Project highlights

  • Develop new algorithm approaches to transform thermal satellite remote sensing
  • Confront the challenge of big data to explore changes in the temperature and composition of the Earth’s surface
  • Opportunity to work with world leading industry in space/aeronautics and data economy

Overview

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 land surface temperature (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 the order of 1 km. Some higher spatial thermal imaging capability for LST measurements is available but their limited temporal sampling and lower accuracy restricts scientific advances and uptake of applications from these missions. Even in regions that are considered well-observed, for example for the study of urban heat islands (UHI) and their impact on human health, detailed observations of surface temperature variability at required scales remain challenging. 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.

Host

University of Leicester

Theme

  • Climate and Environmental Sustainability

Supervisors

Project investigator

Co-investigators

How to apply

Methodology

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.

Training and skills

In the first year, students will be trained on environmental data science, research methods and core skills. Throughout the PhD, training will progress from core skill sets to master classes specific to this project’s themes. Specialist training will include sensor techniques, radiative transfer for infra-red and microwave, non-linear data methods and general remote sensing. The National Centre for Earth Observation will provide access to its Researcher Forum, staff conferences/workshops and national-level training. There is good access to international summer schools, and the student can gain experience from attending European Space Agency meetings and events.

Partners and collaboration

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. Specific collaborations on developing the methods with leading academic centres and world leading space industries.

Further details

Dr Darren Ghent is the lead scientist on the international Climate Change Initiative LST Project, and leads the LST activities for the operational Sentinel-3 satellite mission. The student will have a chance to be part of a national EO community complementing the environmental science focus of CENTA. Professor Remedios is Director of NCEO and Professor at University of Leicester. NCEO is a national centre funded by NERC and distributed across key Earth Observation (EO) groups at Universities and research laboratories.

To apply to this project please visit: https://le.ac.uk/study/research-degrees/funded-opportunities/centa-phd-studentships

Possible timeline

Year 1

Training in software usage and development, and attendance at dedicated workshops. Initial evaluation of high resolution IR data and data analysis to produce a first high resolution LST dataset.

Year 2

Determination of robust relationships and construction of combined LST & Land Surface Emissivity AI retrieval algorithms. Conference attendance, preparation of manuscript for journal submission. Continued development of thesis chapters.

Year 3

AI method for high resolution LST capable of identifying the performances, modelling and design of new satellite sensors. Manuscript submission and revision, International conference attendance, thesis preparation.

Further reading

Ghent, D., Corlett, G., Goettsche, F., & Remedios, J. (2017) Global land surface temperature from the Along-Track Scanning Radiometers. Journal of  Geophysical Research – Atmospheres, 122, 12167-12193

Ghent, D., Veal, K., Trent, T., Dodd, E., Sembhi, H., and Remedios, J. (2019). A New Approach to Defining Uncertainties for MODIS Land Surface Temperature. Remote Sensing, 11, 1021

Hulley, G., Veraverbeke, S., and Hook, S., Thermal-based techniques for land cover change detection using a new dynamic MODIS multispectral emissivity product (MOD21), (2014). Remote Sensing of Environment, 140, 755-765, doi:10.1016/j.rse.2013.10.014.

Perry, M. J. S. (2017). High Spatial Resolution Retrieval of LST and LSE for the Urban Environment (Doctoral dissertation, Department of Physics and Astronomy). https://leicester.figshare.com/articles/thesis/High_Spatial_Resolution_Retrieval_of_LST_and_LSE_for_the_Urban_Environment/10231151

COVID-19

There are no direct impacts of COVID-19 on the successful scheduling of this project. Most activities are expected to be computer-based. This could be supported either physically in the office environment or remotely if the need arises through future COVID-19 restrictions. No impact is envisaged for software and data availability to undertake the research in line with the objectives of the project. Agencies and institutions now have well organised procedures for online meetings, workshops and conferences if future restrictions prevent physical presence.