Supporting traders. Improving Decision. New Data Points.  

Satellite data been available for decades but has either been too complex, too expensive or just not practical to use. HSAT is changing this


HSAT – is focused on providing the answers and not just data and pictures.


HSAT’s team has decades of experience in large and complex trading matters – and can support in varies areas, including:

  • Soft commodities – understanding and predicting crop supply
  • Logistics – monitoring and tracking materials around the world
  • Energy – Understanding production, shipping, and storage.

 Modern satellites provide pictures (optical images), radar (with millimetre precision), and spectroscopy (to monitor pollution).  This data the ability to detect the health of crops, monitor construction or the activity of a city, town, or factory. Fusing this data together with vehicle and foot traffic data, weather data, AIS and drone dates gives the ability to predict supply and demand.

Satellite data is now so sophisticated that it is possible to track the direction of travel of ship, from a single image, based on its wake or provide insights linking factory activity to LNG pricing.


Related Articles

The below article provides examples of about how alternative data – satellites, drones, foot traffic, etc – can be used to support trading decisions.

Providing insights into supply and demand.


Predicting Crops and Crop Yields

Predicting crops and crop yields has been tried for centuries. A hundred years ago regression models were used. Now satellite data, combined with machine learning, is used to predict crops. It's only very recently becoming accurate.
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Case Study: Soft Commodities

A client needed to understand the expected crops across millions of hectares - but there was no reliable data available. HSAT worked the client to produce a bespoke solution to monitor the region for the total area of crops planted, and the growth of the crops throughout the year.
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Predicting Crops - From Brazil to Thailand

Fusing together images from different satellites and using supervised machine learning models enables highly precise information to be obtained about crops. By building an accurate picture of crops, farm by farm, is possible to know exactly what is being grown, across an entire region and plan accordingly.
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