All humans deserve access to clean water but one of the threats, cyanobacterial (blue-green algal) blooms, are of increasing concern in inland waters.

Some species produce potent toxins that pose a major hazard to human health, livestock, wildlife and the aquatic environment.

Traditional field monitoring for algal bloom detection involves identification and cell counting which, whilst reliable, imparts a lag time and is limited in spatial extent.

In Australia, as with other developing and developed countries, overcoming the vast areas and number of water bodies to be monitored is a challenge.

This project, the Algal Early Warning System (AEWS), is a collaboration between CSIRO and the New South Wales (NSW) Department of Primary Industries – Office of Water, to develop a remote sensing approach to monitoring algal blooms in inland waters across large spatial scales.

Earth observation data are used to complement traditional methods for water quality monitoring. The Sentinel 2 (ESA, Copernicus Programme) and Landsat 8 (NASA) satellite sensors offer high resolution, wide scale and frequent monitoring of water quality in inland water bodies in support of early algal bloom alerts for water managers.

Black swan paddling though algal bloom, Lake Burley Griffin, Australia

Earth Observation Data Use

Landsat 7 and 8 data ingested from the Australian Geoscience Data Cube.

Shapefile of the extent of freshwater bodies in NSW.

In the future, data from Sentinel-2 will also be ingested when it becomes available in ‘analysis ready data’ (ARD) form.


The tool is built upon the Australian Geoscience Data Cube (AGDC), a breakthrough innovation to serve standardised and calibrated satellite image data via Australia’s National Computational Infrastructure (NCI) enabling the exploitation of parallel architectures.

In near real time, images are processed in the AGDC to inland water quality outputs (turbidity in the first instance). Data are presented to water managers through a visualisation interface where the turbidity data are translated to relevant bloom alert levels.

The interface allows for visualisation at the regional scale to provide a rapid NSW state-wide overview of algal alert status, or at the scale of the individual water body, to allow the determination of spatial bloom dynamics.

Current data can be displayed within the context of historical data.

Time series of Landsat 7 and 8 turbidity images for Lake Hume using the algal bloom visualisation system, covering the summer to autumn period mid-January to early July 2016. Turbidity in this deep reservoir is primarily driven by changes in phytoplankton concentrations. The time series shows the development of an algal bloom from late February through March and April. This bloom provided the ‘seed’ to stimulate a bloom in the River Murray downstream of the reservoir which affected some ~1600 km of river for three months to June 2016. A full time series of turbidities from January 2015 is also depicted; the solid line represents the median concentration (in mg/L) and the range the full range of turbidities measured spatially.

Key Issues and Results

The main objective of the project is to develop methodologies and procedures with remote sensing technologies to support cyanobacterial monitoring, in order to improve timeliness and a wider spatial coverage of major inland water bodies.

When fully operational, the system will revolutionise the algal management by providing better and earlier information in detection and mitigation.

It will help government agencies (various scales) to monitor wider areas and quickly deploy resources at a local scale when necessary

The development of Data Cube concepts for other parts of the globe would significantly accelerate the speed and creation of similar alert systems, particularly for developing countries.

The development of forecasting services for bloom condition will rely on the integration of satellite data with biogeochemical models.

Analysis, Status, and Outlook

The generic approach developed here for turbidity will be suitable for all optical water quality products into the future.

As new satellite sensor data (e.g. Sentinel 2) become available in ‘analysis ready’ form the power of algal bloom alerting and water quality monitoring will significantly increase.

Current approaches are based on semi-empirical algorithms but future work will adopt the adaptive Linear Matrix Algorithm (aLMI), developed by CSIRO for simultaneous estimation of optically active water quality parameters from remotely sensed data in water bodies with varying water types.

Partners, Contacts, More Information


Tim Malthus

CSIRO Oceans and Atmosphere, Brisbane, Australia



NSW Department of Primary Industries, Office of Water

More Information