PhD Studentship: Statistically self-consistent satellite data, NERC GW4+ DTP PhD studentship for September 2025 Entry Ref: 5434

About the Partnership

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners:  British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology,  the Natural History Museum and Plymouth Marine Laboratory.  The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/

Project details

For information relating to the research project please contact the lead Supervisor via peland@pml.ac.uk

Project Aims and Methods

Processing of satellite data to the mapped images we see everywhere is a complex, multi-step process. Steps are typically undertaken sequentially from raw data to finished product, each making (often implicit) assumptions about the data distribution. From conversion of instrument counts to radiances, through to mapping of multiple overpasses into a daily composite, multiple statistical models are invoked which may not be mutually consistent.  

This project will explore ways to reduce these inconsistencies, reprocessing with consistent statistical models. Initial work will focus on creation of composites, the underlying assumption of which is usually that the surface can be represented as a tessellation of uniform map elements at the chosen spatiotemporal resolution, with values assigned by averaging. We can calculate what each sensor would measure if this model were true, then adjust the values accordingly. This optimises extraction of information from multiple sensors with slightly different viewing geometry, with the potential to more effectively ‘see around’ small clouds and other artifacts. This can be tested by using high-resolution satellite data to create ‘virtual’ lower-resolution data, then comparing processing methods. The model can be extended to address challenges like pixel overlap, stray light, out of band response etc. 

Training

The DTP offers funding to undertake specialist training relating to the student’s specialist area of research. 

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