Monitoring Baltic Sea eutrophication from space- from research to applications
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1 Monitoring Baltic Sea eutrophication from space- from research to applications Susanne Kratzer, Associate Professor Coordinator of NordAquaRemS Department of Systems Ecology, SU
2 Marine remote sensing and management Covers large areas Cost-effective Synoptic view Visual format easy to understand New window into marine ecosystems Cyanobacteria Bloom in the Baltic Sea, MERIS FR image Date _Time094518_Orbits17611, ESA
3 Elements for Ecological Status in the EC Water Framework Directive (WFD): Biological elements: Phytoplankton, aquatic flora, benthic invertebrate fauna, fish fauna Hydro-morphological elements (supporting the biological elements): Morphological conditions, Hydrological and Tidal regime Chemical and physicochemical elements (supporting the biological elements): General: dissolved oxygen, nutrients, transparency, temperature; Specific: synthetic and non-synthetic pollutants Chl-a and Secchi depth can be used as indicators for eutrophication
4 Baltic Sea remote sensing and management Light quality & quantity Uppwelling SST Eutrophication Secchi depth Productivity Large scale dynamics Algal blooms Currents Sediment transport Humic Substances
5 SST in the NW Baltic Sea Derived from AVHRR Himmerfjärden BY31 BY31 BY July 27 July - 5 August 6-15 August BY31 BY31 C August 26 August - 4 Sept Sea Surface Temperature (SST) derived from NOAA/AVHRR data during 7 July-4 September 2001, binned images (10 day composites)
6 Attenuation of light in the sea 90% of TOA signal 10 % of signal from the sea
7 Attenuation of light Case-2 Case-1 water Classification: Morel and Prieur, 1977
8 Light penetration in the open sea and in coastal waters Oceanic Optical Case-1 waters Coastal Optical Case-2 waters Kurt Holacher, 2002
9 Jerlov s water mass classification Clear tropical Baltic Sea Jerlov's optical classification into the oceanic water types I-III and the coastal water types 1-9 (Jerlov, 1976).
10 Baltic Sea remote sensing and management Satellite remote sensing Bio-optical modelling Seatruthing Atmospheric corrections Large scale synoptic information Small and meso scale information Coastal Zone monitoring: Ecological and physiological variables Physics and chemistry Optical biogeochemical variables: CDOM, Chlorophyll, SPM Platforms: Research and monitoring vessels, moorings, ships-of-opportunity Decision making Environmental policies and management Socio-economics (e.g. tourism and fisheries) Public health (e.g. toxic blooms) from Kratzer et al, 2003
11 Main area of investigation: Landsat image of Himmerfjärden 5 May 1986 STP Himmerfjärden and Open Baltic Sea Askö Södertälje H5 H4 STP outlet Trosa H3 H2 B1 BI Landsort Deep BII BIII BY31
12 Secchi Depth map of the Baltic Sea derived from SeaWiFS (end of July/beginning of August 1999) SeaWiFS Kd(490) image In-water algorithm: SD = (0.55 * K d (490) 0.04) -1 Kratzer et al., 2003 m
13 Cyanobacteria Bloom in the Baltic Sea, MERIS FR image Date _Time094518_Orbits17611, ESA
14 Sewage Treatment Plant RR H5 H4 H5 H3 H4 H3 FR H2 B1 FR H2 FR (300 m resolution) B1 RR RR (1.2 km resolution)
15 Sea-truthing onboard Limanda 08/04/
16 Kratzer et al, 2008
17 MERIS Kd490 image (300 m resolution) algorithm base on MERIS channels 3 and 6
18 Testing K d 490 algorithm derived from MERIS against sea-truthing data K d 490 (MERIS FR 19/08/2002, ch3_ch6_taccs) modelled Kd490, m Measured K d (490) MERIS FR transect Distance to sewage outlet, km
19 Attenuation model of the coastal zone inorganic SPM CDOM Chl-a Kd(490) - Kw(490), m Himmerfjärden Open Baltic Sea Horizontal distance from sewage outlet, km Contribution of each optical component to K d (490) modelled from empirical data; polynomial decline of optical components from source (land) to sink (BY31) from Kratzer and Tett, 2009.
