Machine learning approaches to improve retrieval of shelf sea algal biomass from ocean colour remote sensing

The aim of this project is to develop improved ocean colour remote sensing algorithms to retrieve algal biomass from coastal waters using machine learning approaches in support of long term monitoring and compliance with environmental legislation requirements.

Number of places



Home fee, Stipend


6 June 2018



BSc (Hons) 2:1 or equivalent degree in physics/EEE or another numerate discipline



Scholarships (fees and stipend) available on a competitive basis for UK/EU students, please contact supervisor for details.

Project Details

Algal primary production in the marine environment constitutes approximately half of the global total, is therefore a very important element of the global carbon cycle and is the primary source of nutrition for the vast majority of life in the ocean. Various pieces of environmental legislation (EU MSFD, Water Directive etc) require governments to monitor the ecological state of national waters, with algal biomass being used as a proxy for eutrophication. There is also growing interest in monitoring for the presence of harmful algal blooms due to their potential impact on both aquaculture and public health more generally. Ocean colour remote sensing has radically transformed our ability to observe the growth and decay of algal blooms across the globe. However, the performance of standard algorithms for monitoring algal biomass is notoriously variable, with significantly lower performance in optically complex shelf seas. The aim of this project is to use state of the art machine learning approaches to improve understanding of local variability in the optical properties of natural waters and hence to inform interpretation of both historical ocean colour imagery and existing databases of in situ measurements of chlorophyll concentration. This will facilitate construction of a new, water-type specific approach to estimation of algal biomass for Scottish marine waters that will be integrated with regional hydrodynamic and ecosystem models to provide Marine Scotland and other Scottish public bodies with new tools for monitoring and predicting ecosystem status.

Funding Details


This project is jointly funded by the Data Lab and MASTS Industrial Doctorate program and by the University of Strathclyde. The successful candidate will be based at the University of Strathclyde in the Physics Department but will work with a range of experts in machine learning (Dr Jinchang Ren, EEE, Strathclyde), remote sensing (Dr Jacqueline Tweddle, University of Aberdeen) and with Scottish Government scientists (Drs Alejandro Gallego, Matthew Gubbins and Eileen Bresnan, Marine Scotland, Aberdeen). The PhD is open to EU nationals and is fully funded for a total of 3.5 years, with preferred start date of 1st Oct 2018.



Dr David McKee (Physics)Jinchang Ren  (EEE), Jacqui Tweddle (Aberdeen), Alejandro Gallego (Marine Scotland)

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