads (dolomitic mineralogy)

• Four distinct basins varying from mesotrophic to eutrophic

• Kis Balaton is hypertrophic

• Historically high nutrient loads but recent improvements in water quality

Balaton - Characteristics


25.09.2014

Quicklooks Landsat8-OLI acquisition


Data available from the U.S. Geological Survey


25.09.2014


25.09.2014

Quicklooks HICO acquisition


Collaboration with Evangelos Spyrakos, U

STIRLING

Data available from NRL – The U.S. Naval Research Laboratory OSU – Oregon State

University


25.09.2014

Lake Balaton Hungary 2014-07-18 Lake Balaton Hungary 2014-07-02


25.09.2014

APEX acquisitions and in-situ measurements

» 2014-07-19

» 2014-07-25



25.09.2014


Date EO Data acquisition In-situ sampling stations

Comments APEX Landsat-8 HICO USTIR CNR VITO ALL

09/07/2014 X X 3 3

14/07/2014 3 3

15/07/2014 4 4 4 12

Instrument inter-comparison + reference ground targets

16/07/2014 X 4 5 (+3 KB) 4 16 + 6 Kis Balaton macrophyte measurements

17/07/2014 X 4 2 2 8 APEX flights aborted

18/07/2014 X 4 4 (+2 KB) 10

+ 3 Kis Balaton macrophyte measurements + reference ground targets

19/07/2014 X 11 11

Water samples taken at 7 stations. Underway transects with radiometers

21/07/2014 X 5 5

24/07/2014 5 5

25/07/2014 X X 6 6

TOTAL STATIONS SAMPLED 46 20 10 76


25.09.2014

In-situ optics

• U STIRLING » Wetlabs AC-S: (size fractioned) spectral absorption and attenuation

» Wetlabs Eco-BB3: spectral backscattering

» CTD: temperature, salinity, depth

» Trio Satlantic HYPEROCRs: subsurface irradiance reflectance

» Trio Satlanctic HyperSAS and trio TriOS RAMSES: downwelling irradiance, skylight irradiance, total surface radiance for water-leaving reflectance

» (in lab) TriOS OSCAR PSICAM: spectral absorption


25.09.2014

In-situ optics

• CNR » Wetlabs AC-9 and Hobi Labs Hydroscat-6: spectral absorption and

attenuation

» Cyclops-6 fluorometers: phytoplankton pigments (Chla, PC, PE), CDOM fluorescence (+temperature and depth)

» ASD FieldSpec FR and WISP-3: subsurface irradiance reflectance and remote sensing reflectance

» ASD FieldSpec FR, Spectrascan, WISP-3: macrophytes reflectance

• VITO » ASD FieldSpec FR: remote sensing reflectance

» WetLabs ECO-BB3: spectral backscattering


25.09.2014

Water sample analysis

• Chla, PC, HPLC (pigments), particulate absorption (PABS) + flow cytometry

• TSM, CDOM, POC, DOC, phytoplankton cell counts

• Primary production

• Particle size distribution, mycosporine-like amino acids

• Macrophytes: dry weight biomass, pigment and nutrient analysis


25.09.2014

Left: CIMEL CE318 - atmospheric measurements

Middle: ASD FieldSpec FR - water reflectance measurements

Right: Wetlabs AC-S, Wetlabs BB3, Wetlabs AC-9, Hobi Labs Hydroscat, Cyclops-6 fluorometers – intercomparison of IOP measurements


25.09.2014

Left: WetLabs AC-S and ECO-BB3 – absorption and backscatter

Middle: HYPERSAS and RAMSES – downwelling irradiance, skylight radiance,

total surface radiance

Right: In-situ campaign leader Peter Hunter with Evangelos Spyrakos (U

STIRLING)


25.09.2014

Left: Filtering on the USTIR boat for pigments and particulate absorption. Middle: Filtering in the BLI lab for total suspended matter

