Photo: M. Zeising/AWI
https://www.awi.de/en/science/climate-sciences/physical-oceanography/main-research-focus/ocean-optics.html
Phytoplankton refers to microscopically small algae in the ocean that synthesize biomass through photosynthesis, thereby forming the foundation of marine life and global biogeochemical cycles. Phytoplankton is classified into functional groups, which differ in size, shape, and pigment composition, influencing their ecological role and distribution in the ocean. The Phytooptics working group at the Alfred Wegener Institute for Polar and Marine Research (AWI) is developing innovative, bio-optically based methods to detect phytoplankton and other water constituents with high spatial and temporal resolution. The distinction between different phytoplankton groups and constituents is based on spectrally resolved optical measurements that, in addition to Chlorophyll, allow the determination of other pigment and substance concentrations. Satellite data are combined with in-situ measurements from research expeditions to develop and refine algorithms for the determination of optical and biogeochemical properties. The resulting data products are validated in comparison with in-situ data, and the coverage within the water column and under cloud conditions is extended by means of automated optical measurements, machine learning techniques, and coupled ocean-ecosystem modelling. A particular focus is on the long-term (since 1997) analysis of functional phytoplankton groups in the polar regions to understand trends, variability, and climate-related changes. Many collaborations with other working groups and institutes are involved in this effort. The overall objective is to quantify the role of phytoplankton in general, as well as that of different functional groups in the ocean, its interactions with the atmosphere, and thereby allow for informed statements on the impacts of climate change on marine ecosystems and biogeochemical cycles.
Research Objectives
• What changes in the quantity and composition of phytoplankton can be observed in polar seas in the last 30 years, and how do these changes compare to the changes in temperature and sea ice coverage?
• How can functional phytoplankton groups and other water constituents be quantified more comprehensively and over longer periods using optical in-situ and satellite data?
• How can high-resolution optical in-situ and satellite data be improved for satellite product generation by applying machine learning and coupled numerical modeling?
Methods
• Study of long-term datasets on the distribution and composition of phytoplankton and its breakdown products, as well as the analysis of the interaction with the physical properties of the ocean and the atmosphere (AC3, FRAM, DOMSPEC).
• Biogeochemical ocean modeling of the organic carbon cycle and interactions between phytoplankton, particles, and the atmosphere (AC3).
• Testing and further development of bio-optical methods for automated in-situ optical measurements (MUSE, FRAM) and creation of long-term datasets from satellite data (ML-PhytAO, PhytoCCI, S5P-PAL2, 4DMEDSEA, EnMAP4Water, GALENE-SCS).
• Development of satellite data products for operational service (e.g., for the E.U. Copernicus Marine Environment Monitoring Service, ESA, Climate Change Initiative, DLR), especially for use in polar regions (ML-PhytAO, S5P-PAL2, EnMAP4Water-PFT, PhytoCCI).
• In-situ bio-optical measurements during ship campaigns, mainly in polar seas (continuous high spectrally and temporal resolved measurements at surface and in the water column – MUSE, FRAM).
• Evaluation of multispectral and hyperspectral satellite data products with in-situ validation (e.g., EnMAP, Sentinel-3 OLCI, Sentinel-5P TROPOMI – MLPhytAO, EnMAP4Water, S5POC, PhytoCCI, 4DMEDSEA).
• Application of machine learning to improve the determination of functional phytoplankton groups (MLPhytAO), as well as to improve the coverage of satellite data in the water column and under ice and cloud cover (gap filling, data fusion – Phyto4D, DOMSPEC, ML-PhytAO).
•Utilization of coupled atmospheric-water-radiative transport simulations to derive the uncertainties of algorithms (S5P-PAL2) and the sensitivity of satellite sensors (GALENE-SCS).
Responsible person
Prof. Dr. Astrid Bracher, AWI
https://www.awi.de/en/about-us/organisation/staff/single-view/astrid-bracher.html
Current projects
MUSE:
Marine Umweltrobotik und -Sensorik für nachhaltige Erforschung, dem Schutz und Management der Küsten, Meere und Polarregionen
DFG Transregio 172 (AC)3 (2016-2027):
Arctic Amplification – Climate Relevant Atmospheric and Surface Processes, Feedback, and Mechanisms
ESA project GALENE SCS (2026-2027):
A satellite mission proposed at Earth Explorer-ESA program for observing coastal and inland aquatic ecosystems and wetlands
MARDATA/Inspire project DOMSpec (2026-2029):
Deciphering Fram Strait’s Organic Matter Dynamics from Spectral High Resolution Data
ESA project PhytoCCI (2025-2028):
Phytoplankton biomass and diversity Climate Change Initiative
https://climate.esa.int/de/projekte/PHYTO-CCI
CMEMS project MLPhytAO (2024-2026):
Machine Learning based approach towards products of Phytoplankton functional Types in the Arctic Ocean
https://marine.copernicus.eu/about/research-development-projects/2022-2024/ml-phytao
DLR/BMWE project EnMAP4Water (2024-2028):
https://www.awi.de/en/science/climate-sciences/physical-oceanography/main-research-focus/ocean-optics/projects/enmap-calval-water.html
ESA project 4DMED-Sea (2023-2026):
Sustaining the Mediterranean Sea: Fostering Science, Preserving Ocean Life
http://ricerca.ismar.cnr.it/4DMED/index.html
ESA and S&T project S5POC-PAL:
Scientific support for integration of the S5P ocean color spectral diffuse attenuation processor onto S5P-PAL (S5P‐PAL2) (2023-2026)