Constantly fed with Earth observation data,, combined with local measurements and artificial intelligence, Digital Twin Earth will help visualize and forecast natural and human activity on the planet.
The model will be able to monitor the health of the planet, perform simulations of Earth’s interconnected system with human behaviour, and help foster European environmental policies.
Antarctica is a major reservoir of freshwater in the world, with a massive potential to contribute to sea level rise in the future so a digital twin of Antarctica is necessary
Noel Gourmelen, from the University of Edinburgh commented, “By harnessing satellite observations, numerical simulations, and Artificial Intelligence, we have built a twin of the Antarctic ice sheet system, its hydrology, surrounding ocean, atmosphere, and biosphere.”
The Food Systems digital twin simulates agricultural activities and ecosystem interactions on a daily basis. Different models can be run separately for each simulation model, depending on crop, water and irrigation management system.
Luca Brocca, from the National Research Council, Italy, explains the Hydrology Digital Twin, “In the ESA Digital Twin Earth Hydrology project, we have developed a 4D reconstruction of dynamic hydrology at unprecedented resolution through the integration of Earth observation and an advanced modelling system.”
The Climate Impacts Digital Twin will enable decision makers, without expert technical knowledge, to generate and visualise, in real-time, decision-relevant information related to localised impacts of climate change.
Matti Motus, Principal Scientist at VTT, explains the Forest Digital Twin: “This digital twin will be a specialised Digital Twin of Earth, providing a reconstruction of the forest system at levels of detail not possible with generic land surface models. Satellite-based Earth observation, especially the high-quality Copernicus Sentinel data, allows us to get unique and uniform information for all forests of the globe.
This Digital Twin Ocean will focus on exploring the potential of artificial intelligence to learn directly from its data, from the past and the behaviour of the Earth system to predict the future to forecast oceanic events.
https://www.esa.int/Applications/Observing_the_Earth/Working_towards_a_Digital_Twin_of_Earth