D4.5 KUinform.vgt.vito.be/files/documents/INFORM_D4.5_v1.0...D4.5 Improved Monitoring and...

TESTING CAMPAIGN REPORT

D4.5

Reference: INFORM_D4.5_v1.0_withoutANNEX Author(s): Ils REUSEN (VITO), Wesley BOENNE (VITO), Bart BOMANS (VITO), Liesbeth De

KEUKELAERE (VITO), Els KNAEPS (VITO), Sindy STERCKX (VITO), Kristin VREYS (VITO), Peter HUNTER (U STIRLING), Viktor TOTH (MTA OK), Mariano BRESCIANI (CNR), Claudia GIARDINO (CNR), Federica BRAGA (CNR), Monica PINARDI (CNR), Diana VAICIUTE (Klaipedos Universitetas)

Version: 1.0 Date: 31/12/2016

D4.5 Improved Monitoring and Forecasting of Ecological Status of European

Inland Waters by Combining Future Earth Observation Data and Models

Grant Agreement no: 606865

INFORM_D4.5_v1.0 D4.5 of 65

DOCUMENT CONTROL

SIGNATURES Author(s) : Ils REUSEN (VITO), Wesley BOENNE (VITO), Bart BOMANS (VITO), Liesbeth

De KEUKELAERE (VITO), Els KNAEPS (VITO), Sindy STERCKX (VITO), Kristin VREYS (VITO), Peter HUNTER (U STIRLING), Viktor TOTH (MTA OK), Mariano BRESCIANI (CNR), Claudia GIARDINO (CNR), Federica BRAGA (CNR), Monica PINARDI (CNR), Diana VAICIUTE (Klaipedos Universitetas)

Reviewer(s) : Ils REUSEN (VITO) Approver(s) : Ils REUSEN (VITO)

CHANGE RECORD

Release Date Pages Description Editor(s)/Reviewer(s)

1.0 31/12/2016 65 For submission to REA Ils REUSEN





TABLE OF CONTENT

LIST OF FIGURES.............................................................................................................. 5

LIST OF TABLES ............................................................................................................... 7

ACRONYMS & GLOSSARY ................................................................................................ 8

EXECUTIVE SUMMARY .................................................................................................... 9 1. Curonian Lagoon ................................................................................................................................. 10 1.1. Data acquisition .................................................................................................................................. 11

1.1.1. Satellite image acquisition ........................................................................................................... 11 1.1.2. APEX image acquisition ................................................................................................................ 11 1.1.3. APEX data set ............................................................................................................................... 17 1.1.4. APEX processing details ................................................................................................................ 22

1.1.4.1. Radiometric, spectral and geometric calibration ..................................................................... 22 1.1.4.2. Spectral smile ........................................................................................................................... 22 1.1.4.3. Geometric correction ............................................................................................................... 22 1.1.4.4. Atmospheric correction ........................................................................................................... 22 1.1.4.5. Wires ........................................................................................................................................ 23 1.1.4.6. Post-processing ........................................................................................................................ 23

1.1.5. In situ measurements ................................................................................................................... 24 1.1.5.1. Overview .................................................................................................................................. 24 1.1.5.2. In situ optics ............................................................................................................................. 24 1.1.5.3. Water sample analysis ............................................................................................................. 24 1.1.5.4. Monitoring station data ........................................................................................................... 25

2. Lake Balaton and Kis-Balaton .............................................................................................................. 27 2.1. Data acquisition .................................................................................................................................. 28

2.1.1. Satellite image acquisition ........................................................................................................... 28 2.1.2. In situ measurements ................................................................................................................... 28

2.1.2.1. Overview .................................................................................................................................. 28 2.1.2.2. In situ optics ............................................................................................................................. 30 2.1.2.3. Water and macrophytes sample analysis ................................................................................ 31 2.1.2.4. Monitoring station data ........................................................................................................... 31

3. Lake Marken ....................................................................................................................................... 32 3.1. Data acquisition .................................................................................................................................. 32


3.1.2.1. Overview .................................................................................................................................. 32 3.1.2.2. In situ optics ............................................................................................................................. 33 3.1.2.3. Water sample analysis ............................................................................................................. 34 3.1.2.4. Monitoring station data ........................................................................................................... 35

4. Venice Lagoon ..................................................................................................................................... 36 4.1. Data acquisition .................................................................................................................................. 37


4.1.2.1. Overview .................................................................................................................................. 38 4.1.2.2. Monitoring station data ........................................................................................................... 38

5. Lakes Mantua ...................................................................................................................................... 40





5.1. Data acquisition .................................................................................................................................. 41 5.1.1. Satellite image acquisition ........................................................................................................... 41 5.1.2. In situ measurements ................................................................................................................... 42

5.1.2.1. Overview .................................................................................................................................. 42 5.1.2.2. In situ optics ............................................................................................................................. 45 5.1.2.3. Water and macrophytes sample analysis ................................................................................ 45

6. Lake Garda .......................................................................................................................................... 47 6.1. Data acquisition .................................................................................................................................. 48


6.1.2.1. Overview .................................................................................................................................. 48 6.1.2.2. In situ optics ............................................................................................................................. 49 6.1.2.3. Water sample analysis ............................................................................................................. 50 6.1.2.4. Monitoring station data ........................................................................................................... 50 6.1.2.5. Aeronet station ........................................................................................................................ 50

7. Po River delta ...................................................................................................................................... 51 7.1. Data acquisition .................................................................................................................................. 52


7.1.2.1. Overview .................................................................................................................................. 53 7.1.2.2. In situ optics ............................................................................................................................. 54 7.1.2.3. Water sample analysis ............................................................................................................. 54

8. UK Lakes .............................................................................................................................................. 56 8.1. Data acquisition .................................................................................................................................. 57


8.1.2.1. Overview .................................................................................................................................. 57 8.1.2.2. In situ optics ............................................................................................................................. 57 8.1.2.3. Water sample analysis ............................................................................................................. 57

9. Danube Delta & Black Sea ................................................................................................................... 59 9.1. Data acquisition .................................................................................................................................. 59


9.1.2.1. Overview .................................................................................................................................. 59 9.1.2.2. In situ optics ............................................................................................................................ 61 9.1.2.3. Water sample analysis ............................................................................................................. 61

10. Conclusion ........................................................................................................................................... 63 11. References .......................................................................................................................................... 64





LIST OF FIGURES

Figure 1. The Curonian Lagoon. ........................................................................................................ 10 Figure 2. Curonian Lagoon flight planning – Northern (N) region. .................................................... 12 Figure 3. Curonian Lagoon flight planning – Middle (M) region. ....................................................... 13 Figure 4. Curonian Lagoon flight planning – Southern (S) region. ..................................................... 13 Figure 5: Curonian Lagoon flight 01/09 AM (M0174) ........................................................................ 14 Figure 6. Curonian Lagoon flight 01/09 PM (M0175). ....................................................................... 14 Figure 7.Curonian Lagoon flight 02/09 (M0176). ............................................................................... 15 Figure 8. Mosaic of APEX flight lines acquired on 1 September 2016 AM......................................... 19 Figure 9. Mosaic of APEX flight lines acquired on 1 September 2016 PM. ........................................ 20 Figure 10. Mosaic of APEX flight lines acquired on 2 September 2016. ............................................ 21 Figure 11. Lake Balaton in Hungary and the approximate locations of the routine sampling stations used by BLI and the Central Transdanubian Inspectorate for Environmental and Natural Protection (KDT KTF) (adapted from Palmer et al., 2014). .................................................................................. 27 Figure 12. Map of the sampling sites at the Kis-Balaton wetland on 21st July and 22nd July (NL= Nuphar lutea; NA= Nymphaea alba, TN= Trapa natans). ................................................................... 29 Figure 13. Map of the sampling site at the Balaton Lake on 21st July (from W1 to W6 in yellow) and 22nd July (from St1 to St4 in green). ................................................................................................... 29 Figure 14. The 10 locations covered by the 16 stations sampled on Lake Marken by USTIR between 14th and 16th September 2016............................................................................................................ 33 Figure 15. The Lagoon of Venice. Major morphological types (channels, salt marshes and fish farming areas) and modifications (reclaimed area, Malamocco-Marghera channel and MoSE structures at the inlets) are illustrated. 8% of total area is constituted by land above sea level (littorals, reclaimed areas, islands) and 92% by the water system: channels (12%), shallows, mud flats and salt marshes (80%). ............................................................................................................. 37 Figure 16. Maps with automatic monitoring stations in the Lagoon of Venice. ................................ 38 Figure 17. The Mantua Lakes system in the Mincio River watershed, a sub-basin of the Po River (Northern Italy). ................................................................................................................................. 41 Figure 18. Maps with sampling stations in six field campaigns in Mantua Lakes in 2015. In the top and bottom pictures water and macrophyte stations are reported, respectively. .......................... 43 Figure 19. Maps with sampling stations in four field campaigns in Mantua Lakes in 2016. In yellow, green and pink, floating-leaved macrophytes (NL=Nuphar lutea, TN= Trapa natans, NN= Nuphar lutea), helophytes (PA= Phragmites australis), and water sampling sites, respectively. ................... 44 Figure 20. Map of the Garda Lake. ..................................................................................................... 47 Figure 21. Map of the sampling sites at the Garda Lake on 22nd May (from G1 to G3 in yellow), 1st July (from 1 to 5 in green), and 17th August 2016 (from W1 to W4 in light blue). ............................ 49 Figure 22. Po River basin (Viet, 2015; data from: Andrea Coppola, 2008). ....................................... 52 Figure 23. Study area with location of fieldwork activities carried out for validation and monitoring fixed stations (purple and black stars). The five major distributaries of the Po River are indicated. The dotted magenta line denotes the overlapping portion of the two Landsat 8 swaths, available for this site. ........................................................................................................................................ 53 Figure 24. Loch Lomond and Loch Leven in central Scotland. ........................................................... 56





Figure 25. The stations sampled visited by the R/V Mare Nigrum in the Black Sea between the 5th to the 12th May 2016. ............................................................................................................................. 60 Figure 26. The stations sampled in the Danube Delta between the 23rd May to the 3rd June 2016. 60