20 Seasonality of optical constituents- BY31 spring Pigments, µg l Chlorophyll a Total carotenoids /03/ /03/ /04/ /04/ /04/ /04/1999 Spring CDOM SPM G440, m SPM, g m /03/ /03/ /04/ /04/ /04/ /04/ /03/ /03/ /04/ /04/ /04/ /04/1999 Spring 1999 Spring 1999 Kratzer, unpublished
21 Inverted Secchi depth derived from MERIS data 19 August 2002 using a new algorithm (Kratzer et al., 2008) NASA s AERONET-OC station Gustaf Dalén (JRC & Strömbeck Consulting). Inverse Secchi depth (m -1 ) map as derived from the MERIS scene from 22 August 2002, using a Secchi depth algorithm derived from sea-truthing data. Station 7a-7d in Himmerfjärden are the same as H5-H2 in the national monitoring program.
22 Aeronet-OC Gustav Dalén algal bloom Chlorophyll (ug/l) B1 BY31 Model 0 1-Jun Jul Sep-05 Experimental monitoring (reflectance measurements on Gustav Dalén) Regular monitoring data (point sampling, monthly means) Courtesy Niklas Strömbeck
23 Match-ups NW Baltic Sea July 2008 Sampling stations and transects during Askö field campaign 2008 (see PINS on each RR scene). The conditions were very good for sea-truthing. The MERIS RR RGB composites show how patchy the waters become during good conditions in summer.
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25 MERIS FR image 31 July 2008 STN CI- CII-CIII (30 km long transect) Gustav Dalén light house close to CII
26 Testing ICOL processor MERIS RR data, transect on 31 July 2008 near Gustaf Dahlén light house both ICOL and non-icol processed and compared to the sea-truthing data. Processing: Christian Vinterhav; MEGS processing by Gerald Moore
27 Improvement of MERIS processors Chlorophyll a - % error IPF-open sea 2008-FUB-coastal 2008-FUB-open sea 2009-MEGS-open sea 2009-FUB-open sea 2009-MEGS-coastal 2009-MEGS-open sea 2009-FUB ICOL-coastal 2009-FUB ICOL-open sea 2009-BPAC ICOL- open sea
28 Optical variables as indicators of ecosystem state Humic substances (CDOM): terrestrial inputs of freshwater, suspended particulate inorganic matter (SPIM): land drainage and wind-stirring in shallow waters, and phytoplankton pigments: productive status of the pelagic ecosystem, influenced by anthropogenic nutrients from land (Kratzer and Tett, 2009)
29 From research to applications -Development and quality assurance of an operational monitoring system
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32 User consultation meetings Vattenfall Power Consultant s user group EU FP6 SPICOSA stakeholder group Himmerfjärden nitrogen study reference group Direct contact to user, feed-back on what can be improved; need to develop user friendly products Problem: many users are not familiar with the method- many perceive the topic as too technical
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38 Temporal resolution in Hf area Measurements between April and September MERIS passes by every 2-3 day Monitoring Number of measurments Remote.sensing 0 April May June July August September Regular cruises by monitoring vessels: 17 Remote sensing by MERIS: 20 Total set of chlorophyll a measurements: 37
39 How representative is the satellite-derived chlorophyll-a product? In the inner fjord chlorophyll a concentrations are overestimated by 91 %. In open Sea and the outer basin of Himmerfjärden the Chlorophyll a concentrations is overestimated by 25 %. (Kratzer and Vinterhav, 2010) Correct for this in the chl-a estimates
40 12 Monthly average station B1 Monitoring Remote sensing Remote sensing corrected 10 Chl a conc µg m April May June July September Paired t-test Monitoring Df p-value Remote sensing at B1 n.s Remote sensing at B1 corrected n.s Therese Arredal Harvey, PhD student
41 Summary MERIS provides us with a new tool to assess coastal systems from space Indicators for eutrophication, e.g. chl-a and Secchi depth, can be derived from space reliably (if data is validated) Chl-a concentrations from remote sensing not significantly different from conventional monitoring data Remote sensing data provides improve spatial and temporal resolution Together with Vattenfall Power Consultant we have developed a user-friendly operational system Continuation of operational system by joining GMES MarCoast downstream services for Aeronet-OC stations both for validation of satellite data and for providing local continuous time series Continuation of MERIS mission through Sentinel-3 (ocean colour sensor OLCI and SST / (A)ATSR) through 2023
