204
between the Arctic and the North Atlantic oceans to wind
stress forcing through numerical experiments. The tool for
this is the Modini-system, a partial coupling technique that
allows flexible experiments with prescribed wind stress
fields for the ocean in the otherwise fully coupled Earth
System Model of the Max Planck Institute. In this work we
present the first results in investigating the role of atmospheric forcing in shaping freshwater reservoirs and
exchanges between different oceanic subregions by comparing our model results using external wind stress forcing with
the Modini-system, and fully coupled runs.
3 Ocean Optics and Ocean Color Remote
Sensing
Veloisa Mascarenhas
1
, Yangyang Liu
2
, and Therese Keck
3
1
Institut für Chemie und Biologie des Meeres (ICBM),
Universität Oldenburg, Schleusenstrasse 1, 26382
Wilhelmshaven, Germany
2
Alfred Wegener Institute (AWI), Helmholtz Centre for
Polar and Marine Research, P.O. Box 120161, 27570
Bremerhaven, Germany
3
Institute for Space Sciences, Freie Universität Berlin,
Carl-Heinrich-Becker-Weg 6-10, 12165 Berlin, Germany
3.1 Call for Abstracts
Ocean color remote sensing (OCRS) supports many research
fields such as ocean bio-geo-chemistry, physical oceanography, ocean-system modeling and other climate change studies with its unique capability of providing synoptic view of
the aquatic ecosystem. This session invites in-situ and satellite studies of marine bio-optics and OCRS such as hyperspectral radiometric observations, light interactions with
optically active constituents (phytoplankton, colored dissolved organic matter, total suspended matter), inherent and
apparent optical properties, algorithm development & validation, atmospheric correction algorithms, time series analysis, products and applications using multiple platforms and
coupled models.
3.2 Abstracts of Oral Presentations
3.2.1 Dominant Phytoplankton Group
Identification by Using Simulated and in situ
Hyperspectral Remote Sensing Reflectance
Hongyan Xi
1*
1
Institute of Coastal Research, Helmholtz-Zentrum
Geesthacht (HZG), Max-Planck-Str. 1, 21502 Geesthacht,
Germany
*invited speaker, corresponding author: hongyan.xi@
hzg.de
The Environmental Mapping and Analysis Program
(EnMAP) is a German hyperspectral satellite mission that
aims at monitoring and characterizing the Earth’s environment on a global scale. One of its applications focuses on the
aquatic ecosystems. With advanced spectral resolution of
EnMAP hyperspectral imager, it provides much potential to
identify phytoplankton taxonomic groups and improve mapping of phytoplankton community composition both for
global oceans and regional waters. Given that the commonly
used parameter obtained directly from hyperspectral earth
observation sensors is the remote sensing reflectance (Rrs),
this study focused on identification of dominant phytoplankton groups by using Rrs spectra directly. Based on five standard absorption spectra representing five different
phytoplankton spectral groups, a simulated database of Rrs
(C2X database, compiled within the ESA SEOM C2X
Project) that includes 100,000 different water optical conditions was built with HydroLight. A test dataset was constructed using absorption spectra of 128 individual cultures
from different taxonomic phytoplankton groups by simulating Rrs for 120 different water conditions. An identification
approach is proposed to determine phytoplankton groups
with the use of simulated C2X data and the test data; the skill
of the identification is tested by investigating how and to
what extend water optical constituents (Chl, NAP, and
CDOM) impact the accuracy of this identification. The applicability of this approach in natural waters was also tested
with the use of in situ measurements from different regions.
3.2.2 PlanktonID – Combining in situ Imaging,
Deep Learning and Citizen Science for Global
Plankton Research
Svenja Christiansen
1*
, Rainer Kiko
1
, Simon-Martin
Schröder
2
, Reinhard Koch
2
, Lars Stemmann
3
1
GEOMAR, Helmholtz Centre for Ocean Research Kiel,
Hohenbergstraße 2, 24105 Kiel, Germany
2
Department of Computer Science, Christian-Albrechts
University Kiel, Olshausenstr. 4, 24148 Kiel, Germany
3
Sorbonne Universités, UPMC Univ Paris 06, UMR 7093,
France and LOV, Observatoire Océanologique, 06230
Villefranche/mer, France
*corresponding author: schristiansen@geomar.de
Keywords: Zooplankton, Imaging, Citizen science, Deep
learning, Rhizaria
Recent publications revealed the global importance of
single-celled zooplankton, belonging to the super group
Rhizaria and highlighted the need of in-situ imaging to study
these fragile organisms. The advance of in situ plankton
imaging techniques leads to increasing amounts of image
data sets that require identification to different taxonomic
levels. Automatic classification by computer algorithms proAppendices
between the Arctic and the North Atlantic oceans to wind
stress forcing through numerical experiments. The tool for
this is the Modini-system, a partial coupling technique that
allows flexible experiments with prescribed wind stress
fields for the ocean in the otherwise fully coupled Earth
System Model of the Max Planck Institute. In this work we
present the first results in investigating the role of atmospheric forcing in shaping freshwater reservoirs and
exchanges between different oceanic subregions by comparing our model results using external wind stress forcing with
the Modini-system, and fully coupled runs.
