Traders are using more and more data to gain a competitive advantage. 

Satellite data combined with terrestrial data sources (current and historical) can now provide highly accurate predictions.

For example:

Oil wells and drill pads and their operations can be detected – when they are built, when they are active and when they shut down.

  • Satellite images of crops combined with weather data and historical yields can be used to predict future yields based on the current size and state of the crops.
  • Storage and commodities in open-air warehouses can be reported
  • The output of factories, their intensity of work, in entire regions can be identified.

The capabilities of modern satellites to provide radar and optical images, with millimetre precision for measuring elevation, the ability to detect the health of crops and activity of city, town, or factory combined with machine learning gives the ability to predict yields/output, supply and demand.

Satellite data is now so sophisticated that it is possible to track the direction of travel of an individual ship, from a single image, based on its wake.

HSAT offers a wide range of services in this sector, including:

  • Consulting – helping you understand the opportunity to use this data
  • Data Provision – Extraction of the core-satellite data (optical images, radar, elevation, crop identification, temperature, or other relevant data)
  • Analytics – Analytics of all available data to provide reliable information to make decisions

HSAT - Understanding Commodities

Recent Articles

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   Contents Importance of Predicting CropsPredicting Crops – Regression...

Commodities: Solutions to Predicting Crop Yields

Commodities: Crop Predictions Challenges The challenges of predicting crop yields are significant, as outlined in the previous article. Traditionally these challenges result in low accuracy rates -  below 50%, for certain crops.  This article will look at how the accuracy rate can be increased to...

Open Grain Storage: Measuring Commodoties

Understanding and predicting commodity supply requires a wide variety of data points and an understanding of what is stored and where. Using satellite data it is possible to calculate the yield of crops, location and storage volume of commodities (e.g grain, sugar, wheat) and even...