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Using machine learning to develop a global vegetation phenology model

  • Full or part time
  • Application Deadline
    Saturday, January 12, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The prediction, in space and time, of vegetation phenological variables such as: time of onset of ‘greenness’, time of end of ‘greenness’, duration of the growing season, rate of ‘green up’ and rate of senescence can provide the information needed to increase understanding of the effects of climate change on vegetation and accurate estimation of climate-carbon feedback. Such phenological variables can be predicted from ground or remotely sensed data. There are still considerable uncertainty in predicting these phenological variables at global scale. Factors controlling these events can vary across geographical region and time, which makes it difficult to develop a universally applicable model. Availability of continuous observation from satellite and advances in machine learning approach provide a new opportunity to develop a location specific vegetation phenology model. Using an extensive global dataset derived from a combination of satellite, ground observation and meteorological observation over last 30 years and a machine learning/deep learning approach, the project will develop a model of vegetation phenology at a finer spatial resolution (~1km) across the globe. This would be a crucial input to many terrestrial bio-geochemical models, which currently lacks an accurate representation of phenology.

The ECaS research group focusses on climate change impacts and adaptation, sustainability science, and global environmental monitoring including innovative use of Earth observation data, including Earth system science. We have a world-leading reputation for research on climate change impacts and adaptation strategies, with lead authorships in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment report.

Candidates must have or expect to gain a first or strong upper second class degree, in an appropriate discipline, not necessarily Geography. Experience of coding (R, Matlab, Python etc.) and image processing is desirable but not essential as training can be provided. Details on how to apply are available from Julie Drewitt, email . Informal enquiries may be made to Jadu Dash (email ). For the latest information see

Funding Notes

The PhD project will commence September 2019.

This is one of a range of topics currently being advertised. Funding will go to the project(s) with the best applicant(s). The studentship is to be funded at UKRI level, currently £14,777 per annum, with an RTSG of £750. The studentship will fully support British and EU nationals only. International students can apply but they must be able to meet the difference between home/EU and International tuition fees themselves.

Related Subjects

How good is research at University of Southampton in Geography, Environmental Studies and Archaeology?

FTE Category A staff submitted: 32.00

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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