Right: Preparing samples in the BLI lab for dissolved organic carbon analysis


25.09.2014

WP5: Algorithms development and validation

(Leader: U STIRLING)


25.09.2014

Leader: RBINS

• Rationale

– Major source of uncertainity for EO products

– AC is very challenging for inland waters due e.g. to altitude, land adjacency and complex aerosols

• Objective

– To develop an atmospheric correction algorithm for Sentinel-2, Sentinel-3 and EnMAP/PRISMA for inland waters taking TOA radiance data and various auxiliary data as input and providing BOA water reflectance data as output

WP 5.1: Atmospheric correction


25.09.2014

SIMEC adjacency correction

Sterckx et al., RSE, in press


25.09.2014

Leader: RBINS

• Rationale

– Key input for primary production and other ecological models

– Existing algorithms not suited to inland waters and need adaption to hyperspectral sensors

• Objective

– To develop/adapt algorithms for Sentinel-2, EnMAP/PRISMA and APEX taking water reflectance data as input and providing outputs for spectral and PAR diffuse attenuation coefficients (Kd, KdPAR) and euphotic depth (Ze)

WP 5.2: Light attenuation & euphotic depth


25.09.2014


25.09.2014

Leader: VITO

• Rationale

– Key measure of water quality; cal/val of sediment transport and other ecosystem models

– Exploit new sensors, especially SWIR bands for high TSM

• Objective

– To develop/adapt algorithms for Sentinel-2, EnMAP/PRISMA and APEX taking water reflectance data as input and providing outputs for total suspended matter (TSM) concentration and turbidity (TUR).

WP 5.3: TSM & Turbidity


25.09.2014

Varying Total Suspended matter concentration (mg/m3)

0.00

0.01

0.02

0.03

0.04

0.05

0.06

400 500 600 700 800 900 1000 1100 1200

Wavelength (nm)

Re

mo

te s

en

sin

g r

efl

ec

tan

ce

1

10

100

1000

[Credit: RBINS/VITO]


25.09.2014

[Credit: RBINS]


25.09.2014

Leader: PML

• Rationale

– YM = sum of absorption by CDOM and non-algal particles

– CDOM linked to DOC

– Major influence on short wavelength light availability

– Expoit new hyperspectral data products, including UV region

• Objective

– Develop and validate a UV-visible algorithm for yellow matter absorption that decomposes total absorption into pure water, pigments, and yellow substances

WP 5.4: Yellow matter


25.09.2014

Figure PML? + explanation

Dutch lakes data set 2003-2005

Yellow matter absorption

dominant but rarely isolated

in UV-A region -> requires

decomposition approach


25.09.2014

Leader: CNR

• Rationale

– Relative abundance of PFTs (or size classes) important to ecosystem function

– Some toxic bloom-forming species cyanobacteria pose risks to animal and human health – also driver for WFD

• Objective

– To develop/adapt and validate algorithms for Sentinel-2, EnMAP/PRISMA and APEX taking water reflectance data and IOPs as input and providing outputs of Chla, secondary pigments, and size classes.

WP 5.5: Phytoplankton functional types


25.09.2014

Chl-a PE PC [Credit: CNR]


25.09.2014

PC in Esthwaite Water (UK) mapped

using airborne AISA hyperspectral data

[Hunter et al. 2010]


25.09.2014

Leader: EOMAP

• Rationale

– Lakes often have pronounced vertical gradients in dissolved and particulate material due to stratification

– Currently, methods provide no information on depth distribution

• Objective

– Feasibility study to derive information about vertical gradients of TSM using various satellite sensors.