3 Ocean Optics and Ocean Color Remote
Sensing
Veloisa Mascarenhas
1
, Yangyang Liu
2
, and Therese Keck
3
1
Institut für Chemie und Biologie des Meeres (ICBM),
Universität Oldenburg, Schleusenstrasse 1, 26382
Wilhelmshaven, Germany
2
Alfred Wegener Institute (AWI), Helmholtz Centre for
Polar and Marine Research, P.O. Box 120161, 27570
Bremerhaven, Germany
3
Institute for Space Sciences, Freie Universität Berlin,
Carl-Heinrich-Becker-Weg 6-10, 12165 Berlin, Germany
3.1 Call for Abstracts
Ocean color remote sensing (OCRS) supports many research
fields such as ocean bio-geo-chemistry, physical oceanography, ocean-system modeling and other climate change studies with its unique capability of providing synoptic view of
the aquatic ecosystem. This session invites in-situ and satellite studies of marine bio-optics and OCRS such as hyperspectral radiometric observations, light interactions with
optically active constituents (phytoplankton, colored dissolved organic matter, total suspended matter), inherent and
apparent optical properties, algorithm development & validation, atmospheric correction algorithms, time series analysis, products and applications using multiple platforms and
coupled models.
3.2 Abstracts of Oral Presentations
3.2.1 Dominant Phytoplankton Group
Identification by Using Simulated and in situ
Hyperspectral Remote Sensing Reflectance
Hongyan Xi
1*
1
Institute of Coastal Research, Helmholtz-Zentrum
Geesthacht (HZG), Max-Planck-Str. 1, 21502 Geesthacht,
Germany
*invited speaker, corresponding author: hongyan.xi@
hzg.de
The Environmental Mapping and Analysis Program
(EnMAP) is a German hyperspectral satellite mission that
aims at monitoring and characterizing the Earth’s environment on a global scale. One of its applications focuses on the
aquatic ecosystems. With advanced spectral resolution of
EnMAP hyperspectral imager, it provides much potential to
identify phytoplankton taxonomic groups and improve mapping of phytoplankton community composition both for
global oceans and regional waters. Given that the commonly
used parameter obtained directly from hyperspectral earth
observation sensors is the remote sensing reflectance (Rrs),
this study focused on identification of dominant phytoplankton groups by using Rrs spectra directly. Based on five standard absorption spectra representing five different
phytoplankton spectral groups, a simulated database of Rrs
(C2X database, compiled within the ESA SEOM C2X
Project) that includes 100,000 different water optical conditions was built with HydroLight. A test dataset was constructed using absorption spectra of 128 individual cultures
from different taxonomic phytoplankton groups by simulating Rrs for 120 different water conditions. An identification
approach is proposed to determine phytoplankton groups
with the use of simulated C2X data and the test data; the skill
of the identification is tested by investigating how and to
what extend water optical constituents (Chl, NAP, and
CDOM) impact the accuracy of this identification. The applicability of this approach in natural waters was also tested
with the use of in situ measurements from different regions.
3.2.2 PlanktonID – Combining in situ Imaging,
Deep Learning and Citizen Science for Global
Plankton Research
Svenja Christiansen
1*
, Rainer Kiko
1
, Simon-Martin
Schröder
2
, Reinhard Koch
2
, Lars Stemmann
3
1
GEOMAR, Helmholtz Centre for Ocean Research Kiel,
Hohenbergstraße 2, 24105 Kiel, Germany
2
Department of Computer Science, Christian-Albrechts
University Kiel, Olshausenstr. 4, 24148 Kiel, Germany
3
Sorbonne Universités, UPMC Univ Paris 06, UMR 7093,
France and LOV, Observatoire Océanologique, 06230
Villefranche/mer, France
*corresponding author: schristiansen@geomar.de
Keywords: Zooplankton, Imaging, Citizen science, Deep
learning, Rhizaria
Recent publications revealed the global importance of
single-celled zooplankton, belonging to the super group
Rhizaria and highlighted the need of in-situ imaging to study
these fragile organisms. The advance of in situ plankton
imaging techniques leads to increasing amounts of image
data sets that require identification to different taxonomic
levels. Automatic classification by computer algorithms proAppendices