The Use of Artificial Intelligence in ESA’s Climate Change Initiative

Climate change is arguably the greatest environmental challenge facing humankind in the twenty-first century. The United Nations Framework Convention on Climate Change (UNFCCC) facilitates multilateral action to combat climate change and its impacts on humanity and ecosystems. To make decisions on climate change mitigation and adaptation, the UNFCCC requires systematic observations of the global climate system.

The objective of the ESA’s climate programme, currently delivered via the Climate Change Initiative (CCI), is to realise the full potential of the long-term, global-scale, satellite earth observation archive that ESA and its Member States have established over the last 35 years, as a significant and timely contribution to the climate data record required by the UNFCCC.

Since 2010, the programme has contributed to a rapidly expanding body of scientific knowledge on >22 Essential Climate Variables (ECVs), through the production of Climate Data Records (CDRs). Although varying across geophysical parameters, ESA CDRs follow community-driven data standards, facilitating inter- and cross-ECV research of the climate system.

In this work, we highlight the use of artificial intelligence (AI) in the context of the ESA CCI. AI has played a pivotal role in the production and analysis of these Climate Data Records. Eleven CCI projects - Greenhouse Gases (GHG), Aerosols, Clouds, Fire, Ocean Colour, Sea Level, Soil Moisture, High Resolution Landcover, Biomass, Permafrost, and Sea Surface Salinity - have applied AI in their data record production and research or have identified specific AI usage for their research roadmaps.

The use of AI in these CCI projects is varied, for example - GHG CCI algorithms using random forest machine learning techniques; Aerosol CCI algorithms to retrieve dust aerosol optical depth from thermal infrared spectra; Fire CCI algorithms to detect burned areas. Moreover, the ESA climate community has identified climate science gaps in context to ECVs with the potential for meaningful advancement through AI.

We specifically focus on showcasing the use of AI for data homogenization and super-resolution of ESA CCI datasets. For instance, both the land cover and fire CCI dataset were generated globally in low resolution, while high resolution data only exists for specific geographical regions. By adapting super-resolution algorithms to the specific science use cases, we can accelerate the generation of global, high-resolution datasets with the required temporal coverage to support long-term climate studies.