Exceptional events detection using remote sensing and artificial intelligence

Exceptional and extreme events like wildfires, pollution episodes, or volcanic eruptions require near-real-time (NRT) detection to enable effective mitigation and impact reduction. While satellite geophysical products provide valuable information, their NRT availability is limited to targeted atmospheric species. In contrast, radiances (raw satellite data) provide the full spectral information, within of which a wide variety of atmospheric compounds and geophysical parameters simultaneously exist. The IASI atmospheric sounders aboard the Metop satellites provide an extensive archive of such spectra, covering the spectral signature of stable greenhouse gases and highly variable trace gases relevant to extreme events.

This work builds on these observations to develop an AI-based automated detection system. By validating our approach on IASI’s long historical record, we aim to establish a robust framework capable of fully exploiting the higher spectral resolution and enhanced trace-gas sensitivity of the next-generation IASI-NG launched aboard the Metop-SG satellite in summer 2025.

The methodology is organized in two phases. First, the long-term IASI dataset (since 2007) is used to develop AI models for extreme event detection. An event "atlas" is built associating spectral signatures with documented events, and used to train supervised models, including neural networks, directly on radiance data. Unsupervised techniques are also applied to identify unlabeled anomalies and potential unknown atmospheric species.

In the second phase, these models will be adapted to IASI-NG, accounting for instrumental differences and ensuring consistency over the operational overlap period. An operational processing system will then be deployed to provide continuous and reliable monitoring of extreme events.

At this stage, we will focus on presenting the development and results of the fire event atlas produced in the first phase.