The Spatial and Spectral Resolution of ASTER Infrared Image Data: A Paradigm Shift in Volcanological Remote Sensing

Abstract

During the past two decades, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument on the Terra satellite has acquired nearly 320,000 scenes of the world’s volcanoes. This is ~10% of the data in the global ASTER archive. Many of these scenes captured volcanic activity at never before seen spatial and spectral scales, particularly in the thermal infrared (TIR) region. Despite this large archive of data, the temporal resolution of ASTER is simply not adequate to understand ongoing eruptions and assess the hazards to local populations in near real time. However, programs designed to integrate ASTER into a volcanic data sensor web have greatly improved the cadence of the data (in some cases, to as many as 3 scenes in 48 h). This frequency can inform our understanding of what is possible with future systems collecting similar data on the daily or hourly time scales. Here, we present the history of ASTER’s contributions to volcanology, highlighting unique aspects of the instrument and its data. The ASTER archive was mined to provide statistics including the number of observations with volcanic activity, its type, and the average cloud cover. These were noted for more than 2000 scenes over periods of 1, 5 and 20 years.

Mt. Etna Paroxysms of February–April 2021 Monitored and Quantified through a Multi-Platform Satellite Observing System

“…Those sensors, providing Medium Infrared (MIR) and Thermal Infrared (TIR) data at high-temporal resolution (e.g., 15 min for SEVIRI), enable the identification and monitoring of volcanic thermal anomalies and their quantitative characterisation (e.g., [10][11][12]). MODIS data, which are analysed operationally by the MODIS Volcano Thermal Alert System (MODVOLC) and the Middle InfraRed Observation of Volcanic Activity (MIROVA) systems, were often coupled with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (AS-TER) TIR observations (90 m spatial resolution) to assess subtle hotspots (e.g., [13][14][15][16]). On the other hand, recent studies exploited visible/near-infrared (VNIR) and shortwave Infrared (SWIR) observations, at 20/30 m spatial resolution, from the Multispectral Instrument (MSI) and Operational Land Imager (OLI) to better map high-temperature targets (e.g., lava lakes/flows [17][18][19][20]).…”

Implementation of the NHI (Normalized Hot Spot Indices) Algorithm on Infrared ASTER Data: Results and Future Perspectives

“…The high gain setting extends the range of DN output in the presence of a low reflectance target (e.g., dark soil/rocks), whereas the low-gain settings increase the dynamic range and the maximum radiance detectable by the sensor, reducing the saturation (e.g., [ 24 ]). The low gain-2, which was available only for the SWIR bands, was used to detect high temperature targets (e.g., lava lakes) (e.g., [ 17 ]). However, low-gain SWIR observations are less sensitive to subtle thermal anomalies and are not completely free from saturation issues (e.g., [ 25 ]).…”

“…ASTER URP scheduling occurs following a MODIS thermal detection and the ASTER Visible and Near Infrared (VNIR) and TIR observation takes place at the next available opportunity. The URP enables an accurate analysis of different phases of thermal activity and may be used to validate thermal anomalies detected by systems using high-temporal resolution satellite data (e.g., [ 16 , 17 ]).…”

Detection of Thermal Changes Related to the 2011 Shinmoedake Volcano Activity, Japan: Spatiotemporal Variation of Singularity of MODIS Data after Discriminating False Changes Due to Cloud

“…On the other hand, medium-high spatial resolution satellite sensors, such as Thematic Mapper (TM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI), which provide imagery also in the ShortWave InfraRed (SWIR) spectral range, have been demonstrated good ability to map lava flows (see for example [18][19][20]). To understand eruptive precursors and eruptive dynamics of volcanoes, the integration of multiparametric datasets of satellite and ground data can help better monitor volcanic hazards.…”

“…Nighttime data are often used to detect temperature anomalies to avoid sunlight influences accurately, but nighttime cloud discrimination is generally difficult. Various attempts have been performed for cloud discrimination [9][10][11][12][13][14][15][16][18][19][20][21][22][23][24]. However, most of them are available to daytime data for which visible images can be used for verification, and nighttime data are not universally reported.…”