WP 5.6: Stratification


25.09.2014

Shallow view 250m

Deep view 500m

MODIS 08.08.2012 MODIS 250m channels 640-850 nm

MODIS 500m channels 455-850 nm

MODIS 500 m channels “look”

deeper than MODIS 250 m due to

the incorporation of shorter

wavelengths

Applicable also to other sensors with

different band combinations

[Credit: EOMAP]


25.09.2014

Leader: CNR

• Rationale

– Macrophytes fulfill important functional roles in lake ecosystems

– Biological quality element under EU WFD

– High spatial variability and coexistence of different species require high spatial resolution imagery

• Objective

– To develop/adapt classification approach for mapping different groups of macrophyte (emerged and submerged) and evaluate the biomass and health status by applications dedicated indices to aquatic vegetation based on specific endmembers collected in the field and wavelet analysis.

WP 5.7: Macrophytes


25.09.2014

[Credit: CNR-IREA]

Mantua lake system, Water Adjusted Vegetation Index (WAVI) map

derived from APEX data for September 2011 (left). Spectral response of

different aquatic vegetation types and groups derived from APEX (right).

(Villa et al., 2014).


25.09.2014

[Credit: CNR-IREA]

MULTITEMPORAL ASSESSMENT OF

MACROPHYTES USING AQUATIC

VEGETATION INDICES

Multispectral peak of season Multitemporal WAVI series


25.09.2014

Leader: USTIRLING

• Rationale

– C-fixation by phytoplankton is a key contributor to lake ecosystem energetics

– Tightly coupled to meteorology, climate and the catchment

– Model developed for ocean waters, but not tested in lakes

• Objective

– To develop a prototype model for the estimation of phytoplankton primary production in lakes from EO data.

WP 5.8: Primary production


25.09.2014

Tilstone et al. (2009) Deep Sea Res. 56: 918-930

Empirical

VGPM

Wavelength resolved


25.09.2014

Leader: U STIRLING

• Rationale

– Estimation of chlorophyll is problematic in lakes at low concentrations, especially in presence of high CDOM

– Fluorescence signal at 681nm might provide more accurate chlorophyll estimates

– Variability in relationship with chlorophyll related to physiology (photocompensation)

• Objective

– To undertake an evaluation of algorithms for the retrieval of chlorophyll fluorescence and concentration in lakes and explore relations with phytoplankton physiology

WP 5.9: Chlorophyll fluorescence


25.09.2014

[Gons et al 2008]

Chlorophyll-a in the Great

Lakes derived from SICF

peak at 685 nm.


25.09.2014

WP6: EO-model integration (Leader: Deltares)


25.09.2014

Delft3D: tool for effect chain analyses

Physical parameters

Transports (SPM, ..)

Water quality

Ecology

Fish, Birds

other user functions,

etc.


25.09.2014

• Delft3D-FLOW

– Hydrodynamics

• Delft3D-SED

– Suspended particulate matter (SPM)

• Delft3D-WAQ

– Origin of water and residence time

• Delft3D-ECO (BLOOM)

– Nutrients and primary production model

General modelling approach


25.09.2014

BLOOM • BLOOM is a multi-species phytoplankton model

• Competition between phytoplankton types is the guiding principle in BLOOM

• BLOOM selects the optimum composition based on the ratio of the net growth rate and the requirements for each environmental resource

• Trade-off principle between growth and requirement:

– Relatively high potential growth rates may compensate a relatively large requirement hence opportunistic species win when light is high, efficient species win when there is little light


25.09.2014

EO-model integration


25.09.2014

More information and news

http://www.copernicus-inform.eu

http://www.copernicus-inform.eu/















25.09.2014

Thank you For more information: http://www.copernicus-inform.eu Contact: [email protected] [email protected]







25.09.2014

INFORM KO+SAB01 meeting, 23-24 January 2014, VITO, Mol, Belgium

INFORM EUAB01 meeting, 20-21 March 2014, CNR, Venice, Italy

Documents

INFORM project overview and statusinform.vgt.vito.be/...GEOWEBINAR25092014_final.pdf · – lack of adequate analysis methods – lack of adequate low-cost EO data – lack of uncertainty