Heterogeneous Earth observations and deep learning for modeling and understanding of land-atmosphere fluxes
Project descriptionThe MPI-BGC based FLUXCOM initiative develops data-driven methods for simulating fluxes of carbon, water, and energy between the atmosphere and the land-surface and has successfully integrated satellite and site-level observations to create ensembles of global flux estimates (Jung et al., 2020, 2019, Bodesheim et al. 2018, Tramontana et al., 2016, Jung et al., 2010). The global simulations are used widely in Earth System sciences to address questions such as the impact of extreme events or climate change on terrestrial ecosystems, or disentangling anthropogenic and biogenic carbon contributions to the atmospheric concentrations.
Nowadays, a large range of space-borne instruments provide complementary information on the state of the land surface in different spectral regions and acquisition modes, as well as spatial, temporal, and spectral resolutions. The observational capabilities from space have strongly increased and diversified – offering unprecedented opportunities for a comprehensive diagnosis of terrestrial ecosystems with a variety of indicators. Jointly, this variety of satellite measurements can boost modelling performance and the spatiotemporal detail of the simulations. This is of particular relevance in ecosystems that are very heterogeneous and / or undergo strong management. Examples are global crop productivity, selective wood harvest and forest disturbances, or high latitude ecosystems, all of which are notoriously challenging to represent realistically in FLUXCOM but are of high scientific interest as well as of relevance for society, food security, and policy.
At the same time, developments in machine learning open new avenues for the integration of diverse data sources. Novel deep learning architectures that exploit spatio-temporal interactions have the potential to bring flux predictions to the next level.
In this PhD project we seek to explore new ways to synergistically exploit diverse sets of Earth Observation data in FLUXCOM. The candidate will develop methods to explicitly or implicitly account for heterogeneities in acquisition properties of space-borne instruments (e.g., resolution, observation geometries, and overpass times) using machine learning, and to use the resulting flux estimates on targeted regions and specific scientific questions. Depending on the candidate's skills and interest, the project can be focused on ecological or methodological aspects. The thematic orientation is to be defined; possible research questions are:
Global Diagnostic Modeling (GDM)
RequirementsWe are looking for a motivated candidate with a strong interest in data analysis, machine learning, ecology, and climate. The successful candidate will work in close collaboration with an international and diverse research team. More specifically, the requirements are:
Jung et al. (2020), Biogeosciences https://doi.org/10.5194/bg-17-1343-2020
Jung et al. (2019), Scientific Data https://doi.org/10.1038/s41597-019-0076-8
Bodesheim et al. (2018), ESSD https://doi.org/10.5194/essd-10-1327-201
Tramontana et al. (2016) https://doi.org/10.5194/bg-13-4291-2016
Jung et al. (2010), J. Geophys. Res. https://doi.org/10.1029/2010JG001566
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