The FastVRP automatic platform for the thermal monitoring of volcanic activity using VIIRS and SLSTR sensors: FastFRP to monitor volcanic radiative power

Abstract

Satellite thermal remote sensing is widely used to detect and quantify the high-temperature vol- canic features produced during an eruption, e.g. released radiative power. Some space agencies provide Fire Radiative Power (FRP) Products to characterize any thermal anomaly around the world. In particular, Level-2 FRP Products of the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Sea and Land Surface Temperature Radiometer (SLSTR) are freely available online and they allow to monitor high-temperature volcanic features related to the dynamics of volcanic activity. Here, we propose the FastVRP platform developed in Google Colab to process automatically the FRP Products provided by the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) space agencies. FastVRP was designed to monitor the volcanic radiative power (VRP) related to eruptive activity of Mt. Etna (Sicily, Italy). We compared the quality of these FRP Products during a number of recent paroxysmal lava fountains occurred at Etna volcano between February and March 2021. We highlighted the advantages and the limits of each sensor in monitor- ing intense volcanic eruptions lasting a few hours. Furthermore, we combine the mid-high spatial/ low temporal resolution VIIRS and SLSTR with the low spatial-high temporal resolution SEVIRI (Spinning Enhanced Visible and Infrared Radiometer Imager) to improve estimates of the energies released from each paroxysmal episode. In particular, we propose a fitting approach to enhance the accuracy of SEVIRI low spatial-high temporal resolution measurements exploiting the few acqui- sitions from VIIRS and SLSTR high spatial-low temporal resolution during lava fountain cooling phase. We validated the radiative power values forecasted from VIIRS and SLSTR with the radiative power values retrieved using MODIS (Moderate Resolution Imaging Spectroradiometer) sensor.

Quantitative Assessment of Volcanic Thermal Activity from Space Using an Isolation Forest Machine Learning Algorithm

“…Nowadays, satellite remote sensing is widely employed for monitoring volcanic thermal activity globally [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Numerous volcanic hotspot monitoring satellite platforms have been developed for the near real-time monitoring of thermal anomalies, such as MODVOLC [22], HOTVOLC [23], FIRMS [24], MIROVA [25] and LAV@HAZARD [26].…”

A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images

“…Machine learning models were used to identify and map the thermal emissions from active and cooling lava flows [5,7], and to estimate the areal coverage and the total volume of lava fields or deposits [8][9][10][11][12]. Machine learning and deep learning techniques were adopted to detect and characterize the main components of the volcanic plumes during explosive eruptions [13][14][15][16]. Moreover, deep learning algorithms were applied to automatically recognize subtle to intense thermal anomalies exploiting the spatial relationships of the volcanic features [17].…”