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Ocean colour remote sensing for wetland mapping and chlorophyll concentration monitoring with spontaneous fluorescence and stimulated emission

People Involved

Jinchang Ren

Project Description

The environmental status of marine waters plays an important role in monitoring the water extending from the shoreline to deep oceanic waters. Current reporting is based on expensive ship-based surveys that have very limited spatio-temporal coverage. Satellite based ocean colour remote sensing has the potential to significantly enhance monitoring capabilities.
Existing algorithms for chlorophyll concentration break down in turbid coastal waters subject to sediment resuspension or high concentrations of coloured dissolved organics in freshwater run-off. This project will tackle this problem by addressing two critical bottlenecks. Firstly, we will use advanced machine learning techniques to automatically identify optical water types and establish Chl algorithm performance using existing in situ datasets. Secondly, we will map wetland and link it to water quality.
In this project, we will use existing state-of-the-art machine learning approaches to categorise optical signals from ocean colour imagery, for monitoring/inspection of coastal regions.

Type of Project Project
Status Project Live
Funder(s) Royal Society of Edinburgh
Value £12,000.00
Project Dates May 1, 2020 - Oct 31, 2022

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