Abstract
Volcanic clouds are a major hazard to air traffic, public health, infrastructure, and economic sectors. Therefore, monitoring and tracking volcanic clouds and determining eruptive source parameters (e.g., erupted volume, plume height, mass eruption rate) is crucial to characterizing eruption dynamics and assessing associated natural hazards.A literature review is proposed in this study to understand better how Earth Observation (EO) satellite sensors are used to monitor, track, and model ash and SO2 during volcanic eruptions, ranging from optical (multispectral, hyperspectral, and LiDAR) to radar and thermal data. This review seeks to characterize the different sensors algorithms and models, their accuracy, advantages, and limitations. A systematic literature review was carried out to accomplish this goal utilizing the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) standard. To the best of the author'sknowledge, it is the first systematic literature review fully dedicated to satellite Remote Sensing-based approaches (RS) to monitor, track and model volcanic cloud monitoring, prediction, and forecasting methods.The review was performed on academic papers on the Web of Science to find relevant scientific publications on volcanic cloud monitoring, published from January 1 st , 2010, to September 30 th , 2022. The search parameters used were keywords chosen based on the review topic. They were combined as follows: "Volcanic cloud" OR "Volcanic plume" OR "Volcanic Column" AND "Ash plume" OR "Ash cloud" OR "plume" AND "Remote Sensing" OR "Satellite" AND "Monitoring" AND "Eruptive Source Parameters" OR "SO2 mass Flux" OR "SO2 Flux". From this search, 84 papers were chosen, the selection was based on the use of satellites to detect and monitor volcanic clouds, model and forecast, and combining both approaches in order to estimate the eruptive source parameters. This work assesses the state of the art in satellite remote sensing across the globe to identify and comprehend the major gaps, constraints, and prospective advancements in the sensors, algorithms, and models.
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