Bayi Glacier in Qilian Mountain, China (Credit: Xiaoming Wang, distributed via imaggeo.egu.eu)

Job advertisement PhD studentship in MACHINE LEARNING METHODS FOR SATELLITE REMOTE SENSING IN SUPPORT OF CO2 EMISSION MONITORING

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European Geosciences Union

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PhD studentship in MACHINE LEARNING METHODS FOR SATELLITE REMOTE SENSING IN SUPPORT OF CO2 EMISSION MONITORING

Position
PhD studentship in MACHINE LEARNING METHODS FOR SATELLITE REMOTE SENSING IN SUPPORT OF CO2 EMISSION MONITORING

Employer
University of Leicester logo

University of Leicester

The University of Leicester is a leading UK university committed to international excellence through the creation of world changing research and high quality, inspirational teaching; ranked in the top 25 universities in the Times Higher Education REF Research Power rankings. The University of Leicester has a long and distinguished record of discovery in space science. Every year since 1967 has seen a Leicester-built instrument operating in space. We hold, and have held, vital roles in many space missions for space agencies including NASA, European Space Agency, UK Space Agency, ISRO (India) and JAXA (Japan), covering astronomical, planetary and Earth observation science missions. The Earth Observation Science (EOS) group of the University of Leicester is an interdisciplinary group based in the Departments of Physics and Astronomy with academic staff also in the Departments of Chemistry and Geography. The main focus of the EOS group is to conduct integrated research and development, leading the design, build, data analysis and exploitation of increasingly sophisticated and powerful sensors that are now being flown on satellites. Underpinning this work is the utilisation of field instruments, laboratory data and models either in-house or through collaborations. The group has a strong foundation in leading space research at European level both with the European Space Agency and with the European Commission and are strongly involved in Copernicus and the ESA Climate Change Initiative (SST, LST, biomass, water vapour and GHG). The EOS group at University of Leicester is also hosting the National Centre for Earth Observation (NCEO). NCEO is a distributed research centre of approximately 100 scientists from UK institutions that provides the Natural Environment Research Council (NERC) with national capability in Earth Observation science and incorporates world-class capabilities in interpretive EO.

Homepage: https://www.leos.le.ac.uk/GHG


Location
Leicester, United Kingdom of Great Britain and Northern Ireland

Sector
Academic

Relevant divisions
Atmospheric Sciences (AS)
Biogeosciences (BG)
Climate: Past, Present & Future (CL)

Type
Full time

Level
Student / Graduate / Internship

Salary
Annual stipend, currently set at £15,285 (2020/1)

Preferred education
Master

Application deadline
11 January 2021

Posted
24 November 2020

Job description

Since the launch of the first dedicated satellites in 2009, satellite observations of CO2 have become a corner stone of carbon cycle research and upcoming missions are now designed to target anthropogenic CO2 emissions in support of the Paris agreement. One of the fundamental challenges is the development of methods that allow inferring CO2 from the satellite measurements with sufficient and guaranteed accuracy to allow pinpointing where CO2 and CH4 is exchanged between the atmosphere and the surface. The University of Leicester is one of the leading groups that has helped to develop sophisticated physics-based methods that are now employed in major initiatives like the ESA Climate Change Initiative to produce global CO2 records. The next generation of satellites is aimed at producing observations with incredible fine detail and current analysis methods based on traditional physical model-based inference practices are not suitable to deal with the dramatic increase in data volume which is set to rapidly grow further in coming years. We also need to extract information more quickly so that we can more rapidly diagnose emissions and respond to major events (such as the impact of COVID-19).

A fundamental re-think of the approaches to derive information from satellite measurement is needed. Machine Learning (ML) including neural networks has been successfully adopted in many areas for analysis of big data. Such methods have the potential to transform satellite remote sensing and our proof-of-principle research, carried out in the last 3 years, showed immense feasibility of the ML approach to this challenging problem. Building on our previous successful work, we will develop a new-generation ML method for CO2 retrievals from satellites and we tackle perceived limitations of ML that is limiting the uptake of these methods by the community, especially on the quantification of errors and certified robustness in ML. The new ML approach will be applied to data from current missions such as NASA OCO-2 and upcoming missions (French/UK MicroCarb mission and Copernicus CO2 mission) and we will be working closely with the mission teams.