The work on smoke detection

The smoke emitted by fires, even thousands of kilometres from the source, can have a significant impact on the surrounding vegetation. This is particularly the case for vineyards, as the vinasse produced by smoke-affected grapes will have a strongly modified taste.

Fortunately, the Copernicus Atmospheric Monitoring Service (CAMS) provides a large amount of atmospheric data extracted from various physical models, satellite data and in situ observations.

The specific CAMS data indicate the location of smoke at any given time from a selected fire, both at ground level and at several heights above the ground. A first study by the Centre National d’Etudes Spatiales (CNES) showed that the specific CAMS data are difficult to use to determine the affected vineyards.

The QRNN project

The extraction and analysis of geophysical parameters from remote sensing measurements plays a crucial role in the knowledge of the Earth’s physical phenomena. Indeed, greenhouse gases are responsible for important effects on our atmosphere, such as the Earth’s climate change.

In this context, methods based on machine learning algorithms have become important in the scientific community. Multi-layer perceptual neural networks (MLPs) have proven to provide good estimates of atmospheric parameters. Also, they have proven to be more efficient (in terms of computational cost and processing of non-linear systems/models) than classical inversion methods, such as the optimal estimation method (OEM).

However, classical NR techniques do not provide information on the uncertainty of the recovered parameters.
Yet this uncertainty information is essential for the exploitation of scientific products. For example, it is important for their use in systems for analysing and predicting atmospheric composition and/or dynamics.

Within this framework, the unit is in charge of understanding and estimating the potential of obtaining data on the composition of the thermal atmosphere, more precisely, on the content of 𝑪𝑶𝟐 in the troposphere. These measurements are made from infrared hyperspectral survey instruments such as IASI, IASI-NG or OCO-2. Therefore, the unit must be able to determine the uncertainty associated with them, using methods based on neural networks, in order to prepare future missions (e.g. Microcarb).

This work was conducted as part of a CNES project.