Research and Project Groups
Research and Project groups of the Department of Biogeochemical Integration
The Atmosphere-Biosphere Coupling, Climate and Causality research group focuses
on identifying feedbacks and casual links in the exchange of carbon, water and
energy fluxes between the terrestrial biosphere and atmosphere.
Alexander Winkler
The Eco-Meteorology group utilizes different micrometeorological methods and modelling approaches
to investigate land-atmosphere interactions for dryland (e.g., Mediterranean savanna) and wetland ecosystems (e.g. Blue Carbon).
Sung-Ching Lee
The Global Diagnostic Modeling group develops and analyzes global data-driven estimates of carbon,
water and energy fluxes by integrating in-situ measurements, satellite remote sensing and meteorological
reanalysis using machine learning and model data fusion techniques.
Martin Jung
The Model-Data Integration group is strongly motivated by the challenges of
representing terrestrial ecosystem fluxes in space and time in Earth system science.
We explore strategies and develop methods to extract information from data and translate
it into models to improve the understanding of ecosystem function.
Nuno Carvalhais
The Soil Biogeochemistry group aims to understand and quantify the role of subsurface processes
in biogeochemical cycles at different spatial scales. Our main goal is to explain the persistence
of organic matter in soils,m their vulnerability to global environmental and land use changes.
Marion Schrumpf
In the face of increasing environmental and climatic pressures, it is imperative to improve our understanding of the variability and causality underlying
biogeochemical cycles across different spatial and temporal scales. The research of the Machine Learning for Hydrological and Earth Systems (ML4HES) group, which is
also part of the ELLIS Unit Jena, is at its core motivated by the objective of exploring how environmental and climate sciences can benefit from advances in machine
learning and artificial intelligence.
Shijie Jiang
The Ecosystem Function from Earth Observation group aims to rethink how we
can quantify and map the functional properties of ecosystems from space.
(within GDM)
Gregory Duveiller
The Cross-Scale Terrestrial Ecophysiology (XTE) group is focused on taking
understanding gained from on the ground measurements into the context of broad
scale carbon, water and energy cycles, via knowledge guided, data driven methodologies.
Jacob Nelson
The Modelling Interactions in Soil Systems (MISS) project group concentrates on understanding the
dynamics and feedbacks in soil organic carbon (SOC) formation and decomposition. The group emphasizes the role of
mineral-associated organic carbon (MOC) and particulate organic carbon (POC) under changing environmental conditions.
Bernhard Ahrens
Satellite measurements offer indirect but large-scale information about the status and health of ecosystems,
also in inaccessible areas. Observations in different spectral regions give complementary information on different aspects
such as the greenness or water content of the canopy. The vegetation in turn strongly drives the exchange processes of CO2,
water and energy between the land surface and the atmosphere. In the case of CO2, the land ecosystems also compensate a large
part of anthropogenic emissions.
Our main goal is therefore the accurate quantification, and possibly a better understanding, of these exchange processes.
The group "Adapting Machine Learning for the Earth System" adapts machine learning methods to better understand the Earth system.
It particularly focuses on the relationships between weather, climate, and terrestrial vegetation.
Our goal is to analyze the complex interactions between these variables and contribute to our understanding of biogeochemistry.
Christian Reimers
The research of the Scalable Spatiotemporal Data Structures and Analytics focuses on finding
strategies to handle these diverse and very large datasets in an efficient way to build
data analysis pipelines that fulfill the needs of our scientific questions.
light green = project groups dark green = research groups
Former Research Groups
