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Last Update: 2019/04/12   Source: Research Headlines
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Satellite data highlights optimal sites for aquaculture

An EU-funded project has developed a satellite-based service to support the aquaculture industry in finding the best sites and providing continuous monitoring of fish and shellfish farms, helping to ensure global food security for a growing population.


© Richard Carey #209963212, 2019 source:

The global population is projected to grow to nearly 10 billion by 2050, meaning farmers will need to produce a lot more if we are to feed everyone and ensure food security. Fish and shellfish are a popular food source across the globe – and to meet the rising demand, the aquaculture industry is set to experience significant growth.

To help aquaculture planners find the right location for new fish and shellfish farms, both in Europe and worldwide, the EU-funded SAFI project is using remote-sensing data of the Earth taken from satellites to analyse key factors that can influence the choice of location.

‘We have examined reams of satellite data and developed a model that will help the aquaculture industry find the most productive sites to farm fish, molluscs and even seaweed,’ says Antoine Mangin, scientific director at ACRI-ST in France and SAFI project coordinator.

From water temperatures to wave patterns

The EU’s Copernicus satellites gather observations about the Earth’s environment relevant to fields such as weather forecasting, climate-change analysis, mapping changes in land use, forecasting disasters such as floods, forest fires and droughts, and mapping natural disaster zones like earthquakes.

SAFI researchers have evaluated this data and applied it to fisheries. They analysed water temperatures, nutrient levels, marine current speeds, wave patterns and levels of water transparency – all of which have a direct impact on fish, shellfish and marine plant populations. For example, fish kept in cages prefer clearer waters, while most farmed fish need regular marine currents to develop. Different types of farmed fish species also require different water temperature ranges.

The team has produced maps of the best places for fish farms in Portugal, Spain, Morocco, Zanzibar and Madagascar, with a new project under way for environmental monitoring in Chile.

While satellite observations are freely available online, the project developed a business approach to offer premium analysis to customers. For example, project researchers offer early-warning services for common fish-farm problems like algal blooms, which can be toxic to fish. This situation occurred in southern Chile where there was a very large bloom and farmers lost 60 % of their production. If farmers are made aware of such problems before they become too significant, they can take measures such as adapting fish feed or moving populations to different areas.

Monitoring during operation

In addition to detecting the best locations for fish and shellfish farms, SAFI developed an environmental monitoring service that works throughout a farm’s operation. By satellite-gathered information, it can model the species biomass – including its abundance, location and reproduction levels – as well as detect harmful algal blooms. It also monitors water temperatures and nutrients, which allows analysts to forecast the productivity of mussel farms, for example.

The SAFI team is now working with consultants in the aquaculture sector to develop the commercialisation of its services. Target users include industry and public administrators in charge of planning aquaculture or fisheries.

‘The data we use can help pinpoint optimal locations for aquaculture including mussel farms, salmon farms and seaweed farms, helping these industries on every step of the way from obtaining a licence for a new site to continuous monitoring to support their productivity,’ says Mangin.


Project details

  • Project acronym: SAFI
  • Participants: France (Coordinator), Ireland, Portugal, Spain, Morocco
  • Project N°: 607155
  • Total costs: € 2 526 347
  • EU contribution: € 1 959 025
  • Duration: September 2013 to October 2016

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