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Estimating carbon emissions using satellites

We have previously discussed the importance of Big Data analytics using Machine Learning and other AI applications to measure and process data on sustainability and climate change. The collection of spatiotemporal (spatial temporal) data has begun to play an important role in such analyses, yielding significant results in addressing climate change.

Spatiotemporal data are related to both place and time, and can be produced by satellites, which connected to cloud platforms may establish a big data system that enables complex computational processing.

Many scientists are focusing their research on estimating greenhouse gases by converting satellite imagery and sensor data into a format that can be analysed and monitored in real time using advanced computational techniques. The objective is to use a combination of infrared satellite images, satellite images showing smoke released by large emission sources, and ground-based data (building gas sensors) in order to detect and quantify emissions. These sensors will be used for model verification. The data from the combination of these measurements will be analysed using machine learning methods and spatiotemporal analysis to estimate the quantities of gases emitted by a source.

Technological innovations such as IoT and satellite systems are the future of emissions data collection in sectors such as energy, industry, waste, agriculture, forestry and land use.

Using such technological tools enable stakeholders to accurately define the sources and quantities of their emissions, and to easily estimate the carbon footprint of their activities. This will help them focus on the activities that have the biggest carbon footprint and plan accordingly mitigation strategies.

Currently, a number of projects have been launched at the global and European level with the aim of supporting the further development of various high-resolution satellites for monitoring greenhouse gas emissions from large installations (e.g. large coal-fired power plants). These satellites will provide information available to any interested party for further processing.

E-ON Integration can contribute to the requirement of businesses to assess and monitor their carbon footprint, with its expertise and experience in Artificial Intelligence, Machine Learning and Data Analytics techniques required for the analysis of spatiotemporal data (e.g. from satellite imagery) combined with internal business data or other ground-based means (e.g. IoT).

In addition, E-ON has developed cloud platforms, which are used by businesses with the ultimate goal to easily access and manage greenhouse gas emissions data and generate relevant reports for disclosure.


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