LIST OF TABLES

Table 1. Flight line acquisition times for mission M0174 on 01/09/2016 AM. .................................. 16 Table 2. Flight line acquisition times for mission M0175 on 01/09/2016 PM. .................................. 16 Table 3. Flight line acquisition times for mission M0176 on 02/09/2016. ........................................ 16 Table 4. Reflectance cubes file naming. ............................................................................................. 18 Table 5. Summary of the in situ data collected during the INFORM testing campaigns in the Curonian Lagoon. ............................................................................................................................... 24 Table 6. Spectral and spatial characteristics of the acquired satellite data and acquisition date. .... 28 Table 7. Summary of the in situ and lab data measured during the field campaign at Balaton Lake and Kis-Balaton in July 2016............................................................................................................... 30 Table 8. Main features of the Venice Lagoon. ................................................................................... 36 Table 9. Satellite data acquired in the Lagoon of Venice. .................................................................. 37 Table 10. Main features of the Mantua lakes system. ....................................................................... 40 Table 11. Spectral and spatial characteristics of the acquired satellite data and acquisition date. .. 42 Table 12. Summary of the in situ and lab data measured during the campaigns at Mantua Lakes in 2015. The description of measurements protocol and methods can be found in Giardino et al., 2007; Bresciani et al., 2009; Bresciani et al., 2013 ; Giardino et al., 2014. ........................................ 44 Table 13. Summary of the in situ and lab data measured during the campaigns at Mantua Lakes in 2016.................................................................................................................................................... 45 Table 14. Spectral and spatial characteristics of the acquired satellite data and acquisition date. .. 48 Table 15. Summary of the in situ data collected during the campaign at Lake Garda in 2016. The description of measurements protocol and methods can be found in Giardino et al., 2007; Giardino et al., 2014. ......................................................................................................................................... 49 Table 16. Main features of the Po River. ........................................................................................... 51 Table 17. Data acquired in the Po River. ............................................................................................ 52 Table 18. Summary of the in situ and lab data measured during the fieldwork activities in the Po River. The description of measurements protocol and methods can be found in Braga et al., 2013. ............................................................................................................................................................ 53





ACRONYMS & GLOSSARY

AGL: Above Ground Level AOP: Apparent Optical Properties APEX: Airborne Prism EXperiment BLI: Balaton Limnological Institute CDOM: Colored Dissolved Organic Matter Chla: Chlorophyll-a CORPI KU: Klaipeda University Coastal Research and Planning Institute CNR: Italian National Research Council CTD: Conductivity-Temperature-Depth DLR: German Aerospace Center DOC: Dissolved Organic Carbon Eawag: Aquatic Research Institute, Switzerland EO: Earth Observation EPFL: École Polytechnique Fédérale de Lausanne FR: Full Range HICO: Hyperspectral Imager for the Coastal Ocean HPLC: High Performance Liquid Chromatography IOP: Inherent Optical Properties KDT KTF: Central Transdanubian Inspectorate for Environmental and Natural Protection KDT VIZIG: Central-Transdanubian Water Directorate

MAAs: mycosporine-like amino acids

MTA OK: Magyar Tudomanyos Akademia Okologiai Kutatokozpont NASA: National Aeronautics and Space Administration NERC: UK Natural Environment Research Council NERC ARSF: UK NERC Airborne Research and Survey Facility NTU: Nephelometric Turbidity Units OLI: Operational Land Imager PAB: particulate absorption PC: Phycocyanin POC: Particulate Organic Carbon SPIM: Suspended Particulate Inorganic Matter SPOM: Suspended Particulate Organic Matter TIRS: Thermal Infrared Imager TOC: Total Organic Carbon TSM: Total Suspended Matter USTIR: University of Stirling VITO: Flemish Institute for Technological Research





EXECUTIVE SUMMARY

This report details the in situ IOP, AOP, biogeochemical data with concurrent airborne hyperspectral images (at the Curonian lagoon) and satellite (Sentinel-2 MSI, Sentinel-3 OLCI, Landsat-8 OLI and SPOT 5) images that were acquired in 2015 and 2016 during the campaigns organized at Lake Marken in the Netherlands, Lake Balaton and Kis-Balaton in Hungary, Lakes Mantua, Lake Garda, Po River delta and Venice Lagoon in Italy, Loch Leven and Lomond in the UK, Danube Delta and the Black Sea in Romania and the Curonian Lagoon in Lithuania. The data sets acquired in 2015 and 2016 are available for the INFORM project partners for algorithm development and validation within the INFORM project (WP5), for EO-model integration work in (WP6), for demonstration purposes (WP7) and for end-user interaction (WP3).





1. Curonian Lagoon The Curonian Lagoon is a large, shallow water body (total area 1584 km2, mean depth 3.8 m) located along the southeastern coast of the Baltic Sea (Figure 1). Geographically the lagoon is positioned between Lithuania and the Russian Federation. The mixing of fresh riverine and brackish Baltic Sea water masses creates spatially and temporally unstable gradients with salinity ranging from 0 to 7 PSU. The Nemunas River runoff (22.1 km3/year) contributes approximately 96% to the total riverine runoff and 77% to the lagoon's water balance. The lagoon is considered as eutrophic or hyper-eutrophic with chlorophyll a up to 150 (400) µm/l and enhanced turbidity. In spring constantly occurs spring blooms mainly dominated by diatoms (Stephanodiscus hantzschii, Aulacoseira spp., Asterionella spp., etc.) and summer blooms caused by potentially toxic cyanobacteria (mainly Aphanizomenon flos-aquae, Microcystis spp., Planktothrix agardhii). The Curonian Lagoon receives ~20% of its annual particulate organic matter (POM) inputs from the Nemunas River, phytoplankton contribute ~70% of the annual POM inputs.

Figure 1. The Curonian Lagoon.

Five testing field campaigns were organized once or twice per month from June to August 2015 in the Curonian Lagoon and two field campaigns were organized in 24-25 August 2016 concurrent with Landsat 8 (OLI), Sentinel-2A and Sentinel-3A. One of the major testing campaigns with concurrent acquisition of in situ data concurrent with airborne hyperspectral APEX acquisitions (partly funded by EUFAR) and satellite acquisitions took

place from 1 September to 2 September 2016 at the Curonian Lagoon in Lithuania. Field campaigns were organized by KU with collaboration and participation of CNR.





The objectives of the APEX campaign were:

to acquire APEX imagery in support of INFORM atmospheric correction and water quality algorithm development;

to provide in situ data (optical and biogeochemical) in support of the validation of APEX-based products;

to provide in situ data (optical and biogeochemical) in support of the validation of Landsat-8 OLI (Operational Land Imager), Sentinel-2 MSI or Sentinel-3 OLCI.

1.1. Data acquisition

1.1.1. Satellite image acquisition In 2015 totally five cloud free Landsat 8 (OLI) images were concurrent with field campaigns organized by KU. During 24 August 2016 one image of Sentinel-2A and during 1 September 2016 one image of Sentinel-3A concurrent with field sampling are available. The quick-looks of images are present in Annex.

1.1.2. APEX image acquisition The APEX instrument has been developed by a Swiss-Belgian consortium on behalf of ESA as a simulator and a calibration and validation device for spaceborne imagers. APEX records hyperspectral data in approximately 300 spectral bands in the wavelength range between 380 and 2500 nm (Itten et al., 2008). The spatial resolution of the APEX images depends on the flying altitude. The APEX operators (VITO employees) built-in the sensor on a Dornier Do 228-212 aircraft (registered as D-CFFU) from the German Space Agency (DLR). The flight crew has been provided by DLR, but coordination of the flight activities, APEX operations and actual data acquisition was the VITO responsibility. On 01/09/2016 and 02/09/2016, three APEX missions have been performed above the Curonian Lagoon, all at a flying altitude of about 6300 m above mean sea level. This resulted in a swath width of about 3000 m and a GSD of 3 m. The planning of the different flight lines (northern, middle and southern region of the lagoon) is shown in Figure 2, Figure 3 and Figure 4. The missions were carefully planned to avoid BRDF effects as much as possible. The weather conditions on 1 September were excellent, on 2 September however it was more cloudy, as can be seen from the flight line quicklooks in Figure 8, Figure 9 and Figure 10. During the missions some screen shots of the internet site www.flightradar24.com were taken. These are shown in Figure 5, Figure 6 and Figure 7.





Figure 2. Curonian Lagoon flight planning – Northern (N) region.





Figure 3. Curonian Lagoon flight planning – Middle (M) region.

Figure 4. Curonian Lagoon flight planning – Southern (S) region.





Figure 5: Curonian Lagoon flight 01/09 AM (M0174)

Figure 6. Curonian Lagoon flight 01/09 PM (M0175).





Figure 7.Curonian Lagoon flight 02/09 (M0176).

On 01/09/2016, data of 13 flight lines has been acquired before noon (mission M0174) and data of 6 flight lines has been acquired in the afternoon (mission M0175). Image acquisition times range from 9:35 till 12:12 and from 14:54 till 15:54 local time, as shown in Table 1 and Table 2.

On 02/09/2016, data of 5 flight lines has been acquired (mission M0176). Image acquisition times range from 12u17 local time till 13u06 local time, as shown in Table 3.





Table 1. Flight line acquisition times for mission M0174 on 01/09/2016 AM.

ID Name Imaging time (local) Flight direction

N1 M0174_CoolA_160901_a01 9u35 - 9u40 SE -> NW

N2 M0174_CoolA_160901_a02 9u51 - 9u57 SE -> NW

M7 M0174_CoolA_160901_a03 10u10 - 10u11 SE -> NW

M6 M0174_CoolA_160901_a04 10u19 - 10u22 SE -> NW

M5 M0174_CoolA_160901_a05 10u31 - 10u35 SE -> NW

M4 M0174_CoolA_160901_a06 10u44 - 10u48 SE -> NW

M3 M0174_CoolA_160901_a07 10u56 - 10u59 SE -> NW

M2 M0174_CoolA_160901_a08 11u07 - 11u09 SE -> NW

M1 M0174_CoolA_160901_a09 11u17 - 11u18 SE -> NW

S1 M0174_CoolA_160901_a10 11u27 - 11u32 E -> W

S2 M0174_CoolA_160901_a11 11u41 - 11u46 E -> W

S3 M0174_CoolA_160901_a12 11u55 - 11u59 E -> W

N2 M0174_CoolA_160901_a13 12u05 - 12u12 SE -> NW

Table 2. Flight line acquisition times for mission M0175 on 01/09/2016 PM.

Table 3. Flight line acquisition times for mission M0176 on 02/09/2016.


