A major challenge for Earth Observation (EO) is to understand the relative influence of global and local effects within the Earth system. Thermal variations in particular are important to isolate, as they contribute and invariably drive the overall energy balance. Increasing human population and activities contributes very short-term local effects that can have significant long-term global effects. Multi-scale measurements are therefore required in order to understand how these changes evolve over time.
In particular, accurate measurement of LST at local (< 100 m) scales (to resolve fields and cities for example) and knowledge of the composition of the Earth’s surface is lacking. Current operational infrared satellite EO sensors typically offer highly accurate LST but their spatial resolutions are of order 1 km. Some higher spatial thermal imaging capability for LST measurement is available but their limited temporal sampling and lower accuracy restricts scientific advances and uptake of applications from these missions. This lack of long-term, stable, high resolution satellite LST data has been a limiting factor in putting extreme heat wave events, for example, into full climatological context.
This project will develop new methods to study the changing temperature of the Earth’s surface, a need recognised to be very important by international space agencies and environmental scientists. This project will apply new mathematical approaches – optimal estimation (OE) and artificial intelligence (AI) to retrieve LST from remote sensing platforms. AI techniques, such as Machine Learning and neural networks have been successfully applied for big data analysis in many areas of science. Such methods have the potential to transform thermal satellite remote sensing. This project will develop a new AI method to data from current missions and new sensors, and will carry out testing of the methods on both simulations and real data from hyperspectral aircraft measurements. Once verified, the new scheme will be used to identify the performances, modelling and design of new satellite sensors.
This project will fall within the National Centre of Earth Observation (NCEO), which is the leading collective of satellite remote sensing in the UK. Furthermore, the project will feed directly into the preparations for future high resolution thermal sensors, particularly through the European Space Agency (ESA). The student will have an excellent opportunity to work alongside the leading scientists across Europe and beyond in measuring the temperature of the Earth from space.
Entry requirements:
Applicants are required to hold/or expect to obtain a UK Bachelor Degree 2:1 or better (or overseas equivalent) in a relevant subject.
The University of Leicester English language requirements apply where applicable.
Application advice:
To apply, please refer to the guidance at: https://le.ac.uk/study/research-degrees/funded-opportunities/cse-phys-ghent-project-1
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