Several factors, such as weather and climate changes, influence commodity prices. We can forecast price movements and make intelligent decisions when we understand how these factors affect the prices of commodities. One way to monitor some of these factors is using geospatial data from satellite images. These images provide real-time data about agricultural activity, energy, and mining processes, which we can interpret to predict demand and supply in the commodity market. In this article, we will discuss how geospatial data and satellite imagery help in the economic analysis of commodity price movements.
Understanding the Types of Geospatial Imaging
The concept of geospatial data covers information regarding objects, forms, and events on the Earth’s surface. This information is gathered through remote sensing, Geographic Information Sensing (GIS), and satellite images orbiting the Earth. For the purpose of this article, we will only cover how satellite imagery can help forecast commodity prices when combined with other data sources such as TradingView. Images shot from space stations can show us the extent of industrial activities, land use, and energy consumption, but how are these images gotten?
Optical Imagery
Optical imagery is one way to obtain information about vegetation performance, changes in land cover, and industrial activity. Optical imagery can help tell if a crop in certain regions will perform below optimum yield. It does this by identifying stressed or healthy vegetation by analyzing how it reflects specific wavelengths of light. The stress patterns can be caused by drought, diseases, or environmental pollution.
Radar Imaging
Radar imaging, or Synthetic Aperture Radar (SAR), is also useful in tracking the Earth’s mineral components. Initially used for military purposes in the 1960s, this remote sensing technology is now used to measure geological compositions. It uses microwave radiation to take images unaffected by weather or nighttime.
Hyperspectral Imaging
Hyperspectral imaging goes some steps beyond the other forms of imaging. While most satellite imaging forms use three bands, hyperspectral imaging splits these three bands into hundreds of bands. This helps it to provide even more detailed data on commodities like iron ore, crude oil, and crop cover performance.
How Can Satellite Imaging Help Predict Commodity Price Movements?
Here are ways satellite imagery can give insights into commodity price movements for investment decisions.
Agricultural Commodity
Traders and policymakers can forecast the price movement of crops like soybeans, wheat, and barley with the Normalized Difference Vegetation Index (NDVI) analyzed from optical imagery. This can be done by analyzing the possibility of a market surplus or product scarcity in the coming months. A surplus in the market could lead to falling prices, while scarcity could create a price hike. Also, one can predict agricultural supply shocks from satellite imagery of precipitation levels, soil moisture content, and atmospheric heat changes.
Energy Commodity
Satellite imaging helps identify oil extraction activities in geological spaces, even in the energy commodity sector. For example, observing excessive drilling activities in the world’s major oil extraction zones can indicate a period of crude oil surplus in the international market. Thanks to SAR images, we can also acquire information about pipeline oil movements and refinery activities. Also, in the industrial generation of electricity, one can tell periods of heightened energy consumption by analyzing thermal imaging of power plants taken by satellites.
Mining & Industrial Commodity
When we observe increased infrastructural development or urbanization activity from satellite images, it points to one thing – the mining commodity industry is about to receive increased demand for its products, such as iron and coal. Australia’s coal, for instance, has always been the first choice combustion option for Asian markets. We can predict an increased demand or price value of Australian coal when satellite imagery shows us increased urbanization activity in Southeast Asia. This is not to mention keeping track of excavation patterns and transport routes of mined resources.
How Can Financial Markets Use Geospatial Data for Price Predictions?
If you invest in agricultural commodities, for instance, you can use NDVI to predict the performance of specific crops and what might happen to their prices even before the United States Department of Agriculture (USDA) makes official press releases on the crop. This puts you one step ahead of most investors while helping you anticipate price movements. This is also true for crude oil commodities. Thermal imaging and SAR can assist investors in anticipating the demand and supply of the commodity, which can help predict prices. The analysis of geospatial data can also help firms prepare against losses through risk-hedging actions inspired by interpretations of satellite images.
Some commodity-dependent nations have the value of their currency affected by the demand and supply of certain commodities. For instance, the performance of the Russian Ruble and the Canadian Dollar is often affected by the price of crude oil. Currency traders can analyze geospatial data to predict economic trends in these countries and strategize appropriately.
These images can be obtained from public sources such as the National Aeronautics and Space Administration (NASA), European Space Agency (ESA) and U.S. Energy Information Administration Oil & Gas Storage Data (EIA) or private firms like Maxar Technologies and Planet Labs. Bloomberg Terminal users can also receive geospatial data as tables, text alerts, or AI-generated summaries.
Considerations About Using Geospatial Data for Predictions
One area that could be improved is resolution and data accuracy. Images shot from satellites thousands of miles away may not produce the precise results needed for accurate interpretations. Also, there are regulatory and privacy concerns, as some countries or regions have laws prohibiting the capture of their jurisdictions. It is also worth noting that high-resolution images captured in real-time can be expensive, making them only accessible to large corporations and exempting retail traders and small-scale investors. However, concerning accuracy, images can be analyzed together with other predictive data sources for improved accuracy.
Why Geospatial Data Matters for Investors
Geospatial data from satellite imagery, such as optical, SAR, thermal, and hyperspectral imaging, can help investors understand the price movement of certain commodities. When used properly, these insights can help advise investment decisions and safeguard against potential losses.
James is the head of marketing at Tamoco
