Detection and classification of volcanic ash based on satellite UV/VIS measurements

Volcanic eruptions impact human populations and the environment, with volcanic ash contributing to these effects through its influence on air quality, agriculture, and air transportation. Traditional satellite observation methods for monitoring volcanic ash encounter several challenges, including distinguishing ash from other aerosols, coverage area limitations, frequency of observations, and the impact of adverse weather and atmospheric conditions. These issues highlight the need for supplementary satellite-based approaches to improve volcanic ash monitoring. This project introduces a new method that utilizes machine learning techniques to analyze UV and visible satellite data for detecting and classifying volcanic ash. The research focuses on exploring how satellite UV and visible light observations can be used to identify volcanic ash in the atmosphere. A classifier was developed using simulations from a radiative transfer model, which represents various atmospheric scenarios. This classifier is then applied to analyze spectral measurements obtained by the TROPOspheric Monitoring Instrument (TROPOMI) on the ESA Sentinel-5p satellite. The complexity of detecting and classifying volcanic ash arises not only from the presence of other aerosols in the atmosphere but also from the changing characteristics of the ash, influenced by the type of magma and ongoing alterations as the ash remains airborne. This research presents progress in tackling these challenges and in developing a complex algorithm that incorporates a wide range of parameters. The application of this method to the Raikoke eruption case study enables the identification of some volcanic ash, illustrating the potential of this approach. However, this case study also reveals the presence of misclassifications, highlighting the need for continuous improvement in the classifier. This research offers valuable insights into the detection and classification of volcanic ash, contributing to the enhancement of monitoring strategies for hazard mitigation.