Insights from Above: Using Earth Observation to Track Commodity Markets

A Q&A with Carlo Robiati

In recent years, the use of satellite imagery and Earth Observation (EO) has changed the way we track and understand the world around us, including the commodity markets. Traditional and outdated methods of tracking the commodity market today can be augmented with satellite data, which offers a more timely and accurate way of monitoring the global supply and demand of various dry bulk commodities. In this interview, DBX’s Earth Observation Specialist, Carlo Robiati, shares how data from space is bringing transparency and efficiency to the commodity industry.


Q: What role does Earth observation play in the commodity markets?

Earth Observation and Remote Sensing play an ever-growing role in monitoring the supply chain of raw materials and goods. From the near real-time tracking of vessels’ voyages around the globe, to acquiring images of terminals and ports daily, the adoption of satellite technology in monitoring the dry bulk commodities is steadily increasing, with a plethora of remote sensing applications developed at the intersection of supply chain analysis, shipping, and market analytics. Having ‘eyes in the sky’ can lead to accurate and timely data which is critical for informed decision making. The benefit is twofold. On the one hand, the commodity supply chain can be tracked objectively. On the other hand, satellite data can shine a light on a notoriously opaque market, enhancing the traceability of dry bulks and making the entire supply chain more transparent.


Q: What can we measure with satellite imagery and what insights does this provide into the demand of dry commodities?

Satellite imagery can be particularly useful for monitoring the different stages of the dry bulk commodities’ journey around the globe. The market analytics industry makes ample use of spaceborne imaging systems, both passive and active (i.e. optical and radar) to collect and analyse high volumes of data. Overhead images taken from the Earth’s orbit essentially let us see and record the distribution of dry bulk stockpiles over time and space, either through their colour or their geometrical properties. This is essential for anticipating supply and demand scenarios. Being able to consistently track the distribution of raw materials in the supply chain gives a clear understanding of the flows, a key parameter to predict and estimate both trades and commodity prices.


Q: Tell us about DBX’s collaboration with the European Space Agency.

We are proud to have been awarded the European Space Agency (ESA) Space Solutions and Business Application (BASS) ARTES 4.0 Programme’s grant (via, a project designed to leverage on open access EO data applied to stockpile monitoring, while having access to ESA’s development support network. The implementation of this new module is coming to a conclusion as we prepare to release it to the public in the second quarter of 2023.


Q: How does DBX’s data and insights compare to traditional ground-based observations and methods?

In the dry bulk industry, ground based surveys, self-reporting and traditional auditing methods are predominant. Given the sheer size of the dry bulk supply chain this is not surprising, but it leads to significant delays in reporting the data, both at terminal and country-level. The industry needs transparency and objective and timely reporting to be able to track the market and make data-driven decisions. Having an accurate and up-to-date view of the entire supply chain (i.e., iron ore and coal supply chains), from sourcing (i.e., mines) to transportation (i.e., roads, railways and seaborn routes) to the end users (i.e., power plants and steel mills) can provide a competitive edge to physical suppliers and traders and also to the trade authorities and regulators.

DBX leverages satellite technology to provide a digital twin of the global dry bulk supply chain. We use state of the art remote sensing techniques to obtain the most objective and unbiased results. To validate the accuracy and reliability of EO data, we apply advanced statistical and machine learning models, rely on a deep network of reporters and analysts, and we benchmark our data against specific ground truth datasets.