S1 M0175_CoolA_160901_a01 14u54 - 14u58 W -> E

S3 M0175_CoolA_160901_a02 15u02 - 15u07 E -> W

S2 M0175_CoolA_160901_a03 15u11 - 15u15 W -> E

S3 M0175_CoolA_160901_a04 15u20 - 15u24 E -> W

N1 M0175_CoolA_160901_a05 15u31 - 15u37 SE -> NW

N2 M0175_CoolA_160901_a06 15u48 - 15u54 SE -> NW


M4 M0176_CoolA_160902_a01 12u17 - 12u21 SE -> NW

N1 M0176_CoolA_160902_a02 12u28 - 12u35 SE -> NW

N2 M0176_CoolA_160902_a03 12u43 - 12u49 SE -> NW

M0176_CoolA_160902_a04 12u54 - 12u58 NW -> SE

M5 M0176_CoolA_160902_a05 13u01 - 13u06 SE -> NW





1.1.3. APEX data set The data set has been made available to the user through FTP. Account details have been provided to the user by e-mail. The data set contains data of 24 flight lines, that have been atmospherically and geometrically corrected. Furthermore, the spectra have been resampled to the wavelengths, as measured during the sensor spectral calibration on the Calibration Home Base (CHB), and slightly smoothed. Details w.r.t. the applied processing can be found in section 1.1.4 “APEX processing details”. The delivered flight lines are: M0174_CoolA_160901_a01 till M0174_CoolA_160901_a13 M0175_CoolA_160901_a01 till M0175_CoolA_160901_a06 and M0176_CoolA_160902_a01 till M0176_CoolA_160902_a05. Because the large amount of scan lines per flight line, each single flight line has been split in parts, to ease the file handling. There is an overlap of 200 lines between two consecutive flight line parts. In total, 27 flight line parts have been delivered for flight mission 174 on 01/09/2016 AM, 14 flight line parts for flight mission 175 on 01/09/2016 PM, and 14 flight line parts for flight mission 176 on 02/09/2016. Figure 8, Figure 9 and Figure 10 show these flight line parts against a background of Google satellite maps. The reflectance data is delivered in ENVI file format, i.e. pairs of *.img and *.hdr files. For each reflectance cube, a corresponding RGB quicklook has been provided. The table below provides the link between the flight lines as shown on Figure 2 till Figure 4 and the actual file naming of the reflectance cubes and corresponding quicklooks.





Table 4. Reflectance cubes file naming.

ID Name

N1 M0174_CoolA_160901_a01c_v2_Part_[0..2]_Rw


M7 M0174_CoolA_160901_a03c_v2_Part_0_Rw

M6 M0174_CoolA_160901_a04c_v2_Part_[0..1]_Rw






S1 M0174_CoolA_160901_a10c_v2_Part_[0..1]_Rw













M0176_CoolA_160902_a04c_v2_Part_[0..1]_Rw






Figure 8. Mosaic of APEX flight lines acquired on 1 September 2016 AM.





Figure 9. Mosaic of APEX flight lines acquired on 1 September 2016 PM.





Figure 10. Mosaic of APEX flight lines acquired on 2 September 2016.





1.1.4. APEX processing details This section describes briefly the calibration and processing steps performed on the acquired APEX image cubes.

1.1.4.1. Radiometric, spectral and geometric calibration

The APEX radiometric, spectral, and geometric calibration is performed by means of calibration cubes generated from data measured and collected on the APEX Calibration Home Base (CHB) hosted at DLR Oberpfaffenhofen, Germany (Gege et al., 2009). The delivered data contains 98 spectral bands, ranging approximately from 400 nm till 900 nm. The SWIR bands have been removed since they are not carrying relevant information for water applications. The first blue bands (with suspect radiometric quality) have also been removed.

1.1.4.2. Spectral smile

APEX is known to have some smile effects, i.e. the central wavelength depends slightly on the column pixel location. Furthermore, due to spectral instabilities of APEX caused by pressure and/or temperature variations, an in-flight spectral wavelength shift analysis is performed on the basis of atmospheric absorption features to reassign new central wavelengths to each pixel before atmospheric correction. For the delivered reflectance cubes, the ENVI header-file contains the wavelength of pixel 500, as measured during the sensor spectral calibration on the Calibration Home Base (CHB), spectral resampling to this wavelength was performed.

1.1.4.3. Geometric correction

The geometric correction is performed by VITO’s own developed C++ module and is based on direct georeferencing. Input data from the sensor’s GPS/IMU, boresight correction data and the SRTM DTM are further used during the geometric correction process. The data are projected to UTM (34N) with an output pixel size of 3mx3m.

1.1.4.4. Atmospheric correction

The atmospheric correction of the acquired APEX data is performed by the CDPC (Biesemans et al., 2007) with the MODTRAN4 radiative transfer code following the algorithms given in de Haan et al. (1991) and de Haan and Kokke (1996) and taking into account the in-flight determined central wavelengths for each pixel (column) (i.e. smile aware atmospheric correction).





1.1.4.5. Wires

Wires were placed on the entry slit to observe spatial shifts. In the delivered dataset wires are adaptively interpolated during the processing. Some linear artefacts due to interpolation may exist. The across track wire positions are: 334-335 and 676-677; the interpolated region currently encompasses a buffer of 1 pixel around the wire positions. Pixels in the interpolated wire region should be treated with caution.

1.1.4.6. Post-processing

Before delivery, some part of the reflectance spectra were slightly smoothed using the open-source Colibri tool developed by VITO and available at http://sourceforge.net/p/enviidlcodelibr/wiki/Home/. The smoothing coefficients were optimized to remove remaining spikes after atmospheric correction and to ensure a good quality of the water spectra.

http://sourceforge.net/p/enviidlcodelibr/wiki/Home/





1.1.5. In situ measurements

1.1.5.1. Overview

The in situ measurement program was undertaken by KU during 2015 and by KU and CNR during 2016. Table 5 summarizes the exact dates of organized testing campaigns, available airborne or satellite data and number of stations, where in situ optical and biogeochemical data was collected.

Table 5. Summary of the in situ data collected during the INFORM testing campaigns in the Curonian Lagoon.

Date APEX S2A S3A L8 In situ Rrs Stations

2015/06/05 X 11

2015/06/10 X X 13

2015/07/03 X x 3

2015/07/13 x (07/12) x 5

2015/08/04 X x 11

2016/08/24 X x 3

2016/08/25 x 3

2016/08/31 x 4

2016/09/01 x X x 10+3Rrs

2016/09/02 x x 3

1.1.5.2. In situ optics

During four field campaigns on June-August 2015 subsurface irradiance reflectance and remote-sensing reflectance were measured with a WISP-3. The CNR team on 31 august and 1-2 September 2016 made in situ subsurface irradiance reflectance and remote-sensing reflectance measurements with a WISP-3 and SpectralEvolution. The detailed description of methodology can be found in subchapter 2.1.2.2.

1.1.5.3. Water sample analysis

Surface water samples were collected in majority of validation stations for the analysis of bio-optical and biogeochemical parameters in the laboratory. All measured parameters are listed below: Field parameters: - Secchi depth





- water temperature - water salinity - O2, pH, turbidity - Photosynthetically active radiation PAR - Fluorometric measurements of chl-a and algal groups Bio-optical and biogeochemical parameters: - Chlorophyll-a - CDOM (a200-800 & SCDOM) - TSM, PIM, POM - Turbidity ISO 7027 - Phycocyanin - DOC - Particulate absorption - Phytoplankton species, abundance, bio-volume Water for chlorophyll-a and pheopigments was filtered through glass fiber GF/F filters with a nominal pore size 0.7 μm and extracted into 90% acetone. Photosynthetic pigments were measured spectrophotometrically. Additionally phycocyanin, phycoerythrin, carotenoids were assessed. CDOM was measured spectrophotometrically after filtration through 0.22 μm membrane filters. The CDOM absorption coefficient at 440 nm (g440) and slope were derived. Absorption by pigments and non-algal particles (after bleaching) were assessed spectrophotometricaly using PAB method. SHIMADZU UV-2600 dual beam spectrophotometer was used for the analysis of chl-a, CDOM, absorption by pigments and non-algal particle. Concentration of DOC was analysed using SHIMADZU TOC-VHS analyser. Water samples for the analysis of phytoplankton species composition, abundance and biomass were preserved with acid Lugol’s solution. TSM, SPIM and SPOM were assessed gravimetrically. Turbidity (in NTU) was measured with turbiditymeter TN-100 (Eutech Instruments). During field campaigns supplementary environmental parameters (temperature, salinity, pH, O2), vertical profiles of PAR (with Li-COR 192 and Li-COR 193) and Chlorophyll-a of different phytoplankton groups (with FluoroProbe II, BBE Moldaenke GmbH), water transparency (Secchi disk depth) were measured. Weather (wind speed, direction, cloudiness) and water conditions (wave height and direction, current speed and direction etc.) were described. In some sampling stations (3 stations on 10 June 2015, 4 stations on 4 August 2015, 2 stations on 1 September 2016) primary production of phytoplankton was measured using oxygen method.

1.1.5.4. Monitoring station data

Monitoring data, i.e. only chl-a concentration, TSM, phytoplankton counts and supplementary environmental data (temperature, salinity etc.) collected by Department of Marine Research under Environmental Protection Agency (DMR, EPA) is available for 2015. At the moment KU have chl-a concentration and TSM data, other data can be requested. Measurements were taken once per month (during an intensive vegetation period – twice per month) or each second month from





February to November at 10 sampling stations. After the screening the available satellite images, several match-ups were found: 2015/02/25 – L8, 2015/04/14 – L8, 2015/06/10 – L8 concurrent with INFORM field campaign, 2015/08/04 – L8 concurrent with INFORM field campaign, 2015/10/07 – L8 and S2A The data of 2016 will become available from beginning-mid of 2017 due to policy of DMR, EPA respect to the data delivery.





2. Lake Balaton and Kis-Balaton Lake Balaton is Europe’s largest shallow lake at 592 km2 (Figure 11). In spite of its large surface area, it is very shallow with a mean depth of approximately 3.2 m. The lake has historically been subdivided into four basins (west to east): Keszthely, Szigliget, Szemes, and Siófok. The main inflow to the lake is the River Zala in the western Keszthely basin and the only outflow is via a regulated canal in the eastern Siófok basin. In the 1980-90s the western basins became hyper-eutrophic with Chl-a maxima above 200 µg/l due to the high external load of nutrients mainly from the River Zala, with eutro-mesotrophic condition (up to 75 µg/L Chl-a) prevalent in the eastern basins. From the 1990s onwards a number of management measures were undertaken reducing the external P load by approximately 50%. In this post-management period the trophic status of the lake has been reversed. By this time the whole lake became oligo-mesotrophic with slightly higher Chla maxima in the western (up to 20 µg/L) than in the eastern (below 10 µg/L) basins. The lake also receives humic water from the River Zala and as such DOC concentrations in the western basin can be very high. However, the DOC is rapidly diluted and degraded through the system and thus concentrations are typically low and invariant in the eastern regions of the lake. The extreme shallowness of the lake also promotes the resuspension of bottom sediment and as such the lake typically carries a high TSM load (10-50 mg/L) but this can reach up to 200 mg/L of minerogenic particulate matter during high wind events.

Figure 11. Lake Balaton in Hungary and the approximate locations of the routine sampling stations used by BLI and the Central Transdanubian Inspectorate for Environmental and Natural Protection (KDT KTF) (adapted from Palmer et al., 2014).