42 Possible future application using MERIS data to test biogeochemical model Model input: Kd490 as proxy for light forcing External Himmerfjärden SSA conceptual model basin level Sunlight inputs System boundary Advection DIP DIN Uptake Annual light cycle Eukaryotic and pico-phytoplankton Mineralization Annual grazing cycle Advection Temp Temperature control N 2 -fixing cyanobacteria mortality Advection (Denitrification) Mineralization Sedimentation Vertical mixing DIN Mineralization Eukaryotic and DIP pico-phytoplankton Annual nutrient release cycle Sedimentation (Permanent burial) Surface water Bottom water Advection Sediment SPICOSA Conceptual model 2 Courtesy: Jakob Walve Model output: chl-a as proxy for phytoplankton biomass
43 MVT intercalibration work-shop July 2008, Askö
44 COASTCOLOUR Project from ESA Stockholm University and SYKE are champion users of the COASTCOLOUR project Round Robin of different algorithms for Baltic Sea remote sensing products (Sept Feb 2011) Validation of coast colour algorithm in the Baltic Sea
45 COASTCOLOUR sites
46 Thanks for listening! SST bio-sensor, Kratzer Ltd. at Landsort Deep (BY31).
47 Acknowledgements Swedish National Space Board (SNSB): Algorithm development and validation of MERIS data over optically complex waters, Focus: Fundamental research. ESA/ESRIN: Technical Assistance for the validation of MERIS products in lake Vänern and coastal waters of the north-western Baltic Sea (Sweden), mid mid Focus: Validation of MERIS data and intercalibration of radiometers. Swedish Environmental Protection Agency (SEPA) and SYVAB, Focus: Applications. NordForsk: NORDic network for AQUAtic REMote Sensing (NordAquaRemS), Sept 2008-Sept 2010; coordinator: S.Kratzer. Focus: Networking and PhD training. Participation in SPICOSA, and EU FP6 project on integrated coastal zone management (PI: Ragnar Elmgren): Focus: Academic training & developing remote sensing as diagnostic tool for integrated coastal zone management.
48 Close collaborations Petra Philipsson, Vattenfall Power Consultant AB, Niklas Strömbeck, Strömbeck Consultant, Aeronet-OC Stations (in collaboration with Giuseppe Zebordi, JRC, Ispra, Italy and NASA and ESA) Anu Reinart, Tartu Observatory, Estonia Gerald Moore, Bio-Optika, UK Carsten Brockmann, Brockmann Consult, Germany Roland Doerffer, GKSS, Germany Paul Tett, Napier University, UK
49 NORDic network for AQUAtic REMote Sensing (NordAquaRemS) Coordinator: Associated Professor Susanne Kratzer, Department of Systems Ecology, Stockholm University Organizing committee: Prof. Matti Leppäranta, Helsinki University, Finland Dr. Anu Reinart, Tartu Observatory, Estonia Dr. Piotr Kowalczuk, IOPAS, Poland Dr. Are Folkstad, NIVA, Norway Main Communication: & Skype conferences
50 Aims and Objectives- NordAquaRemS (1) Advanced education for PhD students and young researchers, increased mobility and international experience (2) Organize PhD and research training courses and seminars /workshops for students & researchers. (3) Share expertise and define the most relevant common problems when applying remote sensing methods in Nordic conditions. (4) Stimulate the application of new algorithms for the atmospheric correction for satellite images of Nordic water bodies (Baltic Sea and Nordic lakes)
51 Aims and Objectives- NordAquaRemS (5) Share experience, data and instruments according to agreed conditions and rules. (6) Improve exchange between scientists, users and service providers, including small/medium enterprises. (7) Span the research and education across the whole seasonal cycle in Nordic waters, including the winter season. (8) Create a Virtual Nordic Institute in Aquatic Remote Sensing.
Bio-optical monitoring of coastal Baltic Sea waters from research to applications
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