Master Student Internship on Resolving the Inequalities between Aerodynamic Temperature and Radiometric Surface Temperature (M/F)

Reference : ERIN-2020-Intern-015

Type: Intern
Contract type: Internship
Duration: 6 months
Place: Belvaux



Your work environment

The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies.

You will be part of the LIST Environmental Research and Innovation department

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 (e.g. adaptation to climate change, ecosystem resilience, sustainable energy systems, efficient use of renewable resources, 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, bio-resources)
  • Reducing the environmental impact of human consumption and production activities

Within the ERIN department, the ‘Environmental Sensing and Modelling’ (ENVISION) unit contributes to this mission by carrying out impact-driven research, geared towards monitoring, forecasting and predicting environmental systems in a changing world. An interdisciplinary team of around 50 scientists, engineers, post-docs and PhD candidates is developing new environmental process understanding, alongside new tools and technologies – operating at unprecedented spatial and temporal scales.

Embedded into the ENVISION unit, the ‘Remote sensing and natural resources modelling’ research group capitalizes on a blend of remote sensing data obtained from space- and air-borne platforms, as well as in-situ measured data, for producing information on the status of natural resources for public and private stakeholders.




Remotely sensed land surface temperature or radiometric surface temperature (TR) is extensively used to estimate evapotranspiration (E) through the surface energy balance (SEB) models. Estimating E using the SEB models is challenging due to inequalities between TR and aerodynamic temperatures (T0). The most serious assumption of the SEB model concerns the sensible heat flux (H) retrievals, specifically in using TR as a surrogate for the aerodynamic surface temperature. Since T0 cannot be directly measured, very often it is either replaced by TR, or many empirical adjustments are done to compensate the assumptions of their equalities. We have developed a fully analytical SEB model called STIC1.2 (Surface Temperature Initiated Closure), which directly retrieves T0 and is independent of any leafscale empirical parameterization of T0. An evaluation of STIC1.2 against high temporal frequency SEB flux measurements across an aridity gradient in Australia and United States revealed promising performance of the model in all ecosystems.

This internship position will evaluate the aerodynamic temperature retrievals of STIC1.2 with respect to T0 retrievals from the eddy covariance (EC) observations. The objectives of the internship is as follows:

  • Integrating daily MODIS and LANDSAT TR into STIC1.2; testing and validating STIC1.2 based T0 with respect to T0 retrieved from the EC observations.
  • Develop uncertainty framework in T0 estimates due to MODIS and LANDSAT land surface temperature uncertainties.
  • Understanding the differences between TR and T0 in various biomes and climate under different fractional vegetation cover and ecohydrological settings.





  • Be a last year undergraduate student (Master’s) in Environmental Science, or Environmental Engineering, Hydrology, Geography or related disciplines
  • Have skills and interests in environmental / evaporation / soil moisture modeling and remote sensing

Language skills

  • Fluent in English
  • French and German would be an asset

Possible specific skills/knowledge required

  • Good programming skills in Matlab, Python and/or R
  • Basic knowledge and skills in statistical data analysis
  • Knowledge of evapotranspiration and surface energy balance modelling


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Dr Kaniska MALLICK
Dr Kaniska MALLICK