Kis-Balaton is a large, hypertrophic shallow water reservoir system constructed to the south west of Lake Balaton. The system extends to about 76 km2 encompassing open water (some 28 km2) and wetland habitats. The mean depth of the open water is typically about 1m. The system was constructed to reduce the external loading of nutrients and sediment in Lake Balaton from the River Zala. Kis-Balaton is therefore a highly nutrient enriched system and Chl-a concentrations are typically in the order of 100-400 µg/L. The phytoplankton community is dominated by cyanobacteria, although the species composition varies through the system due to the steep trophic gradient.


2.1.1. Satellite image acquisition Landsat-8 OLI and Sentinel-2 data were acquired on 21st and 22nd July 2016, respectively. The data from both date are affected by cloud, although clear areas are present over the eastern part of the lake where the in situ sampling for validation was mainly focused. The dataset includes 10 Landsat-8 images in the period March-October 2016 (31/03; 16/04; 26/05; 05/07; 13/07; 29/07; 14/08; 07/09; 23/09; 01/10).

Table 6. Spectral and spatial characteristics of the acquired satellite data and acquisition date.

Satellite sensor Number of spectral bands

Spectral range (µm)

Spatial resolution (m)

Acquisition date

Landsat-8 OLI 9 0.43-1.38 30 (15, PAN) 21 July 2016

Sentinel-2 13 0.44-2.2

10-20-60 22 July 2016


2.1.2.1. Overview

In Lake Balaton and Kis-Balaton wetland, one field campaign was conducted on 21st and 22nd July 2016, in correspondence of the Landsat-8 and Sentinel-2 overpasses, respectively. Sampling sites for macrophytes in the Kis-Balaton are reported in Figure 12 and for water in Balaton lake in Figure 13. The number of in situ sampled stations for water and macrophytes (Balaton and Kis-Balaton) are shown in Table 7.





Figure 12. Map of the sampling sites at the Kis-Balaton wetland on 21st July and 22nd July (NL= Nuphar lutea; NA= Nymphaea alba, TN= Trapa natans).

Figure 13. Map of the sampling site at the Balaton Lake on 21st July (from W1 to W6 in yellow) and 22nd July (from St1 to St4 in green).





A summary of the in situ data collected over the duration of the Development Campaign at Balaton Lake and Kis Balaton wetland is provided in Table 7.

Table 7. Summary of the in situ and lab data measured during the field campaign at Balaton Lake and Kis-Balaton in July 2016.

21/07 22/07

Wat

er AOPs (Rrs) SE 6 4

Water costituents Chl-a, TSM, CDOM 6 4

Kd

4

Mac

rop

hyt

es AOPs (Reflectance) ASD-FR 3 -

Leaf spectra ASD-FR 7 4

Biomass Leaf biomass 7 4

Pigments Chl-a, Carotenoids and spectral

absorption 7 4

Satellite Data Landsat-8 Sentinel-2


The CNR team made in situ remote-sensing reflectance (Rrs, eq. 1) and subsurface irradiance reflectance (Rr, eq. 2) with an ASD FieldSpec FR (with a 3-5 degree optic lens) and SpectralEvolution spectroradiomete: Rrs(0+)=[Lw(0+)-rLsky(0+)]/[πLref(0+)] (eq. 1) Rr(0-)= Lw(0-)/Ed(0-) (eq. 2)

Where: Radiance emerging from water Lw (0+) with about 42 degree elevation angle from nadir and about 135 degrees azimuth angle from the Sun. Radiance from sky Lsky (0+) with about 42 degree angle from zenith and about 135 degree azimuth angle from the Sun. Radiance from the Spectralon reference panel Lref (0+) from nadir. Upwelling water leaving radiance above the water surface Lw(0+). Upwelling water leaving radiance below the water surface Lw(0-) Downwelling water leaving irradiance below the water surface Ed(0-). Reflectance measurements from different species of macrophytes of Kis-Balaton were taken using an ASD FieldSpec FR.





For each measures in every single stations 5 different replicates were collected.

2.1.2.3. Water and macrophytes sample analysis

Water samples were collected from 10 validation stations in Lake Balaton for the analysis of bio-optical and biogeochemical parameters in the laboratory. Water temperature and conductivity were measured in situ by a multiparameter probe. Secchi disk depth was also measured. 2L of water were collected from the surface using a clean open-necked container. Water samples were stored in the dark on ice until they were returned to the BLI laboratory for analysis. The water samples were processed in the BLI laboratory within 6 hours of sample collection. The samples were filtered through 25 mm GF/F filter papers for spectrophotometric determination of Chl-a following extraction in hot methanol (Iwamura, et al., 1970). TSM was determined gravimetrically following filtration on to pre-combusted 47 mm GF/F filter papers. Samples for CDOM analysis were collected in amber glass bottles and maintained on ice in the dark until they could be processed in the laboratory (always within 12 hours). The samples were subsequently filtered through 0.2 μm membrane filters and CDOM absorption was measured using a dual-beam spectrophotometer against a Milli-Q reference. Macrophyte samples were collected in 11 stations from Kis-Balaton and the dry weight biomass determined in the BLI laboratory. In addition, subsamples of the macrophytes were retained for pigment (Chl-a and carotenoids) analyses.


MTA OK (BLI) provided monitoring station data acquired between 2001 and 2016 for T, pH, conductance, Chl-a, CDOM, attenuation (Kd), Secchi depth and Total Suspended Solids for 6 stations (B.fűzfő, Tihany, Zánka, Szigliget, Keszthely and Zala).





3. Lake Marken Lake Marken (Markermeer) is a 700 km2 large, artificial, shallow lake in the Netherlands. It is part of a former brackish, 4000 km2 inland sea (Zuiderzee) that was dammed and turned into Lake IJssel in 1932. After the construction of the Houtribdijk dam between the cities Lelystad and Enkhuizen, Lake Marken has been separated by sluices from the major inputs of River IJssel. About ten years after closure of the dam an equilibrium between erosion and sedimentation developed, resulting in high, but more or less stable amount of suspended sediment, with a gradient of relatively low concentrations on average in the west to high concentrations in the east. In the last 20 years waterfowl and fish populations in Lake Marken descreased drastically. As a measure to improve the ecology of the lake, a swamp area named ‘Marken Wadden’, is currently constructed in the eastern part of the lake. In 2020 an archipel of 5 islands should be ready. In the period August-September 2016 several field campaigns were organized at Lake Marken by VITO (BE), the University of Stirling (UK), Deltares (NL) and Rijkswaterstaat (NL) to further validate the OPERA atmospheric correction (including adjacency correction) and water quality products based on Sentinel-2A, and to further calibrate the biogeochemical module BLOOM of the DELFT 3D WAQ modelling software suite developed by Deltares for Lake Marken.


3.1.1. Satellite image acquisition There was a Sentinel-3 and Sentinel-2 overpass on 8th September 2016 concurrent to the VITO-lead in situ sampling campaign. The Sentinel-3 and Sentinel-2 overpasses of 26th August 2016 were almost completely obscured by clouds. There was a Sentinel-3 overpass on 14th September 2016 and Sentinel-2 and Sentinel-3 overpassses on 15th September 2016 concurrent to the USTIR-lead in situ sampling campaigns. A further Sentinel-3 overpass on 16th September 2016 was almost completely obscured by clouds.


3.1.2.1. Overview

On 26 August vertical profile and grid-based measurements of turbidity and Chl a at Hoornsche Hop (in the western part of Lake Marken) were performed for further calibration of the bio-geochemical BLOOM module and for comparison of in situ YSI6600V2 turbidity/Chl-a with lab HACH turbidity/Chl-a measurements.





On 8 September field spectroradiometer measurements were acquired from a fixed location near Hoornsche Hop concurrent with water samples for Chl-a retrieval, Total Suspended Matter (TSM) concentration and turbidity measurements. In addition, sun photometer measurements were performed in Hoorn for retrieval of the atmospheric correction parameters aerosol optical thickness and water vapor concentration. Water samples were acquired from a fixed location near Lelystadhaven (in the eastern part of Lake Marken). Water samples were collected 15’ before, during and 15’ after Sentinel-3A and Sentinel-2A overpasses over Lake Marken. On 14-16 September USTIR acquired remote-sensing reflectances and inherent optical properties measurements at 16 stations covering 10 different locations over a biogeochemical gradient on Lake Marken for validation of Sentinel-2A and Sentinel-3A. The stations are shown in Figure 14. Large volume water samples were collected from the surface and analysed for Chl-a, phytoplankton pigments by HPLC, phycocyanin, TSM, CDOM, POC and DOC. The diffuse attenuation coefficient of photosynthetically active radiation Kd(PAR) was measured using a pair of Li-COR quantum sensors. Depth profiles of chlorophyll and CDOM fluorescence, turbidity and water temperature were obtained using Turner C6 sonde and Cyclops-7 sensors. Water depth was measured using an Echotest 2 handheld depth sounder. Shipborne sun photometer measurements were collected to support atmospheric correction.

Figure 14. The 10 locations covered by the 16 stations sampled on Lake Marken by USTIR between 14th and 16th September 2016.


On 8 September turbidity measurements were taken using the HACH 2100Qis instrument. To obtain downwelling irradiance (Ed), skylight radiance (Ls) and total surface radiance (Lt) the ASD





instrument.was used. Water leaving reflectances were derived from the ASD data using according to Mobley (1999). The USTIR team made in situ measurements of spectral absorption and attenuation using a WetLabs AC-S (with and without a 0.2 μm filter for removal of particulates) and spectral backscattering using a Wetlabs ECO-BB3. Temperature, depth and salinity were logged using a SeaBird CTD. Size-fractionated (0.2, 2, 20 and 200 μm) measurements of absorption and attenuation were made at selected stations over the course of the campaign. Subsurface radiance reflectance was measured using a set of Satlantic HyperOCRs. Measurements of downwelling irradiance (Ed), skylight radiance (Ls) and total surface radiance (Lt) for computation of water-leaving reflectance (ρ) were made using a trio of TriOS RAMSES radiometers. Water-leaving reflectances were calculated from remote-sensing reflectance computed according to Simis and Olssen (2013).


Water samples were collected at sampling stations for the analysis of bio-optical and biogeochemical parameters in the laboratory. Triplicate large volume water samples (2L) were collected from the surface using a bucket and immediately decanted into clean, wide-necked amber Nalgene bottles. Samples for CDOM analysis were transferred into 0.1L clean amber glass bottles. Samples were stored on ice in the dark until filtration could be completed. Samples for CDOM analysis were filtered immediately on-board the research vessel and preserved with 1% sodium azide. Sample aliquots for pigment and CDOM analysis were filtered within 12 h; samples for TSM analysis were filtered within 24 h.

