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Examples using ocean colour radiometry from the southern Benguela

By Hayley Evers-King and Marie Smith

Department of Oceanography, University of Cape Town, South Africa

2nd Prize in the LearnEO! Lesson Writing Competition 2013/14

Phytoplankton blooms fuel vast coastal fishery and aquaculture resources. However these blooms can often be harmful, as a result of anoxia or presence of toxins. Thanks to its high temporal and spatial resolution, ocean colour data allows scientists and managers to understand this phenomena and manage risks to resources.

This lesson covers how we can use both in situ and satellite derived ocean colour data to detect high biomass blooms and how we can address the challenges of using this data in the dynamic coastal environment.

Suitable for university students or continued professional development training (intermediate to advanced level).

NOTE: This lesson requires Bilko 3.4 from October 2013 or later, as earlier versions can not open and display all data as described in the lesson.

Google map with St Helena Bay   Yellow  water sample

Figure 1.
Left: The southern Benguela is situated on the west coast of South Africa. Here we focus on the St Helena Bay area (box). Image copyright: Google earth.
Right: A water sample from a Harmful Algal Bloom in St Helena Bay.


The coastal regions of the world's oceans are where society meets the sea. These regions generate vast amounts of natural resources, providing sustenance, fuelling economies and performing ecosystem services vital to the continued habitability of our planet. But these regions are highly diverse and dynamic. Earth Observation data allows scientists to study these regions at suitable spatial and temporal scales to understand these processes and manage our reliance on them.

Coastal waters are usually highly productive. Phytoplankton typically bloom in these waters in response to inputs of nutrients from the adjoining land, and/or from naturally occurring oceanic circulations. The southern Benguela is an example of the latter. Seasonal alongshore winds create an upwelling system, where deep, nutrient rich waters come to the surface, feeding seasonal phytoplankton blooms; the first level in a food chain which results in prolific fisheries that have sustained local populations since early human evolutionary history.

This intensive growth can come at a price. At the event scale, the phytoplankton blooms can become so large that their eventual degradation causes oxygen depletion (anoxia) in the coastal waters. Additionally many of these blooms can involve phytoplankton species that contain harmful toxins. Harmful Algal Blooms (HABs), as these extreme blooms are known, represent significant risks for ecosystems and hence for fishery and aquaculture resources in the region.

When phytoplankton bloom in these extreme events, they can substantially alter the colour of the water. Ocean colour radiometry can be used to detect these changes in a quantifiable way, and many methods (usually known as algorithms) have been developed to relate optical signals to different ocean constituents. Whilst in open ocean waters, phytoplankton are typically the optically dominant component (after water itself), in coastal waters, other components are often present e.g. sediments and coloured dissolved organic matter (CDOM) from riverine inputs or run off. The influence of these other constituents can make it difficult to separate out what parts of the light field are related to which constituents, causing large errors in the estimation of their concentrations. Additionally, near the coast, the atmosphere can be highly variable and contain many different substances, making removal of this part of the optical signature (the atmospheric correction) difficult. Light leaving the land surface can also contaminate the signal that leaves the ocean surface, creating further errors.

The requirements for monitoring and management of Harmful Algal Blooms include:

Lesson Overview

Aim and objectives

This lesson uses reduced resolution (RR, 1km) level 2 ocean colour data from both the Medium Resolution Imaging Spectrometer (MERIS) aboard the ESA ENVISAT satellite and from the Moderate Resolution Imaging Spectrometer (MODIS) aboard NASAs AQUA satellite. In situ radiometric data from a Satlantic Hyperspectral Tethered Surface Radiometer Buoy (H-TSRB), fluorometric chlorophyll-a concentrations, and information on dominant species derived from Coulter Counter and microscopy analysis are also provided for validation and interpretation of the satellite data.

At the end of the lesson you should be able to:

Lesson content

The lesson is divided into the following sections:

  1. Opening and comparing satellite ocean colour data and products.
  2. Validating satellite data with in situ radiometry and using flags to understand likely sources of error.
  3. Comparing in situ chlorophyll-a concentrations with those derived from satellite data
  4. Creating an empirical band ratio algorithm based on in situ data for determining chlorophyll concentrations
  5. Comparing spectra from different blooms to investigate the effect different species can have on optical signatures.

Data and tools for this lesson

MERIS Level 2 Data

MERIS data were acquired from the on-line ESA Optical Data processor (ODESA) using MEGS v8.1.

Supplementary and pre-processed data sets

The following data are needed for the lesson:

Bilko tools

Bilko formula documents

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