Context of intervention
As part of a Research and Technology Organization (RTO), the work of the Environmental Research and Innovation (ERIN) Department tackles some of the major environmental challenges our society is facing today, such as climate change mitigation, ecosystem resilience, sustainable energy systems, the efficient use of renewable resources, and environmental pollution prevention and control. To this end, the mission of the ERIN department is:
(1) To conduct impact-driven scientific research and development, as well as technological innovation
(2) To support companies in the implementation of new environmental regulations and advise governments on determining sustainable policies for the future with the objectives of:
- Analysing, managing and exploiting sustainable resources (water, air, soil, renewable energy, bioresources)
- Reducing the environmental impact of human consumption and production activities
The postdoctoral researcher will intervene in the framework of the European ECOSTRESS Hub (EEH) project funded by the European Space Agency (ESA).
The project will develop land surface temperature (LST) and evapotranspiration (ET) products for Europe and Africa using high spatial and temporal resolution information from multiple thermal infrared (TIR) bands of the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS).
It will combine ECOSTRESS TIR data and Sentinel-2 visible, near infrared and shortwave infrared data in conjunction with ECMWF (European Centre for Medium range Weather Forecasting) environmental data archive for the creation of such a hub.
The targeted products are LST and ET that are used as an indicator for vegetation temperature, water status, water stress, and drought. The LST and ET information generated through this hub will be used to study the temporal response and spatial pattern of vegetation water use and vegetation response to differential water stress.
The EEH project shall be exploited as a support activity for the Copernicus High Priority Candidate Land Surface Temperature Monitoring mission (LSTM). This will support the development of LSTM retrieval techniques by utilizing ECOSTRESS data and enabling researchers to prototype LST and ET products in a cloud environment.
The postdoctoral researcher will be incorporated in the Remote Sensing and Natural Resources Modelling group of the Environmental Sensing and Modelling (ENVISION) RDI unit and she/he is also expected to strongly collaborate with scientists of the Environmental Informatics group.
The postdoctoral researcher will play a central role in the project and its outputs. Her/his main mission is to code LST and ET models in collaboration with computer scientists in Copernicus DIAS cloud environment. He will perform the validation and sensitivity analysis of multiple thermal remote sensing based ET models for Europe and Africa. In addition, the candidate will be asked to exploit information from a wide range of in-situ remotely sensed (e.g., ESA-CCI soil moisture, MODIS Land Surface Temperature, and Sun-Induced Chlorophyll Fluorescence) datasets.
- Implement LST and ET retrieval algorithms using ECOSTRESS and ancillary data
- Perform atmosphere correction of TIR data and implement atmosphere correction methods
- Analyse the LST and ET retrieval performance through validation and uncertainty analysis
- Preparation of LST and ET ATBD (Algorithm and Theoretical Basis Document), validation report, and presentations to the European Space Agency
- Coordinate with computer scientists to help running the LST and ET models in Copernicus DIAS cloud environment
- Prepare the required reports for the European ECOSTRESS Hub Project
Dissemination, valorisation and transfer
- Contribute to dissemination, valorisation and transfer of project results (e.g., participation in scientific conferences, training sessions, and publication in top-field peer-reviewed scientific journals)
- PhD degree in Remote Sensing, Geomatics, LST and ET modelling, Hydrology, Meteorology or Climate Sciences or other similar disciplines
- PhD is necessary, one or two years of postdoctoral experience in LST and ET modeling is an advantage
- Demonstrated technical skills with different programming languages (e.g., Python, R, Matlab)
- Experience in coding LST and ET models
- Experience with Linux-based systems, high performance computing and handling of large datasets is expected
- English is mandatory
- French will be considered as an asset
Candidates interested in the above position can apply online on our website www.list.lu
The application file should include:
- A CV
- A motivation letter
- The names of three referees
- A list of publications, invited talks