Sample aliquots were filtered in triplicate under low vacuum through 25 mm GF/F filter papers for determination of Chla, PC and HPLC (pigments) and in duplicate for the measurement of particulate

absorption. These samples were immediately frozen at -20C until they could be transferred by to

USTIR for long-term preservation. CDOM samples were filtered through 0.2 m Nucleopore membrane filters. Samples aliquots for TSM were filtered in triplicate onto 47 mm pre-weighted

GF/F filters. Filtered samples were transferred to USTIR while frozen and stored at -80C until further analysis could occur.

Chla was determined by spectrophotometry following extraction in hot ethanol according to ISO 10260 (1992). PC was determined according to Horvath et al. (2013). HPLC analysis of phytoplankton pigments was performed on a Dionex Ultimate 3000 system according to the method of Van Heukelem & Thomas (2001). TSM was determined gravimetrically on a calibrated microbalance; the inorganic component was determined gravimetrically after combustion of filter

paper for 6 h at 450C. The organic fraction of TSM was calculated as the difference in mass between the total and inorganic suspended particulate matter. CDOM absorption was measured using a Cary-100 dual-beam spectrophotometer against a Milli-Q reference.






EUAB member Ute Menke from Rijkswaterstaat kindly provided monitoring data from Jan 2013 till Oct 2016. The data contains nutrients, TSM and inorganic fraction, Chl-a, Secchi depth, Kd and vegetation coverage.





4. Venice Lagoon The lagoon of Venice (LV) is a large and shallow coastal lagoon located in the north-eastern Italy (Figure 15). It has a surface area of ca. 540 km2 and it extends for about 50 km along the north-western Adriatic coast. The Lagoon has an average water depth of about 1.1 m and a maximum tidal range of about 1.5 m, with a main period of about 12 h. It maintains a connection to the Adriatic Sea through the inlets of Lido, Malamocco, and Chioggia, and the exchange of water through the inlets in each tidal cycle is about a third of the total volume of the lagoon. Traditionally the lagoon is subdivided into three sub-basins, one for each inlet, separated by two watersheds through which the residual flow is minimum. The LV presents a heterogeneous morphology, characterized by a complex pattern of major (navigable) and minor channels, salt marshes, tidal flats and islands. The lagoon has been subject to intense anthropogenic pressures occurred in the 20th, such as the construction of jetties at the inlets and the dredging of the Malamocco–Marghera channel, which shifted the lagoon towards a prevalent erosion, which lead to a negative sedimentary budget. This, added to natural and anthropogenic subsidence (12 cm) and sea level rise (11 cm), resulted in significant changes of the lagoon morphology, such as the reduction of the area occupied by salt marshes, the increased bathymetry of tidal flats and the siltation of channels, leading to a general flattening of beds and loss of spatial heterogeneity. The last significant intervention on the setting of the Lagoon of Venice is the MoSE project (construction of mobile barrier to safeguard the Lagoon of Venice), which is a long-debated project to defend the city of Venice and the surrounding lagoon from “high water” events. The project entails building mobile barriers at the bottom of each inlet which, when tidal events threaten to become critical, will rise and shut off the lagoon from the sea. Key ecological impacts include the extensive loss of benthic seagrass cover.

Table 8. Main features of the Venice Lagoon.

Site Area (km2)

Perimeter (km)

Mean depth (m)

Venice lagoon 540 157 1.54





Figure 15. The Lagoon of Venice. Major morphological types (channels, salt marshes and fish farming areas) and modifications (reclaimed area, Malamocco-Marghera channel and MoSE structures at the inlets) are illustrated. 8% of total area is constituted by land above sea level

(littorals, reclaimed areas, islands) and 92% by the water system: channels (12%), shallows, mud flats and salt marshes (80%).


4.1.1. Satellite image acquisition The dataset includes 9 Landsat-8 OLI images and 6 Sentinel-2 MSI (S2) images in the period April-September 2016 (Table 9).

Table 9. Satellite data acquired in the Lagoon of Venice.

Sensor ID Date acquired [yyyy mm dd]

time (UTC) Path Row Tidal level

[m] S2

L8 2016112 2016 04 21 09:57 192 28 0.61 -

L8 2016128 2016 05 07 09:58 192 28 0.48 6/5/2016

L8 2016176 2016 06 24 09:58 192 28 0.4 25/6/2016

L8 2016192 2016 07 10 09:58: 192 28 0.24 -

L8 2016201 2016 07 19 09:52 191 29 0.51 18/7/2016

L8 2016208 2016 07 26 09:58 192 28 0.2 25/7/2016

L8 2016224 2016 08 11 09:58 192 28 0.38 -

L8 2016240 2016 08 27 09:58 192 28 0.34 27/8/2016





L8 2016256 2016 09 12 09:58 192 28


4.1.2.1. Overview

In the Lagoon of Venice, no field campaign was performed in the 2016. Data from a network of automatic monitoring stations were gathered for the purpose of assessing the satellite-derived products, including turbidity and fluorescence. In Figure 16 the network of automatic monitoring stations are reported.

Figure 16. Maps with automatic monitoring stations in the Lagoon of Venice.


As part of this activity, monitoring station data were provided. They were collected from the network of automatic monitoring stations (SAMANET) implemented and managed by the “Provveditorato Interregionale alle Opere Pubbliche per il Veneto, Trentino Alto Adige e Friuli Venezia Giulia” (formerly MAV - the Venice Water Authority). The network consists of 7 stations for the measurement of chemical - physical parameters of the lagoon (tide level, temperature, pH, salinity, dissolved oxygen, redox potential, fluorescence, turbidity). The measured values from the





stations are transmitted in real time, stored, validated and processed. The data were provided in correspondence with the acquisition of L8 images. A quality check procedure should be performed in order to eliminate abnormal values due to contingent events (passage of boats, fouling effects, etc.).





5. Lakes Mantua The system of fluvial lakes of Mantua is composed by three small and shallow eutrophic reservoirs located in northern Italy (latitude 45°9’ N, longitude 10°47’ E) (Figure 17, Table 10). The Superior, Middle and Inferior lakes are semi-artificial lakes created from a meander of the Mincio River that was dammed during the 12th century to protect the town of Mantua from enemy invasions and from Po River floods. The Mantua Lakes have features typical of a shallow lentic environment due to low water velocity and limited depth, and of a wetland hosting dense macrophyte meadows, and therefore can be defined as a fluvial lake system. This system is protected as Natural Regional Park and part of World Heritage by UNESCO and is surrounded by two protected wetlands, “Valli del Mincio” and “Vallazza”, which are Site of Community Importance and Natural Reserves (Figure 6.1).

Table 10. Main features of the Mantua lakes system.

Lake Lake area

(km2) Perimeter

(km) Storage volume

(x106 m3) Mean depth

(m)

Superior 3.67 10 14.5 3.6

Middle 1.09 6 3.27 3.0

Inferior 1.45 6 4.36 3.3

At Vasarone dam (between the Superior and the Middle Lake) the mean annual flow was 20±6 m3s-

1 (2000-2006), corresponding to a water residence time in the Superior, Middle and Inferior lakes of about 8.4, 1.9 and 2.5 days, respectively. The water residence time of the fluvial lake system increased in the last decades due to water discharge decrease for irrigation and industrial purposes, and this was coupled to a progressive deterioration of water quality due to high nutrient loads due to agriculture and animal farming activities and to internal load. The lower hydrodynamism of the system, which favors rapid infilling processes and the increase of pollutant loads, triggers a feedback circuit destabilizing. The progressive deterioration of the water quality has impacts on many activities related to the river and lakes as tourism, fishing, industry and agriculture. Eutrophic conditions result in dense phytoplankton communities (Chl-a values up to 100 µg L-1), strongly limiting light availability for benthic macrophytes (water transparency 0.6-1.1 m). The water column hosts phytoplankton communities typical of eutrophic and hypertrophic systems rich in organic matter, including diatoms (e.g. Synedra spp., Aulacoseira spp.), cyanophytes (e.g. Oscillatoria spp.), and chlorophytes (e.g. Scenedesmus spp., Pediastrum simplex). The shallow areas of the fluvial lake system host annual stands of floating-leaved macrophytes that colonize from late April to September. In the Superior Lake Nelumbo nucifera is the main floating-leaved macrophyte which forms two main islands of about 40 and 12 ha. Trapa natans, Nuphar lutea, Nymphaea alba are also present. Cerathophyllum demersum, Myriophyllum spicatum and Potamogetum spp. are the main submersed macrophytes which colonize the Superior Lake. The Middle Lake hosts a ~12





ha monospecific meadow of T. natans and another ~4 ha stand of T. natans, N. lutea and N. alba. The Inferior Lake mainly hosts spread T. natans plants.

Figure 17. The Mantua Lakes system in the Mincio River watershed, a sub-basin of the Po River (Northern Italy).


5.1.1. Satellite image acquisition Satellite cloud free data were acquired on 12th May, 11th June, 16th July 2015 (SPOT-5), 31st July 2015 (Landsat-8) and 6th August 2015 (Sentinel-2) (Tab. 6.2). The dataset includes also 8 Sentinel-2 images in the period July-September 2015 (04/07; 07/07; 24/07; 03/08; 13/08; 26/08; 02/09; 12/09). For macrophyte analysis we also used a series of 19 SPOT-5 images cloud free (12/04; 22/04; 07/05; 17/05; 27/05; 06/06; 16/06; 21/06; 26/06; 01/07; 06/07; 11/07; 21/07; 26/07; 05/08; 15/08; 20/08; 30/08; 04/09). Sentinel-2 cloud free data were acquired on 11th and 28th July, and 19th September 2016 (Table 11). In this latter data, also a Landsat-8 OLI cloud free image was acquired (Table 11). The images of 8th (Landsat-8) and 21st (Sentinel-2) June 2016 were completely affected by cloud.

1 km

Valli del Mincio Mantua Lakes

Vallazza

Northern Italy

Po River Basin

Superior Lake

Middle Lake

Inferior Lake





The dataset of 2016 includes 16 Landsat-8 (04/03; 20/03; 12/04; 21/04; 07/05; 15/06; 24/06; 10/07; 17/07; 26/07; 11/08; 17/08; 19/09; 28/09; 05/10; 21/10) and 20 Sentinel-2 images (13/01; 19/02; 20/03; 23/03; 12/04; 22/04; 29/04; 22/05; 18/06; 28/06; 18/07; 07/08; 17/08; 27/08; 06/09; 09/09; 26/09; 29/09; 06/10; 16/10) in the period January-October, and one RapidEye image acquired on 2nd September 2016.


