Current MDI Projects

Earth System Deep Learning for Seasonal Fire Forecasting in Europe (SeasFire)
Start: March 2022
End: March 2023
PI / Co-PI: Nuno Carvalhais
Participating BGI member: Nuno Carvalhais, Lazaro Alonso
Funding: ESA more
Earth System Models for the Future
ESM2025 – Earth system models for the future is an ambitious European research project on Earth System modelling that will build a novel generation of Earth system models fitted to support the development of mitigation and adaptation strategies in line with the commitments of the Paris Agreement. more
Project Office BIOMASS emerges in the advent of ESA’s BIOMASS mission whose primary objective is to improve our understanding on the role of the land biosphere on the global carbon cycle. The project aims to synthesize and bring forth the current understanding of challenges and opportunities in monitoring biomass in terrestrial ecosystems to further develop our knowledge of the Earth system. Jointly, Stefanie, Hui, and Nuno lead the effort. more
In DeepCube we will be looking at fire risk in the Mediterranean region and at droughts in Africa through deep learning and hybrid modeling approaches, using multivariate data-cubes and cloud data processing workflows. The BGI/MDI team includes Dushyant, Chris, Felix, Fabian, Lazaro, Vitus, Markus and Nuno. more
In CRESCENDO, Sujan and Nuno evaluate the carbon cycle dynamics in European Earth System Models and contribute to the implementation of model evaluation diagnostics of carbon turnover times in the ESMValTool. more


We have been working closely with GDM in building Strategies to Integrate Data and Biogeochemical models in Development (SINDBAD), a modular framework for model-data-integration. Currently, SINDBAD supports several projects on water-carbon coupling and vegetation dynamics across scales: Graph by Sujan and Martin Jung (GDM), and the PhD projects of Tina, Hoontaek and Siyuan.

Other projects

Analysis of climate constraints on gross primary productivity based on light use efficiency models: Shanning analyzes the patterns of climate sensitivities of gross primary productivity using FLUXNET data and global sun-induced fluorescence data. The project focuses on the evaluation and prediction of climate sensitivity functions and model parameters based on model-data fusion strategy.

Ecosystem transpiration from eddy covariance, where Jake develops and assesses methods for partitioning the evapotranspiration flux measured from eddy covariance into the transpiration and abiotic evaporation components. The methods are applied to the FLUXNET dataset, giving a global perspective on plant water use.

Ecological process understanding across time scales: In her PhD project, Nora explores time-scale dependencies between vegetation and climate globally, to answer questions such as: How does the decadal sensitivity of vegetation to climate differ from seasonal sensitivity? Can we extrapolate atmosphere-biosphere relations from one time scale to the other? Where and when do we need to account for such time-scale dependencies in vegetation modeling and monitoring?

Water-carbon-cycle interactions across scales: In his PhD project, Hoontaek attempts to answer 1) how interactions between water and carbon cycles form observations of each cycle and 2) who drives whom at which scales. To do so, Hoontaek will take advantage of the stream of global datasets and a model-data-fusion framework.

Change in global vegetation: Here Siyuan explores data-assimilation to study vegetation processes and ecosystem responses to environment at different spatio-temporal scales. The project investigates the links between ecosystem carbon fluxes and changes in vegetation carbon states, integrating vegetation mortality and metabolic activity based on a novel modular modeling framework and different observational data streams.

Inter-annual variability in terrestrial ecosystem fluxes, in this PhD project, Ranit endeavours to develop a hybrid modelling approach to simulate water and carbon fluxes at the ecosystem level. The project will exploit large multi-variate Earth Observation datasets to explore causal linkages between changing climate and ecosystem responses by taking advantage of both physically based models (physically interpretable) and machine learning approaches (data-adaptive)

Go to Editor View