Overview

Project Highlights:

  • Develop new data techniques, based on Artificial intelligence (AI) and Bayesian estimation, to produce world leading datasets for land surface temperature and surface composition.
  • Apply such datasets to rapidly evolving environmental challenges such as urban and agricultural developments, including work with commercial services.
  • Translate these methods to novel remote sensing, commercial instrumentation capable of delivering improved land surface temperature data to the world continuously.
  • Benefit from the opportunity to work with world leaders in environmental science from satellites in space.

 

Overview:

This project will develop new methods to study the changing land surface temperature (LST) of the Earth’s surface, a need recognised to be very important by international space agencies and environmental scientists. A major challenge for Earth Observation (EO) is to understand the relative influence of global and local effects within the Earth system. 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 are affecting our environment. Thermal variations in particular are important to isolate, as they contribute and invariably drive the overall energy balance at the surface with consequences for disruptive influence from climate change to urban meteorlogy to hydrological controls on agriculture.

This project offers the student the opportunity to work on an acknowledged frontier science topic which is being driven by the exciting new science of LST as a climate indicator and as an expression of environmental change. To understand better the thermal balance of the surface, we need both accurate high-resolution LST (< 0.5 K) measurement at local (< 100 m) scales, for example, to resolve crop fields and urban districts. This involves novel algorithms for simultaneous determination of LST and surface composition (emissivity) and new instrumentation to enable this to happen routinely. Fortunately, there is an excellent starting point by working on data from the Japanese satellite instrument ASTER and understanding its performance; ASTER provides some very good observations but only for some locations around the world.

The project offers an excellent route to publications of topical science, work with leading world-class groups in temperature measurements for environmental science and a broad exposure to new techniques including instrumentation. Our CASE partner, Airbus UK, is actively working on new thermal infra-red instrumentation and space agencies are looking to fly next generation sensors with performances that allow LST and emissivity to be derived globally and in continuous service. Thus the student would be working on projects that aim to make a step change in improvement from the current Landsat instruments operated by the United States.

Figure 1: High-spatial resolution LST image of New York city adjoining New Jersey and Long Island derived from ASTER using an initial algorithm developed by Leicester.

Methodology

This project seeks solutions to science-driven problems in LST data. Progress will be achieved through a state-of-the-art optimal estimation (OE) methodology for simultaneous retrieval of both LST and Land Surface Emissivity (LSE) tested against AI methods; outside of Leicester current methods are limited to semi-empirical approaches for individual estimation. The new approach will be generalised to apply to both existing sensors and new thermal infra-red sensors, continuing the leading nature of our LST research.

The project will carry out testing of the methods on both simulations and real data from hyperspectral aircraft measurements (as well as the new Ecostress instrument to be flown by NASA on the International Space Station or ISS). Once verified, the new scheme will be used to identify the design performances of new satellite sensors, working with Airbus, and key characteristics that will ensure a successful space mission.

Training and Skills

In the first year, students will be trained on environmental science, research methods and core skills. Throughout the PhD, training will progress from core skills sets to master classes specific to this project’s themes. Specialist training will include atmospheric radiative transfer theory, non-linear data methods and infrared radiometry. Leicester Earth observation sits alongside National Centre for Earth Observation activities which can provide Centa students with national-level training/meetings. There is good access to international summer schools. Additional industry skills related to EO data and sateliite mission design will be carried out with the CASE partner, Airbus Space and Defence.

Timeline

Year 1: Basic research skills training; familiarisation with literature, existing LST/LSE datasets and retrieval methods. Development of LST/LSE retrieval and simulator for instrument. First extended visit period to Airbus UK, defining key sensor characteristics. First publication.

Year 2: Completion of combined LST & LSE AI retrieval algorithms. Application to existing data from ASTER and othersensors, analysed for urban areas. Work at CASE Partner, Airbus UK on sensor performances. Presentation at national conference.

Year 3: Finalise data approaches/instrument design. Application of methods to NERC Owl aircraft and ISS Ecostress data. Further work at Airbus UK. Publications and presentation at international conference. Draft key thesis chapters.

Partners and collaboration (including CASE)

This project has been solicited by the designated Case Supervisor, Paolo D’Arrigo, at Airbus UK to enable shared research and development needs in LST. By working with University of Leicester teams, the CASE partner is assured of world-leading expertise supporting their efforts to develop leading sensors. They are interested in galvanizing downstream exploitation of data and upstream sensor technology development to meet the subsequent need. Through the CASE award, the student will spend dedicated time, typically 2 to 3 months per year, working directly with the CASE partner. The equivalent value of the CASE award is up to 45000 Euros.

Further Details

Professor John Remedios, University of Leicester

Email: jjr8@le.ac.uk

https://www2.le.ac.uk/departments/physics/people/johnremedios