Satellite sensor

Number of spectral bands



Acquisition date

Landsat-8 OLI

9 0.43-1.38 30 (15, PAN) 31 July 2015 19 September 2016

Sentinel-2 13 0.44-2.2

10-20-60 06 August 2015 11 July 2016 28 July 2016 19 September 2016

SPOT 5 4 0.50-1.75 10-20 12 May 2015 11 June 2015 16 July 2015


5.1.2.1. Overview

Seven field campaigns were carried out in Mantua Lakes in 2015 (12th May, 11th and 19th June, 16th and 31st July, 6th August, and 9th September), and six were contemporary (12/05; 11/6; 16/07 31/07; 06/08; 09/09) to satellite acquisition (Landsat-8, Sentinel-2 or SPOT-5). The SPOT-5 data of 9th September was cloud covered. In 2016, five field campaigns were conducted on 8th and 21st June, 11th and 28th July, and 19th September 2016. All sampling dates matched a remote sensor overpass of Sentinel-2, and on 8th June and 19th September also a Landsat-8 overpass occurred. In Figure 18 and Figure 19 sampling sites for water and macrophytes are reported.





Figure 18. Maps with sampling stations in six field campaigns in Mantua Lakes in 2015. In the top and bottom pictures water and macrophyte stations are reported, respectively.





Figure 19. Maps with sampling stations in four field campaigns in Mantua Lakes in 2016. In yellow, green and pink, floating-leaved macrophytes (NL=Nuphar lutea, TN= Trapa natans, NN= Nuphar lutea), helophytes (PA= Phragmites australis), and water sampling sites, respectively.

Table 13 and Table 13 show a summary of the in situ data collected for water and macrophytes in all sampling dates in Mantua Lakes system in 2015 and 2016, respectively.

Table 12. Summary of the in situ and lab data measured during the campaigns at Mantua Lakes in 2015. The description of measurements protocol and methods can be found in Giardino et al., 2007; Bresciani et al., 2009; Bresciani et al., 2013 ; Giardino et al., 2014.

12/05 11/06 19/06 16/07 31/07 09/09

Wat

er

AOPs (Rrs) ASD-FR, SE, WISP-3 5 5 2 1 2 3

Water costituents

Chl-a, SPIM, SPOM, CDOM

5 5 2 1 2 3

Water quality Physico-chemical

characteristics 5 5

2 3

Phytoplankton Identification, counts,

HPLC 5 5

2 3

IOPs Absorption (filterPAD) 5 4

2 3

Mac

rop

hyt

es

AOPs (Reflectance)

ASD-FR, SE 27 27

24 27 27

Biomass Leaf biomass 24 27

24 27 27

Pigments Chl-a, Carotenoids and

spectral absorption 27 27

24 27 27

Atmosphere EKO MS-120 x X

x





Table 13. Summary of the in situ and lab data measured during the campaigns at Mantua Lakes in 2016.

08/06 21/06 11/07 28/07 19/09

Wat

er

AOPs (Rrs) ASD-FR, SE, WISP-3 5 5

Water costituents

Chl-a, SPIM, SPOM, CDOM 3 4 3 5 5

Water quality Nutrients + physico-chemical

characteristics 3 4 3 5 5

Phytoplankton Identification, counts, HPLC 5 5

IOPs Absorption (filterPAD) 5

Spectral backscattering (HS6) 5

Kd 5

Mac

rop

hyt

es AOPs

(Reflectance) ASD-FR, SE 6 6

Biomass Leaf biomass 7 4 6 6 6

Pigments Chl-a, Carotenoids and spectral

absorption 6 6

Satellite Data L8 L-8

S-2 S-2 S-2 S-2 S-2


The CNR team made in situ measurements of spectral absorption (via filter pad technique) and attenuation using a Hobi Labs Hydroscat-6. Subsurface irradiance reflectance and remote-sensing reflectance were measured with an ASD FieldSpec FR (with a 3-5 degree optic lens), Spectra Evolution in according with the methodology detailed in 3.1.2.2 paragraph, and a WISP-3 spectroradiometer (Hommerson et al., 2012). Reflectance measurements from different species of macrophytes of Mantua Lakes were taken using an ASD FieldSpec FR, and a Spectra Evolution. For each measures in every single stations 5 different replicates were collected.

5.1.2.3. Water and macrophytes sample analysis

Water samples were collected from stations in Mantua Lakes for the analysis of bio-optical and biogeochemical parameters in the laboratory. Water temperature, conductivity, dissolved oxygen, and pH were measured in situ by a multiparameter probe (HANNA - HI9829). Secchi disk depth was also measured. 2L of water were collected from the surface using a clean open-necked container.





Water samples were stored in the dark on ice until they were returned to the laboratory for analysis. The water samples were processed in laboratory within 6 hours of sample collection. The samples were filtered through 25 mm GF/F filter papers for spectrophotometric determination of Chl-a following extraction in acetone 90% (Lorenzen et al., 1967) and HPLC. Photosynthetic pigments for HPLC analysis were extracted in 90% acetone, overnight in the dark, under nitrogen. The extract obtained was used to quantify Chl and its derivatives (in Chl derivatives units, CD) and total carotenoids by spectrophotometry. Individual carotenoids were detected by revers-phase HPLC with an Ultimate 3000 (Thermo Scientific). Specific pigments were identified by ion pairing, reverse-phase HPLC described in Guilizzoni (2011). PC concentrations were quantified with the spectrophotometer (SAFAS UVmc2) in 1 cm path-length cuvettes using the equations of Bennett and Bogorad (1973). The phytoplankton samples were analyzed (identification and cell count) under the inverted microscope (400x magnification) according to Utermöhl (1958). TSM was determined gravimetrically following filtration on to pre-combusted 47 mm GF/F filter papers; organic and inorganic fractions were determined after incineration in muffle (450°C for 3 hours). Samples for CDOM analysis were collected in amber glass bottles and maintained on ice in the dark until they could be processed in the laboratory (always within 12 hours). The samples were subsequently filtered through 0.2 μm membrane filters and CDOM absorption was measured using a dual-beam spectrophotometer against a Milli-Q reference. Values of aΦ and aNAP (Kishino et al., 1985) were determined spectrophotometrically in laboratory (Trüper and Yentsch, 1967). Macrophyte samples were collected from Mantua Lakes and the dry weight biomass determined in laboratory. In addition, subsamples of the macrophytes were retained for pigment (Chl-a and carotenoids) analyses.





6. Lake Garda Located in the Subalpine ecoregion, Lake Garda is the largest lake in Italy (Figure 20), having an area of 370 km2, a water volume of 50 km3 and a maximum depth of 346 m. Garda catchment is mainly composed by sedimentary rocks (60%), including a minor contribution of crystalline rocks and secondarily deposited sediments (e.g. glacial and fluvial). With respect to morphology the lake can be divided in two different areas: the largest sub-basin extended from north to southwest area, characterised by deepest bottoms, and the south-eastern shallower sub-basin. The northern part of the lake is characterized by mountain slopes mainly covered by forests or rural territories, whilst the southern part of the lake is surrounded by morenic and alluvial plains and low hills with a mix of urbanised and rural land use. It represents an essential strategic water supply for agriculture, industry, energy, fishing and drinking. Moreover, it is an important resource for recreation and tourism with its attractions of landscape, mild climate and water quality. From an ecological point of view, the basin can be categorised as a warm, monomictic and oligomictic basin. Water column mixing takes place once in late winter or in early spring involving exclusively the uppermost portion of the water column down to a depth of about 150-200 m. The complete circulation occurred exclusively in presence of remarkably cold winter times. According to Organisation for Economic Co-operation and Development (OECD) is classified as an oligo-mesotrophic lake: phosphorous concentration in the epilimnium is below or around 10 μg/L, the average concentration of Chl-a is 3 mg/m3, the Secchi disk depths vary between 4–5 m in summer and 15–17 m in late winter.

Figure 20. Map of the Garda Lake.






6.1.1. Satellite image acquisition Landsat-8 OLI cloud free image was acquired on 1st July, and Sentinel-2 data were acquired on 22nd May, 8th June, 1st July, and 17th August 2016 (Table 14). The dataset includes also 8 Sentinel-2 images in the period July-September 2015 (04/07; 07/07; 24/07; 03/08; 13/08; 26/08; 02/09; 12/09).


Satellite sensor

Number of spectral bands



Acquisition date

Landsat-8 OLI

9 0.43-1.38 30 (15, PAN) 1 July 2016

Sentinel-2 13 0.44-2.2

10-20-60 22 May 2016 8 June 2016 1 July 2016 17 August 2016


6.1.2.1. Overview

Four field campaigns were conducted on 22nd May, 8th June, 1st July, and 17th August 2016 in the Garda Lake. All sampling dates matched a remote sensor overpass of Sentinel-2, and on 1st July also a Landsat-8 overpass occurred. The Sentinel-2 image of 8th June was cloud covered. In Figure 21 water sampling sites are reported. In Table 15 a summary of the in situ water data collected in all sampling dates in 2016 in Garda Lake is reported.





Figure 21. Map of the sampling sites at the Garda Lake on 22nd May (from G1 to G3 in yellow), 1st July (from 1 to 5 in green), and 17th August 2016 (from W1 to W4 in light blue).

Table 15. Summary of the in situ data collected during the campaign at Lake Garda in 2016. The description of measurements protocol and methods can be found in Giardino et al., 2007; Giardino

et al., 2014.

22/05 08/06 01/07 17/08

Wat

er

AOPs (Rrs) ASD-FR, SpectraEvolution,

WISP-3 3 4 6 4

Water constituents

Chl-a, SPIM, SPOM, CDOM

4 4

IOPs Absorption (filterPAD) 4 4

Kd 2 5

Atmosphere CIMEL X X X X

Airborne +

Satellite data S-2 S-2 L-8; S-2 S-2


The CNR team made in situ measurements of subsurface irradiance reflectance and remote-sensing reflectance were measured with an ASD FieldSpec FR (with a 3-5 degree optic lens), Spectra Evolution in according with the methodology detailed in 3.1.2.2 paragraph, and a WISP-3 spectroradiometer (Hommerson et al., 2012). For each measures in every single stations 5 different replicates were collected.






Water samples were collected from stations in Garda Lake for the analysis of bio-optical and biogeochemical parameters in the laboratory. 2L of water were collected from the surface using a clean open-necked container. Water samples were stored in the dark on ice until they were returned to the laboratory for analysis. The water samples were processed in laboratory within 6 hours of sample collection. The samples were filtered through 25 mm GF/F filter papers for spectrophotometric determination of Chl-a following extraction in acetone 90% (Lorenzen et al., 1967). TSM was determined gravimetrically following filtration on to pre-combusted 47 mm GF/F filter papers and organic and inorganic fraction after incineration in muffle at 450°C for 3 hours. Samples for CDOM analysis were collected in amber glass bottles and maintained on ice in the dark until they could be processed in the laboratory (always within 12 hours). The samples were subsequently filtered through 0.2 μm membrane filters and CDOM absorption was measured using a dual-beam spectrophotometer against a Milli-Q reference. Values of aΦ and aNAP (Kishino et al., 1985) were determined spectrophotometrically in laboratory (Trüper and Yentsch, 1967).


