Locusts & Monkeys

Soil moisture data from the SMOS satellite and the MODIS instrument acquired between July and October 2016 were used by isardSAT and CIRAD to create this map showing areas with favourable locust swarming conditions (in red) during the November 2016 outbreak. Data courtesy of ESA. Copyright : CIRAD, SMELLS consortium.

Spatial resolution is a key characteristic in remote sensing, as we’ve previously discussed. Often the view is that you need an object to be significantly larger than the resolution to be able to see it on an image. However, this is not always the case as often satellites can identify indicators of objects that are much smaller.

We’ve previously written about satellites identifying phytoplankton in algal blooms, and recently two interesting reports have described how satellites are being used to determine the presence of locusts and monkeys!


Desert locusts are a type of grasshopper, and whilst individually they are harmless as a swarm they can cause huge damage to populations in their paths. Between 2003 and 2005 a swarm in West Africa affected eight million people, with reported losses of 100% for cereals, 90% for legumes and 85% for pasture.

Swarms occur when certain conditions are present; namely a drought, followed by rain and vegetation growth. ESA and the UN Food and Agriculture Organization (FAO) have being working together to determine if data from the Soil Moisture and Ocean Salinity (SMOS) satellite can be used to forecast these conditions. SMOS carries a Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) instrument – a 2D interferometric L-band radiometer with 69 antenna receivers distributed on a Y-shaped deployable antenna array. It observes the ‘brightness temperature’ of the Earth, which indicates the radiation emitted from planet’s surface. It has a temporal resolution of three days and a spatial resolution of around 50 km.

By combining the SMOS soil moisture observations with data from NASA’s MODIS instrument, the team were able to downscale SMOS to 1km spatial resolution and then use this data to create maps. This approach then predicted favourable locust swarming conditions approximately 70 days ahead of the November 2016 outbreak in Mauritania, giving the potential for an early warning system.

This is interesting for us as we’re currently using soil moisture data in a project to provide an early warning system for droughts and floods.


Earlier this month the paper, ‘Connecting Earth Observation to High-Throughput Biodiversity Data’, was published in the journal Nature Ecology and Evolution. It describes the work of scientists from the Universities of Leicester and East Anglia who have used satellite data to help identify monkey populations that have declined through hunting.

The team have used a variety of technologies and techniques to pull together indicators of monkey distribution, including:

  • Earth observation data to map roads and human settlements.
  • Automated recordings of animal sounds to determine what species are in the area.
  • Mosquitos have been caught and analysed to determine what they have been feeding on.

Combining these various datasets provides a huge amount of information, and can be used to identify areas where monkey populations are vulnerable.

These projects demonstrate an interesting capability of satellites, which is not always recognised and understood. By using satellites to monitor certain aspects of the planet, the data can be used to infer things happening on a much smaller scale than individual pixels.

Small Sea Salinity & Satellite Navigation Irrigation

Artists impression of the Soil Moisture and Ocean Salinity (SMOS) satellite. Image courtesy of ESA – P. Carril.

A couple of interesting articles came out in the last week relating to ESA’s Soil Moisture and Ocean Salinity (SMOS) mission. It caught our attention, as we’re currently knee deep in SMOS data at the moment, due to the soil moisture work we’re undertaking.

SMOS was launched in November 2009 and uses the interferometry technique to make worldwide observations of soil moisture over land and salinity over the ocean. Although its data has also been used to measure floating ice and calculate crop-yield forecasts.

The satellite carries the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument, which is a 2D interferometric L-band radiometer with 69 antenna receivers distributed on a Y-shaped deployable antenna array. It has a temporal resolution of three days, with a spatial resolution of around 50 km.

A recent ESA article once again showed the versatility of SMOS, reporting that it was being used to measure the salinity in smaller seas, such as the Mediterranean. This was never an anticipated outcome due to radio interference and the land-sea boundary contamination – where the land and ocean data can’t be distinguished sufficiently to provide high quality measurements.

However, the interference has been reduced by shutting down illegal transmitters interrupting the SMOS signal and the land-sea contamination has been reduced by work at the Barcelona Expert Centre to change the data processing methodology.

All of this has meant that it’s possible to use SMOS to look at how water flows in and out of these smaller seas, and impact on the open oceans. This will help complement the understanding being gained from SMOS on ocean climate change, ocean acidification and the El Niño effect.

A fascinating second article described a new methodology for measuring soil moisture using reflected satellite navigation signals. The idea was originally from ESA engineer Manuel Martin-Neira, who worked on SMOS – which we accept is a bit more of a tenuous link, but we think it works for the blog! Manuel proposed using satellite navigation microwave signals to measure terrestrial features such as the topography of oceans.

This idea was further developed by former ESA employee Javier Marti, and his company Divirod, and they have created a product to try and reduce the overuse of irrigation. According to Javier, the system compares reflected and direct satnav signals to reveal the moisture content of soil and crops and could save around 30% of water and energy costs, and improve crop yields by 10-12%. It is a different methodology to SMOS, but the outcome is the same. The work is currently been tested with farmers around the Ogallala aquifer in America.

For anyone working in soil moisture, this is an interesting idea and shows what a fast moving field remote sensing is with new approaches and products being developed all the time.