Monitoring is performed monthly by regional agencies APPA Trento (one station in the northern part of the lake, http://www.appa.provincia.tn.it/acqua/corpi_lacustri/laboratorio/pagina21.html) and ARPAV (two stations in the Eastern part, http://www.arpa.veneto.it/arpavinforma/bollettini/acqua-1/laghi). Measurements include Secchi Disk, chl-a concentration, dissolved oxygen, water temperature and pH.

6.1.2.5. Aeronet station

A sun-photometers CIMEL CE-318 belonging to AERONET network, has been recently installed (October 2014) on Sirmione Peninsula, on Archeological museum roof. Measuring sun and sky radiance it provides atmospheric properties, such as water vapour, ozone, and optical properties of the aerosol, such as Aerosol Optical Thickness (AOT), refractive index and particle size distribution spectra (http://aeronet.gsfc.nasa.gov/new_web/photo_db/Sirmione_Museo_GC.html).

http://www.arpa.veneto.it/arpavinforma/bollettini/acqua-1/laghi

http://aeronet.gsfc.nasa.gov/new_web/photo_db/Sirmione_Museo_GC.html





7. Po River delta The Po River is the largest river of Italy with a length of 673 km and a watershed of 71000 km2 (Figure 22; Table 16). It flows through one of the most urbanized and productive (mainly agriculture and industry) region of Europe and it represents an important freshwater and sediments source in the Mediterranean Sea. Over one third of its drainage basin is mountainous whereas the remaining is composed of a wide, low-gradient alluvial plain. The Po basin encompasses a variety of Alpine and Apennine streams with different hydrology and lithology. A third of the annual flow is regulated by reservoir management for hydropower and irrigation purposes. Despite this, the river still experiences short-lived event in response to precipitations. The Po River discharge typically exhibits large interannual variability, always characterized by two seasonal floods: one in the autumn driven by intense rainfall with the onset of Polar front and the other in the spring due to both snow melting and frontal rainfall. At the closure river cross section, which is conventionally located at Pontelagoscuro (90 km upstream the river mouth), the flow of the Po River and its tributaries conveys and it is rarely affected by tides and seawater intrusion. At Pontelagoscuro gauging station, hourly discharge is measured since the beginning of 20th century. The river enters the northern Adriatic Sea through a large delta with five major distributaries. From north to south, these are: Maistra, Pila, Tolle, Gnocca, and Goro (Figure 22), each providing different freshwater discharge and sediment loads. The hydrological behaviours of the Po River have been extensively studied, especially for what refers to the flood. Although the river discharge exerts a direct control on the transport of organic and inorganic matter, both qualitative and quantitative estimations of the river material are poorly constrained. This information is very important for many scientific applications: i.e. for assessing the input of the land-derived material entering the Adriatic Sea, for estimating the sediment load, for modeling the sediment transport and partitioning through the distributaries and for understanding the importance of event-dominated transport.

Table 16. Main features of the Po River.

River Length

(km) Watershed

(km2) Mean discharge

(m3 s-1) Min discharge

(m3 s-1) Max discharge

(m3 s-1)

Po 637 71000 1511 275 9780





Figure 22. Po River basin (Viet, 2015; data from: Andrea Coppola, 2008).


7.1.1. Satellite image acquisition The dataset includes 2 Landsat-8 OLI data on the 12th December 2014 and 26th July 2016 (Table 17). For the study area, scenes of two paths (191 and 192) were available providing a revisiting time of 7-9 days for a region extending from the west of Po River delta to about 30 km off the Northern Adriatic Sea coast.

Table 17. Data acquired in the Po River.

Sensor ID Date acquired [yyyy mm dd]

time (UTC) Path Row Hydrometric level at Pontelagoscuro

[m]

Water discharge [m3 s-1]

L8 2014346 2014 12 12 09:58 192 29 -1.7 2885

L8 2016208 2016 07 26 09:58 192 29 -5.98 631






7.1.2.1. Overview

Two field surveys were carried out in the autumn 2014 and summer 2016 in the proximity of the Po River delta (Figure 23) and a set of stations was sampled to collect hydrologic, biological and optical parameters. The investigated area included a segment of the Po River, located at the apex of its delta (the hinge point), the river delta and prodelta, starting where the river leaves the confines of its valley and divides into distributary channels, reaching the sea across very low slopes of the delta plain. All sampling dates matched a remote sensor overpass of Landsat-8. In Figure 23 the sampling sites are reported. In Table 18 a summary of the in situ and lab data measured during the fieldwork activities is reported.

Figure 23. Study area with location of fieldwork activities carried out for validation and monitoring fixed stations (purple and black stars). The five major distributaries of the Po River are indicated.

The dotted magenta line denotes the overlapping portion of the two Landsat 8 swaths, available for this site.

Table 18. Summary of the in situ and lab data measured during the fieldwork activities in the Po River. The description of measurements protocol and methods can be found in Braga et al., 2013.





12/12/2014 26/07/2016

Wat

er

AOPs (Rrs) WISP-3 7 -

Water costituents

Chl-a, SPIM, SPOM, CDOM, particle size distribution and particle volume concentration (LISST)

7 -

TSM and turbidity 12 7

Water quality Physico-chemical characteristics 12 7

IOPs

Spectral backscattering (HS6) 7 -

Absorption (filterPAD) 7 -

Beam attenuation (C-star) 7 -


At 7 stations on the 12 December 2014, the CNR team made in situ measurements of spectral backscattering with a Hobi-Labs Hydroscat-6. Remote-sensing reflectance was measured with a WISP-3 spectroradiometer (Hommerson et al., 2012). Beam attenuation was measured with a C-star (Wet-labs).


At each station, Conductivity-Temperature-Depth (CTD) data were obtained utilizing an Idronaut Ocean Seven 316Plus multi-parameter probe, equipped also with a backscattering optical sensor (Seapoint turbidity meter, operating at 880 nm), previously calibrated utilizing a linear regression with SPM analytical values. Water samples were collected using Niskin bottles within the surface layer (upper 1 m). The SPM concentration was determined by filtering known volumes of seawater (0.5 to 4 L, depending on Secchi disk depth) through precombusted (440°C) and pre-weighed 47 mm fiberglass filters (Whatman, GF/F 0.7 μm nominal pore size) at low vacuum. Each filter was then rinsed with Milli-Q water to remove dissolved salts, and stored at −20°C. In the laboratory, filters were oven-dried for 24 h at 60°C, and then weighed under a dry atmosphere. On the 12 December 2014, water samples were collected from 7 validation stations in the Po River prodelta for the analysis of bio-optical and biogeochemical parameters in the laboratory. Particle size distribution and particle volume concentration were measured at discrete depths with a LISST (Sequoia). Secchi disk depth was also measured. Water samples were collected using Niskin bottles within the surface layer (upper 1 m), filtered immediately in situ and stored for subsequent laboratory analysis.





The samples were filtered through 40 mm GF/F filter papers and the concentration of chl-a was measured using the trichromatic method. Samples for CDOM analysis were filtered through 0.2 μm membrane filters and collected in amber glass bottles. They were maintained on ice in the dark until they could be processed in the laboratory, then measured using a dual-beam spectrophotometer against a Milli-Q reference. The absorption spectra of particles retained on the filters, ap(λ), were obtained using the filter pad technique (Strömbeck and Pierson, 2001) and were calculated according to Babin et al. (2003).





8. UK Lakes Loch Lomond is the largest lake by surface area in Great Britain covering some 71 km2 (Figure 24). It is warm, monomictic and thermally stratifies during summer. The lake is comprised of two major ecologically distinct basins: the northern basin is deep (mean depth c. 130 m) and oligotrophic, while the southern basin is much shallower (mean depth c. 10 m) and mesotrophic. Diatoms, desmids and other green algae dominate the phytoplankton flora of the lake, but recently there has been a progressive shift towards taxa more typical of nutrient enriched waters with an increase in the abundance of cyanobacteria species. The lake is also relatively high in CDOM, particularly in the northern basin and near to river inflows. Loch Leven is a shallow eutrophic lake in central Scotland (Figure 24). It has a surface area of approximately 13.2 km2, a mean depth just over 3 m and a maximum depth of 25 m. The lake is very well mixed and non-stratifying. The lake is eutrophic due to high nutrient inputs from the catchment and internally from bottom sediments. The spring phytoplankton bloom on Loch Leven is dominated by diatom species, but cyanobacterial blooms are common in late summer and early autumn. Chla concentrations can reach approximately 50 μg L-1 in open water during these bloom events and shoreline scums often form on downwind shorelines.

Figure 24. Loch Lomond and Loch Leven in central Scotland.






8.1.1. Satellite image acquisition The sampling campaigns on Loch Lomond and Loch Leven were all conducted coincident to Sentinel-3 overpasses. No Sentinel-2 overpasses occurred during the sampling campaigns.


8.1.2.1. Overview

USTIR undertook a sampling campaign on Loch Lomond on 16th August 2016 during which six station were sampled across the lake. Loch Leven was sampled fortnightly during September and October 2016.


The USTIR team made in situ measurements of spectral absorption and attenuation using a WetLabs AC-S (with and without a 0.2 μm filter for removal of particulates) and spectral backscattering using a Wetlabs ECO-BB3. Temperature, depth and salinity were logged using a SeaBird CTD. Size-fractionated (0.2, 2, 20 and 200 μm) measurements of absorption and attenuation were made at selected stations over the course of the campaign. Subsurface radiance reflectance was measured using a set of Satlantic HyperOCRs. Measurements of downwelling irradiance (Ed), skylight radiance (Ls) and total surface radiance (Lt) for computation of water-leaving reflectance (ρ) were made using a trio of TriOS RAMSES radiometers. Water-leaving reflectances were calculated from remote-sensing reflectance computed according to Simis and Olssen (2013).


Water samples were collected at sampling stations for the analysis of bio-optical and biogeochemical parameters in the laboratory. Triplicate large volume water samples (2L) were collected from the surface using a bucket and immediately decanted into clean, wide-necked amber Nalgene bottles. Samples for CDOM analysis were transferred into 0.1L clean amber glass bottles. Samples were stored on ice in the dark until filtration could be completed. Samples for CDOM analysis were filtered immediately on-board the research vessel and preserved with 1% sodium azide. Sample aliquots for pigment and CDOM analysis were filtered within 12 h; samples for TSM analysis were filtered within 24 h.

Sample aliquots were filtered in triplicate under low vacuum through 25 mm GF/F filter papers for determination of Chla, PC and HPLC (pigments) and in duplicate for the measurement of particulate

absorption. These samples were immediately frozen at -20C until they could be transferred by to

USTIR for long-term preservation. CDOM samples were filtered through 0.2 m Nucleopore membrane filters. Samples aliquots for TSM were filtered in triplicate onto 47 mm pre-weighted

GF/F filters. Filtered samples were transferred to USTIR while frozen and stored at -80C until further analysis could occur.

Chla was determined by spectrophotometry following extraction in hot ethanol according to ISO 10260 (1992). PC was determined according to Horvath et al. (2013). HPLC analysis of





phytoplankton pigments was performed on a Dionex Ultimate 3000 system according to the method of Van Heukelem & Thomas (2001). TSM was determined gravimetrically on a calibrated microbalance; the inorganic component was determined gravimetrically after combustion of filter






9. Danube Delta & Black Sea The Danube Delta is the second largest river delta in Europe (after the Volga) and the largest remaining natural wetland covering an area 4,152 km2; more than 80% of the delta occurs in Romania with the remainder in Ukraine. It is composed of a complex network of river channels, lakes, coastal lagoons interspersed with extensive floodplain wetlands. It has numerous conservation designations including Ramsar status and was listed as a World Heritage Site in 1991. The Danube river enters the Black Sea via three large branches, Chilia, and Sfantu Gheoghe. The Black Sea covers an area of 436,400 km2 and reaches a maximum depth of 2,212 m. The Black Sea drains into the Mediterranean Sea and subsequently the Atlantic Ocean, via the Aegean Sea. The Bosphorus Strait connects the Black Sea to the Sea of Marmara, and the Strait of the Dardanelles connects that sea to the Aegean Sea. The Black Sea is connected to the Sea of Azov by the Strait of Kerch. The lakes and coastal lagoons of the Danube Delta are highly diverse in terms of their physical biogeochemical characteristics. The lakes that occur on the floodplain include relatively “black” waters that maintain only a weak hydrological connection with the main river channel to highly turbid and often relatively productive lakes and coastal lagoons that are strongly connected to the river and/or the coastal Black Sea. The largest extent of inland water is the Razim-Sinoie lake complex situated on the northwestern coast of the Black Sea with a surface area of 863.5 km2 and a maximum depth of 3.5 m. The Romanian Black Sea coast receives significant inputs of sediment from the Danube river and as such is highly turbid and productive. The influence of the Danube River plume extends tens of kilometres into the Black Sea and strongly influences biogeochemical processes on the sea shelf.


9.1.1. Satellite image acquisition Sentinel-2 MSI overpasses over the area of the Black Sea covered by the cruise occurred on the 5th, 8th and 11th May 2016. However, all overpasses were affected to some extent by cloud cover. Sentinel-3 had just commended its commissioning phase at the time of the Black Sea cruise. However, a cloud free Sentinel-3 OCLI overpass occurred on the 11th May 2016.


9.1.2.1. Overview

USTIR led a research cruise on the Black Sea between the 5th to the 12th May 2016 on-board the R/V Mare Nigrum with funding from the EUROFLEETS 2 programme. The cruise occurred on the Romanian Black Sea coastal waters in the region influenced by the Danube plume. In total, 29





stations were sampled during the campaign (see Figure 25). The Black Sea cruise was followed by a sampling campaign in the Danube Delta between 23rd May to the 3rd June aboard the R/V Istros during which 25 stations (see Figure 26) were sampled covering the main river channels, lakes and coastal lagoons.

Figure 25. The stations sampled visited by the R/V Mare Nigrum in the Black Sea between the 5th to the 12th May 2016.

Figure 26. The stations sampled in the Danube Delta between the 23rd May to the 3rd June 2016.






Measurements of the inherent optical properties (IOPs) were made as depth profiles at each

station. The measurement set included: spectral absorption, scattering and beam attenuation at

84 wavelengths (400-730nm) using a Wetlabs AC-S (with and without a 0.2 µm filter for separation

of dissolved and particulate material) and spectral backscattering at 3 wavelengths using a Wetlabs

ECO BB3. The instruments were deployed using a dedicated optics cage and the data logged to a

Wetlabs DH4 logger. The optical measurements adhered as far as possible to standard NASA

protocols. These data were supported by measurements of chlorophyll and CDOM fluorescence

and turbidity from a Turner C6 sonde with Cyclops-7 sensors and salinity, depth and temperature

measurements from a SeaBird CTD. The IOP data will be used as inputs for radiative transfer

modelling as well as for the validation of IOP retrieval algorithms.

Above-water hyperspectral (350-800 nm) radiometric data were collected during the campaign.

Sampling of above water radiometric quantities was carried out using three RAMSES sensors (TriOS

GmbH, Germany), which simultaneously measured downwelling irradiance (Ed), sky radiance (Ls)

and water leaving radiance (Lt). Location and time tracking of the each spectra was realised by a

GPS connected to the system. The optical sensors were mounted on a positioning pole at the front

part of R/V Mare Nigrum (5m above sea level). Above-water remote sensing reflectance data were

collected at each station. The crew ensured the vessel was positioned in such a way that the

sensors were measuring away from the sun. Further finer corrections in viewing zenith and azimuth

angles, in order to minimise sun glint and sky radiance effects, were achieved by manually turning

the positioning pole. Above-water radiometric data were also continuously recorded (from 09:50

to 17:00 local time) along transects. Route between the stations was planned, whenever this was

possible, to obtain measurements within the recommended viewing angles. Moreover, when

conditions were suitable for validation of OLCI and MSI data, reflectance measurements were

collected (with the vessel stopping and positioning away from the sun for 5 minutes) within 1 hour

before/after the satellite overpass. All Ramses radiometers were calibrated by the manufacturer

just before the campaign.


Water samples were collected at sampling stations for the analysis of bio-optical and biogeochemical parameters in the laboratory. In the Black Sea samples were collected on stations at up to 5 depths using a niskin rosette. Samples collected during transects were collected from the surface only using a single 10L niskin. In the Danube Delta large volume water samples (2L) were collected from the surface using a bucket and immediately decanted into clean, wide-necked amber Nalgene bottles.

The samples were stored on ice in the dark and filtered within 3 h of collection on-board the research vessels. Sample aliquots were filtered in triplicate under low vacuum through 25 mm





GF/F filter papers for determination of Chl-a, PC and HPLC (pigments) and in duplicate for the

measurement of particulate absorption. These samples were immediately frozen at -20C until they could be transferred by to USTIR for long-term preservation. CDOM samples were filtered

through 0.2 m Nucleopore membrane filters. Samples aliquots for TSM were filtered in triplicate onto 47 mm pre-weighted GF/F filters. Filtered samples were transferred to USTIR while frozen and

stored at -80C until further analysis could occur.

Chla was determined by spectrophotometry following extraction in hot ethanol according to ISO 10260 (1992). PC was determined according to Horvath et al. (2013). HPLC analysis of phytoplankton pigments was performed on a Dionex Ultimate 3000 system according to the method of Van Heukelem & Thomas (2001). TSM was determined gravimetrically on a calibrated microbalance; the inorganic component was determined gravimetrically after combustion of filter






10. Conclusion In 2015 and 2016 several measurement campaigns were set up to acquire data sets encompassing in situ IOP, AOP and biogeochemical data with concurrent airborne hyperspectral and/or spaceborne images. These data sets bring added value to the INFORM project and will greatly enhance the amount of data available for algorithm development and validation (WP5) within the INFORM project and the other WPs.





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Bresciani M, Rossini M, Morabito G, Matta E, Pinardi M, Cogliati S, Julitta T, Colombo R, Braga F, Giardino C, 2013. Analysis of within- and between-day chlorophyll-a dynamics in Mantua Superior Lake, with a continuous spectroradiometric measurement. Mar Freshwater Res, 64: 1-14.

De Haan J.F., Hovenier J.W, Kokke J.M.M, Van Stokkom H.T.C., 1991. Removal of atmospheric influences on satellite-borne imagery: a radiative transfer approach. Remote Sensing of Environment, 37,1–21. De Haan, J.F., Kokke, J.M.M. (1996). Remote sensing algorithm development toolkit I Operationalization of atmospheric correction methods for tidal and inland waters (Netherlands Remote Sensing Board (BCRS) publication. Rijkswaterstaat Survey Dept. Technical Report) 91p.

Giardino C, Brando VE, Dekker AG, Strömbeck N, Candiani G, 2007. Assessment of water quality in Lake Garda (Italy) using Hyperion. Remote Sens. Environ. 109: 183-195.

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Hommersom A, Kratzer S, Laanen M, Ansko I, Ligi M, Bresciani M, Giardino C, Beltran J, Moore G, Wernand M, Peters S, 2012. An inter-Comparison in the field between the new Wisp-3 and other radiometers (Trios Ramses, Asd Fieldspec, and Taccs). J. Appl. Remote Sens. 6: 063615-1 2012.

Horváth H., Kovács A. W., Riddick C., , and Présing M., 2013. Extraction methods for phycocyanin determination in freshwater filamentous cyanobacteria and their application in a shallow lake. European Journal Of Phycology Vol. 48 , Issue 3,2013.

Itten KI, Dell’Endice F, Hueni A, Kneubühler M, Schläpfer D, Odermatt D, Seidel F, Huber S, Schopfer J, Kellenberger T, Bühler Y, D’Odorico P, Nieke J, Alberti E, Meuleman K, 2008. APEX - the Hyperspectral ESA Airborne Prism Experiment. Sensors 8: 6235-6259. Iwamura T, Nagai H, Ishimura S, 1970. Improved methods for determining the contents of chlorophyll, protein, ribonucleic and deoxyribonucleic acid in planktonic populations. Internationale Revue der gesamten Hyrdobiologie 55: 131–147. Kishino M, Takahashi M, Okami N, Ichimura S, 1985. Estimation of the spectral absorption coefficients of phytoplankton in the sea. Bull. Mar. Sci. 37: 634-642. Lorenzen CJ, 1967. Determination of chlorophyll and pheo-pigments: spectrophotometric equations. Limnol. Oceanogr. 12: 343-346.

Mobley, C.D. ,1999. Estimation of the remote-sensing reflectance from above- surface measurements. Applied Optics, 38, 7442-7455.

Palmer S, Hunter P, Lankester T, Hubbard S, Spyrakos E, Tyler A, Presing M, Horvath H, Lamb A, Balzter H, Toth V, 2015. Validation of Envisat MERIS algorithms for chlorophyll retrieval in a large, turbid and optically-complex shallow lake. Remote Sens. Environ. 157: 158-169. Strömbeck N, Pierson E, 2001. The effects of variability in the inherent optical properties on estimations of chlorophyll a by remote sensing in Swedish freshwater. The Science of the Total Environment 268: 123–137. Trüper HG, Yentsch CS, 1967. Use of glass fiber filters for the rapid preparation of in vivo absorption spectra of photosynthetic bacteria. Journal of Bacteriology 94: 1255-1256. Utermöhl H, 1958. [Zur Vervollkommung der quantitative Phytoplankton Methodik].[Article in German]. Mitt. Int. Verein. Limnol. 9: 1-38.

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